Ion Shuttling Simulation Time Scan 20250117.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db23957a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# Parameters for the simulation\n",
    "T_factor = (121 / 133.93969396934) * 1.65  # The time factor for the simulation\n",
    "NUM_VOLTAGE_POINTS = 500  # The points for voltage sampling\n",
    "T = 150 * 1e-6 * T_factor  # Total time duration in s unit\n",
    "points = 3000000  # Number of points at which to evaluate the solution\n",
    "\n",
    "shuttling_time = np.array([2.0, 1.9, 1.8, 1.7, 1.69, 1.68, 1.675, 1.67, 1.66, 1.6, 1.55, 1.5, 1.45, 1.4, 1.35, 1.3, 1.275, 1.2725, 1.2625, 1.25625, 1.25, 1.24375, 1.2375, 1.225, 1.2, 1.175, 1.1725, 1.1625, 1.15, 1.125, 1.1, 1.075, 1.05, 1.025, 1.0]) * 150 * (121 / 133.93969396934)\n",
    "phonon_number = [3.3162240810623347, 1.1686538823938188, 5.875925464087038, 6.619702650001575, 31.254016433807955, 71.98736367688507, 98.34855030356773, 130.87303629718585, 232.85274899348917, 2391.1302739952052, 147.44020081614985, 629.0087480609346, 268.18965217639294, 48.43543825010925, 206.2857621064642, 95.51753401638237, 36.50945323006546, 28.964561608089042, 7.96054386244553, 1.804352613489891, 0.22117266268485491, 2.8548644300358745, 9.033128088667297, 23.698891504359, 14.689732736191997, 15.435105237356863, 14.186253152704335, 5.481084714468514, 1.6131675500567202, 74.60439276513097, 1871.5136089051891, 7469.869215480245, 9689.46367839787, 3444.424286773976, 822.7717571937637]\n",
    "\n",
    "phonon_number_m1 = [10917.831148500894,\n",
    "                    11204.503136766143,\n",
    "                    11221.463603616474,\n",
    "                    10976.710727993826,\n",
    "                    10497.448718693766,\n",
    "                    9807.351445832617,\n",
    "                    8952.06622021338,\n",
    "                    7974.972236816406,\n",
    "                    6925.580488995814,\n",
    "                    5850.266637011716,\n",
    "                    4798.422598099715,\n",
    "                    3809.8871368057257,\n",
    "                    2919.352046646958,\n",
    "                    2149.1673189952176,\n",
    "                    1513.7667508605855,\n",
    "                    1012.5287491080555,\n",
    "                    638.4661190901367,\n",
    "                    376.151101137252,\n",
    "                    204.90507923133984,\n",
    "                    102.44126052633757,\n",
    "                    47.743716397042185,\n",
    "                    22.646531404729735,\n",
    "                    13.255639171075677,\n",
    "                    10.228019391596268,\n",
    "                    8.48703605235511,\n",
    "                    6.144718720750942,\n",
    "                    3.3450319915578635,\n",
    "                    1.2033559675156116,\n",
    "                    0.7353332177622804,\n",
    "                    2.394402975256187]\n",
    "shuttling_time_m1 = np.array([0.95      , 0.95344828, 0.95689655, 0.96034483, 0.9637931 ,\n",
    "                            0.96724138, 0.97068966, 0.97413793, 0.97758621, 0.98103448,\n",
    "                            0.98448276, 0.98793103, 0.99137931, 0.99482759, 0.99827586,\n",
    "                            1.00172414, 1.00517241, 1.00862069, 1.01206897, 1.01551724,\n",
    "                            1.01896552, 1.02241379, 1.02586207, 1.02931034, 1.03275862,\n",
    "                            1.0362069 , 1.03965517, 1.04310345, 1.04655172, 1.05      ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_m2 = [0.3823764422417888,\n",
    "                    0.32502324693579593,\n",
    "                    1.5604419259381792,\n",
    "                    1.792498084359018,\n",
    "                    1.0318683761106808,\n",
    "                    1.062220981374216,\n",
    "                    2.0513344908511355,\n",
    "                    3.1387355842567772,\n",
