<html><body>
<p>12 new commits in yt:</p>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/046d9ddfc2a5/">https://bitbucket.org/yt_analysis/yt/commits/046d9ddfc2a5/</a> Changeset: 046d9ddfc2a5 Branch: yt User: MatthewTurk Date: 2016-01-21 19:02:45+00:00 Summary: Adding three new colormaps, names to be changed later. Affected #: 1 file</p>
<p>diff -r de823c10fed9f0b5fd4acf0512c45fc7d9cc491a -r 046d9ddfc2a59440d9e203497f2889342fab6719 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -7816,6 +7816,422 @@</p>
<pre>np.ones(256),
)
</pre>
<p>+_parameters = {'xp': [-6.0027356902356814, -42.46106902356901, + 41.393097643097661, 69.344486531986547, 6.15004208754209, + 17.695180976430976], + ‘yp’: [-19.704861111111086, 56.857638888888886, + -8.1597222222222001, 58.680555555555543, -23.958333333333314, + -16.059027777777771], + ‘min_Jp’: 17.1875, + ‘max_Jp’: 82.1875} +color_map_luts["cm_candidate_mjt"] = (array( + [ 0.01845663, 0.01940818, 0.02066025, 0.02218966, 0.02395409, 0.02595033, + 0.02817596, 0.03060653, 0.03322304, 0.03602798, 0.03900455, 0.04208415, + 0.04516324, 0.04823603, 0.05128648, 0.05431253, 0.05730541, 0.06025524, + 0.0631607 , 0.06601581, 0.0688137 , 0.07155484, 0.07423302, 0.07684491, + 0.07939306, 0.08186684, 0.08427203, 0.08660745, 0.08886448, 0.09105658, + 0.09316971, 0.09521672, 0.09719719, 0.09910774, 0.10096841, 0.10275846, + 0.10451309, 0.10621217, 0.10788683, 0.10952759, 0.1111585 , 0.11277895, + 0.11440998, 0.11605498, 0.11773643, 0.11945691, 0.12124361, 0.1230952, + 0.12504265, 0.12708539, 0.12924907, 0.13154308, 0.13398218, 0.13657917, + 0.13934934, 0.14230244, 0.14544595, 0.14880137, 0.15236868, 0.15615269, + 0.16016659, 0.16442043, 0.16890677, 0.1736277 , 0.17858407, 0.18378678, + 0.18922358, 0.19488582, 0.20076673, 0.2068576 , 0.21314778, 0.21962487, + 0.22627485, 0.23308241, 0.24003134, 0.2471049 , 0.25428629, 0.26155934, + 0.26890755, 0.27631616, 0.28377157, 0.29126147, 0.29877494, 0.30630245, + 0.31383585, 0.3213683 , 0.32889415, 0.33640883, 0.34390875, 0.35139113, + 0.35885392, 0.36629566, 0.37371536, 0.38111248, 0.38848676, 0.39583821, + 0.40316703, 0.41047357, 0.41775516, 0.42501568, 0.4322558 , 0.43947622, + 0.44667761, 0.4538607 , 0.46102616, 0.4681747 , 0.4753059 , 0.48242091, + 0.48952159, 0.49660864, 0.50368272, 0.5107445 , 0.51779465, 0.52483372, + 0.53186264, 0.53888257, 0.54589416, 0.55289811, 0.55989507, 0.56688575, + 0.57387113, 0.58085317, 0.58783159, 0.59480708, 0.60178032, 0.60875202, + 0.61572288, 0.62269359, 0.6296661 , 0.63664175, 0.64361962, 0.65060034, + 0.65758453, 0.66457277, 0.67156565, 0.67856369, 0.68556743, 0.69257733, + 0.69959381, 0.70661727, 0.71364828, 0.72068785, 0.7277347 , 0.73478882, + 0.74185008, 0.74891821, 0.7559928 , 0.76307522, 0.77016399, 0.77725777, + 0.78435528, 0.79145495, 0.7985548 , 0.8056524 , 0.81274479, 0.8198318, + 0.82691566, 0.83398271, 0.84102675, 0.84805028, 0.85504479, 0.8619907, + 0.86889642, 0.87572945, 0.88249039, 0.88914374, 0.89568162, 0.90206422, + 0.90825663, 0.91421853, 0.91990203, 0.92525379, 0.93021961, 0.93475119, + 0.93881359, 0.94239114, 0.94548927, 0.94813199, 0.950356 , 0.95220389, + 0.95371848, 0.95494533, 0.955918 , 0.95666379, 0.95722053, 0.95760023, + 0.95783176, 0.95791958, 0.95789564, 0.9577547 , 0.95751076, 0.95718454, + 0.95677095, 0.95627776, 0.95571186, 0.95508526, 0.95439943, 0.95365604, + 0.95285946, 0.9520137 , 0.9511224 , 0.95018892, 0.94921639, 0.94820771, + 0.94716564, 0.94609279, 0.94499169, 0.94386477, 0.9427144 , 0.94154291, + 0.94035262, 0.9391458 , 0.93792477, 0.93669181, 0.93544924, 0.9341994, + 0.93294465, 0.93168737, 0.93042998, 0.92917492, 0.92792467, 0.9266817, + 0.92544809, 0.92422626, 0.92301878, 0.9218282 , 0.92065707, 0.91950796, + 0.91838341, 0.91728597, 0.91621816, 0.91518248, 0.91418144, 0.9132175, + 0.91229316, 0.91141094, 0.91057341, 0.90978329, 0.90904349, 0.90835722, + 0.90772821, 0.90716087, 0.9066524 , 0.90620748, 0.90584124, 0.90556585, + 0.90536904, 0.90529003, 0.90533583, 0.90556318, 0.90603649, 0.90695623, + 0.90913313, 0.91657895, 0.92518702, 0.93347579]), array( + [ 0.14549808, 0.14959758, 0.15353745, 0.15733732, 0.1610376 , 0.16463933, + 0.16813881, 0.17155785, 0.17491483, 0.17819541, 0.18141182, 0.18458839, + 0.18770724, 0.19077643, 0.19381948, 0.19681897, 0.19978209, 0.20272758, + 0.20564068, 0.20852987, 0.21140659, 0.21425995, 0.21710135, 0.21993298, + 0.22274859, 0.22556395, 0.22836995, 0.23116975, 0.23397288, 0.23676848, + 0.23957041, 0.24237069, 0.2451726 , 0.24798083, 0.25078633, 0.2536041, + 0.25641791, 0.25924206, 0.26206407, 0.2648926 , 0.2677197 , 0.27054988, + 0.27337716, 0.27620452, 0.27902542, 0.28184388, 0.28465047, 0.28745244, + 0.29023681, 0.29301229, 0.2957673 , 0.2985035 , 0.30121847, 0.30390569, + 0.30656793, 0.30919798, 0.31179276, 0.31435225, 0.3168706 , 0.31934487, + 0.32177243, 0.32414968, 0.32647317, 0.32873992, 0.3309469 , 0.33309004, + 0.33516695, 0.33717534, 0.33911292, 0.34097773, 0.34276826, 0.34448346, + 0.34612285, 0.34768649, 0.34917502, 0.35058959, 0.35193184, 0.35320373, + 0.35440776, 0.3555465 , 0.35662265, 0.35763896, 0.35859819, 0.35950301, + 0.36035598, 0.36115947, 0.36191571, 0.36262669, 0.36329423, 0.36391995, + 0.36450527, 0.36505146, 0.36555959, 0.36603063, 0.36646538, 0.36686454, + 0.36722872, 0.36755843, 0.36785545, 0.36811875, 0.36834862, 0.36854528, + 0.3687089 , 0.36883963, 0.36893758, 0.36900282, 0.36903596, 0.36903681, + 0.36900473, 0.36893965, 0.36884148, 0.36871009, 0.36854532, 0.36834702, + 0.36811484, 0.3678482 , 0.36754678, 0.36721023, 0.36683815, 0.36643008, + 0.36598535, 0.36550255, 0.36498174, 0.36442226, 0.36382344, 0.36318451, + 0.3625047 , 0.36178315, 0.36101802, 0.36020779, 0.35935264, 0.35845149, + 0.35750326, 0.35650677, 0.35546082, 0.35436416, 0.35321545, 0.35201335, + 0.35075644, 0.34944329, 0.34807215, 0.3466408 , 0.34514904, 0.34359543, + 0.34197858, 0.34029709, 0.33854968, 0.33673306, 0.33484673, 0.33289005, + 0.33086222, 0.3287627 , 0.32659138, 0.3243486 , 0.32203538, 0.31964923, + 0.31718431, 0.31465481, 0.31206608, 0.3094119 , 0.3067005 , 0.30395628, + 0.30116449, 0.29836962, 0.29556856, 0.29281389, 0.29011662, 0.28754017, + 0.28514306, 0.28299549, 0.28118369, 0.27980714, 0.27897084, 0.27877267, + 0.27928815, 0.28055737, 0.28257918, 0.28531437, 0.2886962 , 0.29264343, + 0.29707203, 0.30189644, 0.30704703, 0.31246579, 0.318088 , 0.32388104, + 0.32979805, 0.33582149, 0.34190762, 0.34805406, 0.35424005, 0.36044006, + 0.36665573, 0.37287672, 0.37909448, 0.38529658, 0.39148116, 0.39764648, + 0.40378866, 0.40990456, 0.41599162, 0.42204777, 0.42807135, 0.43406103, + 0.44001575, 0.44593465, 0.45181705, 0.45766241, 0.46347028, 0.46924029, + 0.47497214, 0.48066556, 0.4863203 , 0.49193612, 0.49751281, 0.50305013, + 0.50854783, 0.51400566, 0.51942335, 0.52480059, 0.53013708, 0.53543248, + 0.54068668, 0.54589935, 0.55107004, 0.55619831, 0.56128369, 0.56632567, + 0.57132372, 0.5762773 , 0.58118582, 0.58604867, 0.5908652 , 0.59563471, + 0.60035645, 0.60502957, 0.60965313, 0.61422605, 0.61874705, 0.62321462, + 0.62762687, 0.63198146, 0.63627878, 0.64051559, 0.64468414, 0.64877752, + 0.6527965 , 0.65672131, 0.66054037, 0.66421949, 0.66770704, 0.67086745, + 0.67317041, 0.67282641, 0.67266658, 0.67286793]), array( + [ 0.31784919, 0.31399639, 0.31040105, 0.30704607, 0.30380333, 0.30070875, + 0.29782465, 0.29508233, 0.29241622, 0.28993656, 0.28760832, 0.28531346, + 0.28318494, 0.28120084, 0.27922989, 0.27740774, 0.27570877, 0.27402022, + 0.27246419, 0.27099788, 0.26955239, 0.26822345, 0.26693985, 0.26569724, + 0.2645547 , 0.26340288, 0.26231968, 0.26128857, 0.26024441, 0.2592725, + 0.25827349, 0.25730732, 0.25634957, 0.25536473, 0.25441368, 0.25338488, + 0.25238608, 0.25130798, 0.25022886, 0.24907581, 0.2478929 , 0.24663333, + 0.24532558, 0.24393036, 0.24247998, 0.24092303, 0.23931621, 0.23757501, + 0.23579155, 0.23385836, 0.23186082, 0.22975416, 0.22753042, 0.22523787, + 0.22279557, 0.22026374, 0.21765214, 0.2148911 , 0.21203983, 0.20910422, + 0.20605986, 0.20289957, 0.19965924, 0.19633992, 0.19294399, 0.18945376, + 0.18589623, 0.18228559, 0.17863001, 0.17493902, 0.17122338, 0.16749485, + 0.16376586, 0.16004918, 0.15635755, 0.15270334, 0.14909821, 0.1455525, + 0.14207653, 0.13867843, 0.13536508, 0.13214209, 0.12901386, 0.12598366, + 0.12305378, 0.12022564, 0.11749993, 0.11487682, 0.11235599, 0.10993683, + 0.10761855, 0.10540021, 0.10328085, 0.10125952, 0.09933535, 0.09750756, + 0.09577548, 0.09413856, 0.09259879, 0.09115321, 0.08980156, 0.08854366, + 0.08737943, 0.0863088 , 0.08533173, 0.08444816, 0.08365874, 0.08296295, + 0.08235974, 0.08184879, 0.08142963, 0.0811017 , 0.08086426, 0.08071652, + 0.08065732, 0.08068528, 0.08079915, 0.08099759, 0.08127912, 0.08164221, + 0.08208509, 0.08260558, 0.08320245, 0.08387405, 0.08461872, 0.08543485, + 0.08632086, 0.08727529, 0.08829637, 0.08938269, 0.09053368, 0.09174839, + 0.09302606, 0.09436609, 0.09576809, 0.0972319 , 0.09875759, 0.10034547, + 0.10199611, 0.10371036, 0.10548936, 0.10733453, 0.10924784, 0.11123145, + 0.1132879 , 0.11542014, 0.11763156, 0.1199261 , 0.12230825, 0.12478303, + 0.12735611, 0.13003383, 0.13282332, 0.1357325 , 0.13877022, 0.14194733, + 0.14527745, 0.14877095, 0.15244171, 0.15631008, 0.16039526, 0.16471264, + 0.16929903, 0.17416921, 0.17936813, 0.18491671, 0.19087146, 0.19726367, + 0.20413699, 0.21153645, 0.2195002 , 0.22805202, 0.23719324, 0.24689635, + 0.25710328, 0.26773032, 0.27867898, 0.2898494 , 0.30115214, 0.31251542, + 0.3238872 , 0.33520885, 0.34646952, 0.35766645, 0.36875439, 0.37976247, + 0.39066078, 0.40148085, 0.41217762, 0.42279543, 0.4333259 , 0.44374506, + 0.45408352, 0.46434054, 0.47451587, 0.48459837, 0.49459666, 0.50451736, + 0.51436098, 0.5241281 , 0.53381937, 0.54343549, 0.55297718, 0.56244513, + 0.57184006, 0.58116264, 0.59041354, 0.59959339, 0.60870282, 0.61774243, + 0.62671284, 0.63561464, 0.64444846, 0.65321495, 0.6619148 , 0.67054874, + 0.67911758, 0.68762222, 0.69606366, 0.70444301, 0.71276152, 0.72102061, + 0.7292224 , 0.73736877, 0.74546167, 0.75350327, 0.76149601, 0.76944257, + 0.7773459 , 0.78520926, 0.79303621, 0.80083062, 0.80859672, 0.81633909, + 0.82406267, 0.83177277, 0.83947509, 0.84717571, 0.85488105, 0.86259792, + 0.87033341, 0.87809481, 0.88590073, 0.89376265, 0.90168051, 0.90966175, + 0.9177585 , 0.92595735, 0.93431661, 0.94285311, 0.95166927, 0.96090167, + 0.97095595, 0.97849108, 0.98057884, 0.98147471]), np.ones(256)) + +# Used to reconstruct the colormap in viscm +parameters = {'xp': [17.623025510286254, 20.414094090828513, + -82.390265292478205, -3.3099888437807294, -5.170701230808902], + ‘yp’: [12.406964380648589, -98.305422647527877, 52.412280701754383, + 34.735513024986687, 22.175704412546509], + ‘min_Jp’: 13.5507921715, + ‘max_Jp’: 93.8863000932} + + +color_map_luts["cm_candidate_ng"] = (array( + [ 0.22330277, 0.22677033, 0.23017935, 0.23353169, 0.23681402, 0.2400368, + 0.24320742, 0.24631505, 0.24936304, 0.25236366, 0.25530723, 0.25819299, + 0.2610367 , 0.26382794, 0.26656596, 0.26926798, 0.2719204 , 0.27452761, + 0.27710562, 0.27963477, 0.28213047, 0.28460423, 0.28702736, 0.28943233, + 0.29181274, 0.29415763, 0.29649262, 0.29879558, 0.30108328, 0.30335993, + 0.30560745, 0.30785443, 0.31007522, 0.31229044, 0.31449347, 0.31668024, + 0.3188655 , 0.32102524, 0.32318876, 0.32532296, 0.32745808, 0.32956694, + 0.33167014, 0.33374679, 0.33581262, 0.33784825, 0.33986937, 0.34185319, + 0.34382043, 0.34573964, 0.34763692, 0.34948172, 0.35129044, 0.35304952, + 0.35475122, 0.35640626, 0.3579826 , 0.35949714, 0.36094336, 0.36229195, + 0.3635597 , 0.36473836, 0.36579828, 0.366748 , 0.36758102, 0.36828691, + 0.36883982, 0.3692409 , 0.36948262, 0.36955344, 0.36944189, 0.36913688, + 0.36862806, 0.36790621, 0.36696373, 0.36579487, 0.36439699, 0.36276941, + 0.3609152 , 0.35884058, 0.35655508, 0.35407138, 0.35140496, 0.34857371, + 0.34559732, 0.34249671, 0.33929342, 0.33600904, 0.3326647 , 0.32928363, + 0.32588267, 0.3224787 , 0.31908743, 0.31572297, 0.31239786, 0.30912306, + 0.30590808, 0.30276102, 0.2996887 , 0.2966967 , 0.29378953, 0.29097065, + 0.2882426 , 0.28560705, 0.28306489, 0.28061848, 0.27826751, 0.27600835, + 0.27383916, 0.27175753, 0.26976059, 0.26784495, 0.2660068 , 0.26424192, + 0.26254879, 0.26092124, 0.2593516 , 0.25783427, 0.25636334, 0.25493271, + 0.25353648, 0.25217148, 0.25082665, 0.2494952 , 0.24817023, 0.24684479, + 0.24551313, 0.24416844, 0.2428016 , 0.24140563, 0.23997368, 0.23849902, + 0.23697621, 0.2353966 , 0.23375381, 0.23204161, 0.23025401, 0.22838496, + 0.22642823, 0.22437847, 0.22223061, 0.21997986, 0.21762015, 0.21514715, + 0.21255756, 0.20984779, 0.20701463, 0.20405362, 0.20095999, 0.19773434, + 0.19437503, 0.1908809 , 0.18725135, 0.1834808 , 0.17957218, 0.17552885, + 0.17135306, 0.16704809, 0.1626184 , 0.15806468, 0.15339359, 0.14861956, + 0.14375457, 0.13881352, 0.13381484, 0.12878121, 0.12374048, 0.11871631, + 0.11375875, 0.10891932, 0.10425825, 0.09984694, 0.09576875, 0.09211897, + 0.08900344, 0.08653544, 0.08483018, 0.08399726, 0.08413159, 0.08530458, + 0.08755744, 0.09089837, 0.09530427, 0.10072634, 0.1070978 , 0.11434195, + 0.12237919, 0.13113212, 0.14052892, 0.15050498, 0.16100355, 0.17197546, + 0.18338793, 0.19520014, 0.20738006, 0.21990121, 0.23274066, 0.2459009, + 0.25934606, 0.27305848, 0.28703874, 0.30127492, 0.3157365 , 0.33043715, + 0.34534588, 0.3604517 , 0.37574975, 0.39120759, 0.40682457, 0.42256559, + 0.4384161 , 0.45435289, 0.47033772, 0.48636436, 0.50238444, 0.51837124, + 0.53430503, 0.55014493, 0.56586449, 0.58144061, 0.59684533, 0.61205655, + 0.62705581, 0.64182617, 0.6563544 , 0.67063044, 0.68464533, 0.69839355, + 0.71187229, 0.72507912, 0.73801226, 0.75067321, 0.76306221, 0.77518048, + 0.7870294 , 0.79860909, 0.8099203 , 0.82096426, 0.83173724, 0.84223591, + 0.85245837, 0.86239779, 0.87204252, 0.88137985, 0.89039307, 0.89906019, + 0.90735227, 0.91523111, 0.92264608, 0.92953025, 0.93579566, 0.94132897, + 0.9459897 , 0.94961072, 0.95202797, 0.95313791]), array( + [ 0.02115217, 0.02435766, 0.02770894, 0.03120549, 0.03486176, 0.03866843, + 0.04255067, 0.04636284, 0.05011635, 0.05380853, 0.05745319, 0.06105552, + 0.06460953, 0.06812624, 0.07160913, 0.07505172, 0.07846425, 0.08184654, + 0.08519339, 0.08851553, 0.09180774, 0.09506735, 0.09830606, 0.10151324, + 0.10469233, 0.1078471 , 0.11097106, 0.11407044, 0.11714148, 0.12018399, + 0.12320135, 0.12618924, 0.12915144, 0.13208572, 0.13499318, 0.13787426, + 0.14072906, 0.14355817, 0.14636281, 0.14914232, 0.1518996 , 0.15463338, + 0.1573473 , 0.16004005, 0.16271589, 0.16537333, 0.16801777, 0.17064683, + 0.17326807, 0.1758771 , 0.17848374, 0.1810841 , 0.18368643, 0.18629175, + 0.18890301, 0.19152787, 0.19416467, 0.19682321, 0.19950818, 0.20222144, + 0.20497307, 0.20776897, 0.21061428, 0.21351848, 0.21648954, 0.21953572, + 0.22266685, 0.22589265, 0.22922275, 0.23266724, 0.23623633, 0.23994008, + 0.24378808, 0.24778908, 0.25195046, 0.25627786, 0.26077448, 0.26544111, + 0.27027536, 0.27527178, 0.2804218 , 0.28571395, 0.29113426, 0.29666673, + 0.30229403, 0.30799808, 0.31376071, 0.31956423, 0.3253919 , 0.33122689, + 0.33705636, 0.34286842, 0.34865273, 0.35440061, 0.36010493, 0.36576003, + 0.37136152, 0.3769062 , 0.38239183, 0.38781708, 0.39318132, 0.39848456, + 0.40372731, 0.40891054, 0.41403551, 0.41910295, 0.42411461, 0.42907338, + 0.43398142, 0.43884093, 0.44365421, 0.4484236 , 0.45315147, 0.45784019, + 0.46249111, 0.46710703, 0.4716911 , 0.47624563, 0.48077291, 0.48527518, + 0.4897545 , 0.49421204, 0.49865123, 0.50307409, 0.50748262, 0.51187871, + 0.51626387, 0.52063984, 0.52500891, 0.5293727 , 0.53373275, 0.5380905, + 0.54244704, 0.5468042 , 0.55116316, 0.55552505, 0.55989088, 0.56426165, + 0.56863832, 0.57302164, 0.57741226, 0.58181071, 0.58621776, 0.59063378, + 