Matplotlib - Numerical Analysis with Python
Matplotlib †Pythonã«ã‚ˆã‚‹ã‚°ãƒ©ãƒ•ä½œæˆã¨æ•°å€¤è§£æžãƒ©ã‚¤ãƒ–ラリ。 ã©ã†ã‚„らMatlabã£ã½ã„ã‚‚ã®ã‚’作ã‚ã†ã¨ã—ã¦ã„る模様。 （Matlabを使ã£ãŸã“ã¨ç„¡ã„ã®ã§åˆ†ã‹ã‚Šã¾ã›ã‚“ãŒï¼‰ ↑ è¨å®š †${HOME}/.matplotlib/matplotlibrcãŒè¨å®šãƒ•ã‚¡ã‚¤ãƒ«ã€‚（å¤ã„ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã¯${HOME}/.matplotlibrcã ã£ãŸã®ã§æ³¨æ„） ã¨ã‚Šã‚ãˆãšæœ€åˆã«è¨å®šã™ã‚‹ã®ã¯ backend æç”»ã«ä½¿ã†ãƒ©ã‚¤ãƒ–ラリã®æŒ‡å®šã€‚TkAgg?ã¨ã‹ã€‚ numerix Numeric, numarray, numpyã®ã©ã‚Œã‹ã‚’指定ã™ã‚‹ã€‚ ↑ plot †ã¨ã‚Šã‚ãˆãšè‰²ã€…プãƒãƒƒãƒˆã—ã¦ã¿ã‚‹ã“ã¨ã«ã€‚ >>> x = arange(0.0, 5.0, 0.1) # x, yãã‚Œãžã‚Œã® >>> y = exp(-x**2) # é…列を作る >>> plot(x, y) ã¨ã™ã‚‹ã¨ã“ã‚“ãªã‚°ãƒ©
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pickleモジュールを使ã†ã¨ã€Pythonã®ãƒ‡ãƒ¼ã‚¿æ§‹é€ （タプルã¨ã‹ãƒªã‚¹ãƒˆã¨ã‹ã‚¯ãƒ©ã‚¹ã®ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã¨ã‹ï¼‰ã‚’ä¿å˜ã§ãã¾ã™ã€‚ pickle化ãŒPythonã®ã‚ªãƒ–ジェクトをãƒã‚¤ãƒˆåˆ—ã«å¤‰æ›ã™ã‚‹ã“ã¨ã€éžpickle化ã¯ãã®é€†ã‚’指ã—ã¾ã™ã€‚ Pickle化 dumpを使ã„ã¾ã™ã€‚ dump(オブジェクト, ファイルオブジェクト) 第二引数ã¯open(ファイルå)ã¨ã„ã£ãŸæ„Ÿã˜ã§é–‹ã„ãŸãƒ•ã‚¡ã‚¤ãƒ«ã‚ªãƒ–ジェクトを渡ã—ã¦ãã ã•ã„。 ãªãŠã€dumpsを使ã†ã¨ã€ãƒ•ã‚¡ã‚¤ãƒ«ã«æ›¸ãè¾¼ã¾ãšã«æ–‡å—列ãŒè¿”ã•ã‚Œã¾ã™ã€‚ éžPickle化 loadを使ã„ã¾ã™ã€‚ load(ファイルオブジェクト） ã“ã¡ã‚‰ã‚‚ã€é–‹ã‹ã‚ŒãŸãƒ•ã‚¡ã‚¤ãƒ«ã‚ªãƒ–ジェクトを渡ã™ã‚ˆã†ã«ã—ã¾ã™ã€‚ ãªãŠã€dumpsã§ä½œã£ãŸæ–‡å—列をèªã¿è¾¼ã‚€ã«ã¯loadsを使ã„ã¾ã™ã€‚ 例 クラスã®ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚’ä¿å˜ã—ã¦ã€èªã¿ã ã—ã¦ã„ã¾ã™ã€‚ ã“ã“ã§ã¯ã‚¯ãƒ©ã‚¹ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚’pickle化ã—ã¦ã¾ã™ãŒã€ä»–ã®ãƒ‡ãƒ¼ã‚¿
matplotlibã«ã‚ˆã‚‹ç·šç¨®ã‚„色ãªã©ã®æŒ‡å®šæ–¹æ³•
matplotlibã®ç·šã®è‰²ã€ç·šç¨®ã€ç·šå¹…ã®æŒ‡å®šæ–¹æ³•ã‚’記ã—ã¦ãŠã。 ç·šã®è‰²ã¯plot上ã§ã€é’ãªã‚‰"b"ã€ç·‘ãªã‚‰"g"ã®ã‚ˆã†ã«æŒ‡å®šã™ã‚‹ã€‚ã“ã“ã§ã¯ã€ç™½ã„線を見ã›ã‚‹ãŸã‚ã«èƒŒæ™¯ã‚’ç°è‰²ã«ã—ã¦ã„る。 以下ã€å›³ã®ä½œæˆã«ä½¿ã£ãŸã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã€‚ from pylab import * axes(axisbg="#777777") # 背景をç°è‰²ã«. x = arange(-20, 20, 0.3) plot(x+1, x, "b") # é’. plot(x+2, x, "g") # ç·‘. plot(x+3, x, "r") # 赤. plot(x+4, x, "c") # シアン. plot(x+5, x, "m") # マゼンタ. plot(x+6, x, "y") # 黄. plot(x+7, x, "k") # é»’. plot(x+8, x, "w") # 白. plot(x+9, x, color="#