    "                    4.653112466975929,\n",
    "                    6.184761799729591,\n",
    "                    5.07039360145654,\n",
    "                    1.9932146360298484,\n",
    "                    5.471828005631268,\n",
    "                    22.71604802042077,\n",
    "                    44.84689894517717,\n",
    "                    50.27710133536522,\n",
    "                    30.064086981391384,\n",
    "                    4.993464381091826,\n",
    "                    11.364863358670137,\n",
    "                    68.71847503201228,\n",
    "                    163.59522658303385,\n",
    "                    261.3414385227494,\n",
    "                    328.3085727409664,\n",
    "                    346.77267259278347,\n",
    "                    319.2808926109196,\n",
    "                    264.0782736347852,\n",
    "                    203.4337857971246,\n",
    "                    150.28598330503823,\n",
    "                    104.51495652989107,\n",
    "                    62.36201898299608]\n",
    "shuttling_time_m2 = np.array([1.7       , 1.71034483, 1.72068966, 1.73103448, 1.74137931,\n",
    "                            1.75172414, 1.76206897, 1.77241379, 1.78275862, 1.79310345,\n",
    "                            1.80344828, 1.8137931 , 1.82413793, 1.83448276, 1.84482759,\n",
    "                            1.85517241, 1.86551724, 1.87586207, 1.8862069 , 1.89655172,\n",
    "                            1.90689655, 1.91724138, 1.92758621, 1.93793103, 1.94827586,\n",
    "                            1.95862069, 1.96896552, 1.97931034, 1.98965517, 2.        ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_m3 = [2.394402975256187,\n",
    "                    10.341679736630129,\n",
    "                    17.746883266992494,\n",
    "                    18.879801967995178,\n",
    "                    15.84125682167082,\n",
    "                    15.085175921693596,\n",
    "                    19.641559461240366,\n",
    "                    25.98851885910303,\n",
    "                    27.73577448866504,\n",
    "                    22.197012471177697,\n",
    "                    12.312911991024604,\n",
    "                    3.7304676142863125,\n",
    "                    0.46682114117097395,\n",
    "                    3.93242312592925,\n",
    "                    14.832571322170065,\n",
    "                    34.3920546457663,\n",
    "                    61.34957692396912,\n",
    "                    88.05185399543221,\n",
    "                    100.43843946007372]\n",
    "shuttling_time_m3 = np.array([1.05      , 1.05689655, 1.0637931 , 1.07068966, 1.07758621,\n",
    "                            1.08448276, 1.09137931, 1.09827586, 1.10517241, 1.11206897,\n",
    "                            1.11896552, 1.12586207, 1.13275862, 1.13965517, 1.14655172,\n",
    "                            1.15344828, 1.16034483, 1.16724138, 1.17413793]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_m4 = [113.95230098887484,\n",
    "                    39.61614232918734,\n",
    "                    235.84787876381606,\n",
    "                    404.5630021950763,\n",
    "                    291.14823079863373,\n",
    "                    63.342721596904354,\n",
    "                    235.35569493472556,\n",
    "                    704.8600024415663,\n",
    "                    639.3219657559627,\n",
    "                    122.5695425036581,\n",
    "                    261.8359054904077,\n",
    "                    1332.582851769415,\n",
    "                    2430.9288206784604,\n",
    "                    2661.6464524165476,\n",
    "                    1921.5530191455898,\n",
    "                    905.0772717623831,\n",
    "                    300.0969284581349,\n",
    "                    104.25273864724238,\n",
    "                    31.09823070724036]\n",
    "shuttling_time_m4 = np.array([1.25      , 1.26551724, 1.28103448, 1.29655172, 1.31206897,\n",