0.59505889, 0.59949327, 0.60393701, 0.60839035, 0.61285357, 0.61732602, + 0.62180742, 0.62629743, 0.63079562, 0.63530232, 0.63981655, 0.64433739, + 0.64886414, 0.65339601, 0.65793219, 0.66247241, 0.66701578, 0.6715607, + 0.67610609, 0.68065087, 0.68519387, 0.6897339 , 0.69426971, 0.69880098, + 0.70332548, 0.7078417 , 0.71234818, 0.71684345, 0.72132594, 0.72579406, + 0.73024615, 0.73468052, 0.73909539, 0.74348893, 0.74785925, 0.75220438, + 0.75652229, 0.76081087, 0.76506792, 0.76929116, 0.77347824, 0.77762671, + 0.78173401, 0.78579751, 0.78981448, 0.79378208, 0.79769739, 0.80155739, + 0.80535919, 0.80909916, 0.81277394, 0.81638014, 0.81991431, 0.82337227, + 0.82675067, 0.83004601, 0.8332539 , 0.83637039, 0.83939294, 0.84231614, + 0.84513786, 0.84785477, 0.85046308, 0.85296202, 0.85534788, 0.85762123, + 0.85978086, 0.86182713, 0.86376341, 0.86558903, 0.86731067, 0.86893272, + 0.87045909, 0.87189851, 0.87325813, 0.87454523, 0.87576896, 0.87693806, + 0.87806108, 0.87914681, 0.88020359, 0.88123929, 0.88226176, 0.88327808, + 0.88429462, 0.88531766, 0.88635321, 0.8874062 , 0.88848178, 0.88958464, + 0.89071918, 0.89189007, 0.89310154, 0.89435739, 0.89566299, 0.89702343, + 0.89844289, 0.89992722, 0.90148396, 0.90312081, 0.90484675, 0.90667256, + 0.90861145, 0.91068001, 0.91289948, 0.91529744, 0.91790984, 0.92078319, + 0.92397484, 0.92755584, 0.93160009, 0.93616295]), array( + [ 0.00202189, 0.00551406, 0.00964551, 0.01445093, 0.02004956, 0.0264401, + 0.03362203, 0.04168051, 0.04990578, 0.05800892, 0.06608536, 0.07416013, + 0.0821575 , 0.0901558 , 0.09817059, 0.10611951, 0.11408814, 0.12206225, + 0.12997097, 0.13792374, 0.14585317, 0.15371495, 0.16165622, 0.16953288, + 0.17738626, 0.18527689, 0.19309761, 0.20095943, 0.20879604, 0.21659823, + 0.22445639, 0.2322505 , 0.24010403, 0.2479312 , 0.25576831, 0.26363933, + 0.27148133, 0.27940784, 0.28728597, 0.29527021, 0.30321929, 0.31125895, + 0.3192891 , 0.32740412, 0.33552351, 0.34373191, 0.35194602, 0.36026328, + 0.36857416, 0.37701245, 0.38544163, 0.39398549, 0.40255076, 0.41117865, + 0.41988038, 0.42858781, 0.43740831, 0.44624508, 0.45509757, 0.46404412, + 0.47298933, 0.48193114, 0.49093036, 0.499919 , 0.50887858, 0.51779951, + 0.52670573, 0.53554666, 0.54429458, 0.55292889, 0.56142589, 0.56975888, + 0.57789829, 0.58581211, 0.59346653, 0.60082694, 0.60785824, 0.6145276, + 0.62080425, 0.6266615 , 0.63207769, 0.63703716, 0.64153073, 0.64555596, + 0.64911698, 0.65222401, 0.65489262, 0.65714283, 0.65899809, 0.66048517, + 0.66163121, 0.66246411, 0.66301175, 0.6633014 , 0.66335931, 0.66321038, + 0.66287804, 0.66238411, 0.66174876, 0.66099052, 0.66012634, 0.65917165, + 0.65814046, 0.6570454 , 0.6558979 , 0.65471 , 0.6534912 , 0.65224774, + 0.65098687, 0.64971498, 0.64843772, 0.64716007, 0.64588635, 0.64462029, + 0.64336792, 0.64213086, 0.64090915, 0.63970452, 0.6385183 , 0.6373514, + 0.63620481, 0.63508181, 0.63397823, 0.63289364, 0.63182733, 0.63077828, + 0.62974631, 0.62873002, 0.62772575, 0.62673148, 0.62574501, 0.62476397, + 0.62378672, 0.62280874, 0.62182708, 0.62083861, 0.61984011, 0.61882799, + 0.61779832, 0.61674751, 0.61567191, 0.6145678 , 0.61343038, 0.61225577, + 0.61104058, 0.60978098, 0.60847311, 0.60711217, 0.60569296, 0.60421341, + 0.6026698 , 0.60105844, 0.59937571, 0.59761482, 0.5957737 , 0.59385008, + 0.59184061, 0.58974201, 0.5875511 , 0.58526193, 0.58287108, 0.58037826, + 0.57778061, 0.57507533, 0.57225971, 0.56933109, 0.5662869 , 0.56311922, + 0.55983012, 0.55641792, 0.55288035, 0.5492152 , 0.54542033, 0.54149367, + 0.53743319, 0.53323696, 0.5289031 , 0.52442983, 0.51981546, 0.51505838, + 0.51015712, 0.50511032, 0.4999168 , 0.49457554, 0.4890857 , 0.48344672, + 0.47765827, 0.47172037, 0.46563338, 0.45939808, 0.45301574, 0.44648817, + 0.43980205, 0.43297192, 0.42600399, 0.4189032 , 0.41167556, 0.40430087, + 0.39681183, 0.38922019, 0.38152054, 0.37372536, 0.36586902, 0.35794158, + 0.34998244, 0.34201236, 0.33404905, 0.32613936, 0.31830265, 0.31059314, + 0.30304889, 0.29571869, 0.28866636, 0.28193158, 0.27559011, 0.26970074, + 0.26431775, 0.25950931, 0.25533028, 0.25182957, 0.24905318, 0.24703636, + 0.24580423, 0.24537215, 0.2457446 , 0.24691602, 0.2488721 , 0.25159047, + 0.25504271, 0.25919611, 0.26401499, 0.26946277, 0.27550279, 0.28209979, + 0.28922076, 0.29683505, 0.30491583, 0.31344099, 0.32238968, 0.33174591, + 0.34150092, 0.35164825, 0.36218311, 0.37310746, 0.38442836, 0.39615832, + 0.40831552, 0.42092364, 0.4340111 , 0.44760871, 0.46174485, 0.47643618, + 0.49169001, 0.50743951, 0.52351526, 0.53960817]), np.ones(256)) + +# Used to reconstruct the colormap in viscm +parameters = {'xp': [-2.3569023569023386, 29.24031986531989, 21.948653198653204, -25.44718013468011, -4.78745791245791], + ‘yp’: [-27.604166666666657, -30.642361111111086, 24.652777777777771, -13.6284722222222, 23.4375], + ‘min_Jp’: 15, + ‘max_Jp’: 95} + +color_map_luts['cm_candidate_kk'] = (array( + [ 0.07873808, 0.08503098, 0.09119215, 0.09725944, 0.10324966, 0.10914691, + 0.1149903 , 0.12076614, 0.12647234, 0.13214487, 0.13775951, 0.14331952, + 0.14885405, 0.15434127, 0.15978387, 0.16520148, 0.17058327, 0.17592717, + 0.1812416 , 0.18653223, 0.19178949, 0.19701509, 0.20221806, 0.20739605, + 0.21254477, 0.21766522, 0.22276163, 0.22783646, 0.232884 , 0.23790477, + 0.24289917, 0.24786997, 0.25281796, 0.25773939, 0.26263436, 0.26750288, + 0.27234491, 0.27716076, 0.28195253, 0.28671682, 0.29145343, 0.29616211, + 0.30084257, 0.30549451, 0.31011758, 0.31471143, 0.31927567, 0.32380992, + 0.32831456, 0.3327882 , 0.33723043, 0.34164086, 0.34601907, 0.35036466, + 0.35467722, 0.35895634, 0.36320162, 0.36741265, 0.37158905, 0.37573041, + 0.37983636, 0.38390652, 0.38794052, 0.391938 , 0.3958986 , 0.39982199, + 0.40370783, 0.40755579, 0.41136559, 0.41513702, 0.41886962, 0.42256312, + 0.42621724, 0.42983171, 0.43340628, 0.43694071, 0.44043477, 0.44388826, + 0.44730096, 0.4506727 , 0.4540033 , 0.45729265, 0.46054056, 0.46374691, + 0.4669116 , 0.47003456, 0.47311572, 0.47615505, 0.47915255, 0.48210822, + 0.48502208, 0.4878942 , 0.49072469, 0.49351365, 0.49626124, 0.49896763, + 0.50163303, 0.50425765, 0.50684177, 0.50938571, 0.51188977, 0.51435433, + 0.51677977, 0.5191665 , 0.52151498, 0.52382573, 0.52609922, 0.52833601, + 0.53053664, 0.53270171, 0.53483184, 0.53692768, 0.53898991, 0.54101933, + 0.54301655, 0.5449823 , 0.54691736, 0.54882248, 0.55069849, 0.55254618, + 0.55436641, 0.55616019, 0.55792828, 0.55967151, 0.56139081, 0.56308707, + 0.56476124, 0.56641427, 0.56804712, 0.56966078, 0.57125625, 0.57283474, + 0.5743971 , 0.57594437, 0.57747763, 0.578998 , 0.58050659, 0.58200457, + 0.58349311, 0.58497344, 0.58644679, 0.58791447, 0.58937777, 0.59083808, + 0.59229669, 0.59375499, 0.59521431, 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0.79266478, 0.7988389 , 0.80509156, 0.81142348, 0.81779745, 0.82424433, + 0.83076477, 0.83731914, 0.84395228, 0.85063328, 0.8573683 , 0.86417388, + 0.87100664, 0.87792232, 0.88485711, 0.89186942, 0.898911 , 0.90601831, + 0.91316089, 0.92036241, 0.92760063, 0.93489628, 0.94222522, 0.94961559, + 0.95703072, 0.96451696, 0.97201416, 0.97959794]), array( + [ 0.02380049, 0.02762946, 0.0314955 , 0.03538367, 0.03929263, 0.04314916, + 0.04681625, 0.05034685, 0.05376738, 0.05706764, 0.06028584, 0.06343363, + 0.06649987, 0.06951333, 0.0724811 , 0.07539619, 0.07827446, 0.0811238, + 0.08394364, 0.08673511, 0.08950972, 0.0922702 , 0.09501404, 0.0977463, + 0.10047279, 0.10319545, 0.10591402, 0.10862929, 0.11134673, 0.11406773, + 0.11679361, 0.11952425, 0.12226063, 0.12500588, 0.12776095, 0.13052669, + 0.13330389, 0.13609311, 0.13889411, 0.1417089 , 0.14453802, 0.14738192, + 0.15024103, 0.15311572, 0.15600633, 0.15891314, 0.1618364 , 0.16477634, + 0.16773294, 0.17070662, 0.17369747, 0.17670558, 0.17973101, 0.18277379, + 0.18583395, 0.18891149, 0.19200641, 0.19511868, 0.19824828, 0.20139517, + 0.20455931, 0.20774066, 0.21093915, 0.21415474, 0.21738737, 0.22063697, + 0.22390351, 0.22718691, 0.23048713, 0.23380417, 0.23713792, 0.24048832, + 0.2438553 , 0.24723882, 0.25063881, 0.25405521, 0.25748797, 0.26093702, + 0.26440229, 0.26788372, 0.27138123, 0.27489488, 0.27842445, 0.28196986, + 0.28553099, 0.28910775, 0.29270002, 0.29630767, 0.29993058, 0.30356863, + 0.30722174, 0.31088964, 0.31457215, 0.3182691 , 0.32198029, 0.32570551, + 0.32944456, 0.33319721, 0.33696322, 0.34074232, 0.34453424, 0.34833872, + 0.35215547, 0.35598423, 0.35982468, 0.36367647, 0.36753933, 0.37141294, + 0.37529698, 0.37919113, 0.38309506, 0.38700843, 0.39093088, 0.394862, + 0.39880154, 0.40274917, 0.40670453, 0.41066731, 0.41463715, 0.41861372, + 0.42259669, 0.4265856 , 0.43058022, 0.43458024, 0.43858535, 0.44259523, + 0.44660955, 0.45062802, 0.45465032, 0.45867614, 0.46270517, 0.466737, + 0.47077145, 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0.81217164, 0.81581257, + 0.81942391, 0.82300747, 0.82656492, 0.83009816, 0.83360887, 0.83709793, + 0.84056637, 0.844015 , 0.84744449, 0.85085836, 0.85425577, 0.85763616, + 0.8609997 , 0.8643531 , 0.86769067, 0.87101222, 0.87432566, 0.87762463, + 0.88090892, 0.88418748, 0.88745026, 0.89070437, 0.89394846, 0.89717856, + 0.90040525, 0.90361483, 0.90682325, 0.91001579, 0.9132048 , 0.91638083, + 0.9195519 , 0.92271171, 0.92586627, 0.92900992, 0.93214934, 0.93527665, + 0.93840226, 0.94151285, 0.9446259 , 0.94771918]), array( + [ 0.45890713, 0.46137905, 0.46384563, 0.46630529, 0.46875421, 0.4711862, + 0.47360008, 0.47599069, 0.47835461, 0.48068977, 0.48299219, 0.4852595, + 0.48748847, 0.48967699, 0.49182306, 0.49392292, 0.4959753 , 0.49797881, + 0.4999306 , 0.50182767, 0.50367037, 0.5054572 , 0.50718436, 0.50885097, + 0.51045736, 0.51200237, 0.51348338, 0.51489809, 0.51624834, 0.51753338, + 0.51875253, 0.51990356, 0.52098523, 0.52199968, 0.52294672, 0.52382624, + 0.52463824, 0.52538248, 0.52605675, 0.52666444, 0.52720607, 0.52768224, + 0.5280937 , 0.5284413 , 0.52872599, 0.52894886, 0.52911108, 0.5292139, + 0.52925736, 0.52924451, 0.5291769 , 0.52905617, 0.52888406, 0.52866235, + 0.5283929 , 0.52807762, 0.52771851, 0.52731757, 0.52687689, 0.52639856, + 0.52588473, 0.52533756, 0.52475925, 0.524152 , 0.52351804, 0.5228596, + 0.52217891, 0.5214782 , 0.52075951, 0.52002465, 0.51927646, 0.51851715, + 0.51774891, 0.51697393, 0.51619438, 0.5154124 , 0.5146301 , 0.51384958, + 0.51307291, 0.51230212, 0.51153923, 0.51078544, 0.51004343, 0.50931521, + 0.50860267, 0.50790763, 0.50723191, 0.50657725, 0.50594539, 0.50533791, + 0.50475611, 0.50420208, 0.50367737, 0.50318346, 0.50272179, 0.50229376, + 0.50190069, 0.5015438 , 0.50122435, 0.50094363, 0.50070274, 0.50050273, + 0.50034459, 0.50022925, 0.50015758, 0.50013055, 0.50014881, 0.50021302, + 0.50032381, 0.50048172, 0.50068726, 0.50094086, 0.50124298, 0.50159412, + 0.50199429, 0.50244368, 0.50294243, 0.50349062, 0.5040883 , 0.50473544, + 0.50543197, 0.50617805, 0.5069733 , 0.50781742, 0.50871016, 0.5096512, + 0.51064021, 0.51167679, 0.51276051, 0.5138909 , 0.51506743, 0.51628978, + 0.51755709, 0.51886873, 0.52022401, 0.52162216, 0.52306241, 0.52454388, + 0.52606564, 0.52762668, 0.5292259 , 0.53086207, 0.53253385, 0.53423975, + 0.53597803, 0.53774675, 0.53954371, 0.54136636, 0.54321177, 0.54507652, + 0.54695667, 0.54884775, 0.55074476, 0.55264242, 0.55453558, 0.55642011, + 0.55829453, 0.56016223, 0.56203375, 0.56392814, 0.56587122, 0.56788936, + 0.57000019, 0.57220545, 0.57449055, 0.57683063, 0.57919892, 0.58157275, + 0.58393538, 0.58627588, 0.58858776, 0.59086745, 0.59311274, 0.59532221, + 0.59749564, 0.59963222, 0.60173091, 0.60379008, 0.60580649, 0.60777924, + 0.6097056 , 0.61158212, 0.61340237, 0.6151645 , 0.61686366, 0.61849128, + 0.62004292, 0.62151305, 0.62288934, 0.62416816, 0.62533879, 0.62639158, + 0.62731938, 0.6281096 , 0.62875725, 0.62925097, 0.62958736, 0.62976024, + 0.62976899, 0.62961438, 0.62930119, 0.62883586, 0.62822926, 0.62749232, + 0.62663432, 0.62567778, 0.62461908, 0.62348256, 0.62227864, 0.621005, + 0.6196701 , 0.61828149, 0.61685751, 0.61539115, 0.61388335, 0.61233673, + 0.61075292, 0.60913269, 0.607476 , 0.60579314, 0.60408651, 0.60234546, + 0.60056748, 0.59874959, 0.59688839, 0.59502121, 0.59312158, 0.59117279, + 0.58917064, 0.58718501, 0.58514178, 0.58303558, 0.58094288, 0.57879227, + 0.5765793 , 0.57438349, 0.57210749, 0.56981243, 0.56748232, 0.56508082, + 0.56269289, 0.56020367, 0.55773849, 0.5551784 , 0.55261699, 0.54997983, + 0.54732492, 0.54460356, 0.54185741, 0.53904386, 0.53620824, 0.53329363, + 0.53036982, 0.52734442, 0.52433316, 0.52118636]), np.ones(256)) +</p>
<pre># Aliases
color_map_luts['B-W LINEAR'] = color_map_luts['idl00']
color_map_luts['BLUE'] = color_map_luts['idl01']</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/fbf1412ce0c9/">https://bitbucket.org/yt_analysis/yt/commits/fbf1412ce0c9/</a> Changeset: fbf1412ce0c9 Branch: yt User: MatthewTurk Date: 2016-01-28 16:55:17+00:00 Summary: Adding Cameron's candidate Affected #: 1 file</p>
<p>diff -r 046d9ddfc2a59440d9e203497f2889342fab6719 -r fbf1412ce0c9571b1d0b46ab6e17c7d79176b441 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -8232,6 +8232,149 @@</p>
<pre> 0.54732492, 0.54460356, 0.54185741, 0.53904386, 0.53620824, 0.53329363,
0.53036982, 0.52734442, 0.52433316, 0.52118636]), np.ones(256))
</pre>
<p>+# Used to reconstruct the colormap in viscm +parameters = {'xp': [6.4995757388238928, -16.241760894839473, + -12.632024921242106, -21.656364855235495, 7.5824965309031143, + 6.4995757388238928, 86.274740755325524, 15.884889270177041, + -11.188130531803154, 3.9727605573057474], + ‘yp’: [-0.7838283828382373, -30.022689768976846, + -9.447194719471895, 6.7966171617162274, -0.7838283828382373, + 20.152640264026445, 37.840346534653492, 13.294141914191471, + 40.728135313531396, -0.7838283828382373], + ‘min_Jp’: 3.96624472574, + ‘max_Jp’: 96.2869198312} + +color_map_luts['cm_candidate_ch'] = (array( + [ 0.03522636, 0.03833067, 0.04137086, 0.04422592, 0.0469077 , 0.04949927, + 0.05195494, 0.05435346, 0.05668617, 0.05895159, 0.06118936, 0.06333705, + 0.0654643 , 0.06747923, 0.06945627, 0.07130747, 0.07309864, 0.07473997, + 0.07630552, 0.0776979 , 0.07898537, 0.08010829, 0.08107311, 0.08190198, + 0.08249749, 0.08292767, 0.083176 , 0.08316237, 0.08291788, 0.08242916, + 0.08166884, 0.08060568, 0.07920431, 0.0774235 , 0.07522534, 0.07255839, + 0.06937739, 0.0656378 , 0.06127894, 0.05615142, 0.05030332, 0.04372508, + 0.03661499, 0.03005334, 0.02491817, 0.02190702, 0.02148032, 0.02375703, + 0.02860561, 0.03577661, 0.04479347, 0.05435906, 0.06405802, 0.073712, + 0.083229 , 0.09254701, 0.10166185, 0.11057105, 0.11925526, 0.12774985, + 0.13603271, 0.14414105, 0.15206539, 0.15982306, 0.16742795, 0.17487481, + 0.1821786 , 0.18934939, 0.19639219, 0.20330755, 0.21010013, 0.21677862, + 0.22334613, 0.22980539, 0.2361588 , 0.24240872, 0.24855784, 0.25461006, + 0.26057194, 0.26645523, 0.27228068, 0.27808294, 0.28391451, 0.28984425, + 0.29594531, 0.30227382, 0.30885036, 0.31565791, 0.32265606, 0.32979907, + 0.33704768, 0.34437291, 0.35175534, 0.35917963, 0.36664001, 0.37413354, + 0.38165767, 0.38921099, 0.39679271, 0.40440245, 0.41203793, 0.41969968, + 0.42738983, 0.43510843, 0.44285558, 0.45063135, 0.45843459, 0.46626598, + 0.47412702, 0.48201778, 0.48993829, 0.49788858, 0.50586861, 0.51387941, + 0.52192105, 0.52999354, 0.53809685, 0.54623165, 0.55439949, 0.56259924, + 0.57083088, 0.57909441, 0.58738981, 0.59571933, 0.60408406, 0.61248174, + 0.62091239, 0.62937605, 0.63787278, 0.64640265, 0.65496986, 0.6635733, + 0.67221077, 0.68088234, 0.68958809, 0.69832812, 0.70710251, 0.71591134, + 0.72475464, 0.73363239, 0.74254444, 0.75149049, 0.76046998, 0.76948199, + 0.77852511, 0.7875972 , 0.79669513, 0.80581439, 0.81497372, 0.82415011, + 0.83332691, 0.84252673, 0.85170847, 0.86088203, 0.86999225, 