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10 TV Series Centered Around Plane Crashes. Plane crashes are often used to incite action or set up plots in fiction, and these 10 TV series used the plot device to great effect. R 3D plot with centered axis? I want to represent a series of results computed from y = f(x1, x2, x3), with x1, x2 and x3 being in the -2, +2 range.

Matplotlib has a number of built-in colormaps accessible viamatplotlib.cm.get_cmap. There are also external libraries like[palettable] and [colorcet] that have many extra colormaps.Here we briefly discuss how to choose between the many options. Forhelp on creating your own colormaps, seeCreating Colormaps in Matplotlib.

Overview¶

The idea behind choosing a good colormap is to find a good representation in 3Dcolorspace for your data set. The best colormap for any given data set dependson many things including:

• Whether representing form or metric data ([Ware])
• Your knowledge of the data set (e.g., is there a critical valuefrom which the other values deviate?)
• If there is an intuitive color scheme for the parameter you are plotting
• If there is a standard in the field the audience may be expecting

For many applications, a perceptually uniform colormap is the bestchoice --- one in which equal steps in data are perceived as equalsteps in the color space. Researchers have found that the human brainperceives changes in the lightness parameter as changes in the datamuch better than, for example, changes in hue. Therefore, colormapswhich have monotonically increasing lightness through the colormapwill be better interpreted by the viewer. A wonderful example ofperceptually uniform colormaps is [colorcet].

Color can be represented in 3D space in various ways. One way to represent coloris using CIELAB. In CIELAB, color space is represented by lightness,(L^*); red-green, (a^*); and yellow-blue, (b^*). The lightnessparameter (L^*) can then be used to learn more about how the matplotlibcolormaps will be perceived by viewers.

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An excellent starting resource for learning about human perception of colormapsis from [IBM].

Classes of colormaps¶

Colormaps are often split into several categories based on their function (see,e.g., [Moreland]):

1. Sequential: change in lightness and often saturation of colorincrementally, often using a single hue; should be used forrepresenting information that has ordering.
2. Diverging: change in lightness and possibly saturation of twodifferent colors that meet in the middle at an unsaturated color;should be used when the information being plotted has a criticalmiddle value, such as topography or when the data deviates aroundzero.
3. Cyclic: change in lightness of two different colors that meet inthe middle and beginning/end at an unsaturated color; should beused for values that wrap around at the endpoints, such as phaseangle, wind direction, or time of day.
4. Qualitative: often are miscellaneous colors; should be used torepresent information which does not have ordering orrelationships.

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Sequential¶

For the Sequential plots, the lightness value increases monotonically throughthe colormaps. This is good. Some of the (L^*) values in the colormapsspan from 0 to 100 (binary and the other grayscale), and others start around(L^*=20). Those that have a smaller range of (L^*) will accordinglyhave a smaller perceptual range. Note also that the (L^*) function variesamongst the colormaps: some are approximately linear in (L^*) and othersare more curved.

Sequential2¶

Many of the (L^*) values from the Sequential2 plots are monotonicallyincreasing, but some (autumn, cool, spring, and winter) plateau or even go bothup and down in (L^*) space. Others (afmhot, copper, gist_heat, and hot)have kinks in the (L^*) functions. Data that is being represented in aregion of the colormap that is at a plateau or kink will lead to a perception ofbanding of the data in those values in the colormap (see [mycarta-banding] foran excellent example of this).

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Diverging¶

For the Diverging maps, we want to have monotonically increasing (L^*)values up to a maximum, which should be close to (L^*=100), followed bymonotonically decreasing (L^*) values. We are looking for approximatelyequal minimum (L^*) values at opposite ends of the colormap. By thesemeasures, BrBG and RdBu are good options. coolwarm is a good option, but itdoesn't span a wide range of (L^*) values (see grayscale section below).

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Cyclic¶

For Cyclic maps, we want to start and end on the same color, and meet asymmetric center point in the middle. (L^*) should change monotonicallyfrom start to middle, and inversely from middle to end. It should be symmetricon the increasing and decreasing side, and only differ in hue. At the ends andmiddle, (L^*) will reverse direction, which should be smoothed in(L^*) space to reduce artifacts. See [kovesi-colormaps] for moreinformation on the design of cyclic maps.

The often-used HSV colormap is included in this set of colormaps, although itis not symmetric to a center point. Additionally, the (L^*) values varywidely throughout the colormap, making it a poor choice for representing datafor viewers to see perceptually. See an extension on this idea at[mycarta-jet].

Qualitative¶

Qualitative colormaps are not aimed at being perceptual maps, but looking at thelightness parameter can verify that for us. The (L^*) values move all overthe place throughout the colormap, and are clearly not monotonically increasing.These would not be good options for use as perceptual colormaps.

Miscellaneous¶

Some of the miscellaneous colormaps have particular uses for whichthey have been created. For example, gist_earth, ocean, and terrainall seem to be created for plotting topography (green/brown) and waterdepths (blue) together. We would expect to see a divergence in thesecolormaps, then, but multiple kinks may not be ideal, such as ingist_earth and terrain. CMRmap was created to convert well tograyscale, though it does appear to have some small kinks in(L^*). cubehelix was created to vary smoothly in both lightnessand hue, but appears to have a small hump in the green hue area.