    "                            1.32758621, 1.34310345, 1.35862069, 1.37413793, 1.38965517,\n",
    "                            1.40517241, 1.42068966, 1.4362069 , 1.45172414, 1.46724138,\n",
    "                            1.48275862, 1.49827586, 1.5137931 , 1.52931034])  * 150 * (121 / 133.93969396934)\n",
    "\n",
    "shuttling_time_1 = np.array([1.7       , 1.71578947, 1.73157895, 1.74736842, 1.76315789,\n",
    "                            1.77894737, 1.79473684, 1.81052632, 1.82631579, 1.84210526,\n",
    "                            1.85789474, 1.87368421, 1.88947368, 1.90526316, 1.92105263,\n",
    "                            1.93684211, 1.95263158, 1.96842105, 1.98421053, 2.        ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_1 = [6.622719944242334,\n",
    "                    3.6665789980066514,\n",
    "                    26.40020560905384,\n",
    "                    43.26610387296418,\n",
    "                    32.437882964454495,\n",
    "                    8.074850695091568,\n",
    "                    2.982109653755348,\n",
    "                    11.948611462139123,\n",
    "                    11.74439618130612,\n",
    "                    6.192068357055268,\n",
    "                    3.3273987358235915,\n",
    "                    0.6739697827116691,\n",
    "                    0.27495010337452586,\n",
    "                    1.3850825169641057,\n",
    "                    0.7593143279522443,\n",
    "                    1.2361688787016312,\n",
    "                    2.896015765885327,\n",
    "                    4.935262779795991,\n",
    "                    6.860069819101938,\n",
    "                    3.3229333316504306]\n",
    "\n",
    "shuttling_time_2 = np.array([1.5       , 1.50842105, 1.51684211, 1.52526316, 1.53368421,\n",
    "                            1.54210526, 1.55052632, 1.55894737, 1.56736842, 1.57578947,\n",
    "                            1.58421053, 1.59263158, 1.60105263, 1.60947368, 1.61789474,\n",
    "                            1.62631579, 1.63473684, 1.64315789, 1.65157895, 1.66      ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_2 = [628.9188948472441,\n",
    "                    715.8617235495441,\n",
    "                    625.2224899922396,\n",
    "                    398.8135916458048,\n",
    "                    159.31460165015557,\n",
    "                    49.08760383112478,\n",
    "                    161.56191075045933,\n",
    "                    504.30044461484954,\n",
    "                    1005.1049082936939,\n",
    "                    1549.1785151046683,\n",
    "                    2017.1059972761366,\n",
    "                    2313.9777111072726,\n",
    "                    2387.4370778102857,\n",
    "                    2231.2774143359843,\n",
    "                    1892.4078850788678,\n",
    "                    1454.499200018254,\n",
    "                    1014.2565407174377,\n",
    "                    648.0857324625774,\n",
    "                    390.34507137128696,\n",
    "                    232.89951946244153]\n",
    "\n",
    "shuttling_time_3 = np.linspace(1.1, 1.16, 10) * 150 * (121 / 133.93969396934)\n",
    "phonon_number_3 = [1871.4529251861752, 992.4690490748741, 451.1546616884751, 171.78240593044322, 55.796053192859674, 18.76592586005597, 8.314730748151963, 3.2465493956254425, 0.9834339791532549, 3.4605420534457125]\n",
    "[1871.4529251861752, 992.4690490748741, 451.1546616884751, 171.78240593044322, 55.796053192859674, 18.76592586005597, 8.314730748151963, 3.2465493956254425, 0.9834339791532549, 3.4605420534457125]\n",
    "\n",
    "shuttling_time_4 = np.linspace(1.0, 2.0, 80) * 150 * (121 / 133.93969396934)\n",
