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# PyCon TW 2016 Collaborative Talk Notes <br> Day 2 - R1 > ### Quick Links > - [Portal for Collobration Notes 共筆統整入口](https://hackfoldr.org/pycontw2016) (hosted by [hackfoldr](https://hackfoldr.org/about) and [HackMD](https://hackmd.io/)) > - [Program Schedule 議程時間表](https://tw.pycon.org/2016/events/talks/) > - [PyCon TW 2016 Official Site 官網](https://tw.pycon.org/2016/) > > ### How to update this note? > - Everyone can *freely* update this note. 任何人都能自由地更新內容。 > - Please respect all the participants and follow our [code of conduct](https://tw.pycon.org/2016/about/code-of-conduct/) during discussion. 討論、記錄時,請遵守大會的[行為準則](https://tw.pycon.org/2016/about/code-of-conduct/)。 ## Talk: How to create high available Pycon application with MySQL techniques - Info: https://tw.pycon.org/2016/events/talk/39346130420498441/ - Speaker: 杜修文 - MySQL 高可用階層(可用性由上往下遞增) - Replication - Shared Disk / Virtualization Options - Group Replication - Cluster - MySQL Fabric - 做data shared - Fabric aware connection(支援java,php,python) 或是使用 MySQL Route - MySQL 5.7可做多源複製: - 使用場景,當有很多分公司或是搜集資料的master db,可以把資料都回傳給主公司的slave - Group Replication - 只支援InnoDB - 每個表都要有pk - 需要開啟GTID - 不能線上同時多地DDL - 交易會因為跟其他台衝突而中止 - Cluster(架構由上而下) - client(PHP,python) - Application (MySQL, apache, java, nodes) - Data node ## Talk: 用Google Cloud Platform玩交通資料分析 - Info: https://tw.pycon.org/2016/events/talk/39018216764211208/ - Speaker: 柯維然 - 投影片: https://docs.google.com/presentation/d/19AeaYxblhQ4lbZ_gReZAnBuOmzBu6UjXUWCV61DwV94/edit#slide=id.p - 交通資料介紹 - 資料爬蟲 - App Engine - 資料儲存 - Datastore, BigQuery - 資料分析 - Datalab > 台灣國道是全世界埋感測器最密集的道路。 ## Talk: 連淡水阿嬤都聽得懂的機器學習套件入門 scikit-learn - Info: https://tw.pycon.org/2016/events/talk/69843418095812674/ - Speaker: Cicilia Chia-ying Lee - Slide : http://www.slideshare.net/aacs0130/scikitlearn-62706630 #### Scikit Learn 數字辨識 1. Load data 2. Set a classifier 3. Laern a model 4. Predict the result 5. Evaluate > scikit learn 有很多 classifier,只需要參考 doc 給他參數就能夠使用了 #### 前處理(對原始資料先做些處理) 1. Clean data 2. Feature extraction 3. Convert category and string to number 4. Sparse data 5. Feature selection > raw data -> [前處理] -> 餵進去的向量 ![scikit-learn algorithm](http://scikit-learn.org/stable/_static/ml_map.png) - Reference: - [Scikit-learn 官網](http://scikit-learn.org/stable/index.html) - [Scikit-learn數字範例](http://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) - [選擇機器學習演算法](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) - [林軒田教授的機器學習教學影片](https://www.youtube.com/playlist?list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf) ## Talk: 什麼是色彩管理 & 用 Python 自幹一個好嗎 ? - Info: https://tw.pycon.org/2016/events/talk/66630011804713004/ - Speaker: Kilik Kuo - Slide : https://goo.gl/j0sS6z - Questions: https://goo.gl/slides/s4uqbn #### What is Color Management 在各個裝置上面轉換 color 的轉換系統 #### Why color Management > Obtain a good match across color devices 我想要把鮮豔的紅傳遞給每一個人,不要有色偏,讓大家有相同的 experience MAC OS >>>>>> Windows [Kilik] 補充, Mac 上由於 OSX framework stack 內建了 color management compoent "ColorSync" [1][2], 在Application 層開發透過系統提供的 API 時都會經過 ColorSync 做轉換, 省卻不少問題. Windows 上則有 WIC [3] (win7以前) 用來擷取影像色彩資訊, WIA/WCS [4][5] (win8之後)給應用層使用. 