The often-used jet colormap is included in this set of colormaps. We can seethat the (L^*) values vary widely throughout the colormap, making it apoor choice for representing data for viewers to see perceptually. See anextension on this idea at [mycarta-jet].

First, we'll show the range of each colormap. Note that some seemto change more 'quickly' than others.

Lightness of matplotlib colormaps¶

Here we examine the lightness values of the matplotlib colormaps.Note that some documentation on the colormaps is available([list-colormaps]).

Grayscale conversion¶

It is important to pay attention to conversion to grayscale for colorplots, since they may be printed on black and white printers. If notcarefully considered, your readers may end up with indecipherableplots because the grayscale changes unpredictably through thecolormap.

Conversion to grayscale is done in many different ways [bw]. Some of thebetter ones use a linear combination of the rgb values of a pixel, butweighted according to how we perceive color intensity. A nonlinear method ofconversion to grayscale is to use the (L^*) values of the pixels. Ingeneral, similar principles apply for this question as they do for presentingone's information perceptually; that is, if a colormap is chosen that ismonotonically increasing in (L^*) values, it will print in a reasonablemanner to grayscale.

With this in mind, we see that the Sequential colormaps have reasonablerepresentations in grayscale. Some of the Sequential2 colormaps have decentenough grayscale representations, though some (autumn, spring, summer,winter) have very little grayscale change. If a colormap like this was usedin a plot and then the plot was printed to grayscale, a lot of theinformation may map to the same gray values. The Diverging colormaps mostlyvary from darker gray on the outer edges to white in the middle. Some(PuOr and seismic) have noticeably darker gray on one side than the otherand therefore are not very symmetric. coolwarm has little range of gray scaleand would print to a more uniform plot, losing a lot of detail. Note thatoverlaid, labeled contours could help differentiate between one side of thecolormap vs. the other since color cannot be used once a plot is printed tograyscale. Many of the Qualitative and Miscellaneous colormaps, such asAccent, hsv, and jet, change from darker to lighter and back to darker graythroughout the colormap. This would make it impossible for a viewer tointerpret the information in a plot once it is printed in grayscale.

Color vision deficiencies¶

There is a lot of information available about color blindness (e.g.,[colorblindness]). Additionally, there are tools available to convert imagesto how they look for different types of color vision deficiencies.

The most common form of color vision deficiency involves differentiatingbetween red and green. Thus, avoiding colormaps with both red and green willavoid many problems in general.

References¶

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Before you start your book publishing journey, it’s always a great idea to learn as much as you can about the craft of writing. Whether it’s the importance of outlines, the basics of literary devices or even turning your novel into a screenplay, learning is an important part of the process.

Additionally, determining what will drive your writing style is also a crucial piece of the writing puzzle.

Perhaps the two most common examples are character-driven writing and plot-driven writing.

If you aren’t familiar with these writing styles, here is a quick overview: Character-driven writing is focused on the characters and the internal change, more so than the events and situations that take place while plot-driven writing is focused on the actual happenings and the external changes of the story.

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It can sound confusing. Let’s delve into the details and explore the differences a bit further.

Character-driven –As previously mentioned, character-driven writing focuses on the inner conflict of the characters that you’ve created. If you choose to use this writing style, your reader will spend time thinking about the characters and their attitudes, personal evolutions and decisions, and how those, in turn, change the shape of the plot and the story as a whole.

Plots that are character driven are commonly referred to as “literary fiction” due to the fact that they feature characters that possess multiple layers that are exposed as the story develops.

Here’s an example to give you a better understanding of this writing style. Let’s say that Cruella de Vil from 101 Dalmations, who displays villainous characteristics, all of a sudden began exuding positive qualities. That change in Cruella de Vil would in turn alter the dynamic of the story completely. This internal change of the character is an example of character-driven writing.

Plot driven –Plot-driven stories, on the other hand, place a larger emphasis on the actual plot itself. Factors such as plot twists, action and external conflict are what make up the focus of this style of writing. In most cases, the goals of the story are more external in that they are focused on the development of a situation.

In plot-driven novels, the characters are usually forced to make quick decisions and, as a result, the development of the characters takes a back seat to the rapidly evolving story.

Examples of plot-driven writing are commonly found in genres such as fantasy, romance novels and mysteries, to name a few.

One way to look at this style of writing is to think of plot-driven stories as the tracks of a rollercoaster. The plot is the track and the reader follows that track throughout your book. From the moment your reader boards the rollercoaster car (opens your book), he or she is taken for a ride with your words and ideas through the twists, turns and loops of your plot, which ultimately leads them to the end of their ride. In essence, without the rollercoaster track (plot), the reader could not experience the thrill of the ride (your story).

On one side of the fence you have the character-driven writing style, where the main focus of your story is on the development of characters. And on the other side you have the plot-driven writing style, where the plot takes center stage and the characters are added accessories.

However, whichever side of the fence you choose to be on, the commonality between character- and plot-driven writing is that you’ll be taking the necessary steps to bringing your book idea to life!