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I’m back again!

I was frustrated by the slow speed of the chromaticity diagram plotters, so I have begun to re-implement them. I learned that this requires re-writting most of the color space transforms into a numpy ufunc-like format. Versions of the necessary functions that have this format can be found here. Note these versions require that the input be a numpy array rather than an iterable, but the line var = np.asarray(var) can be added to the top of each function to remove this requriement. Some additional logic could be added so that e.g. if the input was a list, the output is a list, but this is a detail.

The current plotters, as best I understand, generate a 4000x4000 point scatter plot and color the dots with sRGB tones – matplotlib wasn’t made for plotting 16 million data points quickly!

A more efficient scheme is to make a meshgrid in the desired color space (in my case, u’ v’, though this method is general), “block out” values outside of the horseshoe, and shade with sRGB tones. Additional things like plankian locusts can be added, but the performance issue is with the massive number of scatter points.

Progress towards this can be found here. Unfortunately, a warning is thrown in the XYZ->sRGB conversion; I presume this is because there are imaginary colors present. An image of the result, in sRGB, is below.

I would appreciate any help with debugging this and then pulling the changes into colour.

timeit results from my laptop with an i7-7700HQ (4c/8t @ 3.6Ghz)

%timeit CIE_1976_UCS_chromaticity_diagram_plot(show_diagram_colours=True, standalone=False)
>>>502 ms ± 22.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
#from prysm.colorimetry import XYZ_to_xyY, xyY_to_xy, XYZ_to_xy, XYZ_to_uv, Luv_uv_to_xy, xy_to_XYZ
#from prysm.geometry import generate_mask
#from prysm.mathops import nan
samples = 256
xlim = (0,1)
ylim = (0,1)

wvl = np.arange(400, 700, 10)
wvl_XYZ = colour.wavelength_to_XYZ(wvl)
wvl_uv = XYZ_to_uv(wvl_XYZ)
wvl_pts = wvl_uv*samples
wvl_mask = generate_mask(wvl_pts, samples)
mask_idxs = np.where(wvl_mask == 0)

u = np.linspace(xlim[0], xlim[1], samples)
v = np.linspace(ylim[0], ylim[1], samples)
uu, vv = np.meshgrid(u, v)

uu[mask_idxs] = nan
vv[mask_idxs] = nan
shape = uu.shape

# stack u and v for vectorized computations
uuvv = np.stack((uu,vv), axis=len(shape))

# map -> xy -> XYZ -> sRGB
xy = Luv_uv_to_xy(uuvv)
xyz = xy_to_XYZ(xy)
dat = colour.XYZ_to_sRGB(xyz)
dat_2 = np.swapaxes(dat / dat[np.isfinite(dat)].max(), 0, 1)
plt.imshow(dat_2, origin='lower', extent=[*xlim, *ylim])
plt.gca().set(xlim=(0,0.6),ylim=(0,0.6))
plt.grid('off')
>>> 31.5 ms ± 1.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Since this includes expensive warning prints, I assume that this will run in closer to 25ms (a 20x improvement!) when it works properly. A 128x128 grid would also be ~4x faster, bringing the time to less than 10ms. This would allow time to budget e.g. expensive lanczos interpolation of the chromatic surface, achieving similar or superior visual quality in greatly reduced time.

What I don’t know, is why I get imaginary colors. Any help in that regard would be appreciated.