註 : Windows 內建的 image viewer 有做 Color management, 小畫家沒有, 可以用來開圖測試 :) [1] https://developer.apple.com/library/mac/technotes/tn2115/_index.html [2] https://developer.apple.com/library/mac/documentation/GraphicsImaging/Reference/ColorSync_Manager/ [3] https://msdn.microsoft.com/zh-tw/library/windows/desktop/ee719902%28v=vs.85%29.aspx [4] https://msdn.microsoft.com/en-us/library/windows/hardware/ff542835%28v=vs.85%29.aspx [5] https://msdn.microsoft.com/library/windows/desktop/dd372231%28v=vs.85%29.aspx #### Befor We Start - COLOR Light > Eyes > Colors Wavelength(nm) Blue -> Green -> Red (波長短到長) 天色變暗時,人類的尖銳敏銳度對紅色的辨色力會最先喪失 紅色有助於增快暗適應 (光適應 ~ 5min, 暗適應 ~ 20 min) #### Befor We Start - CIE RGB matching function - David Wright / John Guild's experiment 找一群健康的人,讓他們控制三個燈光(RGB)的比重,讓他們盡力控制燈光顏色比例來盡力調出他隨意指定的顏色 到最後算出一個公式,把色光的組合,用波長 input 來算出顏色 ``` Color λ = r(λ') * R + g(λ') * G + b(λ') * B R = 700nm, G = 546.1nm, B = 435.8nm ``` #### Before We Start - CIE XYZ(1931) #### Before We Start - CIE XYZ(imaginary) to xyY(a cousin color space, visible in 2D) - 定義**色度**座標(chromaticity) ``` x = X / (X + Y + Z) y = Y / (X + Y + Z) z = Z / (X + Y + Z) = 1 - x - y X = (Y / y) * x Z = (Y / y) * (1 - x - y) ``` #### Before We Start - Define RGB color Space Matrix in XYZ - Specify a RGB color space matrix - Reddest red / Greenest green / Bluest blue - 3 vertices 要找到三個覺得最紅、最綠、最藍 的點 - 定義黑白 - Darkest dark / Lighest light - A space where Y1 < Y2 - RGB from XYZ - Define (0, 0, 0) -> (1, 1, 1) - 000(black) / 100(red) / 010(blue) / 001(green) - 111(white) ... 110 / 101 / 011 - 8 vertices, 6 sides > 所以我們可以藉由 color management ,跳到其他 color space 而不會有 color(data) loss #### Before We Start - Reference white point - 什麼是參考白 - What we perceived as 'white' depends on the type of light that's illuminating a scene - D50 / 5003K, D65 / 6504 K - Changing colors to match a new ref. white is called **adaptation**. - K (KelvinScale) #### Before - ICC Profile ( 定義 RGB 的 space ) 定義白點是甚麼、定義 RGB、 ...,預設的白點是 D50 #### Before - TRC( Tone Response Curve ) - 25 個燈泡,一個代表 RGB + 10 - 25 盞燈全開 = Max value - 人類對於低暗度的變化感受比較強,高亮度變化比較弱 - ICC profile 裡面會放 curve,表示變化的曲線 #### Color Managment Flow 1. Non-linear Color space A 2. Linear Color Space A' 3. D65 - XYZ 4. D50 - XYZ 5. Linear Color Space B' 6. Non-linear Color space B #### Pillow.ImageCms - The ImageCms module - LittleCMS2 color management engine, based on PyCMS library. #### ImageCms APIs 1. [Information] Profile Information 2. [Creation] Default profile 3. [Transformation] Directly from profile A to B 4. ... https://github.com/kilikkuo/Python_ColorManagement [Kilik] answer to 用純 python 自幹一個, 好嗎 ? A : 效能上來, 拜偷不要 Orz... 但如果用一種追尋心靈平靜的角度來看, 還不錯 ! 讓我挖掘了一段深刻的色彩與影像知識. 1) 為了用 OpenCL 硬體加速轉換色彩空間, 需要一個 CPU 版本對照, 所以開始自幹. 2) 為了確認 CPU 版本的正確性, 先得搞懂 Color Space & ICC Profile 相關知識, 然後自幹 3) 為了讀取於影像中的 ICC profile, 用 python 開始寫 metadata parser, 又是一個 K spec後自幹. 