Usage — Matplotlib 2.0.2 documentation
General Concepts¶ matplotlib has an extensive codebase that can be daunting to many new users. However, most of matplotlib can be understood with a fairly simple conceptual framework and knowledge of a few important points. Plotting requires action on a range of levels, from the most general (e.g., ‘contour this 2-D array’) to the most specific (e.g., ‘color this screen pixel red’). The purpose of
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Overview This page aims to provide basic information on various services available to those who use mtheory for their primary email and web serving needs. Hopefully this page will make you aware of everything that is available to you, so you can get more from the web with a view to making life easier. It should provide a handy reference for address and details that you may have otherwise forgot as
Mac OS X Python Packages for Universal Python 2.4 on Mac OS X 10.3.9 and later (Intel and PPC)
matplotlib matplotlib-0.91.1-py2.5-macosx10.4-2007-12-04.dmg (MD5: f7d4a1727192a155f54f33a7237cb852) numarray numarray-1.5.2-py2.5-macosx10.4-2007-01-30.dmg (MD5: 0ff4194a398f21b55286ba7f648d5f7b) Numeric Numeric-24.2-py2.5-macosx10.4.dmg (MD5: ee3f8efd086fbf6b46ce5e85be747dab) numpy numpy-1.0.4-py2.5-macosx10.4-2007-11-07.dmg (MD5: f35f679b7b64ef984e3a4eeb56a26657) PIL PIL-1.1.6-py2.5-macosx10.4-
Matplotlib: Python plotting — Matplotlib 2.0.0 documentation
Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display. As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matp
pythonグラフライブラリ「matplotlibã€è¦šæ›¸ - Pashango’s Blog
http://matplotlib.sourceforge.net/ よã使ã†æ©Ÿèƒ½ã®ã¿ãƒ”ックアップ。 グラフ(plot) from pylab import * x = arange(-10, 10, 0.1) y = sin(x) plot(x,y, '--') show() 棒グラフ(bar) from pylab import * datas = {"Tim":7, "Jack":10, "Matthew":4} width = 1 bar(arange(3), datas.values(), width) xticks(0.5+arange(3), datas.keys()) show() 散布図(scatter) from pylab import * N = 20 x = rand(N) y = rand(N) scatter(x, y, marker="+") show()
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