Surface NMR processing and inversion GUI
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  1. # New matplotlib colormaps by Nathaniel J. Smith, Stefan van der Walt,
  2. # and (in the case of viridis) Eric Firing.
  3. #
  4. # This file and the colormaps in it are released under the CC0 license /
  5. # public domain dedication. We would appreciate credit if you use or
  6. # redistribute these colormaps, but do not impose any legal restrictions.
  7. #
  8. # To the extent possible under law, the persons who associated CC0 with
  9. # mpl-colormaps have waived all copyright and related or neighboring rights
  10. # to mpl-colormaps.
  11. #
  12. # You should have received a copy of the CC0 legalcode along with this
  13. # work. If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
  14. __all__ = ['magma', 'magma_r', 'inferno', 'inferno_r', 'plasma', 'plasma_r', 'viridis', 'viridis_r']
  15. _magma_data = [[0.001462, 0.000466, 0.013866],
  16. [0.002258, 0.001295, 0.018331],
  17. [0.003279, 0.002305, 0.023708],
  18. [0.004512, 0.003490, 0.029965],
  19. [0.005950, 0.004843, 0.037130],
  20. [0.007588, 0.006356, 0.044973],
  21. [0.009426, 0.008022, 0.052844],
  22. [0.011465, 0.009828, 0.060750],
  23. [0.013708, 0.011771, 0.068667],
  24. [0.016156, 0.013840, 0.076603],
  25. [0.018815, 0.016026, 0.084584],
  26. [0.021692, 0.018320, 0.092610],
  27. [0.024792, 0.020715, 0.100676],
  28. [0.028123, 0.023201, 0.108787],
  29. [0.031696, 0.025765, 0.116965],
  30. [0.035520, 0.028397, 0.125209],
  31. [0.039608, 0.031090, 0.133515],
  32. [0.043830, 0.033830, 0.141886],
  33. [0.048062, 0.036607, 0.150327],
  34. [0.052320, 0.039407, 0.158841],
  35. [0.056615, 0.042160, 0.167446],
  36. [0.060949, 0.044794, 0.176129],
  37. [0.065330, 0.047318, 0.184892],
  38. [0.069764, 0.049726, 0.193735],
  39. [0.074257, 0.052017, 0.202660],
  40. [0.078815, 0.054184, 0.211667],
  41. [0.083446, 0.056225, 0.220755],
  42. [0.088155, 0.058133, 0.229922],
  43. [0.092949, 0.059904, 0.239164],
  44. [0.097833, 0.061531, 0.248477],
  45. [0.102815, 0.063010, 0.257854],
  46. [0.107899, 0.064335, 0.267289],
  47. [0.113094, 0.065492, 0.276784],
  48. [0.118405, 0.066479, 0.286321],
  49. [0.123833, 0.067295, 0.295879],
  50. [0.129380, 0.067935, 0.305443],
  51. [0.135053, 0.068391, 0.315000],
  52. [0.140858, 0.068654, 0.324538],
  53. [0.146785, 0.068738, 0.334011],
  54. [0.152839, 0.068637, 0.343404],
  55. [0.159018, 0.068354, 0.352688],
  56. [0.165308, 0.067911, 0.361816],
  57. [0.171713, 0.067305, 0.370771],
  58. [0.178212, 0.066576, 0.379497],
  59. [0.184801, 0.065732, 0.387973],
  60. [0.191460, 0.064818, 0.396152],
  61. [0.198177, 0.063862, 0.404009],
  62. [0.204935, 0.062907, 0.411514],
  63. [0.211718, 0.061992, 0.418647],
