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Reading Results

Reading Results¤

Metrics¤

The metrics are returned as list of dictionaries. For example, if we use methods=[benchmark.LightnessBenchmark] as the metrics argument, we get a length one list for ["#208eb7", "#8bd0eb", "#214a65", "#52dcbc"],

[
    {
        'method': 'lightness',
        'data': {
            'lightness': [84.91983092093982, 29.92196529863262, 84.89371481804541, 49.84008327873316, 74.84871458198634, 59.995102078239626],
            'min_lightness': 25,
            'max_lightness': 85,
            'smaller_than_max': [True, True, True, True, True, True],
            'greater_than_min': [True, True, True, True, True, True],
            'bounded_by_min_max': [True, True, True, True, True, True]
        }
    }
]

If multiple benchmarks are provided, i.e., methods=[benchmark.PerceptualDistanceBenchmark, benchmark.LightnessBenchmark], we get multiple metrics returned as a list.

[
    {
        'method': 'perceptual_distance',
        'data': {
            'colors': ['#8de4d3', '#344b46', '#74ee65', '#238910', '#a6c363', '#509d99'],
            'lab': [(84.91983092093982, -30.077937807876264, 0.02004498485879136), (29.92196529863262, -10.125651525571849, 0.11432219452202075), (84.89371481804541, -59.80516907528527, 55.02104980247336), (49.84008327873316, -49.880004054854155, 49.88542716044376), (74.84871458198634, -24.89418244566888, 44.67650807158665), (59.995102078239626, -24.750918732142523, -5.256244862912585)],
            'distances': [[0.0, 51.595487359709644, 22.65815239947601, 35.416816157500605, 23.89289149811552, 19.300512151508368], [51.595487359709644, 0.0, 56.39906000723282, 29.903204552554257, 49.4276053685809, 30.206424477640848], [22.658152399476016, 56.39906000723282, 0.0, 28.124661697115705, 13.768615584198283, 32.78257261407205], [35.41681615750062, 29.903204552554257, 28.124661697115705, 0.0, 23.282610044487893, 27.59833645833781], [23.89289149811552, 49.4276053685809, 13.768615584198294, 23.282610044487903, 0.0, 28.627463185209976], [19.300512151508364, 30.206424477640848, 32.78257261407205, 27.5983364583378, 28.62746318520996, 0.0]],
            'noticable': [[False, True, True, True, True, True], [True, False, True, True, True, True], [True, True, False, True, True, True], [True, True, True, False, True, True], [True, True, True, True, False, True], [True, True, True, True, True, False]]
        }
    },
    {
        'method': 'lightness',
        'data': {
            'lightness': [84.91983092093982, 29.92196529863262, 84.89371481804541, 49.84008327873316, 74.84871458198634, 59.995102078239626],
            'min_lightness': 25,
            'max_lightness': 85,
            'smaller_than_max': [True, True, True, True, True, True],
            'greater_than_min': [True, True, True, True, True, True],
            'bounded_by_min_max': [True, True, True, True, True, True]
        }
    }
]

Visualizations¤

Perceptual Distance¤

The following results are the results for ["#208eb7", "#8bd0eb", "#214a65", "#52dcbc"].

The deltaE distance matrix is plotted as a heatmap. In general, the larger the distance, the easier for us to distinguish.

We ceil all the distance values to 10 in this chart as large distances in Lab space desn't have an intuitive meaning.

If two colors are two close to each in a perceptual uniform color space, it is hard to distinguish them.

This heatmap shows whether any pair of colors are distinguishable.

Lightness¤

WIP

Not yet visualized.

Emperical¤

We will automatically create several charts using the color palette.

The following charts are the results for ["#208eb7", "#8bd0eb", "#214a65", "#52dcbc"].