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# Default Color changes

As discussed at length elsewhere [insert links], `jet` is an empirically bad color map and should not be the default color map. Due to the position that changing the appearance of the plot breaks backward compatibility, this change has been put off for far longer than it should have been. In addition to changing the default color map we plan to take the chance to change the default color-cycle on plots and to adopt a different color map for filled plots (`imshow`, `pcolor`, `contourf`, etc) and for scatter like plots.

## Default Heat Map Colormap

The choice of a new color map is fertile ground to bike-shedding (“No, it should be _this_ color”) so we have a proposed set criteria (via Nathaniel Smith) to evaluate proposed color maps.

- it should be a sequential colormap, because diverging colormaps are really misleading unless you know where the “center” of the data is, and for a default colormap we generally won’t.
- it should be perceptually uniform, i.e., human subjective judgments of how far apart nearby colors are should correspond as linearly as possible to the difference between the numerical values they represent, at least locally.
- it should have a perceptually uniform luminance ramp, i.e. if you convert to greyscale it should still be uniform. This is useful both in practical terms (greyscale printers are still a thing!) and because luminance is a very strong and natural cue to magnitude.
- it should also have some kind of variation in hue, because hue variation is a really helpful additional cue to perception, having two cues is better than one, and there’s no reason not to do it.
- the hue variation should be chosen to produce reasonable results even for viewers with the more common types of colorblindness. (Which rules out things like red-to-green.)
- For bonus points, it would be nice to choose a hue ramp that still works if you throw away the luminance variation, because then we could use the version with varying luminance for 2d plots, and the version with just hue variation for 3d plots. (In 3d plots you really want to reserve the luminance channel for lighting/shading, because your brain is really good at extracting 3d shape from luminance variation. If the 3d surface itself has massively varying luminance then this screws up the ability to see shape.)
- Not infringe any existing IP

### Example script

### Proposed Colormaps

## Default Scatter Colormap

For heat-map like applications it can be desirable to cover as much of the luminence scale as possible, however when color mapping markers, having markers too close to white can be a problem. For that reason we propose using a different (but maybe related) color map to the heat map for marker-based. The design parameters are the same as above, only with a more limited luminence variation.

### Example script

    import numpy as np
    import matplotlib.pyplot as plt

    np.random.seed(1234)

    fig, (ax1, ax2) = plt.subplots(1, 2)

    N = 50
    x = np.random.rand(N)
    y = np.random.rand(N)
    colors = np.random.rand(N)
    area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radiuses

    ax1.scatter(x, y, s=area, c=colors, alpha=0.5)

    X,Y = np.meshgrid(np.arange(0, 2*np.pi, .2),
                      np.arange(0, 2*np.pi, .2))
    U = np.cos(X)
    V = np.sin(Y)
    Q = ax2.quiver(X, Y, U, V, units='width')
    qd = np.random.rand(np.prod(X.shape))
    Q.set_array(qd)

### Proposed Colormaps

## Color Cycle / Qualitative color map

When plotting lines it is frequently desirable to plot multiple lines or artists which need to be distinguishable, but there is no inherent ordering.

### Example script

    import numpy as np
    import matplotlib.pyplot as plt

    fig, (ax1, ax2) = plt.subplots(1, 2)

    x = np.linspace(0, 1, 10)

    for j in range(10):
        ax1.plot(x, x * j)

    th = np.linspace(0, 2*np.pi, 1024)
    for j in np.linspace(0, np.pi, 10):
        ax2.plot(th, np.sin(th + j))

    ax2.set_xlim(0, 2*np.pi)

### Proposed Color cycle