GitHub - rougier/scientific-visualization-book: An open access book on scientific visualization using python and matplotlib
Nicolas P. Rougier, Bordeaux, November 2021. The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop only, some deal with
Python-matplotlib：scatterã€æ•£å¸ƒå›³ã€‘ - リファレンス メモ
トップ 言語 ç‰ CSS HTML JavaScript Python ライブラリ・プラットフォームjQuery micro:bit Xamarin ドã‚ュメンテーション Doxygen JSDoc Markdown SHFB (Sandcastle Help File Builder) XML ドã‚ュメント コメント ãã®ä»– å„種資料 åサイト・SNS 簡易リファレンス・Tips Facebook X サãƒãƒ¼ãƒˆå¯„付 ãƒ¡ã‚¤ãƒ³æ¦‚è¦ ç›®æ¬¡ 実行ãƒãƒ¼ã‚¸ãƒ§ãƒ³ 外部リンク 構文ã€æ§‹æ–‡ä¸€è¦§ã€‘ 空行 行継続 複数文 インデント 代入文 (=) 代入文 (代入演算å) ä»£å…¥å¼ (:=) 3.8 #ã€ã‚³ãƒ¡ãƒ³ãƒˆã€‘ # codingã€ã‚¨ãƒ³ã‚³ãƒ¼ãƒ‰å®£è¨€ã€‘ '''ã€ãƒ‰ã‚ュメンテーション文å—列】 assertã€è¨ºæ– (デãƒãƒƒã‚°)】 asyncã€ã‚³ãƒ«ãƒ¼ãƒãƒ³ã€‘3.5 breakã€ãƒ«ãƒ¼ãƒ—終了】 classã€ã‚¯ãƒ©ã‚¹å®šç¾©ã€‘ c
matplotlibã®ã‚ã£ã¡ã‚ƒã¾ã¨ã‚ - Qiita
0. ã¯ã˜ã‚ã« ã‚„ã‚ŠãŸã„ã“ã¨ãŒã‚ã‚‹ãŸã³ã«ã„ã¡ã„ã¡Googleã‚„å…¬å¼ã‚µã‚¤ãƒˆã§æ¤œç´¢ã—ã¦ãã‚Œã£ã½ã„ã®ã‚’探ã™ã®ã¯ã‚‚ã†é¢å€’ã 。 ã‚„ã£ã¨ãã‚Œã£ã½ã„ã®ã‚’見ã¤ã‘ãŸã®ã«ã€ä¸€è¡Œã§æ¸ˆã‚€ã‚ˆã†ãªã“ã¨ã‚’「plt.ãªã‚“ã¡ã‚ƒã‚‰ã€ã ã®ã€Œset_ãªã‚“ã¡ã‚ƒã‚‰ã€ã‚’ãŸãã•ã‚“並ã¹ã¾ã—ãŸãªã‚“ã¦ãƒ–ãƒã‚°ã¯ã‚‚ã¯ã‚„検索妨害ã 。 Qiitaã«ã™ã‚‰åƒ•ã®ãŸã‚ã®ã„ã„æ„Ÿã˜ã®ã¾ã¨ã‚ãŒãªã„ãªã‚“ã¦â€¦â€¦ よã考ãˆãŸã‚‰è‡ªåˆ†ãŒæ™®æ®µä½¿ã†ã‚ˆã†ãªãƒ¡ã‚½ãƒƒãƒ‰ãªã‚“ã¦é™ã‚‰ã‚Œã¦ã„ã‚‹ã˜ã‚ƒãªã„ã‹ã€‚ ã‚‚ã†è‡ªåˆ†ã§ã¾ã¨ã‚ã‚‹ã‚。自分ã®ãŸã‚ã«ã€‚ ã¨ã„ã†ã‚ã‘ã§ã‚¤ãƒ³ãƒãƒ¼ãƒˆã€‚
matplotlibã§è‰²ã‚’ãŸãã•ã‚“使ㆠ- iMind Developers Blog
æ¦‚è¦ matplotlibã§è‰²åˆ†ã‘ã—ã¦ã‚°ãƒ©ãƒ•ã‚’表示ã—ãŸã„時ã«ã€è‰²ã‚’ãŸãã•ã‚“å–ã£ã¦ãる方法ã«ã¤ã„ã¦èª¿ã¹ã‚‹ã€‚ ãƒãƒ¼ã‚¸ãƒ§ãƒ³æƒ…å ± matplotlib==3.0.3 8色ã¾ã§ matplotlib.colors.BASE_COLORSã§8色ãŒå‡ºã›ã‚‹ã€‚用æ„ã•ã‚Œã¦ã„る色ã¯b, g, r, c, m, y, k, w import matplotlib from matplotlib import pylab as plt matplotlib.colors.BASE_COLORS #=> {'b': (0, 0, 1), #=> 'g': (0, 0.5, 0), #=> 'r': (1, 0, 0), #=> 'c': (0, 0.75, 0.75), #=> 'm': (0.75, 0, 0.75), #=> 'y': (0.75, 0.75, 0), #=> 'k': (0, 0, 0), #=
ã€Matplotlib】注釈ã¨çŸ¢å°
グラフã®ã‚る特定ã®ä½ç½®ã‚’明示ã™ã‚‹ãŸã‚ã«ã€Axes.annotate() を使ã£ã¦æ³¨é‡ˆ (annotation)ã¨çŸ¢å° (arrow) を表示ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ã¾ãšã¯ç°¡å˜ãªä¾‹ã‚’示ã—ã¾ã™ã€‚ # MATPLOTLIB_ANNOTATE # In[1] import numpy as np import matplotlib.pyplot as plt # Figureã¨Axesã‚’æç”» fig, ax = plt.subplots(figsize=(5, 5)) ax.grid() ax.set_xlim(0, 10) ax.set_ylim(0, 10) # 指定ã—ãŸä½ç½®ã«æ³¨é‡ˆ(annotation)を入れる ax.annotate("point A", xy=(4, 4), size=15, color="red") plt.show() ã“ã®ã‚ˆã†ã«ã€Axes.annotate() ã¯æ³¨é‡ˆãƒ†
ã€xkcd】pythonコードã«ãŸã£ãŸä¸€è¡Œã§æ¼«ç”»ã®ã‚ˆã†ãªã‚°ãƒ©ãƒ•ã‚’ä½œã‚‹ï¼ - プãƒã‚¯ãƒ©ã‚·ã‚¹ãƒˆ