  64. [0.218512, 0.061158, 0.425392],
  65. [0.225302, 0.060445, 0.431742],
  66. [0.232077, 0.059889, 0.437695],
  67. [0.238826, 0.059517, 0.443256],
  68. [0.245543, 0.059352, 0.448436],
  69. [0.252220, 0.059415, 0.453248],
  70. [0.258857, 0.059706, 0.457710],
  71. [0.265447, 0.060237, 0.461840],
  72. [0.271994, 0.060994, 0.465660],
  73. [0.278493, 0.061978, 0.469190],
  74. [0.284951, 0.063168, 0.472451],
  75. [0.291366, 0.064553, 0.475462],
  76. [0.297740, 0.066117, 0.478243],
  77. [0.304081, 0.067835, 0.480812],
  78. [0.310382, 0.069702, 0.483186],
  79. [0.316654, 0.071690, 0.485380],
  80. [0.322899, 0.073782, 0.487408],
  81. [0.329114, 0.075972, 0.489287],
  82. [0.335308, 0.078236, 0.491024],
  83. [0.341482, 0.080564, 0.492631],
  84. [0.347636, 0.082946, 0.494121],
  85. [0.353773, 0.085373, 0.495501],
  86. [0.359898, 0.087831, 0.496778],
  87. [0.366012, 0.090314, 0.497960],
  88. [0.372116, 0.092816, 0.499053],
  89. [0.378211, 0.095332, 0.500067],
  90. [0.384299, 0.097855, 0.501002],
  91. [0.390384, 0.100379, 0.501864],
  92. [0.396467, 0.102902, 0.502658],
  93. [0.402548, 0.105420, 0.503386],
  94. [0.408629, 0.107930, 0.504052],
  95. [0.414709, 0.110431, 0.504662],
  96. [0.420791, 0.112920, 0.505215],
  97. [0.426877, 0.115395, 0.505714],
  98. [0.432967, 0.117855, 0.506160],
  99. [0.439062, 0.120298, 0.506555],
  100. [0.445163, 0.122724, 0.506901],
  101. [0.451271, 0.125132, 0.507198],
  102. [0.457386, 0.127522, 0.507448],
  103. [0.463508, 0.129893, 0.507652],
  104. [0.469640, 0.132245, 0.507809],
  105. [0.475780, 0.134577, 0.507921],
  106. [0.481929, 0.136891, 0.507989],
  107. [0.488088, 0.139186, 0.508011],
  108. [0.494258, 0.141462, 0.507988],
  109. [0.500438, 0.143719, 0.507920],
  110. [0.506629, 0.145958, 0.507806],
  111. [0.512831, 0.148179, 0.507648],
  112. [0.519045, 0.150383, 0.507443],
  113. [0.525270, 0.152569, 0.507192],
  114. [0.531507, 0.154739, 0.506895],
  115. [0.537755, 0.156894, 0.506551],
  116. [0.544015, 0.159033, 0.506159],
  117. [0.550287, 0.161158, 0.505719],
  118. [0.556571, 0.163269, 0.505230],
  119. [0.562866, 0.165368, 0.504692],
  120. [0.569172, 0.167454, 0.504105],
  121. [0.575490, 0.169530, 0.503466],
  122. [0.581819, 0.171596, 0.502777],
  123. [0.588158, 0.173652, 0.502035],
  124. [0.594508, 0.175701, 0.501241],
  125. [0.600868, 0.177743, 0.500394],
  126. [0.607238, 0.179779, 0.499492],
  127. [0.613617, 0.181811, 0.498536],
  128. [0.620005, 0.183840, 0.497524],
  129. [0.626401, 0.185867, 0.496456],
  130. [0.632805, 0.187893, 0.495332],
  131. [0.639216, 0.189921, 0.494150],
  132. [0.645633, 0.191952, 0.492910],
  133. [0.652056, 0.193986, 0.491611],
  134. [0.658483, 0.196027, 0.490253],
  135. [0.664915, 0.198075, 0.488836],
  136. [0.671349, 0.200133, 0.487358],
