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Merge pull request #14 from matplotlib/nicolas
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‎README.md

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![](./handout-intermediate.png)
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![](./handout-tips.png)
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# How to compile
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1. You need to create a `fonts` repository with:

‎handout-tips.pdf

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‎handout-tips.png

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‎handout-tips.tex

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\documentclass[10pt,landscape,a4paper]{article}
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\usepackage[right=10mm, left=10mm, top=10mm, bottom=10mm]{geometry}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage[english]{babel}
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\usepackage[rm,light]{roboto}
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\usepackage{xcolor}
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\usepackage{graphicx}
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\graphicspath{{./figures/}}
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\usepackage{multicol}
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\usepackage{colortbl}
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\usepackage{array}
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\setlength\parindent{0pt}
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\setlength{\tabcolsep}{2pt}
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\baselineskip=0pt
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\setlength\columnsep{1em}
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\definecolor{Gray}{gray}{0.85}
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% --- Listing -----------------------------------------------------------------
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\usepackage{listings}
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\lstset{
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frame=tb, framesep=4pt, framerule=0pt,
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backgroundcolor=\color{black!5},
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basicstyle=\ttfamily\footnotesize,
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commentstyle=\ttfamily\color{black!50},
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breakatwhitespace=false,
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breaklines=true,
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extendedchars=true,
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keepspaces=true,
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language=Python,
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rulecolor=\color{black},
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showspaces=false,
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showstringspaces=false,
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showtabs=false,
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tabsize=2,
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%
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emph = {
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plot, scatter, imshow, bar, contourf, pie, subplots, spines,
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add_gridspec, add_subplot, set_xscale, set_minor_locator, linestyle,
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dash_capstyle, projection, Stroke, Normal, add_axes, label, savefig,
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get_cmap, histtype, annotate, set_minor_formatter, tick_params,
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fill_betweenx, text, legend, errorbar, boxplot, hist, title, xlabel,
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ylabel, suptitle, fraction, pad, set_fontname, get_xticklabels},
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emphstyle = {\ttfamily\bfseries}
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}
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% --- Fonts -------------------------------------------------------------------
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\usepackage{fontspec}
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\usepackage[babel=true]{microtype}
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\defaultfontfeatures{Ligatures = TeX, Mapping = tex-text}
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\setsansfont{Roboto} [ Path = fonts/roboto/Roboto-,
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Extension = .ttf,
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UprightFont = Light,
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ItalicFont = LightItalic,
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BoldFont = Regular,
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BoldItalicFont = Italic ]
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\setromanfont{RobotoSlab} [ Path = fonts/roboto-slab/RobotoSlab-,
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Extension = .ttf,
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UprightFont = Light,
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BoldFont = Bold ]
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\setmonofont{RobotoMono} [ Path = fonts/roboto-mono/RobotoMono-,
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Extension = .ttf,
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Scale = 0.90,
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UprightFont = Light,
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ItalicFont = LightItalic,
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BoldFont = Regular,
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BoldItalicFont = Italic ]
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\renewcommand{\familydefault}{\sfdefault}
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% -----------------------------------------------------------------------------
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\begin{document}
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\thispagestyle{empty}
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\section*{\LARGE \rmfamily
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Matplotlib \textcolor{orange}{\mdseries tips \& tricks}}
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\begin{multicols*}{3}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Transparency}
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Scatter plots can be enhanced by using transparency (alpha) in order
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to show area with higher density and multiple scatter plots can be
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used to delineate a frontier.
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\begin{tabular}{@{}m{.774\linewidth}m{.216\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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X = np.random.normal(-1,1,500)
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Y = np.random.normal(-1,1,500)
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ax.scatter(X, Y, 50, "0.0", lw=2) # optional
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ax.scatter(X, Y, 50, "1.0", lw=0) # optional
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ax.scatter(X, Y, 40, "C1", lw=0, alpha=0.1)
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-transparency.pdf}}
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\end{tabular}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Rasterization}
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If your figure is made of a lot graphical elements such as a huge
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scatter, you can rasterize them to save memory and keep other elements
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in vector format.
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\begin{lstlisting}
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X = np.random.normal(-1, 1, 10_000)
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Y = np.random.normal(-1, 1, 10_000)
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ax.scatter(X, Y, rasterized=True)
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fig.savefig("rasterized-figure.pdf", dpi=600)
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\end{lstlisting}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Offline rendering}
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Use the Agg backend to render a figure directly in an array.
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\begin{lstlisting}
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from matplotlib.backends.backend_agg import FigureCanvas