    "phonon_number_4 = [822.8831820325785, 553.1855352141761, 3542.171964861885, 7418.750265052354, 9748.075347736132, 9550.291315380848, 7250.075046199817, 4158.847362108642, 1676.5578137796924, 417.3286389873755, 56.62349334331037, 9.04329312526159, 1.1110347310223108, 7.380877247751045, 15.97618959458514, 13.190498898531047, 16.456690491244057, 26.031210712333166, 20.983015437829177, 5.69234210323791, 0.4792879869919452, 13.311145676331547, 48.19142786474047, 90.1869494360309, 87.08293990635754, 27.439289193761844, 7.943963101493556, 110.59330008038978, 245.44588671044565, 249.18604885410141, 121.19406518440272, 30.174987850247856, 90.65755646185869, 248.02176020203984, 366.8975992970766, 345.02453786359933, 187.05368708804002, 53.45598584149631, 153.89604931299039, 477.0539761262665, 710.1349472245089, 576.5589089972765, 210.53031634995466, 55.093199254533324, 405.46048180363704, 1151.5731883360443, 1922.1306278358686, 2357.619586558614, 2284.2525232653193, 1775.4381028688915, 1105.4180049957135, 562.8893512682979, 259.0906372646809, 124.47093631889408, 55.891271781258354, 13.86489918227468, 0.0674895740823596, 10.593581390571051, 30.409641898581075, 43.07678390368917, 37.392256734106184, 17.760404734702387, 3.106450318201271, 4.344128275799964, 11.791978557101324, 12.772113827422457, 8.299232790727718, 4.895337790600413, 2.853086195149589, 0.7113415941668045, 0.06745745452049429, 1.0813954662514385, 1.2722384550374848, 0.6668664137922837, 1.2210296544611676, 2.5616014949176296, 3.95699332661789, 6.022500602985368, 6.629504719797712, 3.3229333316504306]\n",
    "\n",
    "shuttling_time_5 = np.array([1.25      , 1.26315789, 1.27631579, 1.28947368, 1.30263158,\n",
    "                    1.31578947, 1.32894737, 1.34210526, 1.35526316, 1.36842105,\n",
    "                    1.38157895, 1.39473684, 1.40789474, 1.42105263, 1.43421053,\n",
    "                    1.44736842, 1.46052632, 1.47368421, 1.48684211, 1.5       ]) * 150 * (121 / 133.93969396934)\n",
    "phonon_number_5 = [0.21557833883358168,\n",
    "                    8.889765338956778,\n",
    "                    40.75899549486657,\n",
    "                    85.97241022810252,\n",
    "                    90.36925017194477,\n",
    "                    30.741205457865956,\n",
    "                    7.4235807726708,\n",
    "                    114.52988917197142,\n",
    "                    251.0120685367646,\n",
    "                    239.6263609060765,\n",
    "                    101.36244663037469,\n",
    "                    29.40305106016917,\n",
    "                    121.99408817955025,\n",
    "                    288.4724147117752,\n",
    "                    377.5509234345375,\n",
    "                    301.3072286120741,\n",
    "                    120.33170525893043,\n",
    "                    59.96463300694118,\n",
    "                    289.01736659950575,\n",
    "                    628.9188948472441]\n",
    "\n",
    "shuttling_time_0 = np.array([1.7       , 1.71578947, 1.73157895, 1.74736842, 1.76315789,\n",
    "                            1.77894737, 1.79473684, 1.81052632, 1.82631579, 1.84210526,\n",
    "                            1.85789474, 1.87368421, 1.88947368, 1.90526316, 1.92105263,\n",
    "                            1.93684211, 1.95263158, 1.96842105, 1.98421053, 2.        ]) * 150 * (121 / 133.93969396934)\n",
    "phonon_number_0 = [6.611800019088033,\n",
    "                    3.6582972482942595,\n",
    "                    26.369318017544657,\n",
    "                    43.184343173550495,\n",
    "                    32.4023308321295,\n",
    "                    8.073363888764545,\n",
    "                    2.9772072524057354,\n",
    "                    11.940665437935861,\n",
    "                    11.725732978507285,\n",
    "                    6.1844655645330295,\n",
    "                    3.3125373916212957,\n",
    "                    0.6704987522671576,\n",
    "                    0.2719823324680728,\n",
    "                    1.38020543375336,\n",
    "                    0.7582715754115237,\n",
    "                    1.2323576315610096,\n",
    "                    2.8905536554362423,\n",
    "                    4.920834741776568,\n",