  137. [0.677786, 0.202203, 0.485819],
  138. [0.684224, 0.204286, 0.484219],
  139. [0.690661, 0.206384, 0.482558],
  140. [0.697098, 0.208501, 0.480835],
  141. [0.703532, 0.210638, 0.479049],
  142. [0.709962, 0.212797, 0.477201],
  143. [0.716387, 0.214982, 0.475290],
  144. [0.722805, 0.217194, 0.473316],
  145. [0.729216, 0.219437, 0.471279],
  146. [0.735616, 0.221713, 0.469180],
  147. [0.742004, 0.224025, 0.467018],
  148. [0.748378, 0.226377, 0.464794],
  149. [0.754737, 0.228772, 0.462509],
  150. [0.761077, 0.231214, 0.460162],
  151. [0.767398, 0.233705, 0.457755],
  152. [0.773695, 0.236249, 0.455289],
  153. [0.779968, 0.238851, 0.452765],
  154. [0.786212, 0.241514, 0.450184],
  155. [0.792427, 0.244242, 0.447543],
  156. [0.798608, 0.247040, 0.444848],
  157. [0.804752, 0.249911, 0.442102],
  158. [0.810855, 0.252861, 0.439305],
  159. [0.816914, 0.255895, 0.436461],
  160. [0.822926, 0.259016, 0.433573],
  161. [0.828886, 0.262229, 0.430644],
  162. [0.834791, 0.265540, 0.427671],
  163. [0.840636, 0.268953, 0.424666],
  164. [0.846416, 0.272473, 0.421631],
  165. [0.852126, 0.276106, 0.418573],
  166. [0.857763, 0.279857, 0.415496],
  167. [0.863320, 0.283729, 0.412403],
  168. [0.868793, 0.287728, 0.409303],
  169. [0.874176, 0.291859, 0.406205],
  170. [0.879464, 0.296125, 0.403118],
  171. [0.884651, 0.300530, 0.400047],
  172. [0.889731, 0.305079, 0.397002],
  173. [0.894700, 0.309773, 0.393995],
  174. [0.899552, 0.314616, 0.391037],
  175. [0.904281, 0.319610, 0.388137],
  176. [0.908884, 0.324755, 0.385308],
  177. [0.913354, 0.330052, 0.382563],
  178. [0.917689, 0.335500, 0.379915],
  179. [0.921884, 0.341098, 0.377376],
  180. [0.925937, 0.346844, 0.374959],
  181. [0.929845, 0.352734, 0.372677],
  182. [0.933606, 0.358764, 0.370541],
  183. [0.937221, 0.364929, 0.368567],
  184. [0.940687, 0.371224, 0.366762],
  185. [0.944006, 0.377643, 0.365136],
  186. [0.947180, 0.384178, 0.363701],
  187. [0.950210, 0.390820, 0.362468],
  188. [0.953099, 0.397563, 0.361438],
  189. [0.955849, 0.404400, 0.360619],
  190. [0.958464, 0.411324, 0.360014],
  191. [0.960949, 0.418323, 0.359630],
  192. [0.963310, 0.425390, 0.359469],
  193. [0.965549, 0.432519, 0.359529],
  194. [0.967671, 0.439703, 0.359810],
  195. [0.969680, 0.446936, 0.360311],
  196. [0.971582, 0.454210, 0.361030],
  197. [0.973381, 0.461520, 0.361965],
  198. [0.975082, 0.468861, 0.363111],
  199. [0.976690, 0.476226, 0.364466],
  200. [0.978210, 0.483612, 0.366025],
  201. [0.979645, 0.491014, 0.367783],
  202. [0.981000, 0.498428, 0.369734],
  203. [0.982279, 0.505851, 0.371874],
  204. [0.983485, 0.513280, 0.374198],
  205. [0.984622, 0.520713, 0.376698],
  206. [0.985693, 0.528148, 0.379371],
  207. [0.986700, 0.535582, 0.382210],
  208. [0.987646, 0.543015, 0.385210],