0.87899789, + 0.88778843, 0.89611225, 0.90337493, 0.90860929, 0.91175598, 0.91370041, + 0.91501944, 0.91599762, 0.91675056, 0.91732023, 0.91778036, 0.91811232, + 0.91837528, 0.91855668, 0.91865995, 0.91869408, 0.91866521, 0.91857744, + 0.91843343, 0.91823488, 0.91798277, 0.91767766, 0.91731981, 0.91690936, + 0.91644637, 0.91593091, 0.91536311, 0.91474318, 0.91407949, 0.91336534, + 0.91259789, 0.9117775 , 0.91090473, 0.90998027, 0.90900929, 0.90798541, + 0.90690915, 0.90578172, 0.90460452, 0.90337725, 0.90209964, 0.90077411, + 0.8994031 , 0.89798939, 0.89653293, 0.89503377, 0.8935014 , 0.89194155, + 0.89036098, 0.88876764, 0.88716861, 0.88557213, 0.88399934, 0.88246708, + 0.88099466, 0.87960391, 0.87831891, 0.87716549, 0.87617292, 0.87537354, + 0.87479129, 0.87444814, 0.87436113, 0.87454145, 0.87499843, 0.87576485, + 0.87680691, 0.87811326, 0.87968158, 0.88153454, 0.88361008, 0.88590523, + 0.88844166, 0.89115133, 0.89406565, 0.89714468, 0.90038245, 0.90377593, + 0.907298 , 0.91095738, 0.91472653, 0.91860958, 0.92259009, 0.92665895, + 0.93081455, 0.93503101, 0.93931993, 0.9436451 , 0.94800146, 0.9523663, + 0.95670453, 0.96101117, 0.96524557, 0.96939757, 0.97351615, 0.97761533, + 0.9817668 , 0.98605907, 0.99050657, 0.99513559]), array( + [ 0.00717629, 0.00941214, 0.01190817, 0.01466782, 0.01769325, 0.02096064, + 0.02448619, 0.02824312, 0.03223296, 0.0364546 , 0.04088298, 0.04530612, + 0.04965416, 0.05396345, 0.05821592, 0.06244244, 0.06662853, 0.07080188, + 0.07494638, 0.07908871, 0.08321444, 0.08733926, 0.09146285, 0.09558174, + 0.09971703, 0.10385498, 0.10799951, 0.11216606, 0.11634821, 0.12054804, + 0.12476983, 0.12901801, 0.13329717, 0.13761219, 0.1419667 , 0.14636562, + 0.15081199, 0.1553078 , 0.1598563 , 0.16447093, 0.16913797, 0.17385302, + 0.17859758, 0.18333009, 0.18798922, 0.19250917, 0.19683753, 0.20095108, + 0.20485138, 0.2085565 , 0.21208884, 0.21547059, 0.2187219 , 0.22186023, + 0.22490038, 0.22785619, 0.23073769, 0.23355385, 0.23631507, 0.23902539, + 0.2416948 , 0.24432528, 0.24692442, 0.24949553, 0.25204168, 0.25456886, + 0.25707918, 0.25957507, 0.26205962, 0.26453663, 0.26700889, 0.26947802, + 0.27194668, 0.2744176 , 0.27689351, 0.27937719, 0.28187144, 0.28437889, + 0.28690162, 0.28944039, 0.2919933 , 0.29455367, 0.29710773, 0.29963342, + 0.30210294, 0.30448964, 0.30677635, 0.3089592 , 0.31104521, 0.31304651, + 0.31497559, 0.31684275, 0.31865561, 0.32042051, 0.32214032, 0.32381667, + 0.32545088, 0.32704374, 0.32859564, 0.33010679, 0.33157808, 0.33300923, + 0.33439917, 0.33574763, 0.33705434, 0.33831896, 0.33954175, 0.34072209, + 0.34185885, 0.34295161, 0.34399994, 0.34500336, 0.34596145, 0.34687315, + 0.34773789, 0.34855512, 0.34932428, 0.35004438, 0.35071386, 0.35133271, + 0.35190023, 0.35241573, 0.35287848, 0.35328622, 0.35363738, 0.35393254, + 0.35417082, 0.35435127, 0.3544729 , 0.35453467, 0.35453246, 0.3544659, + 0.35433547, 0.35413995, 0.35387806, 0.35354849, 0.35314988, 0.35268084, + 0.35213998, 0.35152592, 0.35083734, 0.35007306, 0.34923213, 0.34831394, + 0.34731844, 0.34624641, 0.34509977, 0.34388221, 0.3425743 , 0.34119867, + 0.33977344, 0.33827666, 0.33675521, 0.33520455, 0.33369577, 0.33229408, + 0.3311592 , 0.33064529, 0.3315562 , 0.33510297, 0.34120532, 0.34863721, + 0.35665329, 0.36490048, 0.37324447, 0.38164035, 0.39000738, 0.39837187, + 0.4066732 , 0.41493037, 0.42314374, 0.43130788, 0.43942037, 0.44748078, + 0.4554899 , 0.46344929, 0.4713609 , 0.47922684, 0.48704928, 0.49483028, + 0.50257176, 0.51027545, 0.51794292, 0.52557548, 0.53316833, 0.54072793, + 0.5482575 , 0.55575771, 0.56322906, 0.57067187, 0.57808347, 0.58546876, + 0.59282791, 0.60016055, 0.60746619, 0.61474529, 0.6219982 , 0.62922358, + 0.6364201 , 0.64358621, 0.65072187, 0.65782681, 0.66489576, 0.67192551, + 0.67891244, 0.68585233, 0.69274151, 0.69957569, 0.70634483, 0.71304131, + 0.71965675, 0.72618213, 0.73260803, 0.73892495, 0.7451228 , 0.75119134, + 0.75712447, 0.76291767, 0.76856894, 0.77407887, 0.77944929, 0.78467669, + 0.78977658, 0.79475738, 0.79962537, 0.80438334, 0.8090495 , 0.81362981, + 0.81812939, 0.822561 , 0.82692955, 0.83124292, 0.83550709, 0.83972927, + 0.84391222, 0.84806526, 0.8521879 , 0.85628939, 0.8603707 , 0.86443499, + 0.86849289, 0.8725333 , 0.87658679, 0.88063259, 0.88469332, 0.88877587, + 0.89287903, 0.8970406 , 0.90125214, 0.90552454, 0.90987168, 0.91426387, + 0.91867981, 0.92310252, 0.92749634, 0.93184435]), array( + [ 0.01748575, 0.02188322, 0.02672053, 0.0320049 , 0.0377461 , 0.04374005, + 0.04967379, 0.05549667, 0.06125422, 0.06697896, 0.07265592, 0.07836626, + 0.08404944, 0.08980628, 0.09555785, 0.10139442, 0.1072383 , 0.1131781, + 0.11912453, 0.12517083, 0.13123399, 0.13736417, 0.14354194, 0.14973423, + 0.15601774, 0.16231425, 0.16862456, 0.17499629, 0.1813827 , 0.18777277, + 0.19416216, 0.20054403, 0.20690794, 0.21323963, 0.21951507, 0.22570772, + 0.23177787, 0.23767532, 0.24334283, 0.24872311, 0.25369558, 0.25814289, + 0.26191917, 0.26487715, 0.26692571, 0.26808099, 0.2684773 , 0.26831057, + 0.26778035, 0.26703712, 0.26618953, 0.26531302, 0.26445711, 0.26365339, + 0.26292142, 0.26228611, 0.26174391, 0.26129748, 0.26096479, 0.26072575, + 0.26060175, 0.26057155, 0.26064668, 0.26081902, 0.2610823 , 0.26144311, + 0.26189315, 0.26242706, 0.26304235, 0.26373904, 0.26451357, 0.26535872, + 0.26627022, 0.2672428 , 0.26826982, 0.26934258, 0.27044939, 0.27157424, + 0.27269501, 0.27378145, 0.27479358, 0.27568221, 0.27639444, 0.27688646, + 0.27714116, 0.27717994, 0.27705717, 0.27683892, 0.27658103, 0.27631892, + 0.27606894, 0.27583456, 0.27561248, 0.27539915, 0.27518661, 0.27496758, + 0.27473682, 0.27448999, 0.27422348, 0.27393436, 0.27362188, 0.27328355, + 0.2729159 , 0.27251738, 0.27208666, 0.27162255, 0.27112496, 0.27059242, + 0.2700227 , 0.26941486, 0.26876801, 0.26808128, 0.26735382, 0.26658388, + 0.26577047, 0.26491267, 0.26400955, 0.26305951, 0.26206006, 0.26101115, + 0.25991162, 0.25876029, 0.25755586, 0.25629478, 0.25497448, 0.25359554, + 0.25215625, 0.25065475, 0.24908901, 0.24745683, 0.24575146, 0.24397141, + 0.24211619, 0.24018264, 0.23816727, 0.23606623, 0.23387523, 0.23158951, + 0.22920377, 0.22671207, 0.22410782, 0.22138361, 0.21853118, 0.2155413, + 0.21240363, 0.20910667, 0.20563757, 0.20198213, 0.19808683, 0.19395191, + 0.18956227, 0.18482577, 0.17973484, 0.17416272, 0.16804944, 0.16121776, + 0.15345263, 0.14450283, 0.13440947, 0.124886 , 0.11850527, 0.11530169, + 0.11429895, 0.11482246, 0.11642615, 0.11883084, 0.12186164, 0.12538522, + 0.12930681, 0.1335561 , 0.13808108, 0.14284015, 0.1478005 , 0.15293632, + 0.15822735, 0.16365776, 0.16921525, 0.1748903 , 0.1806757 , 0.18656606, + 0.19255748, 0.1986473 , 0.2048339 , 0.2111165 , 0.21749036, 0.22396009, + 0.23052897, 0.23719906, 0.2439729 , 0.25085347, 0.25784138, 0.26494541, + 0.27217054, 0.27952188, 0.28700505, 0.29462764, 0.30239803, 0.31032349, + 0.31841207, 0.3266725 , 0.33511695, 0.34375899, 0.35260525, 0.36166667, + 0.37095463, 0.38048072, 0.39025901, 0.40030417, 0.41061811, 0.42120758, + 0.43207661, 0.44322563, 0.45465067, 0.46634268, 0.47828317, 0.49044181, + 0.50279317, 0.51530648, 0.52795044, 0.54069578, 0.55350777, 0.56628119, + 0.57905927, 0.59183607, 0.60457689, 0.61715156, 0.62970523, 0.64220124, + 0.65448412, 0.66677747, 0.67887524, 0.6909046 , 0.70283761, 0.71461871, + 0.72635938, 0.73792219, 0.74945264, 0.76083717, 0.77214931, 0.7834029, + 0.79449887, 0.80567531, 0.81656784, 0.82755657, 0.83843574, 0.84923937, + 0.86013357, 0.87076849, 0.88140532, 0.89198331, 0.90217129, 0.91220561, + 0.922003 , 0.93137403, 0.94060562, 0.94975586]), np.ones(256)) +</p>
<pre># Aliases
color_map_luts['B-W LINEAR'] = color_map_luts['idl00']
color_map_luts['BLUE'] = color_map_luts['idl01']</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/7ff5f3c9da79/">https://bitbucket.org/yt_analysis/yt/commits/7ff5f3c9da79/</a> Changeset: 7ff5f3c9da79 Branch: yt User: MatthewTurk Date: 2016-01-28 17:03:03+00:00 Summary: Anonymizing candidates Affected #: 1 file</p>
<p>diff -r fbf1412ce0c9571b1d0b46ab6e17c7d79176b441 -r 7ff5f3c9da7905fc7ee654ecdf82c2ca03210c4d yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -7824,7 +7824,7 @@</p>
<pre> -16.059027777777771],
'min_Jp': 17.1875,
'max_Jp': 82.1875}</pre>
<p>-color_map_luts["cm_candidate_mjt"] = (array( +color_map_luts["cm_candidate_1"] = (array(</p>
<pre>[ 0.01845663, 0.01940818, 0.02066025, 0.02218966, 0.02395409, 0.02595033,
0.02817596, 0.03060653, 0.03322304, 0.03602798, 0.03900455, 0.04208415,
0.04516324, 0.04823603, 0.05128648, 0.05431253, 0.05730541, 0.06025524,</pre>
<p>@@ -7964,7 +7964,7 @@</p>
<pre> 'max_Jp': 93.8863000932}
</pre>
<p>-color_map_luts["cm_candidate_ng"] = (array( +color_map_luts["cm_candidate_2"] = (array(</p>
<pre>[ 0.22330277, 0.22677033, 0.23017935, 0.23353169, 0.23681402, 0.2400368,
0.24320742, 0.24631505, 0.24936304, 0.25236366, 0.25530723, 0.25819299,
0.2610367 , 0.26382794, 0.26656596, 0.26926798, 0.2719204 , 0.27452761,</pre>
<p>@@ -8101,7 +8101,7 @@</p>
<pre> 'min_Jp': 15,
'max_Jp': 95}
</pre>
<p>-color_map_luts['cm_candidate_kk'] = (array( +color_map_luts['cm_candidate_3'] = (array(</p>
<pre>[ 0.07873808, 0.08503098, 0.09119215, 0.09725944, 0.10324966, 0.10914691,
0.1149903 , 0.12076614, 0.12647234, 0.13214487, 0.13775951, 0.14331952,
0.14885405, 0.15434127, 0.15978387, 0.16520148, 0.17058327, 0.17592717,</pre>
<p>@@ -8244,7 +8244,7 @@</p>
<pre> 'min_Jp': 3.96624472574,
'max_Jp': 96.2869198312}
</pre>
<p>-color_map_luts['cm_candidate_ch'] = (array( +color_map_luts['cm_candidate_4'] = (array(</p>
<pre>[ 0.03522636, 0.03833067, 0.04137086, 0.04422592, 0.0469077 , 0.04949927,
0.05195494, 0.05435346, 0.05668617, 0.05895159, 0.06118936, 0.06333705,
0.0654643 , 0.06747923, 0.06945627, 0.07130747, 0.07309864, 0.07473997,</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/9047bc1c4b5b/">https://bitbucket.org/yt_analysis/yt/commits/9047bc1c4b5b/</a> Changeset: 9047bc1c4b5b Branch: yt User: brittonsmith Date: 2016-03-22 20:26:33+00:00 Summary: Adding cmap kwarg to all imshow calls. Affected #: 4 files</p>
<p>diff -r 7ff5f3c9da7905fc7ee654ecdf82c2ca03210c4d -r 9047bc1c4b5b114f215805a30562d5555fbbcbbb yt/visualization/plot_modifications.py --- a/yt/visualization/plot_modifications.py +++ b/yt/visualization/plot_modifications.py @@ -1590,7 +1590,7 @@</p>
<pre> plot._axes.imshow(image, zorder=1,
extent=[xx0, xx1, yy0, yy1],</pre>
<ul><li><p>origin='lower',</p></li></ul>
<p>+ origin='lower', cmap=self.cmap,</p>
<pre> interpolation='nearest')
</pre>
<p>@@ -2276,7 +2276,7 @@</p>
<pre>lic_data_clip_rescale = (lic_data_clip - self.lim[0]) \
/ (self.lim[1] - self.lim[0])
lic_data_rgba[...,3] = lic_data_clip_rescale * self.alpha</pre>
<ul><li><p>plot._axes.imshow(lic_data_rgba, extent=extent)</p></li></ul>
<p>+ plot._axes.imshow(lic_data_rgba, extent=extent, cmap=self.cmap)</p>
<pre> plot._axes.hold(False)
return plot</pre>
<p>diff -r 7ff5f3c9da7905fc7ee654ecdf82c2ca03210c4d -r 9047bc1c4b5b114f215805a30562d5555fbbcbbb yt/visualization/volume_rendering/old_camera.py --- a/yt/visualization/volume_rendering/old_camera.py +++ b/yt/visualization/volume_rendering/old_camera.py @@ -691,7 +691,8 @@</p>
<pre>del nz
else:
nim = im</pre>
<ul><li><p>ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper')</p></li></ul>
<p>+ ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper', + cmap=self.cmap)</p>
<pre> return ax
def draw(self):</pre>
<p>@@ -1103,7 +1104,7 @@</p>
<pre>pylab.draw()
im = Camera.snapshot(self, fn, clip_ratio)
pylab.figure(1)</pre>
<ul><li><p>pylab.imshow(im / im.max())</p></li></ul>
<p>+ pylab.imshow(im / im.max(), cmap=self.cmap)</p>
<pre> pylab.draw()
self.frames.append(im)
</pre>
<p>@@ -1780,7 +1781,8 @@</p>
<pre>if take_log: func = np.log10
else: func = lambda a: a
implot = ax.imshow(func(img), extent=(-np.pi,np.pi,-np.pi/2,np.pi/2),</pre>
<ul><li><p>clip_on=False, aspect=0.5, vmin=cmin, vmax=cmax)</p></li></ul>
<p>+ clip_on=False, aspect=0.5, vmin=cmin, vmax=cmax, + cmap=self.cmap)</p>
<pre>cb = fig.colorbar(implot, orientation='horizontal')
cb.set_label(label)
ax.xaxis.set_ticks(())</pre>
<p>diff -r 7ff5f3c9da7905fc7ee654ecdf82c2ca03210c4d -r 9047bc1c4b5b114f215805a30562d5555fbbcbbb yt/visualization/volume_rendering/scene.py --- a/yt/visualization/volume_rendering/scene.py +++ b/yt/visualization/volume_rendering/scene.py @@ -419,7 +419,8 @@</p>
<pre>del nz
else:
nim = im</pre>
<ul><li><p>axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), interpolation="nearest")</p></li></ul>
<p>+ axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), interpolation="nearest", + cmap=self.cmap)</p>
<pre> return axim
</pre>
<p>diff -r 7ff5f3c9da7905fc7ee654ecdf82c2ca03210c4d -r 9047bc1c4b5b114f215805a30562d5555fbbcbbb yt/visualization/volume_rendering/transfer_functions.py --- a/yt/visualization/volume_rendering/transfer_functions.py +++ b/yt/visualization/volume_rendering/transfer_functions.py @@ -493,7 +493,7 @@</p>
<pre>i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)</pre>
<ul><li><p>ax.imshow(i_data, origin='lower')</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower', cmap=self.cmap)</p>
<pre>ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
ax.set_xlim(0, self.alpha.x.size)
xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]</pre>
<p>@@ -534,7 +534,7 @@</p>
<pre>i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)</pre>
<ul><li><p>ax.imshow(i_data, origin='lower')</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower', cmap=self.cmap)</p>
<pre>ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
ax.set_xlim(0, self.alpha.x.size)
xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]</pre>
<p>@@ -582,7 +582,7 @@</p>
<pre>i_data[:,:,0] = np.outer(self.funcs[0].y, np.ones(self.alpha.x.size))
i_data[:,:,1] = np.outer(self.funcs[1].y, np.ones(self.alpha.x.size))
i_data[:,:,2] = np.outer(self.funcs[2].y, np.ones(self.alpha.x.size))</pre>
<ul><li><p>ax.imshow(i_data, origin='lower', aspect='auto')</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower', aspect='auto', cmap=self.cmap)</p>
<pre> ax.plot(alpha, np.arange(self.alpha.y.size), 'w')
# Set TF limits based on what is visible</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/183cbe63270e/">https://bitbucket.org/yt_analysis/yt/commits/183cbe63270e/</a> Changeset: 183cbe63270e Branch: yt User: MatthewTurk Date: 2016-03-22 20:23:10+00:00 Summary: Updating candidates 2 & 4 Affected #: 1 file</p>
<p>diff -r 9047bc1c4b5b114f215805a30562d5555fbbcbbb -r 183cbe63270e9437b27156d8652492b043f9ff04 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -7955,145 +7955,145 @@</p>
<pre> 0.9177585 , 0.92595735, 0.93431661, 0.94285311, 0.95166927, 0.96090167,
0.97095595, 0.97849108, 0.98057884, 0.98147471]), np.ones(256))
</pre>
<p>-# Used to reconstruct the colormap in viscm -parameters = {'xp': [17.623025510286254, 20.414094090828513,</p>
<ul><li><p>-82.390265292478205, -3.3099888437807294, -5.170701230808902],</p></li>
<li><p>‘yp’: [12.406964380648589, -98.305422647527877, 52.412280701754383,</p></li>
<li><p>34.735513024986687, 22.175704412546509],</p></li>
<li><p>‘min_Jp’: 13.5507921715,</p></li>
<li><p>‘max_Jp’: 93.8863000932}</p></li></ul>
<p>– +parameters = {'xp': [25.813729633909759, 31.169191027506741, + -75.940036844432967, -15.794085808651431, + -6.7309972964103792], + ‘yp’: [14.230225988700568, -99.470338983050823, + 9.2867231638418275, 41.007532956685509, + 31.532485875706215], + ‘min_Jp’: 27.2243940579, + ‘max_Jp’: 94.7771696638}</p>
<pre>color_map_luts["cm_candidate_2"] = (array(</pre>
<ul><li><p>[ 0.22330277, 0.22677033, 0.23017935, 0.23353169, 0.23681402, 0.2400368,</p></li>
<li><p>0.24320742, 0.24631505, 0.24936304, 0.25236366, 0.25530723, 0.25819299,</p></li>
<li><p>0.2610367 , 0.26382794, 0.26656596, 0.26926798, 0.2719204 , 0.27452761,</p></li>
