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physics_based_augmentation
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seqgen
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pypulseq
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Sequence
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parula.py

parula.py 3.2 KB
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  1. from matplotlib.colors import LinearSegmentedColormap
    2. 

  2. 

  3. 

  4. def main(N: int) -> LinearSegmentedColormap:
    5. 

  5.     """
    6. 

  6.     Returns a Parula colormap to be used with matplotlib's `cycler`. `cm_data` has values copied from MATLAB for
    7. 

  7.     `parula(64)`.
    8. 

  8. 

  9.     Parameters
    10. 

  10.     ----------
    11. 

  11.     N : int
    12. 

  12.         Number of RGB quantization levels.
    13. 

  13. 

  14.     Returns
    15. 

  15.     -------
    16. 

  16.     LinearSegmentedColormap
    17. 

  17.         Parula color map.
    18. 

  18.     """
    19. 

  19.     cm_data = [
    20. 

  20.         [0.2422, 0.1504, 0.6603],
    21. 

  21.         [0.25039048, 0.16499524, 0.70761429],
    22. 

  22.         [0.25777143, 0.18178095, 0.7511381],
    23. 

  23.         [0.26472857, 0.19775714, 0.79521429],
    24. 

  24.         [0.27064762, 0.21467619, 0.83637143],
    25. 

  25.         [0.27511429, 0.2342381, 0.87098571],
    26. 

  26.         [0.2783, 0.25587143, 0.89907143],
    27. 

  27.         [0.28033333, 0.27823333, 0.9221],
    28. 

  28.         [0.2813381, 0.30059524, 0.94137619],
    29. 

  29.         [0.28101429, 0.32275714, 0.95788571],
    30. 

  30.         [0.27946667, 0.34467143, 0.97167619],
    31. 

  31.         [0.27597143, 0.36668095, 0.98290476],
    32. 

  32.         [0.26991429, 0.3892, 0.9906],
    33. 

  33.         [0.26024286, 0.41232857, 0.99515714],
    34. 

  34.         [0.24403333, 0.43583333, 0.99883333],
    35. 

  35.         [0.22064286, 0.46025714, 0.99728571],
    36. 

  36.         [0.19633333, 0.48471905, 0.98915238],
    37. 

  37.         [0.18340476, 0.50737143, 0.97979524],
    38. 

  38.         [0.17864286, 0.52885714, 0.96815714],
    39. 

  39.         [0.1764381, 0.54990476, 0.95201905],
    40. 

  40.         [0.16874286, 0.5702619, 0.93587143],
    41. 

  41.         [0.154, 0.5902, 0.9218],
    42. 

  42.         [0.14602857, 0.60911905, 0.90785714],
    43. 

  43.         [0.13802381, 0.62762857, 0.89729048],
    44. 

  44.         [0.12481429, 0.64592857, 0.88834286],
    45. 

  45.         [0.11125238, 0.6635, 0.87631429],
    46. 

  46.         [0.09520952, 0.67982857, 0.85978095],
    47. 

  47.         [0.06887143, 0.69477143, 0.83935714],
    48. 

  48.         [0.02966667, 0.70816667, 0.81633333],
    49. 

  49.         [0.00357143, 0.72026667, 0.7917],
    50. 

  50.         [0.00665714, 0.73121429, 0.76601429],
    51. 

  51.         [0.04332857, 0.74109524, 0.73940952],
    52. 

  52.         [0.09639524, 0.75, 0.7120381],
    53. 

  53.         [0.14077143, 0.7584, 0.68415714],
    54. 

  54.         [0.1717, 0.7669619, 0.65544286],
    55. 

  55.         [0.19376667, 0.77576667, 0.6251],
    56. 

  56.         [0.21608571, 0.7843, 0.5923],
    57. 

  57.         [0.24695714, 0.79179524, 0.55674286],
    58. 

  58.         [0.29061429, 0.79729048, 0.51882857],
    59. 

  59.         [0.34064286, 0.8008, 0.47885714],
    60. 

  60.         [0.3909, 0.80287143, 0.43544762],
    61. 

  61.         [0.44562857, 0.80241905, 0.39091905],
    62. 

  62.         [0.5044, 0.7993, 0.348],
    63. 

  63.         [0.5615619, 0.79423333, 0.30448095],
    64. 

  64.         [0.61739524, 0.78761905, 0.2612381],
    65. 

  65.         [0.67198571, 0.77927143, 0.2227],
    66. 

  66.         [0.7242, 0.76984286, 0.19102857],
    67. 

  67.         [0.77383333, 0.75980476, 0.16460952],
    68. 

  68.         [0.82031429, 0.74981429, 0.15352857],
    69. 

  69.         [0.86343333, 0.7406, 0.15963333],
    70. 

  70.         [0.90354286, 0.73302857, 0.17741429],
    71. 

  71.         [0.93925714, 0.72878571, 0.20995714],
    72. 

  72.         [0.97275714, 0.72977143, 0.23944286],
    73. 

  73.         [0.99564762, 0.74337143, 0.23714762],
    74. 

  74.         [0.99698571, 0.76585714, 0.21994286],
    75. 

  75.         [0.99520476, 0.78925238, 0.2027619],
    76. 

  76.         [0.9892, 0.81356667, 0.18853333],
    77. 

  77.         [0.97862857, 0.83862857, 0.17655714],
    78. 

  78.         [0.96764762, 0.8639, 0.16429048],
    79. 

  79.         [0.96100952, 0.88901905, 0.15367619],
    80. 

  80.         [0.95967143, 0.91345714, 0.14225714],
    81. 

  81.         [0.96279524, 0.9373381, 0.12650952],
    82. 

  82.         [0.96911429, 0.96062857, 0.1063619],
    83. 

  83.         [0.9769, 0.9839, 0.0805],
    84. 

  84.     ]
    85. 

  85. 

  86.     return LinearSegmentedColormap.from_list(name="parula", colors=cm_data, N=N)
    87.