xkcdã¨ã¯ 世界一ã§æœ€ã‚‚人気ã®ã‚るウェブ漫画ã®ä¸€ã¤ã§ã™ xkcd: Code Quality 3 ランダル・マンãƒãƒ¼ãŒ2005å¹´9月ã«é–‹è¨ 皮肉や風刺ãŒå¾—æ„。ç†ç³»ãƒã‚¿ãŒçµæ§‹å¤šã„。 ç¾åœ¨ã¯é€±3回更新ã•ã‚Œã¦ã„ã‚‹ ã‚ャラやフォントãŒç‰¹å¾´çš„ ã“ã‚Œã¨ã‹çš®è‚‰ãŒåŠ¹ã„ã¦ã¦ã„ã„ã§ã™ãï¼å¥½ãã§ã™ A : å¯ãªã„ã®ï¼Ÿ B : å¯ã‚‰ã‚Œãªã„ã‚“ã ã€å¤§äº‹ãªã“ã¨ãŒã‚ã‚‹ A : ãªã«ã‚ˆï¼Ÿ B : 誰ã‹ãŒã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆã§ãƒœãƒã‚’出ã—ã¦ã‚‹ã‚“ã xkcd: Duty Calls 実ã¯matplotlibを使ãˆã°ã€ã‚°ãƒ©ãƒ•ã‚’xkcd風ã«ä»•ç«‹ã¦ã‚‰ã‚Œã¾ã™ã€‚ã—ã‹ã‚‚ãŸã£ãŸä¸€è¡Œã§ï¼ä»Šå›žã¯ãã®ç´¹ä»‹ã‚’ã—ã¾ã™ xkcdã¨ã¯ matplotlibã§ã€xkcd requirement 使ã„æ–¹ Matplotlibã®HPã‚‚xkcd風㫠サンプルを見ã¦ã¿ã‚‹ 3D 円グラフ 最後㫠matplotlibã§ã€xkcd requirement matplotli
ã€matplotlib】軸ã®ç›®ç››ã‚Šã‚’指数表記ã«ã—ãŸã„ã§ã™ - ãã‚‹ãã‚‹ãã‚‹ãã‚’ãらãšã«ãã†ãœ
HDF5ã®èªã¿è¾¼ã¿ã¯æ¬¡å›žã«ã¾ã‚ã—ã¾ã™ã€‚ã”ã‚ã‚“ã。 今回ã¯ã€å€‹äººçš„ã«ã‚€ã£ã¡ã‚ƒè‹¦åŠ´ã—ãŸãŠè©±ã«ã¤ã„ã¦ã€ãƒ¡ãƒ¢ã€‚ 目的ã¯ã€ Scientific notationã®æŒ‡æ•°ã‚’10^{n}ã«ã™ã‚‹ Scientific notationã®æŒ‡æ•°ã®æ–‡å—ã‚’ã§ã‹ãã™ã‚‹ ã§ã™ã€‚ ã¾ãšã¯çŠ¶æ³è¨å®š import numpy as np import matplotlib.pyplot as plt x = np.arange(1,1000,1) y = 10e-7*x fig, ax=plt.subplots(figsize=(5*1.618,5)) ax.plot(x,y) ã“ã‚Œã€y軸ã®ç›®ç››ã‚Šãƒ»ãƒ»ãƒ»æŒ‡æ•°è¡¨è¨˜ã—ãŸããªã„？ã—ãŸã„よã？ ã¨ã„ã†ã‚ã‘ã§ã€ã“ã„ã¤ã‚’ã†ã¾ã指数表記ã™ã‚‹æ–¹æ³•ã‚’ã“ã“ã§ã¯è¿°ã¹ã¾ã™ã€‚ çµè«– 以下ã®ã‚ˆã†ã«ã‚„れ。 import numpy as np import matplotlib.pyplot as pl
Legend guide — Matplotlib 2.0.2 documentation
Legend guide¶ This legend guide is an extension of the documentation available at legend() - please ensure you are familiar with contents of that documentation before proceeding with this guide. This guide makes use of some common terms, which are documented here for clarity: legend entry A legend is made up of one or more legend entries. An entry is made up of exactly one key and one label. legen
pylab_examples example code: subplots_demo.py — Matplotlib 2.0.2 documentation
"""Examples illustrating the use of plt.subplots(). This function creates a figure and a grid of subplots with a single call, while providing reasonable control over how the individual plots are created. For very refined tuning of subplot creation, you can still use add_subplot() directly on a new figure. """ import matplotlib.pyplot as plt import numpy as np # Simple data to display in various fo
1
ランã‚ング
ランã‚ング
ランã‚ング
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
Overviews — PyViz 0.0.1 documentation
5 Magical Tricks to improve your Visualization Design using Matplotlib
http://www.mathgram.xyz/entry/plotly
pylab_examples example code: figlegend_demo.py — Matplotlib 2.0.2 documentation
Examples — Matplotlib 3.9.2 documentation
{{#tags}}- {{label}}
{{/tags}}