<li><p>0.27710562, 0.27963477, 0.28213047, 0.28460423, 0.28702736, 0.28943233,</p></li>
<li><p>0.29181274, 0.29415763, 0.29649262, 0.29879558, 0.30108328, 0.30335993,</p></li>
<li><p>0.30560745, 0.30785443, 0.31007522, 0.31229044, 0.31449347, 0.31668024,</p></li>
<li><p>0.3188655 , 0.32102524, 0.32318876, 0.32532296, 0.32745808, 0.32956694,</p></li>
<li><p>0.33167014, 0.33374679, 0.33581262, 0.33784825, 0.33986937, 0.34185319,</p></li>
<li><p>0.34382043, 0.34573964, 0.34763692, 0.34948172, 0.35129044, 0.35304952,</p></li>
<li><p>0.35475122, 0.35640626, 0.3579826 , 0.35949714, 0.36094336, 0.36229195,</p></li>
<li><p>0.3635597 , 0.36473836, 0.36579828, 0.366748 , 0.36758102, 0.36828691,</p></li>
<li><p>0.36883982, 0.3692409 , 0.36948262, 0.36955344, 0.36944189, 0.36913688,</p></li>
<li><p>0.36862806, 0.36790621, 0.36696373, 0.36579487, 0.36439699, 0.36276941,</p></li>
<li><p>0.3609152 , 0.35884058, 0.35655508, 0.35407138, 0.35140496, 0.34857371,</p></li>
<li><p>0.34559732, 0.34249671, 0.33929342, 0.33600904, 0.3326647 , 0.32928363,</p></li>
<li><p>0.32588267, 0.3224787 , 0.31908743, 0.31572297, 0.31239786, 0.30912306,</p></li>
<li><p>0.30590808, 0.30276102, 0.2996887 , 0.2966967 , 0.29378953, 0.29097065,</p></li>
<li><p>0.2882426 , 0.28560705, 0.28306489, 0.28061848, 0.27826751, 0.27600835,</p></li>
<li><p>0.27383916, 0.27175753, 0.26976059, 0.26784495, 0.2660068 , 0.26424192,</p></li>
<li><p>0.26254879, 0.26092124, 0.2593516 , 0.25783427, 0.25636334, 0.25493271,</p></li>
<li><p>0.25353648, 0.25217148, 0.25082665, 0.2494952 , 0.24817023, 0.24684479,</p></li>
<li><p>0.24551313, 0.24416844, 0.2428016 , 0.24140563, 0.23997368, 0.23849902,</p></li>
<li><p>0.23697621, 0.2353966 , 0.23375381, 0.23204161, 0.23025401, 0.22838496,</p></li>
<li><p>0.22642823, 0.22437847, 0.22223061, 0.21997986, 0.21762015, 0.21514715,</p></li>
<li><p>0.21255756, 0.20984779, 0.20701463, 0.20405362, 0.20095999, 0.19773434,</p></li>
<li><p>0.19437503, 0.1908809 , 0.18725135, 0.1834808 , 0.17957218, 0.17552885,</p></li>
<li><p>0.17135306, 0.16704809, 0.1626184 , 0.15806468, 0.15339359, 0.14861956,</p></li>
<li><p>0.14375457, 0.13881352, 0.13381484, 0.12878121, 0.12374048, 0.11871631,</p></li>
<li><p>0.11375875, 0.10891932, 0.10425825, 0.09984694, 0.09576875, 0.09211897,</p></li>
<li><p>0.08900344, 0.08653544, 0.08483018, 0.08399726, 0.08413159, 0.08530458,</p></li>
<li><p>0.08755744, 0.09089837, 0.09530427, 0.10072634, 0.1070978 , 0.11434195,</p></li>
<li><p>0.12237919, 0.13113212, 0.14052892, 0.15050498, 0.16100355, 0.17197546,</p></li>
<li><p>0.18338793, 0.19520014, 0.20738006, 0.21990121, 0.23274066, 0.2459009,</p></li>
<li><p>0.25934606, 0.27305848, 0.28703874, 0.30127492, 0.3157365 , 0.33043715,</p></li>
<li><p>0.34534588, 0.3604517 , 0.37574975, 0.39120759, 0.40682457, 0.42256559,</p></li>
<li><p>0.4384161 , 0.45435289, 0.47033772, 0.48636436, 0.50238444, 0.51837124,</p></li>
<li><p>0.53430503, 0.55014493, 0.56586449, 0.58144061, 0.59684533, 0.61205655,</p></li>
<li><p>0.62705581, 0.64182617, 0.6563544 , 0.67063044, 0.68464533, 0.69839355,</p></li>
<li><p>0.71187229, 0.72507912, 0.73801226, 0.75067321, 0.76306221, 0.77518048,</p></li>
<li><p>0.7870294 , 0.79860909, 0.8099203 , 0.82096426, 0.83173724, 0.84223591,</p></li>
<li><p>0.85245837, 0.86239779, 0.87204252, 0.88137985, 0.89039307, 0.89906019,</p></li>
<li><p>0.90735227, 0.91523111, 0.92264608, 0.92953025, 0.93579566, 0.94132897,</p></li>
<li><p>0.9459897 , 0.94961072, 0.95202797, 0.95313791]), array(</p></li>
<li><p>[ 0.02115217, 0.02435766, 0.02770894, 0.03120549, 0.03486176, 0.03866843,</p></li>
<li><p>0.04255067, 0.04636284, 0.05011635, 0.05380853, 0.05745319, 0.06105552,</p></li>
<li><p>0.06460953, 0.06812624, 0.07160913, 0.07505172, 0.07846425, 0.08184654,</p></li>
<li><p>0.08519339, 0.08851553, 0.09180774, 0.09506735, 0.09830606, 0.10151324,</p></li>
<li><p>0.10469233, 0.1078471 , 0.11097106, 0.11407044, 0.11714148, 0.12018399,</p></li>
<li><p>0.12320135, 0.12618924, 0.12915144, 0.13208572, 0.13499318, 0.13787426,</p></li>
<li><p>0.14072906, 0.14355817, 0.14636281, 0.14914232, 0.1518996 , 0.15463338,</p></li>
<li><p>0.1573473 , 0.16004005, 0.16271589, 0.16537333, 0.16801777, 0.17064683,</p></li>
<li><p>0.17326807, 0.1758771 , 0.17848374, 0.1810841 , 0.18368643, 0.18629175,</p></li>
<li><p>0.18890301, 0.19152787, 0.19416467, 0.19682321, 0.19950818, 0.20222144,</p></li>
<li><p>0.20497307, 0.20776897, 0.21061428, 0.21351848, 0.21648954, 0.21953572,</p></li>
<li><p>0.22266685, 0.22589265, 0.22922275, 0.23266724, 0.23623633, 0.23994008,</p></li>
<li><p>0.24378808, 0.24778908, 0.25195046, 0.25627786, 0.26077448, 0.26544111,</p></li>
<li><p>0.27027536, 0.27527178, 0.2804218 , 0.28571395, 0.29113426, 0.29666673,</p></li>
<li><p>0.30229403, 0.30799808, 0.31376071, 0.31956423, 0.3253919 , 0.33122689,</p></li>
<li><p>0.33705636, 0.34286842, 0.34865273, 0.35440061, 0.36010493, 0.36576003,</p></li>
<li><p>0.37136152, 0.3769062 , 0.38239183, 0.38781708, 0.39318132, 0.39848456,</p></li>
<li><p>0.40372731, 0.40891054, 0.41403551, 0.41910295, 0.42411461, 0.42907338,</p></li>
<li><p>0.43398142, 0.43884093, 0.44365421, 0.4484236 , 0.45315147, 0.45784019,</p></li>
<li><p>0.46249111, 0.46710703, 0.4716911 , 0.47624563, 0.48077291, 0.48527518,</p></li>
<li><p>0.4897545 , 0.49421204, 0.49865123, 0.50307409, 0.50748262, 0.51187871,</p></li>
<li><p>0.51626387, 0.52063984, 0.52500891, 0.5293727 , 0.53373275, 0.5380905,</p></li>
<li><p>0.54244704, 0.5468042 , 0.55116316, 0.55552505, 0.55989088, 0.56426165,</p></li>
<li><p>0.56863832, 0.57302164, 0.57741226, 0.58181071, 0.58621776, 0.59063378,</p></li>
<li><p>0.59505889, 0.59949327, 0.60393701, 0.60839035, 0.61285357, 0.61732602,</p></li>
<li><p>0.62180742, 0.62629743, 0.63079562, 0.63530232, 0.63981655, 0.64433739,</p></li>
<li><p>0.64886414, 0.65339601, 0.65793219, 0.66247241, 0.66701578, 0.6715607,</p></li>
<li><p>0.67610609, 0.68065087, 0.68519387, 0.6897339 , 0.69426971, 0.69880098,</p></li>
<li><p>0.70332548, 0.7078417 , 0.71234818, 0.71684345, 0.72132594, 0.72579406,</p></li>
<li><p>0.73024615, 0.73468052, 0.73909539, 0.74348893, 0.74785925, 0.75220438,</p></li>
<li><p>0.75652229, 0.76081087, 0.76506792, 0.76929116, 0.77347824, 0.77762671,</p></li>
<li><p>0.78173401, 0.78579751, 0.78981448, 0.79378208, 0.79769739, 0.80155739,</p></li>
<li><p>0.80535919, 0.80909916, 0.81277394, 0.81638014, 0.81991431, 0.82337227,</p></li>
<li><p>0.82675067, 0.83004601, 0.8332539 , 0.83637039, 0.83939294, 0.84231614,</p></li>
<li><p>0.84513786, 0.84785477, 0.85046308, 0.85296202, 0.85534788, 0.85762123,</p></li>
<li><p>0.85978086, 0.86182713, 0.86376341, 0.86558903, 0.86731067, 0.86893272,</p></li>
<li><p>0.87045909, 0.87189851, 0.87325813, 0.87454523, 0.87576896, 0.87693806,</p></li>
<li><p>0.87806108, 0.87914681, 0.88020359, 0.88123929, 0.88226176, 0.88327808,</p></li>
<li><p>0.88429462, 0.88531766, 0.88635321, 0.8874062 , 0.88848178, 0.88958464,</p></li>
<li><p>0.89071918, 0.89189007, 0.89310154, 0.89435739, 0.89566299, 0.89702343,</p></li>
<li><p>0.89844289, 0.89992722, 0.90148396, 0.90312081, 0.90484675, 0.90667256,</p></li>
<li><p>0.90861145, 0.91068001, 0.91289948, 0.91529744, 0.91790984, 0.92078319,</p></li>
<li><p>0.92397484, 0.92755584, 0.93160009, 0.93616295]), array(</p></li>
<li><p>[ 0.00202189, 0.00551406, 0.00964551, 0.01445093, 0.02004956, 0.0264401,</p></li>
<li><p>0.03362203, 0.04168051, 0.04990578, 0.05800892, 0.06608536, 0.07416013,</p></li>
<li><p>0.0821575 , 0.0901558 , 0.09817059, 0.10611951, 0.11408814, 0.12206225,</p></li>
<li><p>0.12997097, 0.13792374, 0.14585317, 0.15371495, 0.16165622, 0.16953288,</p></li>
<li><p>0.17738626, 0.18527689, 0.19309761, 0.20095943, 0.20879604, 0.21659823,</p></li>
<li><p>0.22445639, 0.2322505 , 0.24010403, 0.2479312 , 0.25576831, 0.26363933,</p></li>
<li><p>0.27148133, 0.27940784, 0.28728597, 0.29527021, 0.30321929, 0.31125895,</p></li>
<li><p>0.3192891 , 0.32740412, 0.33552351, 0.34373191, 0.35194602, 0.36026328,</p></li>
<li><p>0.36857416, 0.37701245, 0.38544163, 0.39398549, 0.40255076, 0.41117865,</p></li>
<li><p>0.41988038, 0.42858781, 0.43740831, 0.44624508, 0.45509757, 0.46404412,</p></li>
<li><p>0.47298933, 0.48193114, 0.49093036, 0.499919 , 0.50887858, 0.51779951,</p></li>
<li><p>0.52670573, 0.53554666, 0.54429458, 0.55292889, 0.56142589, 0.56975888,</p></li>
<li><p>0.57789829, 0.58581211, 0.59346653, 0.60082694, 0.60785824, 0.6145276,</p></li>
<li><p>0.62080425, 0.6266615 , 0.63207769, 0.63703716, 0.64153073, 0.64555596,</p></li>
<li><p>0.64911698, 0.65222401, 0.65489262, 0.65714283, 0.65899809, 0.66048517,</p></li>
<li><p>0.66163121, 0.66246411, 0.66301175, 0.6633014 , 0.66335931, 0.66321038,</p></li>
<li><p>0.66287804, 0.66238411, 0.66174876, 0.66099052, 0.66012634, 0.65917165,</p></li>
<li><p>0.65814046, 0.6570454 , 0.6558979 , 0.65471 , 0.6534912 , 0.65224774,</p></li>
<li><p>0.65098687, 0.64971498, 0.64843772, 0.64716007, 0.64588635, 0.64462029,</p></li>
<li><p>0.64336792, 0.64213086, 0.64090915, 0.63970452, 0.6385183 , 0.6373514,</p></li>
<li><p>0.63620481, 0.63508181, 0.63397823, 0.63289364, 0.63182733, 0.63077828,</p></li>
<li><p>0.62974631, 0.62873002, 0.62772575, 0.62673148, 0.62574501, 0.62476397,</p></li>
<li><p>0.62378672, 0.62280874, 0.62182708, 0.62083861, 0.61984011, 0.61882799,</p></li>
<li><p>0.61779832, 0.61674751, 0.61567191, 0.6145678 , 0.61343038, 0.61225577,</p></li>
<li><p>0.61104058, 0.60978098, 0.60847311, 0.60711217, 0.60569296, 0.60421341,</p></li>
<li><p>0.6026698 , 0.60105844, 0.59937571, 0.59761482, 0.5957737 , 0.59385008,</p></li>
<li><p>0.59184061, 0.58974201, 0.5875511 , 0.58526193, 0.58287108, 0.58037826,</p></li>
<li><p>0.57778061, 0.57507533, 0.57225971, 0.56933109, 0.5662869 , 0.56311922,</p></li>
<li><p>0.55983012, 0.55641792, 0.55288035, 0.5492152 , 0.54542033, 0.54149367,</p></li>
<li><p>0.53743319, 0.53323696, 0.5289031 , 0.52442983, 0.51981546, 0.51505838,</p></li>
<li><p>0.51015712, 0.50511032, 0.4999168 , 0.49457554, 0.4890857 , 0.48344672,</p></li>
<li><p>0.47765827, 0.47172037, 0.46563338, 0.45939808, 0.45301574, 0.44648817,</p></li>
<li><p>0.43980205, 0.43297192, 0.42600399, 0.4189032 , 0.41167556, 0.40430087,</p></li>
<li><p>0.39681183, 0.38922019, 0.38152054, 0.37372536, 0.36586902, 0.35794158,</p></li>
<li><p>0.34998244, 0.34201236, 0.33404905, 0.32613936, 0.31830265, 0.31059314,</p></li>
<li><p>0.30304889, 0.29571869, 0.28866636, 0.28193158, 0.27559011, 0.26970074,</p></li>
<li><p>0.26431775, 0.25950931, 0.25533028, 0.25182957, 0.24905318, 0.24703636,</p></li>
<li><p>0.24580423, 0.24537215, 0.2457446 , 0.24691602, 0.2488721 , 0.25159047,</p></li>
<li><p>0.25504271, 0.25919611, 0.26401499, 0.26946277, 0.27550279, 0.28209979,</p></li>
<li><p>0.28922076, 0.29683505, 0.30491583, 0.31344099, 0.32238968, 0.33174591,</p></li>
<li><p>0.34150092, 0.35164825, 0.36218311, 0.37310746, 0.38442836, 0.39615832,</p></li>
<li><p>0.40831552, 0.42092364, 0.4340111 , 0.44760871, 0.46174485, 0.47643618,</p></li>
<li><p>0.49169001, 0.50743951, 0.52351526, 0.53960817]), np.ones(256))</p></li></ul>
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0.4419283 , 0.43252618, + 0.42302131, 0.41340722, 0.40368844, 0.3938839 , 0.38398203, 0.37399931, + 0.36394886, 0.35381793, 0.34364247, 0.33340639, 0.32314442, 0.31285132, + 0.30255889, 0.2922684 , 0.28202116, 0.27181653, 0.26171116, 0.25171982, + 0.24189603, 0.23229831, 0.22296839, 0.21399898, 0.20548226, 0.19752366, + 0.19024226, 0.18379968, 0.17836522, 0.17412601, 0.17128095, 0.17003118, + 0.17056788, 0.17305931, 0.17763999, 0.18441057, 0.19343386, 0.2047358, + 0.21833687, 0.2341978 , 0.25223614, 0.2722682]), np.ones(256))</p>
<pre># Used to reconstruct the colormap in viscm
parameters = {'xp': [-2.3569023569023386, 29.24031986531989, 21.948653198653204, -25.44718013468011, -4.78745791245791],</pre>
<p>@@ -8233,147 +8233,148 @@</p>
<pre> 0.53036982, 0.52734442, 0.52433316, 0.52118636]), np.ones(256))
# Used to reconstruct the colormap in viscm</pre>
<p>-parameters = {'xp': [6.4995757388238928, -16.241760894839473,</p>
<ul><li><p>-12.632024921242106, -21.656364855235495, 7.5824965309031143,</p></li>
<li><p>6.4995757388238928, 86.274740755325524, 15.884889270177041,</p></li>
<li><p>-11.188130531803154, 3.9727605573057474],</p></li>
<li><p>‘yp’: [-0.7838283828382373, -30.022689768976846,</p></li>
<li><p>-9.447194719471895, 6.7966171617162274, -0.7838283828382373,</p></li>
<li><p>20.152640264026445, 37.840346534653492, 13.294141914191471,</p></li>
<li><p>40.728135313531396, -0.7838283828382373],</p></li>
<li><p>‘min_Jp’: 3.96624472574,</p></li>
<li><p>‘max_Jp’: 96.2869198312}</p></li></ul>
<p>+parameters = {'xp': [ 3.444773825208614, -17.207400087834856, + -12.632024921242106, -21.656364855235495, + 16.850570926657895, 55.256368028107175, + 14.676657883179644, 12.502744839701393, + 40.401295564339051, 0.90854194115064502], + ‘yp’: [-1.6304347826086598, -24.818840579710098, + -9.447194719471895, 6.796617161716227, + -5.6159420289854722, 57.065217391304373, + 13.224637681159436, 3.4420289855072781, + 58.514492753623216, 0.1811594202898732], + ‘min_Jp’: 3.96624472574, ‘max_Jp’: 96.5975103734}</p>
<pre>color_map_luts['cm_candidate_4'] = (array(</pre>
<ul><li><p>[ 0.03522636, 0.03833067, 0.04137086, 0.04422592, 0.0469077 , 0.04949927,</p></li>
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0.87598709, + 0.88439937, 0.89263161, 0.90038304, 0.90687168, 0.91166375, 0.91561895, + 0.91938826, 0.92318052, 0.9269946 , 0.93070173, 0.93405155, 0.93665497, + 0.93818572, 0.93886516, 0.93923641, 0.93951639, 0.93979528, 0.94008554, + 0.9403692 , 0.94071214, 0.94104779, 0.94137354, 0.94172077, 0.9420958, + 0.94244563, 0.94274487, 0.94309703, 0.94344151, 0.94369514, 0.94407978, + 0.94432487, 0.94468376, 0.9449135 , 0.94526127, 0.94544804, 0.945808, + 0.94601776, 0.94631082, 0.94656632, 0.9467505 , 0.94706511, 0.94725625, + 0.9474877 , 0.94776129, 0.9479231 , 0.94816834, 0.94843142, 0.94859414, + 0.9488144 , 0.94909331, 0.9492829 , 0.94943682, 0.94975423, 0.94999285, + 0.95015253, 0.9504141 , 0.95072075, 0.95095877, 0.95112839, 0.95145943, + 0.95179158, 0.95206534, 0.95228128, 0.95264039, 0.95303147, 0.95337439, + 0.95367005, 0.95401622, 0.95449739, 0.95494063, 0.95534717, 0.95571839, + 0.95624412, 0.95681749, 0.95736483, 0.9578879 , 0.95838862, 0.95906407, + 0.95978233, 0.96048784, 0.96118301, 0.96187054, 0.96267628, 0.96359786, + 0.96452242, 0.96545346, 0.9663949 , 0.96735109, 0.96853232, 0.96975252, + 0.97100046, 0.9722811 , 0.97360166, 0.97499094]), array( + [ 0.01131879, 0.01391783, 0.01674789, 0.01980761, 0.02309557, 0.02659703, + 0.03032697, 0.03427332, 0.03844574, 0.04276988, 0.04704341, 0.05129054, + 0.05550078, 0.05969933, 0.06386921, 0.06803423, 0.07217737, 0.07632217, + 0.08045053, 0.08458326, 0.0887074 , 0.09283339, 0.0969619 , 0.10108667, + 0.10522618, 0.10936788, 0.1135134 , 0.1176759 , 0.12185004, 0.12603586, + 0.13023651, 0.1344553 , 0.13869563, 0.14296085, 0.14725413, 0.15157814, + 0.15593465, 0.16032384, 0.16475544, 0.16922528, 0.17373863, 0.17827184, + 0.1827844 , 0.18715937, 0.19126004, 0.19503246, 0.19852956, 0.20182311, + 0.20496848, 0.20800345, 0.21095494, 0.21383778, 0.21666923, 0.21945445, + 0.22220507, 0.22492545, 0.22761943, 0.23029139, 0.232945 , 0.23558391, + 0.23821051, 0.24082617, 0.24343288, 0.24603239, 0.24862627, 0.25121599, + 0.25380393, 0.25639065, 0.25897721, 0.26156482, 0.2641547 , 0.26674815, + 0.26934657, 0.27195171, 0.274567 , 0.27719243, 0.27983033, 0.28248333, + 0.2851544 , 0.28784764, 0.29056213, 0.2932949 , 0.29603493, 0.29876097, + 0.30144013, 0.30404208, 0.30655158, 0.3089731 , 0.31131738, 0.31359848, + 0.31582875, 0.31801758, 0.32016861, 0.32228546, 0.3243719 , 0.32642829, + 0.32845517, 0.33045378, 