One's information perceptually that is, if a colormap is chosen that is General, similar principles apply for this question as they do for presenting A nonlinear method ofĬonversion to grayscale is to use the \(L^*\) values of the pixels. Weighted according to how we perceive color intensity. Some of theīetter ones use a linear combination of the rgb values of a pixel, but Plots because the grayscale changes unpredictably through theĬonversion to grayscale is done in many different ways. If notĬarefully considered, your readers may end up with indecipherable Plots, since they may be printed on black and white printers. It is important to pay attention to conversion to grayscale for color tight_layout ( h_pad = 0.0, pad = 1.5 ) plt. set_xlabel ( cmap_category + ' colormaps', fontsize = 14 ) fig. set_ylabel ( 'Lightness $L^*$', fontsize = 12 ) ax. set_major_locator ( ticker ) formatter = mpl. set_ticks_position ( 'top' ) ticker = mpl. set_ylim ( 0.0, 100.0 ) # Set up labels for colormaps ax. append ( x + j * dc ) # Set up the axis limits: # * the 1st subplot is used as a reference for the x-axis limits # * lightness values goes from 0 to 100 (y-axis limits) ax. append ( x + j * dc ) elif cmap_category in ( 'Diverging', 'Qualitative', 'Cyclic', 'Miscellaneous', 'Sequential (2)' ): locs. scatter ( x + j * dc, y_, c = c_, cmap = cmap, s = 300, linewidths = 0.0 ) # Store locations for colormap labels if cmap_category in ( 'Perceptually Uniform Sequential', 'Sequential' ): locs. get ( cmap_category, 1.4 ) # cmaps horizontal spacing ax. y_ = lab c_ = x else : y_ = lab c_ = x dc = _DC. To make scatter markers change # color along plot: # if cmap_category = 'Sequential' : # These colormaps all start at high lightness, but we want them # reversed to look nice in the plot, so reverse the order. Do separately for each category # so each plot can be pretty. colormaps ( x ) lab = cspace_converter ( "sRGB1", "CAM02-UCS" )( rgb ) # Plot colormap L values. flat ): locs = # locations for text labels for j, cmap in enumerate ( cmap_list ): # Get RGB values for colormap and convert the colormap in # CAM02-UCS colorspace. subplots ( nrows = nsubplots, squeeze = False, figsize = ( 7, 2.6 * nsubplots )) for i, ax in enumerate ( axs. ceil ( len ( cmap_list ) / dsub )) # squeeze=False to handle similarly the case of a single subplot fig, axs = plt. get ( cmap_category, 6 ) nsubplots = int ( np. items (): # Do subplots so that colormaps have enough space. linspace ( 0.0, 1.0, 100 ) # Do plot for cmap_category, cmap_list in cmaps. Represent information which does not have ordering orĬmaps = # Indices to step through colormap x = np. Qualitative: often are miscellaneous colors should be used to Used for values that wrap around at the endpoints, such as phase The middle and beginning/end at an unsaturated color should be Middle value, such as topography or when the data deviates aroundĬyclic: change in lightness of two different colors that meet in Should be used when the information being plotted has a critical Representing information that has ordering.ĭiverging: change in lightness and possibly saturation of twoĭifferent colors that meet in the middle at an unsaturated color Incrementally, often using a single hue should be used for Sequential: change in lightness and often saturation of color Parameter \(L^*\) can then be used to learn more about how the matplotlibĪn excellent starting resource for learning about human perception of colormapsĬolormaps are often split into several categories based on their function (see, In CIELAB, color space is represented by lightness, Perceptually uniform colormaps can be found in theĬolor can be represented in 3D space in various ways. Will be better interpreted by the viewer. Which have monotonically increasing lightness through the colormap Much better than, for example, changes in hue. Perceives changes in the lightness parameter as changes in the data Researchers have found that the human brain a colormap in which equal steps in data are perceived as equal If there is a standard in the field the audience may be expectingįor many applications, a perceptually uniform colormap is the best choice If there is an intuitive color scheme for the parameter you are plotting Your knowledge of the data set ( e.g., is there a critical value Whether representing form or metric data ( ) The best colormap for any given data set depends The idea behind choosing a good colormap is to find a good representation in 3DĬolorspace for your data set. Here we briefly discuss how to choose between the many options. Third-party colormaps section of the Matplotlib documentation. Have many extra colormaps, which can be viewed in the Matplotlib has a number of built-in colormaps accessible via To download the full example code Choosing Colormaps in Matplotlib #
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