Equidistant Cylindrical Projection — Basemap Matplotlib Toolkit 1.2.1 documentation
Equidistant Cylindrical Projection¶ The simplest projection, just displays the world in latitude/longitude coordinates. from mpl_toolkits.basemap import Basemap import numpy as np import matplotlib.pyplot as plt # llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon # are the lat/lon values of the lower left and upper right corners # of the map. # resolution = 'c' means use crude resolution coastlines. m = Bas
Text rendering With LaTeX — Matplotlib 2.0.2 documentation
Text rendering With LaTeX¶ Matplotlib has the option to use LaTeX to manage all text layout. This option is available with the following backends: Agg PS PDF The LaTeX option is activated by setting text.usetex : True in your rc settings. Text handling with matplotlib’s LaTeX support is slower than matplotlib’s very capable mathtext, but is more flexible, since different LaTeX packages (font packa
[Python]Matplotlibで複数のグラフを描画する方法 - Qiita
import matplotlib.pyplot as plt import numpy as np t = np.linspace(-np.pi, np.pi, 1000) x1 = np.sin(2*t) x2 = np.cos(2*t) fig, (axL, axR) = plt.subplots(ncols=2, figsize=(10,4)) axL.plot(t, x1, linewidth=2) axL.set_title('sin') axL.set_xlabel('t') axL.set_ylabel('x') axL.set_xlim(-np.pi, np.pi) axL.grid(True) axR.plot(t, x2, linewidth=2) axR.set_title('cos') axR.set_xlabel('t') axR.set_ylabel('x')
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Multiple subplots — Matplotlib 3.10.1 documentation
Multiple subplots# Simple demo with multiple subplots. For more options, see Create multiple subplots using plt.subplots. import matplotlib.pyplot as plt import numpy as np # Create some fake data. x1 = np.linspace(0.0, 5.0) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) x2 = np.linspace(0.0, 2.0) y2 = np.cos(2 * np.pi * x2)
images_contours_and_fields example code: pcolormesh_levels.py — Matplotlib 2.0.2 documentation
""" Shows how to combine Normalization and Colormap instances to draw "levels" in pcolor, pcolormesh and imshow type plots in a similar way to the levels keyword argument to contour/contourf. """ import matplotlib.pyplot as plt from matplotlib.colors import BoundaryNorm from matplotlib.ticker import MaxNLocator import numpy as np # make these smaller to increase the resolution dx, dy = 0.05, 0.05
mplot3d tutorial — Matplotlib 2.0.2 documentation
You are reading an old version of the documentation (v2.0.2). For the latest version see https://matplotlib.org/stable/
matplotlib.pyplot.legend — Matplotlib 2.2.2 documentation
matplotlib.pyplot.legend# matplotlib.pyplot.legend(*args, **kwargs)[source]# Place a legend on the Axes. Call signatures: legend() legend(handles, labels) legend(handles=handles) legend(labels) The call signatures correspond to the following different ways to use this method: 1. Automatic detection of elements to be shown in the legend The elements to be added to the legend are automatically deter
matplotlib.figure.Figure — Matplotlib 3.3.4 documentation
matplotlib.figure.Figure¶ class matplotlib.figure.Figure(figsize=None, dpi=None, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None)[source]¶ Bases: matplotlib.artist.Artist The top level container for all the plot elements. The Figure instance supports callbacks through a callbacks attribute which is a CallbackRegistry instanc
Matplotlib: Python plotting — Matplotlib 2.0.0 documentation