0.33242493, 0.33436795, 0.33628328, 0.33817163, + 0.34003234, 0.34186568, 0.34367166, 0.34544999, 0.34720092, 0.34892444, + 0.35061943, 0.35228681, 0.35392679, 0.35553735, 0.35711946, 0.35867374, + 0.36019909, 0.36169371, 0.36315988, 0.36459771, 0.36600339, 0.36737862, + 0.36872465, 0.37004148, 0.37132268, 0.37257345, 0.37379387, 0.37498382, + 0.37613623, 0.37725628, 0.37834438, 0.37940027, 0.38042109, 0.38140311, + 0.38235118, 0.38326484, 0.38414358, 0.38498682, 0.38579085, 0.38655416, + 0.38728007, 0.38796794, 0.3886171 , 0.38922686, 0.38979653, 0.39032549, + 0.39081315, 0.39125906, 0.39166297, 0.39202487, 0.39234513, 0.39262465, + 0.39285875, 0.39304214, 0.39318804, 0.39330134, 0.3933541 , 0.39338391, + 0.39336059, 0.3933133 , 0.39323839, 0.39313869, 0.39303156, 0.39295965, + 0.39295557, 0.39315799, 0.39391119, 0.39605497, 0.39998337, 0.40475771, + 0.40971377, 0.41464651, 0.41955776, 0.42457034, 0.42991332, 0.43592086, + 0.44281731, 0.45031271, 0.45790211, 0.46542398, 0.47282633, 0.48011752, + 0.48732483, 0.49440541, 0.50141998, 0.50837558, 0.51525348, 0.52205415, + 0.52881949, 0.53556909, 0.54223396, 0.54885896, 0.55550043, 0.56201556, + 0.56858101, 0.57503769, 0.58153866, 0.58793414, 0.59439323, 0.60071983, + 0.60710417, 0.61341299, 0.61971646, 0.6260332 , 0.63225511, 0.63852001, + 0.64474137, 0.65092034, 0.65713626, 0.66329112, 0.66941973, 0.67558021, + 0.68169667, 0.68777066, 0.69387233, 0.69997643, 0.70599397, 0.71203573, + 0.71810111, 0.72411175, 0.73009422, 0.73609755, 0.74212117, 0.74807095, + 0.75401458, 0.75997634, 0.7659557 , 0.77187449, 0.77777791, 0.78369726, + 0.78963205, 0.79554605, 0.80140962, 0.80728734, 0.81317876, 0.8190834, + 0.82493514, 0.83077307, 0.83662327, 0.84248528, 0.84835861, 0.85417838, + 0.85998992, 0.86581204, 0.87164424, 0.877486 , 0.88329814, 0.88908291, + 0.8948766 , 0.9006787 , 0.90648868, 0.91230608, 0.91806899, 0.9238354, + 0.92961071, 0.93539672, 0.94119732, 0.94701577]), array( + [ 0.02001135, 0.02410049, 0.0285127 , 0.03327199, 0.03840892, 0.04377278, + 0.04914302, 0.05449872, 0.05987322, 0.06527853, 0.07069504, 0.07616998, + 0.08164232, 0.08718911, 0.0927269 , 0.09833499, 0.10393204, 0.10960072, + 0.11525772, 0.12097641, 0.12669592, 0.13244873, 0.13822862, 0.14400333, + 0.14983392, 0.15566404, 0.16149121, 0.16735218, 0.17321659, 0.17907296, + 0.18491908, 0.19075078, 0.19656126, 0.20233991, 0.20807104, 0.21373203, + 0.21929102, 0.22470403, 0.22993048, 0.2348811 , 0.23944453, 0.24342373, + 0.24654791, 0.24854699, 0.24944069, 0.24964107, 0.24954394, 0.24938897, + 0.24925926, 0.24920029, 0.24923631, 0.24934712, 0.24956187, 0.24984924, + 0.25022917, 0.25068854, 0.25121928, 0.25182018, 0.25248939, 0.25322692, + 0.25402939, 0.25489122, 0.25581039, 0.25678491, 0.25781293, 0.25889267, + 0.26002567, 0.26120803, 0.26243772, 0.26371326, 0.26503318, 0.26639597, + 0.26779993, 0.26924355, 0.27072854, 0.27224553, 0.27378857, 0.27534839, + 0.27691073, 0.27845714, 0.27994688, 0.28132934, 0.28254139, 0.28352939, + 0.28426343, 0.28477016, 0.28511888, 0.2853897 , 0.28562523, 0.28584846, + 0.28606765, 0.28628369, 0.28648659, 0.2866717 , 0.28683846, 0.28698041, + 0.28709378, 0.28717794, 0.2872321 , 0.28725271, 0.28723935, 0.28719253, + 0.28710992, 0.28699145, 0.2868367 , 0.28664459, 0.28641544, 0.28614894, + 0.28584249, 0.28549799, 0.28511576, 0.28469141, 0.28422699, 0.28372376, + 0.28317939, 0.28259016, 0.28196082, 0.28129151, 0.28057453, 0.27981338, + 0.27901054, 0.27816595, 0.27726695, 0.2763238 , 0.27533659, 0.27430498, + 0.27321529, 0.27207747, 0.27089221, 0.26965877, 0.26837161, 0.26702355, + 0.26562346, 0.26417019, 0.2626624 , 0.26109861, 0.25947144, 0.25777762, + 0.25602258, 0.25420436, 0.25232078, 0.25036953, 0.2483481 , 0.24625386, + 0.24408403, 0.24183578, 0.23950621, 0.23709248, 0.23459188, 0.23200196, + 0.22930941, 0.22649861, 0.22358618, 0.22057215, 0.21739359, 0.2141051, + 0.21063096, 0.20699978, 0.20316855, 0.19908944, 0.19471496, 0.19000133, + 0.1847768 , 0.17886461, 0.17199335, 0.16449989, 0.15797658, 0.15243265, + 0.1469976 , 0.14126571, 0.13521428, 0.12907937, 0.12349501, 0.11987508, + 0.12047985, 0.12617587, 0.1352476 , 0.14599545, 0.15747549, 0.16925814, + 0.18117203, 0.19298222, 0.20476193, 0.21648737, 0.22808422, 0.23953607, + 0.25094286, 0.26235208, 0.27357466, 0.28472765, 0.29596437, 0.30689052, + 0.31799337, 0.32882896, 0.33982619, 0.35056047, 0.36151349, 0.37212065, + 0.38293088, 0.39355561, 0.40419893, 0.41491671, 0.42538398, 0.43601357, + 0.44654349, 0.4569744 , 0.46755003, 0.47796579, 0.48832714, 0.49881686, + 0.50919369, 0.51945918, 0.52983799, 0.54024888, 0.55039796, 0.56064657, + 0.57099376, 0.58117647, 0.59128027, 0.60146991, 0.6117441 , 0.62177734, + 0.63179964, 0.64189483, 0.65206133, 0.66202135, 0.67193144, 0.68190219, + 0.69193176, 0.70188796, 0.71165938, 0.72147981, 0.73134717, 0.74125921, + 0.75096714, 0.76061378, 0.77029569, 0.78001026, 0.78975461, 0.79927776, + 0.80875027, 0.81824276, 0.82775166, 0.83727291, 0.84665036, 0.85588868, + 0.86512674, 0.87435845, 0.88357649, 0.89277169, 0.90169092, 0.91054951, + 0.91934683, 0.92805132, 0.93660318, 0.9448656 ]), np.ones(256))</p>
<pre># Aliases
color_map_luts['B-W LINEAR'] = color_map_luts['idl00']</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/df7f40e328e9/">https://bitbucket.org/yt_analysis/yt/commits/df7f40e328e9/</a> Changeset: df7f40e328e9 Branch: yt User: MatthewTurk Date: 2016-03-22 20:45:30+00:00 Summary: Rename and set default from algae to arbre Affected #: 3 files</p>
<p>diff -r 183cbe63270e9437b27156d8652492b043f9ff04 -r df7f40e328e9b856003b2676723c3295279f67c7 yt/config.py --- a/yt/config.py +++ b/yt/config.py @@ -61,6 +61,7 @@</p>
<pre>ignore_invalid_unit_operation_errors = 'False',
chunk_size = '1000',
xray_data_dir = '/does/not/exist',</pre>
<p>+ default_colormap = ‘arbre’,</p>
<pre> )
# Here is the upgrade. We're actually going to parse the file in its entirety
# here. Then, if it has any of the Forbidden Sections, it will be rewritten</pre>
<p>diff -r 183cbe63270e9437b27156d8652492b043f9ff04 -r df7f40e328e9b856003b2676723c3295279f67c7 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -7824,7 +7824,7 @@</p>
<pre> -16.059027777777771],
'min_Jp': 17.1875,
'max_Jp': 82.1875}</pre>
<p>-color_map_luts["cm_candidate_1"] = (array( +color_map_luts["octarine"] = (array(</p>
<pre>[ 0.01845663, 0.01940818, 0.02066025, 0.02218966, 0.02395409, 0.02595033,
0.02817596, 0.03060653, 0.03322304, 0.03602798, 0.03900455, 0.04208415,
0.04516324, 0.04823603, 0.05128648, 0.05431253, 0.05730541, 0.06025524,</pre>
<p>@@ -7964,7 +7964,7 @@</p>
<pre> 'min_Jp': 27.2243940579,
'max_Jp': 94.7771696638}
</pre>
<p>-color_map_luts["cm_candidate_2"] = (array( +color_map_luts["arbre"] = (array(</p>
<pre>[ 0.44131774, 0.44370177, 0.44605933, 0.44839054, 0.45067478, 0.45293504,
0.45516891, 0.45735977, 0.45952958, 0.46167133, 0.46377662, 0.4658642,
0.4679203 , 0.46994924, 0.4719642 , 0.47394237, 0.47590549, 0.47785176,</pre>
<p>@@ -8101,7 +8101,7 @@</p>
<pre> 'min_Jp': 15,
'max_Jp': 95}
</pre>
<p>-color_map_luts['cm_candidate_3'] = (array( +color_map_luts["kelp"] = (array(</p>
<pre>[ 0.07873808, 0.08503098, 0.09119215, 0.09725944, 0.10324966, 0.10914691,
0.1149903 , 0.12076614, 0.12647234, 0.13214487, 0.13775951, 0.14331952,
0.14885405, 0.15434127, 0.15978387, 0.16520148, 0.17058327, 0.17592717,</pre>
<p>@@ -8245,7 +8245,7 @@</p>
<pre> 58.514492753623216, 0.1811594202898732],
'min_Jp': 3.96624472574, 'max_Jp': 96.5975103734}
</pre>
<p>-color_map_luts['cm_candidate_4'] = (array( +color_map_luts["sunset"] = (array(</p>
<pre>[ 0.02379297, 0.0261157 , 0.02850455, 0.03095137, 0.0334476 , 0.0360304,
0.03863824, 0.04128529, 0.04384689, 0.04631624, 0.04870907, 0.05097181,
0.05316059, 0.05519077, 0.05714277, 0.05892787, 0.06062771, 0.06214595,</pre>
<p>diff -r 183cbe63270e9437b27156d8652492b043f9ff04 -r df7f40e328e9b856003b2676723c3295279f67c7 yt/visualization/plot_container.py --- a/yt/visualization/plot_container.py +++ b/yt/visualization/plot_container.py @@ -28,6 +28,8 @@</p>
<pre>from ._mpl_imports import FigureCanvasAgg
from .tick_locators import LogLocator, LinearLocator
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
get_image_suffix, \
get_ipython_api_version, iterable, \</pre>
<p>@@ -190,7 +192,8 @@</p>
<pre>self.plots = PlotDictionary(data_source)
self._callbacks = []
self._field_transform = {}</pre>
<ul><li><p>self._colormaps = defaultdict(lambda: ‘algae’)</p></li></ul>
<p>+ self._colormaps = defaultdict( + lambda: ytcfg.get("yt", "default_colormap"))</p>
<pre>font_path = matplotlib.get_data_path() + '/fonts/ttf/STIXGeneral.ttf'
self._font_properties = FontProperties(size=fontsize, fname=font_path)
self._font_color = None</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/9263ba4d9676/">https://bitbucket.org/yt_analysis/yt/commits/9263ba4d9676/</a> Changeset: 9263ba4d9676 Branch: yt User: MatthewTurk Date: 2016-03-22 20:46:18+00:00 Summary: Unset the default colormap in matplotlib Affected #: 1 file</p>
<p>diff -r df7f40e328e9b856003b2676723c3295279f67c7 -r 9263ba4d967626d5d5b54ce065b6651f2efbbdbf yt/visualization/color_maps.py --- a/yt/visualization/color_maps.py +++ b/yt/visualization/color_maps.py @@ -72,9 +72,6 @@</p>
<pre>add_cmap('bds_highcontrast', cdict)
add_cmap('algae', cdict)
</pre>
<p>-# Set the default colormap to be algae. -matplotlib.rc('image', cmap="algae") –</p>
<pre># This next colormap was designed by Tune Kamae and converted here by Matt
_vs = np.linspace(0,1,255)
_kamae_red = np.minimum(255,</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/6c610b7d4f0c/">https://bitbucket.org/yt_analysis/yt/commits/6c610b7d4f0c/</a> Changeset: 6c610b7d4f0c Branch: yt User: MatthewTurk Date: 2016-03-22 20:47:41+00:00 Summary: Add new native colormaps Affected #: 1 file</p>
<p>diff -r 9263ba4d967626d5d5b54ce065b6651f2efbbdbf -r 6c610b7d4f0c22f29b7d586f4ff9bd509773d2f6 doc/source/visualizing/colormaps/index.rst --- a/doc/source/visualizing/colormaps/index.rst +++ b/doc/source/visualizing/colormaps/index.rst @@ -97,7 +97,8 @@</p>
<pre>.. code-block:: python
import yt</pre>
<ul><li><p>yt.show_colormaps(subset=['algae', ‘kamae’, 'spectral'],</p></li></ul>
<p>+ yt.show_colormaps(subset=['algae', ‘kamae’, ‘spectral’, + ‘arbre’, ‘sunset’, ‘octarine’, 'kelp'],</p>
<pre> filename="yt_native.png")
Applying a Colormap to your Rendering</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/28d03f133f6f/">https://bitbucket.org/yt_analysis/yt/commits/28d03f133f6f/</a> Changeset: 28d03f133f6f Branch: yt User: MatthewTurk Date: 2016-03-22 20:52:41+00:00 Summary: Pulling a handful of cmap args out, adding a default. Affected #: 4 files</p>
<p>diff -r 6c610b7d4f0c22f29b7d586f4ff9bd509773d2f6 -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 yt/visualization/plot_modifications.py --- a/yt/visualization/plot_modifications.py +++ b/yt/visualization/plot_modifications.py @@ -27,6 +27,8 @@</p>
<pre>from matplotlib import cm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
mylog, iterable
from yt.extern.six import add_metaclass</pre>
<p>@@ -1535,9 +1537,12 @@</p>
<pre> """
_type_name = "mesh_lines"
</pre>
<ul><li><p>def __init__(self, thresh=0.1):</p></li></ul>
<p>+ def __init__(self, thresh=0.1, cmap=None):</p>
<pre>super(MeshLinesCallback, self).__init__()
self.thresh = thresh</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”) + self.cmap = cmap</p>
<pre> def __call__(self, plot):
</pre>
<p>diff -r 6c610b7d4f0c22f29b7d586f4ff9bd509773d2f6 -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 yt/visualization/volume_rendering/old_camera.py --- a/yt/visualization/volume_rendering/old_camera.py +++ b/yt/visualization/volume_rendering/old_camera.py @@ -691,8 +691,7 @@</p>
<pre>del nz
else:
nim = im</pre>
<ul><li><p>ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper',</p></li>
<li><p>cmap=self.cmap)</p></li></ul>
<p>+ ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper')</p>
<pre> return ax
def draw(self):</pre>
<p>@@ -1104,7 +1103,7 @@</p>
<pre>pylab.draw()
im = Camera.snapshot(self, fn, clip_ratio)
pylab.figure(1)</pre>
<ul><li><p>pylab.imshow(im / im.max(), cmap=self.cmap)</p></li></ul>
<p>+ pylab.imshow(im / im.max())</p>
<pre> pylab.draw()
self.frames.append(im)
</pre>
<p>@@ -1781,8 +1780,7 @@</p>
<pre>if take_log: func = np.log10
else: func = lambda a: a
implot = ax.imshow(func(img), extent=(-np.pi,np.pi,-np.pi/2,np.pi/2),</pre>
<ul><li><p>clip_on=False, aspect=0.5, vmin=cmin, vmax=cmax,</p></li>
<li><p>cmap=self.cmap)</p></li></ul>
<p>+ clip_on=False, aspect=0.5, vmin=cmin, vmax=cmax)</p>
<pre>cb = fig.colorbar(implot, orientation='horizontal')
cb.set_label(label)
ax.xaxis.set_ticks(())</pre>
<p>diff -r 6c610b7d4f0c22f29b7d586f4ff9bd509773d2f6 -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 yt/visualization/volume_rendering/scene.py --- a/yt/visualization/volume_rendering/scene.py +++ b/yt/visualization/volume_rendering/scene.py @@ -419,8 +419,8 @@</p>
<pre>del nz
else:
nim = im</pre>
<ul><li><p>axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), interpolation="nearest",</p></li>
<li><p>cmap=self.cmap)</p></li></ul>
<p>+ axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), + interpolation="nearest")</p>
<pre> return axim
</pre>
<p>diff -r 6c610b7d4f0c22f29b7d586f4ff9bd509773d2f6 -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 yt/visualization/volume_rendering/transfer_functions.py --- a/yt/visualization/volume_rendering/transfer_functions.py +++ b/yt/visualization/volume_rendering/transfer_functions.py @@ -493,7 +493,7 @@</p>
<pre>i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)</pre>
<ul><li><p>ax.imshow(i_data, origin='lower', cmap=self.cmap)</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower')</p>
<pre>ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
ax.set_xlim(0, self.alpha.x.size)
xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]</pre>
<p>@@ -534,7 +534,7 @@</p>
<pre>i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)</pre>
<ul><li><p>ax.imshow(i_data, origin='lower', cmap=self.cmap)</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower')</p>
<pre>ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
ax.set_xlim(0, self.alpha.x.size)
xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]</pre>
<p>@@ -582,7 +582,7 @@</p>
<pre>i_data[:,:,0] = np.outer(self.funcs[0].y, np.ones(self.alpha.x.size))
i_data[:,:,1] = np.outer(self.funcs[1].y, np.ones(self.alpha.x.size))
i_data[:,:,2] = np.outer(self.funcs[2].y, np.ones(self.alpha.x.size))</pre>
<ul><li><p>ax.imshow(i_data, origin='lower', aspect='auto', cmap=self.cmap)</p></li></ul>
<p>+ ax.imshow(i_data, origin='lower', aspect='auto')</p>
<pre> ax.plot(alpha, np.arange(self.alpha.y.size), 'w')
# Set TF limits based on what is visible</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/1c76ceca3070/">https://bitbucket.org/yt_analysis/yt/commits/1c76ceca3070/</a> Changeset: 1c76ceca3070 Branch: yt User: brittonsmith Date: 2016-03-22 21:15:41+00:00 Summary: Changing default colormap from algae to using ytcfg value. Affected #: 11 files</p>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/analysis_modules/cosmological_observation/light_cone/light_cone.py --- a/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py +++ b/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py @@ -17,6 +17,8 @@</p>
<pre>import numpy as np
import os
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
mylog, \
only_on_root</pre>
<p>@@ -233,7 +235,7 @@</p>
<pre>weight_field=None, photon_field=False,
save_stack=True, save_final_image=True,
save_slice_images=False,</pre>
<ul><li><p>cmap_name="algae",</p></li></ul>
<p>+ cmap_name=None,</p>
<pre> njobs=1, dynamic=False):
r"""Create projections for light cone, then add them together.