    "                    6.82717941312589,\n",
    "                    3.3061360201165386]\n",
    "\n",
    "phonon_number_6 = [960.412857263177,\n",
    "                    5565.232793973766,\n",
    "                    9689.214239600225,\n",
    "                    9624.276920402259,\n",
    "                    5825.74543517543,\n",
    "                    1763.4641674800007,\n",
    "                    379.6590828241329,\n",
    "                    1960.0707266309487,\n",
    "                    4732.660373963657,\n",
    "                    6621.267216334895,\n",
    "                    6431.442126373816,\n",
    "                    4289.753644587272,\n",
    "                    1612.4612789634377,\n",
    "                    376.0004976343955,\n",
    "                    1649.6030567060948,\n",
    "                    4488.424703040916,\n",
    "                    6522.137598909649,\n",
    "                    6008.294403782537,\n",
    "                    3384.3072592824346,\n",
    "                    822.8831820325785]\n",
    "shuttling_time_6 = np.array([0.8       , 0.81052632, 0.82105263, 0.83157895, 0.84210526,\n",
    "                            0.85263158, 0.86315789, 0.87368421, 0.88421053, 0.89473684,\n",
    "                            0.90526316, 0.91578947, 0.92631579, 0.93684211, 0.94736842,\n",
    "                            0.95789474, 0.96842105, 0.97894737, 0.98947368, 1.        ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_7 = [925.5835720576033,\n",
    "                540.9867789523305,\n",
    "                379.5372771205698,\n",
    "                459.00594628758756,\n",
    "                776.7624213452759,\n",
    "                1311.9872709975712,\n",
    "                2025.5274353012371]\n",
    "shuttling_time_7 = np.array([0.93, 0.93315789, 0.93631579, 0.93947368, 0.94263158, 0.94578947, 0.94894737]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_8 = [540.7838157182764,\n",
    "                    484.81804175249084,\n",
    "                    440.35394049977776,\n",
    "                    407.19763007056184,\n",
    "                    385.5767372568061,\n",
    "                    376.54684034588297,\n",
    "                    379.40067427425936,\n",
    "                    393.4928146330621,\n",
    "                    420.26803538991174,\n",
    "                    459.04890258792]\n",
    "shuttling_time_8 = np.array([0.93315789, 0.93385964, 0.9345614 , 0.93526315, 0.93596491, 0.93666666, 0.93736842, 0.93807017, 0.93877193, 0.93947368]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_9 = [1652.4772295961702,\n",
    "                    1416.916987380497,\n",
    "                    1201.199446708044,\n",
    "                    1006.5449084799493,\n",
    "                    835.9621789276681,\n",
    "                    690.6854743866578,\n",
    "                    572.1272837340895,\n",
    "                    481.70899237514504,\n",
    "                    420.50727639426356,\n",
    "                    389.4163396944165,\n",
    "                    388.6817234402452,\n",
    "                    418.93463545608853,\n",
    "                    479.6264969829406,\n",
    "                    570.3723559646336,\n",
    "                    690.9544373690151,\n",
    "                    839.8290183040909,\n",
    "                    1016.232803286527,\n",
    "                    1217.3605466777492,\n",
    "                    1443.1019558783248,\n",
    "                    1690.3865706072002]\n",
    "shuttling_time_9 = np.array([0.92631579, 0.92742382, 0.92853186, 0.92963989, 0.93074792,\n",
    "                            0.93185596, 0.93296399, 0.93407202, 0.93518006, 0.93628809,\n",
    "                            0.93739612, 0.93850415, 0.93961219, 0.94072022, 0.94182825,\n",
    "                            0.94293629, 0.94404432, 0.94515235, 0.94626039, 0.94736842]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_10 = [1920.456286688493,\n",