Matplotlib: Visualization with Python Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Create publication quality plots. Make interactive figures that can zoom, pan, update. Customize visual style and layout. Export to many file formats. Embed in JupyterLab and Graphical User I
matplotlibだけで画像からマウスで指定した領域を抜き出す - Qiita
import numpy as np import matplotlib.pyplot as plt # 押した時 def Press(event): global x1,y1,DragFlag # 値がNoneなら終了 if (event.xdata is None) or (event.ydata is None): return # 丸める cx = int(round(event.xdata)) cy = int(round(event.ydata)) x1 = cx y1 = cy # フラグをたてる DragFlag = True # ドラッグした時 def Drag(event): global x1,y1,x2,y2,DragFlag # ドラッグしていなければ終了 if DragFlag == False: return # 値がNoneなら終了 if (event.xd
3分でmatplotlibを日本語対応させる - Qiita
pythonのmatplotlibで日本語を扱う Qiitaにもいくつかおんなじような記事あるんですが、なんか分かりにくかったので自分の為にまとめておく 状況としては、macOS Sierra でmatplotlibのデフォルトのフォントが日本語対応しておらず、▫︎▫︎▫︎に(豆腐というらしい) とりあえず、グラフに日本語が表示されてほしい！ 手順 http://ipafont.ipa.go.jp/old/ipafont/download.html に行って、TTFファイルの4書体パックをダウンロード |font |name |family| ---|---|---|---| |ipagp.ttf | IPAPGothic| [sans-serif] |ipamp.ttf | IPAPMincho | [sans-serif] |ipam.ttf | IPAMincho | [sans-s
matplotlibを使って時系列グラフを書いてみる - Qiita
Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. import sys from datetime import datetime import matplotlib.pyplot as plt impo
Horizontal bar chart — Matplotlib 3.10.3 documentation
Horizontal bar chart# This example showcases a simple horizontal bar chart. import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) fig, ax = plt.subplots() # Example data people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim') y_pos = np.arange(len(people)) performance = 3 + 10 * np.random.rand(len(people)) error = np.random.rand(len(people))
Tight Layout guide — Matplotlib 3.5.0 documentation
Tight Layout guide¶ How to use tight-layout to fit plots within your figure cleanly. tight_layout automatically adjusts subplot params so that the subplot(s) fits in to the figure area. This is an experimental feature and may not work for some cases. It only checks the extents of ticklabels, axis labels, and titles. An alternative to tight_layout is constrained_layout. Simple Example¶ In matplotli
matplotlib.patches.FancyArrowPatch — Matplotlib 3.10.1 documentation
matplotlib.patches.FancyArrowPatch# class matplotlib.patches.FancyArrowPatch(posA=None, posB=None, *, path=None, arrowstyle='simple', connectionstyle='arc3', patchA=None, patchB=None, shrinkA=2, shrinkB=2, mutation_scale=1, mutation_aspect=1, **kwargs)[source]# Bases: Patch A fancy arrow patch. It draws an arrow using the ArrowStyle. It is primarily used by the annotate method. For most purposes,
【入門者向け解説】openCV顔検出の仕組と実践(detectMultiScale) - Qiita
概要と環境 openCVの顔検出方法について調べて試してみました。入門者向けに解説します。 以下はopenCV関連記事です。 OpenCV3.3とPython3.6をAnacondaでWindows10へインストール openCVの顔検出でパラメータを指定して手っ取り早く検出精度を高める openCVで複数画像ファイルから顔検出をして切り出し保存 openCVで効率的に大量画像を顔検出するためのtips 検証環境は下記のとおりです。 openCVの顔検出でできたこと 人物画像に対して顔検知して四角枠で囲みました。 複数人でも大丈夫。すごい！ 変顔だって検知します！(後ろの人も検知できてます) 顔検知から鼻検知に切り替えることもできます。 oepnCVの顔検出の仕組み 論文や具体的なアルゴリズムを見て調べて確認していないので、以降の解説は誤りがあるかもしれません(指摘いただけたら嬉しいです)
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matplotlib ログスケール表示とグリッド表示 - Qiita
shapes_and_collections example code: scatter_demo.py — Matplotlib 2.0.2 documentation
pylab_examples example code: dashpointlabel.py — Matplotlib 2.0.2 documentation
https://matplotlib.org/devdocs/api/axes_api.html