</pre>
<p>@@ -266,7 +268,7 @@</p>
<pre>Default: False.
cmap_name : string
color map for images.</pre>
<ul><li><p>Default: “algae”.</p></li></ul>
<p>+ Default: your default colormap.</p>
<pre> njobs : int
The number of parallel jobs over which the light cone projection
will be split. Choose -1 for one processor per individual</pre>
<p>@@ -279,6 +281,9 @@</p>
<pre> """
</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”) +</p>
<pre> if isinstance(field_of_view, tuple) and len(field_of_view) == 2:
field_of_view = self.simulation.quan(field_of_view[0],
field_of_view[1])</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/analysis_modules/sunyaev_zeldovich/projection.py --- a/yt/analysis_modules/sunyaev_zeldovich/projection.py +++ b/yt/analysis_modules/sunyaev_zeldovich/projection.py @@ -18,6 +18,8 @@</p>
<pre># The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.utilities.physical_constants import sigma_thompson, clight, hcgs, kboltz, mh, Tcmb
from yt.funcs import fix_axis, get_pbar
from yt.visualization.volume_rendering.off_axis_projection import \</pre>
<p>@@ -391,7 +393,7 @@</p>
<pre> fib.writeto(filename, clobber=clobber)
@parallel_root_only</pre>
<ul><li><p>def write_png(self, filename_prefix, cmap_name="algae",</p></li></ul>
<p>+ def write_png(self, filename_prefix, cmap_name=None,</p>
<pre>axes_units="kpc", log_fields=None):
r""" Export images to PNG files. Writes the SZ distortion in all
specified frequencies as well as the mass-weighted temperature and the</pre>
<p>@@ -406,6 +408,9 @@</p>
<pre>--------
>>> szprj.write_png("SZsloshing")
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”) +</p>
<pre>import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/data_objects/construction_data_containers.py --- a/yt/data_objects/construction_data_containers.py +++ b/yt/data_objects/construction_data_containers.py @@ -1212,7 +1212,7 @@</p>
<pre> return vv
def export_obj(self, filename, transparency = 1.0, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>@@ -1292,6 +1292,8 @@</p>
<pre> ... dist_fac = distf, plot_index = i)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
if color_field is not None:
self.get_data(color_field,"face")</pre>
<p>@@ -1366,10 +1368,12 @@</p>
<pre>@parallel_root_only
def _export_obj(self, filename, transparency, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if plot_index is None:
plot_index = 0
if isinstance(filename, io.IOBase):</pre>
<p>@@ -1460,7 +1464,7 @@</p>
<pre>def export_blender(self, transparency = 1.0, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>@@ -1540,6 +1544,8 @@</p>
<pre> ... dist_fac = distf, plot_index = i)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
if color_field is not None:
self.get_data(color_field,"face")</pre>
<p>@@ -1559,10 +1565,12 @@</p>
<pre> return fullverts, colors, alpha, emisses, colorindex
def _export_blender(self, transparency, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if plot_index is None:
plot_index = 0
ftype = [("cind", "uint8"), ("emit", "float")]</pre>
<p>@@ -1607,7 +1615,7 @@</p>
<pre>def export_ply(self, filename, bounds = None, color_field = None,</pre>
<ul><li><p>color_map = “algae”, color_log = True, sample_type = “face”,</p></li></ul>
<p>+ color_map = None, color_log = True, sample_type = “face”,</p>
<pre>no_ghost=False):
r"""This exports the surface to the PLY format, suitable for visualization
in many different programs (e.g., MeshLab).</pre>
<p>@@ -1639,6 +1647,8 @@</p>
<pre>... sp.center[i] + 5.0*kpc) for i in range(3)]
>>> surf.export_ply("my_galaxy.ply", bounds = bounds)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
self.get_data(color_field, sample_type, no_ghost=no_ghost)
elif color_field is not None:</pre>
<p>@@ -1663,7 +1673,9 @@</p>
<pre>@parallel_root_only
def _export_ply(self, filename, bounds = None, color_field = None,</pre>
<ul><li><p>color_map = “algae”, color_log = True, sample_type = "face"):</p></li></ul>
<p>+ color_map = None, color_log = True, sample_type = “face"): + if color_map is None: + color_map = ytcfg.get("yt”, “default_colormap”)</p>
<pre> if hasattr(filename, 'read'):
f = filename
else:</pre>
<p>@@ -1727,7 +1739,7 @@</p>
<pre> f.close()
def export_sketchfab(self, title, description, api_key = None,</pre>
<ul><li><p>color_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, color_map = None,</p>
<pre>color_log = True, bounds = None, no_ghost = False):
r"""This exports Surfaces to SketchFab.com, where they can be viewed
interactively in a web browser.</pre>
<p>@@ -1784,6 +1796,8 @@</p>
<pre>... bounds = bounds)
...
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> api_key = api_key or ytcfg.get("yt","sketchfab_api_key")
if api_key in (None, "None"):
raise YTNoAPIKey("SketchFab.com", "sketchfab_api_key")</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/data_objects/image_array.py --- a/yt/data_objects/image_array.py +++ b/yt/data_objects/image_array.py @@ -13,6 +13,8 @@</p>
<pre>import warnings
import numpy as np</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.visualization.image_writer import write_bitmap, write_image
from yt.units.yt_array import YTArray
</pre>
<p>@@ -307,7 +309,7 @@</p>
<pre> return write_bitmap(out.swapaxes(0, 1), filename)
def write_image(self, filename, color_bounds=None, channel=None,</pre>
<ul><li><p>cmap_name="algae", func=lambda x: x):</p></li></ul>
<p>+ cmap_name=None, func=lambda x: x):</p>
<pre> r"""Writes a single channel of the ImageArray to a png file.
Parameters</pre>
<p>@@ -348,6 +350,8 @@</p>
<pre> >>> im_arr.write_image('test_ImageArray.png')
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre> if filename[-4:] != '.png':
filename += '.png'
</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/utilities/command_line.py --- a/yt/utilities/command_line.py +++ b/yt/utilities/command_line.py @@ -50,6 +50,8 @@</p>
<pre>if ytcfg.getboolean("yt","loadfieldplugins"):
enable_plugins()
</pre>
<p>+_default_colormap = ytcfg.get("yt", “default_colormap”) +</p>
<pre>def _fix_ds(arg):
if os.path.isdir("%s" % arg) and \
os.path.exists("%s/%s" % (arg,arg)):</pre>
<p>@@ -160,7 +162,7 @@</p>
<pre>help="Field to weight projections with"),
cmap = dict(longname="--colormap",
action="store", type=str,</pre>
<ul><li><p>dest="cmap", default="algae",</p></li></ul>
<p>+ dest="cmap", default=_default_colormap,</p>
<pre>help="Colormap name"),
zlim = dict(short="-z", longname="--zlim",
action="store", type=float,</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/eps_writer.py --- a/yt/visualization/eps_writer.py +++ b/yt/visualization/eps_writer.py @@ -19,6 +19,8 @@</p>
<pre>import matplotlib.pyplot as plt
from ._mpl_imports import FigureCanvasAgg
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.utilities.logger import ytLogger as mylog
from .plot_window import PlotWindow
from .profile_plotter import PhasePlot, ProfilePlot</pre>
<p>@@ -725,7 +727,7 @@</p>
<pre>if plot.cmap is not None:
_cmap = plot.cmap.name
if _cmap is None:</pre>
<ul><li><p>_cmap = ‘algae’</p></li></ul>
<p>+ _cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> if isinstance(plot, (PlotWindow, PhasePlot)):
if isinstance(plot, PlotWindow):
try:</pre>
<p>@@ -1345,7 +1347,7 @@</p>
<pre> return d
#=============================================================================</pre>
<p>-def return_cmap(cmap="algae", label="", range=(0,1), log=False): +def return_cmap(cmap=None, label="", range=(0,1), log=False):</p>
<pre> r"""Returns a dict that describes a colorbar. Exclusively for use with
multiplot.
</pre>
<p>@@ -1364,5 +1366,7 @@</p>
<pre>--------
>>> cb = return_cmap("algae", "Density [cm$^{-3}$]", (0,10), False)
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre>return {'cmap': cmap, 'name': label, 'range': range, 'log': log}
</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/image_writer.py --- a/yt/visualization/image_writer.py +++ b/yt/visualization/image_writer.py @@ -15,6 +15,8 @@</p>
<pre>import numpy as np
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, get_image_suffix
from yt.units.yt_array import YTQuantity
from yt.utilities.exceptions import YTNotInsideNotebook</pre>
<p>@@ -175,7 +177,7 @@</p>
<pre> return pw.write_png_to_string(bitmap_array.copy())
return bitmap_array
</pre>
<p>-def write_image(image, filename, color_bounds = None, cmap_name = “algae”, func = lambda x: x): +def write_image(image, filename, color_bounds = None, cmap_name = None, func = lambda x: x):</p>
<pre> r"""Write out a floating point array directly to a PNG file, scaling it and
applying a colormap.
</pre>
<p>@@ -210,6 +212,8 @@</p>
<pre>(1024, 1024))
>>> write_image(frb1["Density"], "saved.png")
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre>if len(image.shape) == 3:
mylog.info("Using only channel 1 of supplied image")
image = image[:,:,0]</pre>
<p>@@ -217,7 +221,7 @@</p>
<pre> pw.write_png(to_plot, filename)
return to_plot
</pre>
<p>-def apply_colormap(image, color_bounds = None, cmap_name = ‘algae’, func=lambda x: x): +def apply_colormap(image, color_bounds = None, cmap_name = None, func=lambda x: x):</p>
<pre> r"""Apply a colormap to a floating point image, scaling to uint8.
This function will scale an image and directly call libpng to write out a</pre>
<p>@@ -242,6 +246,8 @@</p>
<pre> to_plot : uint8 image with colorbar applied.
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre>from yt.data_objects.image_array import ImageArray
image = ImageArray(func(image))
if color_bounds is None:</pre>
<p>@@ -333,7 +339,7 @@</p>
<pre>def write_projection(data, filename, colorbar=True, colorbar_label=None,
title=None, limits=None, take_log=True, figsize=(8,6),</pre>
<ul><li><p>dpi=100, cmap_name='algae', extent=None, xlabel=None,</p></li></ul>
<p>+ dpi=100, cmap_name=None, extent=None, xlabel=None,</p>
<pre>ylabel=None):
r"""Write a projection or volume rendering to disk with a variety of
pretty parameters such as limits, title, colorbar, etc. write_projection</pre>
<p>@@ -378,6 +384,8 @@</p>
<pre>title="Offaxis Projection", limits=(1e-5,1e-3),
take_log=True)
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre> import matplotlib
from ._mpl_imports import FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/tests/test_callbacks.py --- a/yt/visualization/tests/test_callbacks.py +++ b/yt/visualization/tests/test_callbacks.py @@ -18,6 +18,8 @@</p>
<pre>from numpy.testing import \
assert_raises
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.testing import \
fake_amr_ds
import yt.units as u</pre>
<p>@@ -357,7 +359,7 @@</p>
<pre> p = SlicePlot(ds, "x", "density")
p.annotate_line_integral_convolution("velocity_x", "velocity_y",
kernellen=100., lim=(0.4,0.7),</pre>
<ul><li><p>cmap='algae', alpha=0.9,</p></li>
<li><p>const_alpha=True)</p></li></ul>
<p>+ cmap=ytcfg.get("yt", "default_colormap"), + alpha=0.9, const_alpha=True)</p>
<pre> p.save(prefix)
</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/volume_rendering/old_camera.py --- a/yt/visualization/volume_rendering/old_camera.py +++ b/yt/visualization/volume_rendering/old_camera.py @@ -16,6 +16,8 @@</p>
<pre>from yt.extern.six.moves import builtins
import numpy as np
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
iterable, mylog, get_pbar, \
get_num_threads, ensure_numpy_array</pre>
<p>@@ -236,7 +238,7 @@</p>
<pre> px = (res[1]*(dy/self.width[1])).astype('int')
return px, py, dz
</pre>
<ul><li><p>def draw_grids(self, im, alpha=0.3, cmap='algae', min_level=None,</p></li></ul>
<p>+ def draw_grids(self, im, alpha=0.3, cmap=None, min_level=None,</p>
<pre> max_level=None):
r"""Draws Grids on an existing volume rendering.
</pre>
<p>@@ -269,6 +271,8 @@</p>
<pre> >>> write_bitmap(im, 'render_with_grids.png')
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre>region = self.data_source
corners = []
levels = []</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/volume_rendering/render_source.py --- a/yt/visualization/volume_rendering/render_source.py +++ b/yt/visualization/volume_rendering/render_source.py @@ -12,6 +12,8 @@</p>
<pre># -----------------------------------------------------------------------------
import numpy as np</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, ensure_numpy_array
from yt.utilities.parallel_tools.parallel_analysis_interface import \
ParallelAnalysisInterface</pre>
<p>@@ -355,7 +357,7 @@</p>
<pre> self.current_image = None
# default color map</pre>
<ul><li><p>self._cmap = ‘algae’</p></li></ul>
<p>+ self._cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> self._color_bounds = None
# default mesh annotation options</pre>
<p>@@ -939,7 +941,7 @@</p>
<pre> """
</pre>
<ul><li><p>def __init__(self, data_source, alpha=0.3, cmap='algae',</p></li></ul>
<p>+ def __init__(self, data_source, alpha=0.3, cmap=None,</p>
<pre>min_level=None, max_level=None):
self.data_source = data_source_or_all(data_source)
corners = []</pre>
<p>@@ -959,6 +961,8 @@</p>
<pre>levels.append(block.Level)
corners = np.dstack(corners)
levels = np.array(levels)</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> if max_level is not None:
subset = levels <= max_level</pre>
<p>diff -r 28d03f133f6f3b1e5ec4d7bc77c39a0013135f63 -r 1c76ceca30705cd3c6372b26a69056818ce906f2 yt/visualization/volume_rendering/scene.py --- a/yt/visualization/volume_rendering/scene.py +++ b/yt/visualization/volume_rendering/scene.py @@ -15,6 +15,8 @@</p>
<pre>import functools
import numpy as np
from collections import OrderedDict</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, get_image_suffix
from yt.extern.six import iteritems, itervalues, string_types
from yt.units.dimensions import \</pre>
<p>@@ -643,7 +645,7 @@</p>
<pre> self.add_source(box_source)
return self
</pre>
<ul><li><p>def annotate_grids(self, data_source, alpha=0.3, cmap='algae',</p></li></ul>
<p>+ def annotate_grids(self, data_source, alpha=0.3, cmap=None,</p>
<pre> min_level=None, max_level=None):
r"""
</pre>
<p>@@ -677,6 +679,8 @@</p>
<pre> >>> im = sc.render()
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> grids = GridSource(data_source, alpha=alpha, cmap=cmap,
min_level=min_level, max_level=max_level)
self.add_source(grids)</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/fdc75297cbf7/">https://bitbucket.org/yt_analysis/yt/commits/fdc75297cbf7/</a> Changeset: fdc75297cbf7 Branch: yt User: MatthewTurk Date: 2016-03-23 11:49:21+00:00 Summary: Changing sunset->dusk Affected #: 2 files</p>
<p>diff -r 1c76ceca30705cd3c6372b26a69056818ce906f2 -r fdc75297cbf75142d1d79a94f9861153ac6008f0 doc/source/visualizing/colormaps/index.rst --- a/doc/source/visualizing/colormaps/index.rst +++ b/doc/source/visualizing/colormaps/index.rst @@ -98,7 +98,7 @@</p>
<pre>import yt
yt.show_colormaps(subset=['algae', 'kamae', 'spectral',</pre>
<ul><li><p>‘arbre’, ‘sunset’, ‘octarine’, 'kelp'],</p></li></ul>
<p>+ ‘arbre’, ‘dusk’, ‘octarine’, 'kelp'],</p>
<pre> filename="yt_native.png")
Applying a Colormap to your Rendering</pre>
<p>diff -r 1c76ceca30705cd3c6372b26a69056818ce906f2 -r fdc75297cbf75142d1d79a94f9861153ac6008f0 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -8245,7 +8245,7 @@</p>
<pre> 58.514492753623216, 0.1811594202898732],
'min_Jp': 3.96624472574, 'max_Jp': 96.5975103734}
</pre>
<p>-color_map_luts["sunset"] = (array( +color_map_luts["dusk"] = (array(</p>
<pre>[ 0.02379297, 0.0261157 , 0.02850455, 0.03095137, 0.0334476 , 0.0360304,
0.03863824, 0.04128529, 0.04384689, 0.04631624, 0.04870907, 0.05097181,
0.05316059, 0.05519077, 0.05714277, 0.05892787, 0.06062771, 0.06214595,</pre>
<p><a href="https://bitbucket.org/yt_analysis/yt/commits/b608c33bfeae/">https://bitbucket.org/yt_analysis/yt/commits/b608c33bfeae/</a> Changeset: b608c33bfeae Branch: yt User: ngoldbaum Date: 2016-03-28 16:52:24+00:00 Summary: Merged in MatthewTurk/yt (pull request #2067)</p>
<p>Add colormaps and change default to ‘arbre’ Affected #: 18 files</p>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 doc/source/visualizing/colormaps/index.rst --- a/doc/source/visualizing/colormaps/index.rst +++ b/doc/source/visualizing/colormaps/index.rst @@ -97,7 +97,8 @@</p>
<pre>.. code-block:: python
import yt</pre>
<ul><li><p>yt.show_colormaps(subset=['algae', ‘kamae’, 'spectral'],</p></li></ul>
<p>+ yt.show_colormaps(subset=['algae', ‘kamae’, ‘spectral’, + ‘arbre’, ‘dusk’, ‘octarine’, 'kelp'],</p>
<pre> filename="yt_native.png")
Applying a Colormap to your Rendering</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/analysis_modules/cosmological_observation/light_cone/light_cone.py --- a/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py +++ b/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py @@ -17,6 +17,8 @@</p>
<pre>import numpy as np
import os
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
mylog, \
only_on_root</pre>
<p>@@ -233,7 +235,7 @@</p>
<pre>weight_field=None, photon_field=False,
save_stack=True, save_final_image=True,
save_slice_images=False,</pre>
<ul><li><p>cmap_name="algae",</p></li></ul>
<p>+ cmap_name=None,</p>
<pre> njobs=1, dynamic=False):
r"""Create projections for light cone, then add them together.
</pre>
<p>@@ -266,7 +268,7 @@</p>
<pre>Default: False.
cmap_name : string
color map for images.</pre>
<ul><li><p>Default: “algae”.</p></li></ul>
<p>+ Default: your default colormap.</p>
<pre> njobs : int
The number of parallel jobs over which the light cone projection
will be split. Choose -1 for one processor per individual</pre>
<p>@@ -279,6 +281,9 @@</p>
<pre> """
</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”) +</p>
<pre> if isinstance(field_of_view, tuple) and len(field_of_view) == 2:
field_of_view = self.simulation.quan(field_of_view[0],
field_of_view[1])</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/analysis_modules/sunyaev_zeldovich/projection.py --- a/yt/analysis_modules/sunyaev_zeldovich/projection.py +++ b/yt/analysis_modules/sunyaev_zeldovich/projection.py @@ -18,6 +18,8 @@</p>
<pre># The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.utilities.physical_constants import sigma_thompson, clight, hcgs, kboltz, mh, Tcmb
from yt.funcs import fix_axis, get_pbar
from yt.visualization.volume_rendering.off_axis_projection import \</pre>
<p>@@ -391,7 +393,7 @@</p>
<pre> fib.writeto(filename, clobber=clobber)
@parallel_root_only</pre>
<ul><li><p>def write_png(self, filename_prefix, cmap_name="algae",</p></li></ul>
<p>+ def write_png(self, filename_prefix, cmap_name=None,</p>
<pre>axes_units="kpc", log_fields=None):
r""" Export images to PNG files. Writes the SZ distortion in all
specified frequencies as well as the mass-weighted temperature and the</pre>
<p>@@ -406,6 +408,9 @@</p>
<pre>--------
>>> szprj.write_png("SZsloshing")
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”) +</p>
<pre>import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/config.py --- a/yt/config.py +++ b/yt/config.py @@ -61,6 +61,7 @@</p>
<pre>ignore_invalid_unit_operation_errors = 'False',
chunk_size = '1000',
xray_data_dir = '/does/not/exist',</pre>
<p>+ default_colormap = ‘arbre’,</p>
<pre> )
# Here is the upgrade. We're actually going to parse the file in its entirety
# here. Then, if it has any of the Forbidden Sections, it will be rewritten</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/data_objects/construction_data_containers.py --- a/yt/data_objects/construction_data_containers.py +++ b/yt/data_objects/construction_data_containers.py @@ -1225,7 +1225,7 @@</p>
<pre> return vv
def export_obj(self, filename, transparency = 1.0, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>@@ -1305,6 +1305,8 @@</p>
<pre> ... dist_fac = distf, plot_index = i)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
if color_field is not None:
self.get_data(color_field,"face")</pre>
<p>@@ -1379,10 +1381,12 @@</p>
<pre>@parallel_root_only
def _export_obj(self, filename, transparency, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if plot_index is None:
plot_index = 0
if isinstance(filename, io.IOBase):</pre>
<p>@@ -1473,7 +1477,7 @@</p>
<pre>def export_blender(self, transparency = 1.0, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>@@ -1553,6 +1557,8 @@</p>
<pre> ... dist_fac = distf, plot_index = i)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
if color_field is not None:
self.get_data(color_field,"face")</pre>
<p>@@ -1572,10 +1578,12 @@</p>
<pre> return fullverts, colors, alpha, emisses, colorindex
def _export_blender(self, transparency, dist_fac = None,</pre>
<ul><li><p>color_field = None, emit_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, emit_field = None, color_map = None,</p>
<pre>color_log = True, emit_log = True, plot_index = None,
color_field_max = None, color_field_min = None,
emit_field_max = None, emit_field_min = None):</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if plot_index is None:
plot_index = 0
ftype = [("cind", "uint8"), ("emit", "float")]</pre>
<p>@@ -1620,7 +1628,7 @@</p>
<pre>def export_ply(self, filename, bounds = None, color_field = None,</pre>
<ul><li><p>color_map = “algae”, color_log = True, sample_type = “face”,</p></li></ul>
<p>+ color_map = None, color_log = True, sample_type = “face”,</p>
<pre>no_ghost=False):
r"""This exports the surface to the PLY format, suitable for visualization
in many different programs (e.g., MeshLab).</pre>
<p>@@ -1652,6 +1660,8 @@</p>
<pre>... sp.center[i] + 5.0*kpc) for i in range(3)]
>>> surf.export_ply("my_galaxy.ply", bounds = bounds)
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> if self.vertices is None:
self.get_data(color_field, sample_type, no_ghost=no_ghost)
elif color_field is not None:</pre>
<p>@@ -1676,7 +1686,9 @@</p>
<pre>@parallel_root_only
def _export_ply(self, filename, bounds = None, color_field = None,</pre>
<ul><li><p>color_map = “algae”, color_log = True, sample_type = "face"):</p></li></ul>
<p>+ color_map = None, color_log = True, sample_type = “face"): + if color_map is None: + color_map = ytcfg.get("yt”, “default_colormap”)</p>
<pre> if hasattr(filename, 'read'):
f = filename
else:</pre>
<p>@@ -1740,7 +1752,7 @@</p>
<pre> f.close()
def export_sketchfab(self, title, description, api_key = None,</pre>
<ul><li><p>color_field = None, color_map = “algae”,</p></li></ul>
<p>+ color_field = None, color_map = None,</p>
<pre>color_log = True, bounds = None, no_ghost = False):
r"""This exports Surfaces to SketchFab.com, where they can be viewed
interactively in a web browser.</pre>
<p>@@ -1797,6 +1809,8 @@</p>
<pre>... bounds = bounds)
...