  209. [0.988533, 0.550446, 0.388365],
  210. [0.989363, 0.557873, 0.391671],
  211. [0.990138, 0.565296, 0.395122],
  212. [0.990871, 0.572706, 0.398714],
  213. [0.991558, 0.580107, 0.402441],
  214. [0.992196, 0.587502, 0.406299],
  215. [0.992785, 0.594891, 0.410283],
  216. [0.993326, 0.602275, 0.414390],
  217. [0.993834, 0.609644, 0.418613],
  218. [0.994309, 0.616999, 0.422950],
  219. [0.994738, 0.624350, 0.427397],
  220. [0.995122, 0.631696, 0.431951],
  221. [0.995480, 0.639027, 0.436607],
  222. [0.995810, 0.646344, 0.441361],
  223. [0.996096, 0.653659, 0.446213],
  224. [0.996341, 0.660969, 0.451160],
  225. [0.996580, 0.668256, 0.456192],
  226. [0.996775, 0.675541, 0.461314],
  227. [0.996925, 0.682828, 0.466526],
  228. [0.997077, 0.690088, 0.471811],
  229. [0.997186, 0.697349, 0.477182],
  230. [0.997254, 0.704611, 0.482635],
  231. [0.997325, 0.711848, 0.488154],
  232. [0.997351, 0.719089, 0.493755],
  233. [0.997351, 0.726324, 0.499428],
  234. [0.997341, 0.733545, 0.505167],
  235. [0.997285, 0.740772, 0.510983],
  236. [0.997228, 0.747981, 0.516859],
  237. [0.997138, 0.755190, 0.522806],
  238. [0.997019, 0.762398, 0.528821],
  239. [0.996898, 0.769591, 0.534892],
  240. [0.996727, 0.776795, 0.541039],
  241. [0.996571, 0.783977, 0.547233],
  242. [0.996369, 0.791167, 0.553499],
  243. [0.996162, 0.798348, 0.559820],
  244. [0.995932, 0.805527, 0.566202],
  245. [0.995680, 0.812706, 0.572645],
  246. [0.995424, 0.819875, 0.579140],
  247. [0.995131, 0.827052, 0.585701],
  248. [0.994851, 0.834213, 0.592307],
  249. [0.994524, 0.841387, 0.598983],
  250. [0.994222, 0.848540, 0.605696],
  251. [0.993866, 0.855711, 0.612482],
  252. [0.993545, 0.862859, 0.619299],
  253. [0.993170, 0.870024, 0.626189],
  254. [0.992831, 0.877168, 0.633109],
  255. [0.992440, 0.884330, 0.640099],
  256. [0.992089, 0.891470, 0.647116],
  257. [0.991688, 0.898627, 0.654202],
  258. [0.991332, 0.905763, 0.661309],
  259. [0.990930, 0.912915, 0.668481],
  260. [0.990570, 0.920049, 0.675675],
  261. [0.990175, 0.927196, 0.682926],
  262. [0.989815, 0.934329, 0.690198],
  263. [0.989434, 0.941470, 0.697519],
  264. [0.989077, 0.948604, 0.704863],
  265. [0.988717, 0.955742, 0.712242],
  266. [0.988367, 0.962878, 0.719649],
  267. [0.988033, 0.970012, 0.727077],
  268. [0.987691, 0.977154, 0.734536],
  269. [0.987387, 0.984288, 0.742002],
  270. [0.987053, 0.991438, 0.749504]]
  271. _inferno_data = [[0.001462, 0.000466, 0.013866],
  272. [0.002267, 0.001270, 0.018570],
  273. [0.003299, 0.002249, 0.024239],
  274. [0.004547, 0.003392, 0.030909],
  275. [0.006006, 0.004692, 0.038558],
  276. [0.007676, 0.006136, 0.046836],
  277. [0.009561, 0.007713, 0.055143],
  278. [0.011663, 0.009417, 0.063460],
  279. [0.013995, 0.011225, 0.071862],
