Good Scientific Visualization Practices + Python
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1 Good Scientific Visualization Practices + Python Kristen Thyng Python in Geosciences September 19, 2013 Kristen Thyng (Texas A&M) Visualization September 19, / 29
2 Outline Overview of Bad Plotting 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
3 Overview of Bad Plotting There are lots of bad plots out there Using 3D when unnecessary Kristen Thyng (Texas A&M) Visualization September 19, / 29
4 Overview of Bad Plotting There are lots of bad plots out there What is the dominant feature of this plot? jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
5 Overview of Bad Plotting There are lots of bad plots out there Inconsistent representations jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
6 Overview of Bad Plotting There are lots of bad plots out there Unintuitive representation Kristen Thyng (Texas A&M) Visualization September 19, / 29
7 Overview of Bad Plotting There are lots of bad plots out there Chart junk Tufte, Edward R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Kristen Thyng (Texas A&M) Visualization September 19, / 29
8 Goals in Visualization Overview of Bad Plotting Honestly present the information - no cheating! Clear presentation - nothing unnecessary Label axes, units, etc, with large lettering Intuitive, consistent markers/coloring Give context Kristen Thyng (Texas A&M) Visualization September 19, / 29
9 Better plots Overview of Bad Plotting Using 3D when unnecessary Kristen Thyng (Texas A&M) Visualization September 19, / 29
10 Better plots Overview of Bad Plotting Better presentation, though pie charts not great Kristen Thyng (Texas A&M) Visualization September 19, / 29
11 Better plots Overview of Bad Plotting What is the dominant feature of this plot? jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
12 Better plots Overview of Bad Plotting Simpler is often better jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
13 Better plots Overview of Bad Plotting Pick a representation and stick with it jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
14 Better plots Overview of Bad Plotting Unintuitive representation Kristen Thyng (Texas A&M) Visualization September 19, / 29
15 Better plots Overview of Bad Plotting Color choices are key Kristen Thyng (Texas A&M) Visualization September 19, / 29
16 Better plots Overview of Bad Plotting Color choices are key rkriz/projects/create color table/color 07.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
17 Better plots Overview of Bad Plotting Chart junk Tufte, Edward R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Kristen Thyng (Texas A&M) Visualization September 19, / 29
18 Outline Perceptually-based colormaps 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
19 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
20 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
21 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
22 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
23 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
24 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Equal steps in data should correspond to equal steps in perception Kristen Thyng (Texas A&M) Visualization September 19, / 29
25 Color is 3D Perceptually-based colormaps r: saturation/color intensity, z: luminance/brightness, θ: color hue Kristen Thyng (Texas A&M) Visualization September 19, / 29
26 Color is 3D Perceptually-based colormaps r: saturation/color intensity, z: luminance/brightness, θ: color hue good for representing continuous variations in data magnitude Kristen Thyng (Texas A&M) Visualization September 19, / 29
27 Jet Luminance Perceptually-based colormaps files/28982/16/spectrum vs cubicyf.png Kristen Thyng (Texas A&M) Visualization September 19, / 29
28 Perceptually-based colormaps Hue-based Colormaps Perceptually Distort Information Kristen Thyng (Texas A&M) Visualization September 19, / 29
29 Perceptually-based colormaps Luminance and Saturation Colormaps Kristen Thyng (Texas A&M) Visualization September 19, / 29
30 Perceptually-based colormaps Improved Colormapping Kristen Thyng (Texas A&M) Visualization September 19, / 29
31 Outline Matplotlib Tools 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
32 Types of Colormaps Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
33 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
34 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
35 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
36 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
37 Subplot Matplotlib Tools axes and figures/subplot demo.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
38 Axes Grid Toolbox Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
39 Inset Axes Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
40 Overlaid Axes Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
41 Matplotlib Tools Overlaid Axes: Colorbar Placement Kristen Thyng (Texas A&M) Visualization September 19, / 29
42 Outline Miscellaneous Considerations 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
43 Miscellaneous Considerations Use Colormaps for Different Purposes Kristen Thyng (Texas A&M) Visualization September 19, / 29
44 Cube Helix Miscellaneous Considerations Proper intensity scaling, though do consider application dag/cubehelix/ Kristen Thyng (Texas A&M) Visualization September 19, / 29
45 Give Context Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
46 Be Consistent Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
47 Miscellaneous Considerations Discrete vs. Continuous Colorbar Kristen Thyng (Texas A&M) Visualization September 19, / 29
48 Miscellaneous Considerations Diverging Colormap: Critical value treatment Kristen Thyng (Texas A&M) Visualization September 19, / 29
49 Miscellaneous Considerations Printing in Black and White from Color Change the design choices altogether, or be sure the colors will print to different grayscale values Kristen Thyng (Texas A&M) Visualization September 19, / 29
50 Color Blindness Miscellaneous Considerations Problems distinguishing green-yellow-red, and rarely blue-yellow Kristen Thyng (Texas A&M) Visualization September 19, / 29
51 Resources Miscellaneous Considerations Edward Tufte s books IBM articles: Color Brewer: Color advice for maps: IEEE Computer Society article: rkriz/projects/create color table/color 07.pdf MatLab colormap function, with lots of references: perceptually-improved-colormaps/content/pmkmp.m Cube Helix colormap: dag/cubehelix/+ Test plots for sensitivity to color blindness: Matplotlib example gallery: Useful design blog: Figures writeup: Kristen Thyng (Texas A&M) Visualization September 19, / 29
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