"""</pre>
<p>+ if color_map is None: + color_map = ytcfg.get("yt", “default_colormap”)</p>
<pre> api_key = api_key or ytcfg.get("yt","sketchfab_api_key")
if api_key in (None, "None"):
raise YTNoAPIKey("SketchFab.com", "sketchfab_api_key")</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/data_objects/image_array.py --- a/yt/data_objects/image_array.py +++ b/yt/data_objects/image_array.py @@ -13,6 +13,8 @@</p>
<pre>import warnings
import numpy as np</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.visualization.image_writer import write_bitmap, write_image
from yt.units.yt_array import YTArray
</pre>
<p>@@ -307,7 +309,7 @@</p>
<pre> return write_bitmap(out.swapaxes(0, 1), filename)
def write_image(self, filename, color_bounds=None, channel=None,</pre>
<ul><li><p>cmap_name="algae", func=lambda x: x):</p></li></ul>
<p>+ cmap_name=None, func=lambda x: x):</p>
<pre> r"""Writes a single channel of the ImageArray to a png file.
Parameters</pre>
<p>@@ -348,6 +350,8 @@</p>
<pre> >>> im_arr.write_image('test_ImageArray.png')
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre> if filename[-4:] != '.png':
filename += '.png'
</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/utilities/command_line.py --- a/yt/utilities/command_line.py +++ b/yt/utilities/command_line.py @@ -50,6 +50,8 @@</p>
<pre>if ytcfg.getboolean("yt","loadfieldplugins"):
enable_plugins()
</pre>
<p>+_default_colormap = ytcfg.get("yt", “default_colormap”) +</p>
<pre>def _fix_ds(arg):
if os.path.isdir("%s" % arg) and \
os.path.exists("%s/%s" % (arg,arg)):</pre>
<p>@@ -160,7 +162,7 @@</p>
<pre>help="Field to weight projections with"),
cmap = dict(longname="--colormap",
action="store", type=str,</pre>
<ul><li><p>dest="cmap", default="algae",</p></li></ul>
<p>+ dest="cmap", default=_default_colormap,</p>
<pre>help="Colormap name"),
zlim = dict(short="-z", longname="--zlim",
action="store", type=float,</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/_colormap_data.py --- a/yt/visualization/_colormap_data.py +++ b/yt/visualization/_colormap_data.py @@ -7816,6 +7816,566 @@</p>
<pre>np.ones(256),
)
</pre>
<p>+_parameters = {'xp': [-6.0027356902356814, -42.46106902356901, + 41.393097643097661, 69.344486531986547, 6.15004208754209, + 17.695180976430976], + ‘yp’: [-19.704861111111086, 56.857638888888886, + -8.1597222222222001, 58.680555555555543, -23.958333333333314, + -16.059027777777771], + ‘min_Jp’: 17.1875, + ‘max_Jp’: 82.1875} +color_map_luts["octarine"] = (array( + [ 0.01845663, 0.01940818, 0.02066025, 0.02218966, 0.02395409, 0.02595033, + 0.02817596, 0.03060653, 0.03322304, 0.03602798, 0.03900455, 0.04208415, + 0.04516324, 0.04823603, 0.05128648, 0.05431253, 0.05730541, 0.06025524, + 0.0631607 , 0.06601581, 0.0688137 , 0.07155484, 0.07423302, 0.07684491, + 0.07939306, 0.08186684, 0.08427203, 0.08660745, 0.08886448, 0.09105658, + 0.09316971, 0.09521672, 0.09719719, 0.09910774, 0.10096841, 0.10275846, + 0.10451309, 0.10621217, 0.10788683, 0.10952759, 0.1111585 , 0.11277895, + 0.11440998, 0.11605498, 0.11773643, 0.11945691, 0.12124361, 0.1230952, + 0.12504265, 0.12708539, 0.12924907, 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0.90644047, 0.90902303, 0.91152628, 0.91394863, 0.91628739, 0.91854149, + 0.92071018, 0.92279108, 0.92478281, 0.92668571, 0.92849682, 0.93021638, + 0.93184454, 0.9333779 , 0.93481986, 0.93616638, 0.93742098, 0.9385809, + 0.93964931, 0.94062391, 0.94150928, 0.94230167, 0.94300773, 0.9436249, + 0.9441573 , 0.94460885, 0.94497728, 0.9452696 , 0.94548972, 0.94564084, + 0.94572458, 0.94574964, 0.94572195, 0.94564856, 0.94553784, 0.94539999, + 0.94524753, 0.94509613, 0.94496566, 0.94487644, 0.94486297, 0.94496862, + 0.9452415 , 0.94575723, 0.94660423, 0.94788672]), array( + [ 0.04951266, 0.06083219, 0.07165053, 0.08210915, 0.09247329, 0.10260581, + 0.11257765, 0.12255122, 0.13238498, 0.14215586, 0.15194141, 0.16162489, + 0.17131665, 0.18099838, 0.19059467, 0.20026607, 0.20988044, 0.21946015, + 0.22909981, 0.23866289, 0.24827349, 0.25786718, 0.2674192 , 0.27704703, + 0.28660208, 0.2962198 , 0.30581382, 0.31540414, 0.3250423 , 0.33462062, + 0.34430922, 0.35393291, 0.36363845, 0.37332276, 0.38305586, 0.39280948, + 0.40258886, 0.41242099, 0.42226577, 0.43218573, 0.44211511, 0.45213185, + 0.46216464, 0.47228627, 0.48244044, 0.49267364, 0.50296577, 0.51331525, + 0.52375993, 0.53422779, 0.54483694, 0.55545388, 0.56620423, 0.57700138, + 0.5878611 , 0.59884035, 0.60984329, 0.62095468, 0.63213636, 0.6433441, + 0.6546616 , 0.6660279 , 0.67741372, 0.68886382, 0.70036612, 0.71186928, + 0.72336666, 0.73489587, 0.74641297, 0.75788429, 0.76929516, 0.7806286, + 0.79188503, 0.80303085, 0.81402588, 0.82484364, 0.83545535, 0.84583016, + 0.85593546, 0.86573722, 0.87520057, 0.88429036, 0.89297186, 0.90121149, + 0.90897763, 0.91624135, 0.92297716, 0.92916362, 0.93478393, 0.93982627, + 0.944284 , 0.9481558 , 0.95144555, 0.95416177, 0.95631799, 0.95793177, + 0.95902407, 0.95961865, 0.95974148, 0.95942014, 0.95868324, 0.95756071, + 0.95608217, 0.9542758 , 0.95216987, 0.94979177, 0.94716782, 0.94432316, + 0.94128162, 0.93806566, 0.93469637, 0.93119344, 0.92757518, 0.92385855, + 0.92005924, 0.91619167, 0.91226907, 0.90830358, 0.90430622, 0.90028706, + 0.8962552 , 0.89221888, 0.88818549, 0.88416167, 0.88015334, 0.87616574, + 0.87220349, 0.86827062, 0.86437059, 0.86050638, 0.85668045, 0.85289483, + 0.84915193, 0.84545249, 0.84179674, 0.83818509, 0.83461764, 0.83109413, + 0.82761399, 0.82417636, 0.82078008, 0.81742372, 0.81410558, 0.81082372, + 0.80757594, 0.80435989, 0.80117258, 0.79801115, 0.79487252, 0.79175341, + 0.78865036, 0.78555973, 0.78247771, 0.77940035, 0.77632353, 0.77324301, + 0.77015423, 0.76705215, 0.76393282, 0.76079161, 0.75762382, 0.75442468, + 0.7511894 , 0.74791315, 0.74459109, 0.74121838, 0.73779019, 0.73430173, + 0.73074825, 0.72712503, 0.72342746, 0.71965096, 0.71579107, 0.71184237, + 0.70780168, 0.70366489, 0.69942802, 0.69508722, 0.69063878, 0.68607916, + 0.68140497, 0.67661297, 0.67170012, 0.66666354, 0.66150054, 0.65620859, + 0.65078539, 0.64522878, 0.63953554, 0.63370386, 0.62773308, 0.62162183, + 0.61536895, 0.60897349, 0.60243471, 0.59575208, 0.5889253 , 0.58195428, + 0.57483914, 0.56757997, 0.56016891, 0.55261464, 0.54491821, 0.53708088, + 0.52910417, 0.52098984, 0.51272693, 0.50432884, 0.49579898, 0.48714013, + 0.47834844, 0.46942272, 0.46037716, 0.45121575, 0.4419283 , 0.43252618, + 0.42302131, 0.41340722, 0.40368844, 0.3938839 , 0.38398203, 0.37399931, + 0.36394886, 0.35381793, 0.34364247, 0.33340639, 0.32314442, 0.31285132, + 0.30255889, 0.2922684 , 0.28202116, 0.27181653, 0.26171116, 0.25171982, + 0.24189603, 0.23229831, 0.22296839, 0.21399898, 0.20548226, 0.19752366, + 0.19024226, 0.18379968, 0.17836522, 0.17412601, 0.17128095, 0.17003118, + 0.17056788, 0.17305931, 0.17763999, 0.18441057, 0.19343386, 0.2047358, + 0.21833687, 0.2341978 , 0.25223614, 0.2722682]), np.ones(256)) + +# Used to reconstruct the colormap in viscm +parameters = {'xp': [-2.3569023569023386, 29.24031986531989, 21.948653198653204, -25.44718013468011, -4.78745791245791], + ‘yp’: [-27.604166666666657, -30.642361111111086, 24.652777777777771, -13.6284722222222, 23.4375], + ‘min_Jp’: 15, + ‘max_Jp’: 95} + +color_map_luts["kelp"] = (array( + [ 0.07873808, 0.08503098, 0.09119215, 0.09725944, 0.10324966, 0.10914691, + 0.1149903 , 0.12076614, 0.12647234, 0.13214487, 0.13775951, 0.14331952, + 0.14885405, 0.15434127, 0.15978387, 0.16520148, 0.17058327, 0.17592717, + 0.1812416 , 0.18653223, 0.19178949, 0.19701509, 0.20221806, 0.20739605, + 0.21254477, 0.21766522, 0.22276163, 0.22783646, 0.232884 , 0.23790477, + 0.24289917, 0.24786997, 0.25281796, 0.25773939, 0.26263436, 0.26750288, + 0.27234491, 0.27716076, 0.28195253, 0.28671682, 0.29145343, 0.29616211, + 0.30084257, 0.30549451, 0.31011758, 0.31471143, 0.31927567, 0.32380992, + 0.32831456, 0.3327882 , 0.33723043, 0.34164086, 0.34601907, 0.35036466, + 0.35467722, 0.35895634, 0.36320162, 0.36741265, 0.37158905, 0.37573041, + 0.37983636, 0.38390652, 0.38794052, 0.391938 , 0.3958986 , 0.39982199, + 0.40370783, 0.40755579, 0.41136559, 0.41513702, 0.41886962, 0.42256312, + 0.42621724, 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0.83731914, 0.84395228, 0.85063328, 0.8573683 , 0.86417388, + 0.87100664, 0.87792232, 0.88485711, 0.89186942, 0.898911 , 0.90601831, + 0.91316089, 0.92036241, 0.92760063, 0.93489628, 0.94222522, 0.94961559, + 0.95703072, 0.96451696, 0.97201416, 0.97959794]), array( + [ 0.02380049, 0.02762946, 0.0314955 , 0.03538367, 0.03929263, 0.04314916, + 0.04681625, 0.05034685, 0.05376738, 0.05706764, 0.06028584, 0.06343363, + 0.06649987, 0.06951333, 0.0724811 , 0.07539619, 0.07827446, 0.0811238, + 0.08394364, 0.08673511, 0.08950972, 0.0922702 , 0.09501404, 0.0977463, + 0.10047279, 0.10319545, 0.10591402, 0.10862929, 0.11134673, 0.11406773, + 0.11679361, 0.11952425, 0.12226063, 0.12500588, 0.12776095, 0.13052669, + 0.13330389, 0.13609311, 0.13889411, 0.1417089 , 0.14453802, 0.14738192, + 0.15024103, 0.15311572, 0.15600633, 0.15891314, 0.1618364 , 0.16477634, + 0.16773294, 0.17070662, 0.17369747, 0.17670558, 0.17973101, 0.18277379, + 0.18583395, 0.18891149, 0.19200641, 0.19511868, 0.19824828, 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0.83709793, + 0.84056637, 0.844015 , 0.84744449, 0.85085836, 0.85425577, 0.85763616, + 0.8609997 , 0.8643531 , 0.86769067, 0.87101222, 0.87432566, 0.87762463, + 0.88090892, 0.88418748, 0.88745026, 0.89070437, 0.89394846, 0.89717856, + 0.90040525, 0.90361483, 0.90682325, 0.91001579, 0.9132048 , 0.91638083, + 0.9195519 , 0.92271171, 0.92586627, 0.92900992, 0.93214934, 0.93527665, + 0.93840226, 0.94151285, 0.9446259 , 0.94771918]), array( + [ 0.45890713, 0.46137905, 0.46384563, 0.46630529, 0.46875421, 0.4711862, + 0.47360008, 0.47599069, 0.47835461, 0.48068977, 0.48299219, 0.4852595, + 0.48748847, 0.48967699, 0.49182306, 0.49392292, 0.4959753 , 0.49797881, + 0.4999306 , 0.50182767, 0.50367037, 0.5054572 , 0.50718436, 0.50885097, + 0.51045736, 0.51200237, 0.51348338, 0.51489809, 0.51624834, 0.51753338, + 0.51875253, 0.51990356, 0.52098523, 0.52199968, 0.52294672, 0.52382624, + 0.52463824, 0.52538248, 0.52605675, 0.52666444, 0.52720607, 0.52768224, + 0.5280937 , 0.5284413 , 0.52872599, 0.52894886, 0.52911108, 0.5292139, + 0.52925736, 0.52924451, 0.5291769 , 0.52905617, 0.52888406, 0.52866235, + 0.5283929 , 0.52807762, 0.52771851, 0.52731757, 0.52687689, 0.52639856, + 0.52588473, 0.52533756, 0.52475925, 0.524152 , 0.52351804, 0.5228596, + 0.52217891, 0.5214782 , 0.52075951, 0.52002465, 0.51927646, 0.51851715, + 0.51774891, 0.51697393, 0.51619438, 0.5154124 , 0.5146301 , 0.51384958, + 0.51307291, 0.51230212, 0.51153923, 0.51078544, 0.51004343, 0.50931521, + 0.50860267, 0.50790763, 0.50723191, 0.50657725, 0.50594539, 0.50533791, + 0.50475611, 0.50420208, 0.50367737, 0.50318346, 0.50272179, 0.50229376, + 0.50190069, 0.5015438 , 0.50122435, 0.50094363, 0.50070274, 0.50050273, + 0.50034459, 0.50022925, 0.50015758, 0.50013055, 0.50014881, 0.50021302, + 0.50032381, 0.50048172, 0.50068726, 0.50094086, 0.50124298, 0.50159412, + 0.50199429, 0.50244368, 0.50294243, 0.50349062, 0.5040883 , 0.50473544, + 0.50543197, 0.50617805, 0.5069733 , 0.50781742, 0.50871016, 0.5096512, + 0.51064021, 0.51167679, 0.51276051, 0.5138909 , 0.51506743, 0.51628978, + 0.51755709, 0.51886873, 0.52022401, 0.52162216, 0.52306241, 0.52454388, + 0.52606564, 0.52762668, 0.5292259 , 0.53086207, 0.53253385, 0.53423975, + 0.53597803, 0.53774675, 0.53954371, 0.54136636, 0.54321177, 0.54507652, + 0.54695667, 0.54884775, 0.55074476, 0.55264242, 0.55453558, 0.55642011, + 0.55829453, 0.56016223, 0.56203375, 0.56392814, 0.56587122, 0.56788936, + 0.57000019, 0.57220545, 0.57449055, 0.57683063, 0.57919892, 0.58157275, + 0.58393538, 0.58627588, 0.58858776, 0.59086745, 0.59311274, 0.59532221, + 0.59749564, 0.59963222, 0.60173091, 0.60379008, 0.60580649, 0.60777924, + 0.6097056 , 0.61158212, 0.61340237, 0.6151645 , 0.61686366, 0.61849128, + 0.62004292, 0.62151305, 0.62288934, 0.62416816, 0.62533879, 0.62639158, + 0.62731938, 0.6281096 , 0.62875725, 0.62925097, 0.62958736, 0.62976024, + 0.62976899, 0.62961438, 0.62930119, 0.62883586, 0.62822926, 0.62749232, + 0.62663432, 0.62567778, 0.62461908, 0.62348256, 0.62227864, 0.621005, + 0.6196701 , 0.61828149, 0.61685751, 0.61539115, 0.61388335, 0.61233673, + 0.61075292, 0.60913269, 0.607476 , 0.60579314, 0.60408651, 0.60234546, + 0.60056748, 0.59874959, 0.59688839, 0.59502121, 0.59312158, 0.59117279, + 0.58917064, 0.58718501, 0.58514178, 0.58303558, 0.58094288, 0.57879227, + 0.5765793 , 0.57438349, 0.57210749, 0.56981243, 0.56748232, 0.56508082, + 0.56269289, 0.56020367, 0.55773849, 0.5551784 , 0.55261699, 0.54997983, + 0.54732492, 0.54460356, 0.54185741, 0.53904386, 0.53620824, 0.53329363, + 0.53036982, 0.52734442, 0.52433316, 0.52118636]), np.ones(256)) + +# Used to reconstruct the colormap in viscm +parameters = {'xp': [ 3.444773825208614, -17.207400087834856, + -12.632024921242106, -21.656364855235495, + 16.850570926657895, 55.256368028107175, + 14.676657883179644, 12.502744839701393, + 40.401295564339051, 0.90854194115064502], + ‘yp’: [-1.6304347826086598, -24.818840579710098, + -9.447194719471895, 6.796617161716227, + -5.6159420289854722, 57.065217391304373, + 13.224637681159436, 3.4420289855072781, + 58.514492753623216, 0.1811594202898732], + ‘min_Jp’: 3.96624472574, ‘max_Jp’: 96.5975103734} + +color_map_luts["dusk"] = (array( + [ 0.02379297, 0.0261157 , 0.02850455, 0.03095137, 0.0334476 , 0.0360304, + 0.03863824, 0.04128529, 0.04384689, 0.04631624, 0.04870907, 0.05097181, + 0.05316059, 0.05519077, 0.05714277, 0.05892787, 0.06062771, 0.06214595, + 0.06357023, 0.06480933, 0.06592827, 0.06687993, 0.06766224, 0.06830946, + 0.06872415, 0.0689718 , 0.06904334, 0.06886045, 0.06844799, 0.06780027, + 0.06689054, 0.06568777, 0.06415629, 0.06225567, 0.05994102, 0.05716433, + 0.0538775 , 0.05003897, 0.04551728, 0.04029295, 0.03446229, 0.02874366, + 0.02385447, 0.02127502, 0.02242508, 0.0275301 , 0.03578777, 0.0460108, + 0.05640918, 0.06656043, 0.07634217, 0.08577769, 0.0948332 , 0.10358394, + 0.11202107, 0.1201887 , 0.12811962, 0.13583301, 0.14334681, 0.15067405, + 0.15783121, 0.16483645, 0.17170112, 0.17843538, 0.18504822, 0.19154762, + 0.19793598, 0.20422296, 0.21041472, 0.21651599, 0.22253073, 0.22846216, + 0.23431275, 0.24008353, 0.24576942, 0.25137838, 0.25691024, 0.26236467, + 0.2677416 , 0.27303884, 0.27827346, 0.28346958, 0.28867439, 0.29395695, + 0.29940706, 0.30508966, 0.31101811, 0.31715338, 0.32345196, 0.32986905, + 0.33636963, 0.34292913, 0.34953992, 0.35619361, 0.36288108, 0.36960242, + 0.37635671, 0.38314097, 0.38995328, 0.39679542, 0.40366619, 0.41056374, + 0.41748952, 0.4244427 , 0.43142299, 0.43843086, 0.44546541, 0.45252642, + 0.45961609, 0.46673201, 0.47387346, 0.48104441, 0.4882424 , 0.4954658, + 0.5027165 , 0.50999774, 0.51730448, 0.52463621, 0.53199987, 0.53939166, + 0.54680881, 0.55425097, 0.56172938, 0.56923401, 0.57676432, 0.58432014, + 0.59191314, 0.59953375, 0.60718079, 0.6148543 , 0.62255842, 0.63029849, + 0.63806608, 0.64586138, 0.65368467, 0.66153629, 0.66942125, 0.67734101, + 0.68528996, 0.69326842, 0.70127674, 0.70931527, 0.71738431, 0.72548409, + 0.73361473, 0.74177618, 0.74996816, 0.75819004, 0.76644077, 0.77471875, + 0.78302933, 0.79137839, 0.79974926, 0.80813611, 0.81657205, 0.82501275, + 0.83349386, 0.84198258, 0.85048492, 0.8590003 , 0.86751392, 0.87598709, + 0.88439937, 0.89263161, 0.90038304, 0.90687168, 0.91166375, 0.91561895, + 0.91938826, 0.92318052, 0.9269946 , 0.93070173, 0.93405155, 0.93665497, + 0.93818572, 0.93886516, 0.93923641, 0.93951639, 0.93979528, 0.94008554, + 0.9403692 , 0.94071214, 0.94104779, 0.94137354, 0.94172077, 0.9420958, + 0.94244563, 0.94274487, 0.94309703, 0.94344151, 0.94369514, 0.94407978, + 0.94432487, 0.94468376, 0.9449135 , 0.94526127, 0.94544804, 0.945808, + 0.94601776, 0.94631082, 0.94656632, 0.9467505 , 0.94706511, 0.94725625, + 0.9474877 , 0.94776129, 0.9479231 , 0.94816834, 0.94843142, 0.94859414, + 0.9488144 , 0.94909331, 0.9492829 , 0.94943682, 0.94975423, 0.94999285, + 0.95015253, 0.9504141 , 0.95072075, 0.95095877, 0.95112839, 0.95145943, + 0.95179158, 0.95206534, 0.95228128, 0.95264039, 0.95303147, 0.95337439, + 0.95367005, 0.95401622, 0.95449739, 0.95494063, 0.95534717, 0.95571839, + 0.95624412, 0.95681749, 0.95736483, 0.9578879 , 0.95838862, 0.95906407, + 0.95978233, 0.96048784, 0.96118301, 0.96187054, 0.96267628, 0.96359786, + 0.96452242, 0.96545346, 0.9663949 , 0.96735109, 0.96853232, 0.96975252, + 0.97100046, 0.9722811 , 0.97360166, 0.97499094]), array( + [ 0.01131879, 0.01391783, 0.01674789, 0.01980761, 0.02309557, 0.02659703, + 0.03032697, 0.03427332, 0.03844574, 0.04276988, 0.04704341, 0.05129054, + 0.05550078, 0.05969933, 0.06386921, 0.06803423, 0.07217737, 0.07632217, + 0.08045053, 0.08458326, 0.0887074 , 0.09283339, 0.0969619 , 0.10108667, + 0.10522618, 0.10936788, 0.1135134 , 0.1176759 , 0.12185004, 0.12603586, + 0.13023651, 0.1344553 , 0.13869563, 0.14296085, 0.14725413, 