  280. [0.016561, 0.013136, 0.080282],
  281. [0.019373, 0.015133, 0.088767],
  282. [0.022447, 0.017199, 0.097327],
  283. [0.025793, 0.019331, 0.105930],
  284. [0.029432, 0.021503, 0.114621],
  285. [0.033385, 0.023702, 0.123397],
  286. [0.037668, 0.025921, 0.132232],
  287. [0.042253, 0.028139, 0.141141],
  288. [0.046915, 0.030324, 0.150164],
  289. [0.051644, 0.032474, 0.159254],
  290. [0.056449, 0.034569, 0.168414],
  291. [0.061340, 0.036590, 0.177642],
  292. [0.066331, 0.038504, 0.186962],
  293. [0.071429, 0.040294, 0.196354],
  294. [0.076637, 0.041905, 0.205799],
  295. [0.081962, 0.043328, 0.215289],
  296. [0.087411, 0.044556, 0.224813],
  297. [0.092990, 0.045583, 0.234358],
  298. [0.098702, 0.046402, 0.243904],
  299. [0.104551, 0.047008, 0.253430],
  300. [0.110536, 0.047399, 0.262912],
  301. [0.116656, 0.047574, 0.272321],
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  1010. [0.709898, 0.868751, 0.169257],
  1011. [0.720391, 0.870350, 0.162603],
  1012. [0.730889, 0.871916, 0.156029],
  1013. [0.741388, 0.873449, 0.149561],
  1014. [0.751884, 0.874951, 0.143228],
  1015. [0.762373, 0.876424, 0.137064],
  1016. [0.772852, 0.877868, 0.131109],
  1017. [0.783315, 0.879285, 0.125405],
  1018. [0.793760, 0.880678, 0.120005],
  1019. [0.804182, 0.882046, 0.114965],
  1020. [0.814576, 0.883393, 0.110347],
  1021. [0.824940, 0.884720, 0.106217],
  1022. [0.835270, 0.886029, 0.102646],
  1023. [0.845561, 0.887322, 0.099702],
  1024. [0.855810, 0.888601, 0.097452],
  1025. [0.866013, 0.889868, 0.095953],
  1026. [0.876168, 0.891125, 0.095250],
  1027. [0.886271, 0.892374, 0.095374],
  1028. [0.896320, 0.893616, 0.096335],
  1029. [0.906311, 0.894855, 0.098125],
  1030. [0.916242, 0.896091, 0.100717],
  1031. [0.926106, 0.897330, 0.104071],
  1032. [0.935904, 0.898570, 0.108131],
  1033. [0.945636, 0.899815, 0.112838],
  1034. [0.955300, 0.901065, 0.118128],
  1035. [0.964894, 0.902323, 0.123941],
  1036. [0.974417, 0.903590, 0.130215],
  1037. [0.983868, 0.904867, 0.136897],
  1038. [0.993248, 0.906157, 0.143936]]
  1039. from matplotlib.colors import ListedColormap
  1040. cmaps = {}
  1041. for (name, data) in (('magma', _magma_data),
  1042. ('magma_r', _magma_data[::-1]),
  1043. ('inferno', _inferno_data),
  1044. ('inferno_r', _inferno_data[::-1]),
  1045. ('plasma', _plasma_data),
  1046. ('plasma_r', _plasma_data[::-1]),
  1047. ('viridis', _viridis_data),
  1048. ('viridis_r', _viridis_data[::-1])):
  1049. cmaps[name] = ListedColormap(data, name=name)
  1050. magma = cmaps['magma']
  1051. magma_r = cmaps['magma_r']
  1052. inferno = cmaps['inferno']
  1053. inferno_r = cmaps['inferno_r']
  1054. plasma = cmaps['plasma']
  1055. plasma_r = cmaps['plasma_r']
  1056. viridis = cmaps['viridis']
  1057. viridis_r = cmaps['viridis_r']