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canvas = FigureCanvas(Figure()))
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... # draw som stuff
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canvas.draw()
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Z = np.array(canvas.renderer.buffer_rgba())
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\end{lstlisting}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Range of continuous colors}
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You can use colormap to pick a range of continuous colors.
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\begin{tabular}{@{}m{.774\linewidth}m{.216\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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X = np.random.randn(1000, 4)
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cmap = plt.get_cmap("Blues")
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colors = [cmap(i) for in in [.2,.4,.6,.8]]
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ax.hist(X, 2, histtype='bar', color=colors)
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-color-range.pdf}}
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\end{tabular}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Text outline}
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Use text outline to make text more visible.
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\begin{tabular}{@{}m{.774\linewidth}m{.216\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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import matplotlib.patheffects as fx
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text = ax.text(0.5, 0.1, "Label")
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text.set_path_effects([
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fx.Stroke(linewidth=3, foreground='1.0'),
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fx.Normal()])
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-outline.pdf}}
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\end{tabular}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Multiline plot}
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You can plot several lines at once using None as separator.
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\begin{lstlisting}
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X,Y = [], []
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for x in np.linspace(0, 10*np.pi, 100):
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X.extend([x, x, None]), Y.extend([0, sin(x), None])
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plt.plot(X, Y, "black")
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\end{lstlisting}
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\includegraphics[width=\linewidth]{tip-multiline.pdf}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Dotted lines}
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To have rounded dotted lines, use a custom {\ttfamily linestyle} and
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modify {\ttfamily dash\_capstyle}.
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\begin{lstlisting}
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plt.plot([0,1], [0,0], "C1",
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linestyle = (0, (0.01, 1)), dash_capstyle="round")
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plt.plot([0,1], [1,1], "C1",
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linestyle = (0, (0.01, 2)), dash_capstyle="round")
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\end{lstlisting}
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\includegraphics[width=\linewidth]{tip-dotted.pdf}
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% -----------------------------------------------------------------------------
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\subsection*{\rmfamily Combining axes}
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You can use overlaid axes with different projections.
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\begin{tabular}{@{}m{.774\linewidth}m{.216\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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ax1 = fig.add_axes([0,0,1,1],
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label="cartesian")
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ax2 = fig.add_axes([0,0,1,1],
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label="polar",
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projection="polar")
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-dual-axis.pdf}}
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\end{tabular}
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\subsection*{\rmfamily Colorbar adjustment}
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You can adjust colorbar aspect when adding it.
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\begin{tabular}{@{}m{.754\linewidth}m{.236\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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im = ax.imshow(Z)
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cb = plt.colorbar(im,
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fraction=0.046, pad=0.04)
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cb.set_ticks([])
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-colorbar.pdf}}
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\end{tabular}
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\subsection*{\rmfamily Taking advantage of typography}
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You can use a condensed face such as Roboto
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Condensed to save space on tick labels.
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\begin{lstlisting}
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for tick in ax.get_xticklabels(which='both'):
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tick.set_fontname("Roboto Condensed")
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\end{lstlisting}
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\includegraphics[width=\linewidth]{tip-font-family.pdf}
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\subsection*{\rmfamily Getting rid of margins}
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Once your figure is finished, you can call {\ttfamily tight\_layout()}
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to remove white margins. If there are remaining margins, you can use
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the {\ttfamily pdfcrop} utility (comes with TeX live).
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\subsection*{\rmfamily Hatching}
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You can achieve nice visual effect with thick hatch patterns.
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\begin{tabular}{@{}m{.774\linewidth}m{.216\linewidth}}
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\begin{lstlisting}[belowskip=-\baselineskip]
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cmap = plt.get_cmap("Oranges")
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plt.rcParams['hatch.color'] = cmap(0.2)
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plt.rcParams['hatch.linewidth'] = 8
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ax.bar(X, Y, color=cmap(0.6), hatch="/" )
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\end{lstlisting} &
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\raisebox{-0.75em}{\includegraphics[width=\linewidth]{tip-hatched.pdf}}
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\end{tabular}
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\subsection*{\rmfamily Read the documentation}
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Matplotlib comes with an extensive documenation explaining every
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detals of each command and is generally accompanied by examples
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with. Together with the huge online gallery, this documenation is a
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gold-mine.
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\vfill
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%
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{\scriptsize Matplotlib 3.2 handout for tips \& tricks.
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Copyright (c) 2020 Nicolas P. Rougier.
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Released under a CC-BY 4.0 License.
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Supported by NumFocus Grant \#12345.\par}
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\end{multicols*}
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\end{document}
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‎scripts/tip-color-range.py