0.15157814, + 0.15593465, 0.16032384, 0.16475544, 0.16922528, 0.17373863, 0.17827184, + 0.1827844 , 0.18715937, 0.19126004, 0.19503246, 0.19852956, 0.20182311, + 0.20496848, 0.20800345, 0.21095494, 0.21383778, 0.21666923, 0.21945445, + 0.22220507, 0.22492545, 0.22761943, 0.23029139, 0.232945 , 0.23558391, + 0.23821051, 0.24082617, 0.24343288, 0.24603239, 0.24862627, 0.25121599, + 0.25380393, 0.25639065, 0.25897721, 0.26156482, 0.2641547 , 0.26674815, + 0.26934657, 0.27195171, 0.274567 , 0.27719243, 0.27983033, 0.28248333, + 0.2851544 , 0.28784764, 0.29056213, 0.2932949 , 0.29603493, 0.29876097, + 0.30144013, 0.30404208, 0.30655158, 0.3089731 , 0.31131738, 0.31359848, + 0.31582875, 0.31801758, 0.32016861, 0.32228546, 0.3243719 , 0.32642829, + 0.32845517, 0.33045378, 0.33242493, 0.33436795, 0.33628328, 0.33817163, + 0.34003234, 0.34186568, 0.34367166, 0.34544999, 0.34720092, 0.34892444, + 0.35061943, 0.35228681, 0.35392679, 0.35553735, 0.35711946, 0.35867374, + 0.36019909, 0.36169371, 0.36315988, 0.36459771, 0.36600339, 0.36737862, + 0.36872465, 0.37004148, 0.37132268, 0.37257345, 0.37379387, 0.37498382, + 0.37613623, 0.37725628, 0.37834438, 0.37940027, 0.38042109, 0.38140311, + 0.38235118, 0.38326484, 0.38414358, 0.38498682, 0.38579085, 0.38655416, + 0.38728007, 0.38796794, 0.3886171 , 0.38922686, 0.38979653, 0.39032549, + 0.39081315, 0.39125906, 0.39166297, 0.39202487, 0.39234513, 0.39262465, + 0.39285875, 0.39304214, 0.39318804, 0.39330134, 0.3933541 , 0.39338391, + 0.39336059, 0.3933133 , 0.39323839, 0.39313869, 0.39303156, 0.39295965, + 0.39295557, 0.39315799, 0.39391119, 0.39605497, 0.39998337, 0.40475771, + 0.40971377, 0.41464651, 0.41955776, 0.42457034, 0.42991332, 0.43592086, + 0.44281731, 0.45031271, 0.45790211, 0.46542398, 0.47282633, 0.48011752, + 0.48732483, 0.49440541, 0.50141998, 0.50837558, 0.51525348, 0.52205415, + 0.52881949, 0.53556909, 0.54223396, 0.54885896, 0.55550043, 0.56201556, + 0.56858101, 0.57503769, 0.58153866, 0.58793414, 0.59439323, 0.60071983, + 0.60710417, 0.61341299, 0.61971646, 0.6260332 , 0.63225511, 0.63852001, + 0.64474137, 0.65092034, 0.65713626, 0.66329112, 0.66941973, 0.67558021, + 0.68169667, 0.68777066, 0.69387233, 0.69997643, 0.70599397, 0.71203573, + 0.71810111, 0.72411175, 0.73009422, 0.73609755, 0.74212117, 0.74807095, + 0.75401458, 0.75997634, 0.7659557 , 0.77187449, 0.77777791, 0.78369726, + 0.78963205, 0.79554605, 0.80140962, 0.80728734, 0.81317876, 0.8190834, + 0.82493514, 0.83077307, 0.83662327, 0.84248528, 0.84835861, 0.85417838, + 0.85998992, 0.86581204, 0.87164424, 0.877486 , 0.88329814, 0.88908291, + 0.8948766 , 0.9006787 , 0.90648868, 0.91230608, 0.91806899, 0.9238354, + 0.92961071, 0.93539672, 0.94119732, 0.94701577]), array( + [ 0.02001135, 0.02410049, 0.0285127 , 0.03327199, 0.03840892, 0.04377278, + 0.04914302, 0.05449872, 0.05987322, 0.06527853, 0.07069504, 0.07616998, + 0.08164232, 0.08718911, 0.0927269 , 0.09833499, 0.10393204, 0.10960072, + 0.11525772, 0.12097641, 0.12669592, 0.13244873, 0.13822862, 0.14400333, + 0.14983392, 0.15566404, 0.16149121, 0.16735218, 0.17321659, 0.17907296, + 0.18491908, 0.19075078, 0.19656126, 0.20233991, 0.20807104, 0.21373203, + 0.21929102, 0.22470403, 0.22993048, 0.2348811 , 0.23944453, 0.24342373, + 0.24654791, 0.24854699, 0.24944069, 0.24964107, 0.24954394, 0.24938897, + 0.24925926, 0.24920029, 0.24923631, 0.24934712, 0.24956187, 0.24984924, + 0.25022917, 0.25068854, 0.25121928, 0.25182018, 0.25248939, 0.25322692, + 0.25402939, 0.25489122, 0.25581039, 0.25678491, 0.25781293, 0.25889267, + 0.26002567, 0.26120803, 0.26243772, 0.26371326, 0.26503318, 0.26639597, + 0.26779993, 0.26924355, 0.27072854, 0.27224553, 0.27378857, 0.27534839, + 0.27691073, 0.27845714, 0.27994688, 0.28132934, 0.28254139, 0.28352939, + 0.28426343, 0.28477016, 0.28511888, 0.2853897 , 0.28562523, 0.28584846, + 0.28606765, 0.28628369, 0.28648659, 0.2866717 , 0.28683846, 0.28698041, + 0.28709378, 0.28717794, 0.2872321 , 0.28725271, 0.28723935, 0.28719253, + 0.28710992, 0.28699145, 0.2868367 , 0.28664459, 0.28641544, 0.28614894, + 0.28584249, 0.28549799, 0.28511576, 0.28469141, 0.28422699, 0.28372376, + 0.28317939, 0.28259016, 0.28196082, 0.28129151, 0.28057453, 0.27981338, + 0.27901054, 0.27816595, 0.27726695, 0.2763238 , 0.27533659, 0.27430498, + 0.27321529, 0.27207747, 0.27089221, 0.26965877, 0.26837161, 0.26702355, + 0.26562346, 0.26417019, 0.2626624 , 0.26109861, 0.25947144, 0.25777762, + 0.25602258, 0.25420436, 0.25232078, 0.25036953, 0.2483481 , 0.24625386, + 0.24408403, 0.24183578, 0.23950621, 0.23709248, 0.23459188, 0.23200196, + 0.22930941, 0.22649861, 0.22358618, 0.22057215, 0.21739359, 0.2141051, + 0.21063096, 0.20699978, 0.20316855, 0.19908944, 0.19471496, 0.19000133, + 0.1847768 , 0.17886461, 0.17199335, 0.16449989, 0.15797658, 0.15243265, + 0.1469976 , 0.14126571, 0.13521428, 0.12907937, 0.12349501, 0.11987508, + 0.12047985, 0.12617587, 0.1352476 , 0.14599545, 0.15747549, 0.16925814, + 0.18117203, 0.19298222, 0.20476193, 0.21648737, 0.22808422, 0.23953607, + 0.25094286, 0.26235208, 0.27357466, 0.28472765, 0.29596437, 0.30689052, + 0.31799337, 0.32882896, 0.33982619, 0.35056047, 0.36151349, 0.37212065, + 0.38293088, 0.39355561, 0.40419893, 0.41491671, 0.42538398, 0.43601357, + 0.44654349, 0.4569744 , 0.46755003, 0.47796579, 0.48832714, 0.49881686, + 0.50919369, 0.51945918, 0.52983799, 0.54024888, 0.55039796, 0.56064657, + 0.57099376, 0.58117647, 0.59128027, 0.60146991, 0.6117441 , 0.62177734, + 0.63179964, 0.64189483, 0.65206133, 0.66202135, 0.67193144, 0.68190219, + 0.69193176, 0.70188796, 0.71165938, 0.72147981, 0.73134717, 0.74125921, + 0.75096714, 0.76061378, 0.77029569, 0.78001026, 0.78975461, 0.79927776, + 0.80875027, 0.81824276, 0.82775166, 0.83727291, 0.84665036, 0.85588868, + 0.86512674, 0.87435845, 0.88357649, 0.89277169, 0.90169092, 0.91054951, + 0.91934683, 0.92805132, 0.93660318, 0.9448656 ]), np.ones(256)) +</p>
<pre># Aliases
color_map_luts['B-W LINEAR'] = color_map_luts['idl00']
color_map_luts['BLUE'] = color_map_luts['idl01']</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/color_maps.py --- a/yt/visualization/color_maps.py +++ b/yt/visualization/color_maps.py @@ -72,9 +72,6 @@</p>
<pre>add_cmap('bds_highcontrast', cdict)
add_cmap('algae', cdict)
</pre>
<p>-# Set the default colormap to be algae. -matplotlib.rc('image', cmap="algae") –</p>
<pre># This next colormap was designed by Tune Kamae and converted here by Matt
_vs = np.linspace(0,1,255)
_kamae_red = np.minimum(255,</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/eps_writer.py --- a/yt/visualization/eps_writer.py +++ b/yt/visualization/eps_writer.py @@ -19,6 +19,8 @@</p>
<pre>import matplotlib.pyplot as plt
from ._mpl_imports import FigureCanvasAgg
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.utilities.logger import ytLogger as mylog
from .plot_window import PlotWindow
from .profile_plotter import PhasePlot, ProfilePlot</pre>
<p>@@ -725,7 +727,7 @@</p>
<pre>if plot.cmap is not None:
_cmap = plot.cmap.name
if _cmap is None:</pre>
<ul><li><p>_cmap = ‘algae’</p></li></ul>
<p>+ _cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> if isinstance(plot, (PlotWindow, PhasePlot)):
if isinstance(plot, PlotWindow):
try:</pre>
<p>@@ -1345,7 +1347,7 @@</p>
<pre> return d
#=============================================================================</pre>
<p>-def return_cmap(cmap="algae", label="", range=(0,1), log=False): +def return_cmap(cmap=None, label="", range=(0,1), log=False):</p>
<pre> r"""Returns a dict that describes a colorbar. Exclusively for use with
multiplot.
</pre>
<p>@@ -1364,5 +1366,7 @@</p>
<pre>--------
>>> cb = return_cmap("algae", "Density [cm$^{-3}$]", (0,10), False)
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre>return {'cmap': cmap, 'name': label, 'range': range, 'log': log}
</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/image_writer.py --- a/yt/visualization/image_writer.py +++ b/yt/visualization/image_writer.py @@ -15,6 +15,8 @@</p>
<pre>import numpy as np
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, get_image_suffix
from yt.units.yt_array import YTQuantity
from yt.utilities.exceptions import YTNotInsideNotebook</pre>
<p>@@ -175,7 +177,7 @@</p>
<pre> return pw.write_png_to_string(bitmap_array.copy())
return bitmap_array
</pre>
<p>-def write_image(image, filename, color_bounds = None, cmap_name = “algae”, func = lambda x: x): +def write_image(image, filename, color_bounds = None, cmap_name = None, func = lambda x: x):</p>
<pre> r"""Write out a floating point array directly to a PNG file, scaling it and
applying a colormap.
</pre>
<p>@@ -210,6 +212,8 @@</p>
<pre>(1024, 1024))
>>> write_image(frb1["Density"], "saved.png")
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre>if len(image.shape) == 3:
mylog.info("Using only channel 1 of supplied image")
image = image[:,:,0]</pre>
<p>@@ -217,7 +221,7 @@</p>
<pre> pw.write_png(to_plot, filename)
return to_plot
</pre>
<p>-def apply_colormap(image, color_bounds = None, cmap_name = ‘algae’, func=lambda x: x): +def apply_colormap(image, color_bounds = None, cmap_name = None, func=lambda x: x):</p>
<pre> r"""Apply a colormap to a floating point image, scaling to uint8.
This function will scale an image and directly call libpng to write out a</pre>
<p>@@ -242,6 +246,8 @@</p>
<pre> to_plot : uint8 image with colorbar applied.
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre>from yt.data_objects.image_array import ImageArray
image = ImageArray(func(image))
if color_bounds is None:</pre>
<p>@@ -333,7 +339,7 @@</p>
<pre>def write_projection(data, filename, colorbar=True, colorbar_label=None,
title=None, limits=None, take_log=True, figsize=(8,6),</pre>
<ul><li><p>dpi=100, cmap_name='algae', extent=None, xlabel=None,</p></li></ul>
<p>+ dpi=100, cmap_name=None, extent=None, xlabel=None,</p>
<pre>ylabel=None):
r"""Write a projection or volume rendering to disk with a variety of
pretty parameters such as limits, title, colorbar, etc. write_projection</pre>
<p>@@ -378,6 +384,8 @@</p>
<pre>title="Offaxis Projection", limits=(1e-5,1e-3),
take_log=True)
"""</pre>
<p>+ if cmap_name is None: + cmap_name = ytcfg.get("yt", “default_colormap”)</p>
<pre> import matplotlib
from ._mpl_imports import FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/plot_container.py --- a/yt/visualization/plot_container.py +++ b/yt/visualization/plot_container.py @@ -28,6 +28,8 @@</p>
<pre>from ._mpl_imports import FigureCanvasAgg
from .tick_locators import LogLocator, LinearLocator
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
get_image_suffix, \
get_ipython_api_version, iterable, \</pre>
<p>@@ -190,7 +192,8 @@</p>
<pre>self.plots = PlotDictionary(data_source)
self._callbacks = []
self._field_transform = {}</pre>
<ul><li><p>self._colormaps = defaultdict(lambda: ‘algae’)</p></li></ul>
<p>+ self._colormaps = defaultdict( + lambda: ytcfg.get("yt", "default_colormap"))</p>
<pre>font_path = matplotlib.get_data_path() + '/fonts/ttf/STIXGeneral.ttf'
self._font_properties = FontProperties(size=fontsize, fname=font_path)
self._font_color = None</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/plot_modifications.py --- a/yt/visualization/plot_modifications.py +++ b/yt/visualization/plot_modifications.py @@ -27,6 +27,8 @@</p>
<pre>from matplotlib import cm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
mylog, iterable
from yt.extern.six import add_metaclass</pre>
<p>@@ -1591,9 +1593,12 @@</p>
<pre> """
_type_name = "mesh_lines"
</pre>
<ul><li><p>def __init__(self, thresh=0.1):</p></li></ul>
<p>+ def __init__(self, thresh=0.1, cmap=None):</p>
<pre>super(MeshLinesCallback, self).__init__()
self.thresh = thresh</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”) + self.cmap = cmap</p>
<pre> def __call__(self, plot):
</pre>
<p>@@ -1646,7 +1651,7 @@</p>
<pre> plot._axes.imshow(image, zorder=1,
extent=[xx0, xx1, yy0, yy1],</pre>
<ul><li><p>origin='lower',</p></li></ul>
<p>+ origin='lower', cmap=self.cmap,</p>
<pre> interpolation='nearest')
</pre>
<p>@@ -2352,7 +2357,7 @@</p>
<pre>lic_data_clip_rescale = (lic_data_clip - self.lim[0]) \
/ (self.lim[1] - self.lim[0])
lic_data_rgba[...,3] = lic_data_clip_rescale * self.alpha</pre>
<ul><li><p>plot._axes.imshow(lic_data_rgba, extent=extent)</p></li></ul>
<p>+ plot._axes.imshow(lic_data_rgba, extent=extent, cmap=self.cmap)</p>
<pre> plot._axes.hold(False)
return plot</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/tests/test_callbacks.py --- a/yt/visualization/tests/test_callbacks.py +++ b/yt/visualization/tests/test_callbacks.py @@ -18,6 +18,8 @@</p>
<pre>from numpy.testing import \
assert_raises
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.testing import \
fake_amr_ds
import yt.units as u</pre>
<p>@@ -357,7 +359,7 @@</p>
<pre> p = SlicePlot(ds, "x", "density")
p.annotate_line_integral_convolution("velocity_x", "velocity_y",
kernellen=100., lim=(0.4,0.7),</pre>
<ul><li><p>cmap='algae', alpha=0.9,</p></li>
<li><p>const_alpha=True)</p></li></ul>
<p>+ cmap=ytcfg.get("yt", "default_colormap"), + alpha=0.9, const_alpha=True)</p>
<pre> p.save(prefix)
</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/volume_rendering/old_camera.py --- a/yt/visualization/volume_rendering/old_camera.py +++ b/yt/visualization/volume_rendering/old_camera.py @@ -16,6 +16,8 @@</p>
<pre>from yt.extern.six.moves import builtins
import numpy as np
</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import \
iterable, mylog, get_pbar, \
get_num_threads, ensure_numpy_array</pre>
<p>@@ -236,7 +238,7 @@</p>
<pre> px = (res[1]*(dy/self.width[1])).astype('int')
return px, py, dz
</pre>
<ul><li><p>def draw_grids(self, im, alpha=0.3, cmap='algae', min_level=None,</p></li></ul>
<p>+ def draw_grids(self, im, alpha=0.3, cmap=None, min_level=None,</p>
<pre> max_level=None):
r"""Draws Grids on an existing volume rendering.
</pre>
<p>@@ -269,6 +271,8 @@</p>
<pre> >>> write_bitmap(im, 'render_with_grids.png')
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre>region = self.data_source
corners = []
levels = []</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/volume_rendering/render_source.py --- a/yt/visualization/volume_rendering/render_source.py +++ b/yt/visualization/volume_rendering/render_source.py @@ -12,6 +12,8 @@</p>
<pre># -----------------------------------------------------------------------------
import numpy as np</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, ensure_numpy_array
from yt.utilities.parallel_tools.parallel_analysis_interface import \
ParallelAnalysisInterface</pre>
<p>@@ -355,7 +357,7 @@</p>
<pre> self.current_image = None
# default color map</pre>
<ul><li><p>self._cmap = ‘algae’</p></li></ul>
<p>+ self._cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> self._color_bounds = None
# default mesh annotation options</pre>
<p>@@ -939,7 +941,7 @@</p>
<pre> """
</pre>
<ul><li><p>def __init__(self, data_source, alpha=0.3, cmap='algae',</p></li></ul>
<p>+ def __init__(self, data_source, alpha=0.3, cmap=None,</p>
<pre>min_level=None, max_level=None):
self.data_source = data_source_or_all(data_source)
corners = []</pre>
<p>@@ -959,6 +961,8 @@</p>
<pre>levels.append(block.Level)
corners = np.dstack(corners)
levels = np.array(levels)</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> if max_level is not None:
subset = levels <= max_level</pre>
<p>diff -r c956b4087291b6717fd870f09c9fd5176057d1f6 -r b608c33bfeaed20a0e588602b89ceec19d3714d0 yt/visualization/volume_rendering/scene.py --- a/yt/visualization/volume_rendering/scene.py +++ b/yt/visualization/volume_rendering/scene.py @@ -15,6 +15,8 @@</p>
<pre>import functools
import numpy as np
from collections import OrderedDict</pre>
<p>+from yt.config import \ + ytcfg</p>
<pre>from yt.funcs import mylog, get_image_suffix
from yt.extern.six import iteritems, itervalues, string_types
from yt.units.dimensions import \</pre>
<p>@@ -420,7 +422,8 @@</p>
<pre>del nz
else:
nim = im</pre>
<ul><li><p>axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), interpolation="nearest")</p></li></ul>
<p>+ axim = plt.imshow(nim[:,:,:3]/nim[:,:,:3].max(), + interpolation="nearest")</p>
<pre> return axim
</pre>
<p>@@ -643,7 +646,7 @@</p>
<pre> self.add_source(box_source)
return self
</pre>
<ul><li><p>def annotate_grids(self, data_source, alpha=0.3, cmap='algae',</p></li></ul>
<p>+ def annotate_grids(self, data_source, alpha=0.3, cmap=None,</p>
<pre> min_level=None, max_level=None):
r"""
</pre>
<p>@@ -677,6 +680,8 @@</p>
<pre> >>> im = sc.render()
"""</pre>
<p>+ if cmap is None: + cmap = ytcfg.get("yt", “default_colormap”)</p>
<pre> grids = GridSource(data_source, alpha=alpha, cmap=cmap,
min_level=min_level, max_level=max_level)
self.add_source(grids)</pre>
<p>Repository URL: <a href="https://bitbucket.org/yt_analysis/yt/">https://bitbucket.org/yt_analysis/yt/</a></p>
<p>—</p>
<p>This is a commit notification from bitbucket.org. You are receiving this because you have the service enabled, addressing the recipient of this email.</p>
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