4) 為了不讓演講沒有太多 python, 找一個別人寫的 python CMM 使用看看. 對我來說, frameworks / tools 的汰換日新月異, 除了"知道"這些春筍, 找一個題目往下挖掘1~3年, 整個人生的職涯可以累積數個"根基穩固"的技術本, 甚至能在其他工作經驗中交互發揮, 很有好處, 分享一點心得給大家. ## Talk: Jupyter kernel: How to speak in another language - Info: https://tw.pycon.org/2016/en-us/events/talk/56754637675429904/ - Slides: http://www.slideshare.net/AdrianLiaw/jupyter-kernel-how-to-speak-in-another-language - Speaker: 廖偉涵 Adrian Liaw #### agenda 1. Jupyter, IPython, notebook, console, clinet, kernel 2. The Interactive Computing Protocol 3. Implementing a kernet 4. Live Demo #### Jupyter > Client <- $\varnothing MQ$ Socket -> Kernel - 也可以支援多個 client 連到相同的 Kernel 上面 #### What's inside IPython - The interactive ipthon shell (No $\varnothing MQ$) - Magic commands, ( e.g. ls, pwd, cd ) - Auto word completer - beautiful traceback - shell history management [IPyKernel's repo](https://github.com/ipython/ipykernel) #### What's inside jupyter - web-based notebook interface & nbconvert #### interactive computing protocol >定義 kernel 和 client 要怎麼溝通 >有提供一些 pattern: request, reply, ... - i.e. jupyter messaging protocol - Communication between Kernel and Client - Base on $\varnothing MQ$ and JSON ```sequence Note left of Client: DEALER Client->Kernel: SHELL Note right of Kernel: ROUTER Kernel->Client: SHELL Note left of Client: DEALER Note right of Kernel: PUB Kernel->Client: IOPub Note left of Client: SUB Note right of Kernel: IOPub 會推\n執行的 Status Note right of Kernel: ROUTER Kernel->Client: stdin Note left of Client: DEALER Client->Kernel: stdin Note right of Kernel: ROUTER Note left of Client: DEALER Client->Kernel: control Note right of Kernel: ROUTER Kernel->Client: control Note left of Client: DEALER Note left of Client: REQ Client->Kernel: heartbeat Note right of Kernel: REP Kernel->Client: heartbeat Note left of Client: REQ ``` [Jupyter 官方說明](http://jupyter-client.readthedocs.io/en/latest/api/client.html) #### Kernel Types - Native Kernel - Python Wrapper Kernel - 在 IPyKernel 這個套件裡 - REPL Wrapper Kernel ## Talk: Authentication with JSON Web Tokens - Info: https://tw.pycon.org/2016/events/talk/67002934621110318/ - Speaker: Shuhsi Lin - Slide: http://www.slideshare.net/sucitw/2016-pycontw-web-api-authentication #### IAA - Identity - Authentication - Authorization #### Server Based Authentication 需要在 server 上面儲存 session 資訊,容易被公ㄐㄧ #### Token Based Authentication Server 上不用存東西 ## Talk: You Might Not Want Async (in Python) - Info: https://tw.pycon.org/2016/events/talk/69195601836769336/ - Speaker: Tzu-ping Chung ### 共筆 > `asyncio`很雷~~~ #### async * 整個python都會變成async,沒有partial * is not parallelism * 3rd party support * 很難做unit test * 需要用decorator * add coverage to test code * consider asynctest * 使用pytest-asyncio 或 pytest > 推pytest! (可參考Day1 R1 Talk: We Made the PyCon TW 2016 Website) #### alternatives * concurrency with multiprocessing * Greenlets: 用coroutine做concurrency * threading * pypy: good choice,自動變multithread > async 會被 standard IO blcok