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# -----------------------------------------------------------------------------
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# Matplotlib cheat sheet
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# Released under the BSD License
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# -----------------------------------------------------------------------------
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# Scripts to generate all the basic plots
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=(2,2))
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mpl.rcParams['axes.linewidth'] = 1.5
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d = 0.01
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ax = fig.add_axes([d,d,1-2*d,1-2*d], xticks=[], yticks=[])
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X = np.random.seed(1)
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X = np.random.randn(1000, 4)
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cmap = plt.get_cmap("Oranges")
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colors = [cmap(i) for i in [.1,.3,.5,.7]]
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ax.hist(X, 2, density=True, histtype='bar', color=colors)
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plt.savefig("../figures/tip-color-range.pdf")
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# plt.show()

‎scripts/tip-colorbar.py

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# -----------------------------------------------------------------------------
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# Matplotlib cheat sheet
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# Released under the BSD License
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# -----------------------------------------------------------------------------
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# Scripts to generate all the basic plots
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.patheffects as path_effects
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fig = plt.figure(figsize=(2.15,2))
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mpl.rcParams['axes.linewidth'] = 1.5
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d = 0.01
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ax = fig.add_axes([d,d,1-2*d,1-2*d], xticks=[], yticks=[])
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np.random.seed(1)
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Z = np.random.uniform(0,1,(8,8))
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cmap = plt.get_cmap("Oranges")
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im = ax.imshow(Z, interpolation="nearest", cmap=cmap, vmin=0, vmax=2)
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cb = fig.colorbar(im, fraction=0.046, pad=0.04)
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cb.set_ticks([])
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plt.savefig("../figures/tip-colorbar.pdf")
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# plt.show()

‎scripts/tip-dotted.py

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# -----------------------------------------------------------------------------
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# Matplotlib cheat sheet
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# Released under the BSD License
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# -----------------------------------------------------------------------------
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# Scripts to generate all the basic plots
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=(5,.25))
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ax = fig.add_axes([0,0,1,1], frameon=False,
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xticks=[], yticks=[], xlim=[0,1], ylim=[-.5,1.5])
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epsilon=1e-12
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plt.plot([0,1], [0,0], "black", clip_on=False, lw=8,
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ls=(.5,(epsilon, 1)), dash_capstyle="round")
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plt.plot([0,1], [1,1], "black", clip_on=False, lw=8,
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ls=(-.5,(epsilon, 2)), dash_capstyle="round")
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plt.savefig("../figures/tip-dotted.pdf")
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plt.show()

‎scripts/tip-dual-axis.py

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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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mpl.rcParams['axes.linewidth'] = 1.5
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fig = plt.figure(figsize=(2,2))
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d = 0.01
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ax1 = fig.add_axes([d,d,1-2*d,1-2*d], label="cartesian")
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ax2 = fig.add_axes([d,d,1-2*d,1-2*d], projection="polar", label="polar")
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ax1.set_xticks([]) #np.linspace(0.0, 0.4, 5))
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ax1.set_yticks([]) #np.linspace(0.0, 1.0, 11))
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ax2.set_rorigin(0)
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ax2.set_thetamax(90)
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ax2.set_ylim(0.5,1.0)
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ax2.set_xticks(np.linspace(0, np.pi/2, 10))
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ax2.set_yticks(np.linspace(0.5, 1.0, 5))
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ax2.set_xticklabels([])
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ax2.set_yticklabels([])
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plt.savefig("../figures/tip-dual-axis.pdf")
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plt.show()

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