    "                    19406.050090061202,\n",
    "                    11549.868445267326,\n",
    "                    769.5182118228291,\n",
    "                    9581.306724941125,\n",
    "                    12788.971323715221,\n",
    "                    3075.80833112831,\n",
    "                    3486.6881439897925,\n",
    "                    13118.459178404775,\n",
    "                    6465.730063659422]\n",
    "shuttling_time_10 = np.array([0.8       , 0.82111111, 0.84222222, 0.86333333, 0.88444444,\n",
    "                            0.90555556, 0.92666667, 0.94777778, 0.96888889, 0.99      ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_11 = [9568.328141283175,\n",
    "                    8563.478251157201,\n",
    "                    1786.557164266618,\n",
    "                    24.143707752822532,\n",
    "                    0.9226381644066272,\n",
    "                    17.15466195231177,\n",
    "                    25.125977877617014,\n",
    "                    4.99492143709375,\n",
    "                    22.376485596003374,\n",
    "                    95.46146530501147,\n",
    "                    2.5974705857599454,\n",
    "                    257.4997523929176,\n",
    "                    80.11713138534785,\n",
    "                    180.32989742056165,\n",
    "                    372.23198390284574,\n",
    "                    52.30088213756731,\n",
    "                    585.1094444973797,\n",
    "                    401.2456461285471,\n",
    "                    295.58568261081183,\n",
    "                    2081.683960353963]\n",
    "shuttling_time_11 = np.array([0.99      , 1.01684211, 1.04368421, 1.07052632, 1.09736842,\n",
    "                            1.12421053, 1.15105263, 1.17789474, 1.20473684, 1.23157895,\n",
    "                            1.25842105, 1.28526316, 1.31210526, 1.33894737, 1.36578947,\n",
    "                            1.39263158, 1.41947368, 1.44631579, 1.47315789, 1.5       ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_12 = [2.726979296482458,\n",
    "                    6.20912002423036,\n",
    "                    10.86111341545978,\n",
    "                    14.362937801097196,\n",
    "                    15.40970522868022,\n",
    "                    14.086232480250871]\n",
    "shuttling_time_12 = np.array([1.7       , 1.70612245, 1.7122449 , 1.71836735, 1.7244898 , 1.73061224]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_13 = [9567.73064611388,\n",
    "                    10735.330424443164,\n",
    "                    9832.64787629046,\n",
    "                    7423.384802129073,\n",
    "                    4507.598029273467,\n",
    "                    2082.1323897876505,\n",
    "                    659.918578995693]\n",
    "shuttling_time_13 = np.array([0.99      , 1.00040816, 1.01081633, 1.02122449, 1.03163265, 1.04204082, 1.05244898]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_14 = [901.7163254961342,\n",
    "                    14.277885527396183,\n",
    "                    0.9363572309346078,\n",
    "                    18.154305170914057,\n",
    "                    19.969890977981976,\n",
    "                    16.9619135738993,\n",
    "                    2.286897905772318,\n",
    "                    59.259627176040254,\n",
    "                    74.33825567929853,\n",
    "                    20.504558432119925,\n",
    "                    266.7064531471633,\n",
    "                    97.0870707143194,\n",
    "                    115.55097127559138,\n",
    "                    385.08659426655197,\n",
    "                    169.21136800818573,\n",
    "                    181.01348086765677,\n",
    "                    738.1633370739263,\n",
    "                    197.28639927428566,\n",
    "                    453.17792272569903,\n",
    "                    2081.6519058654426]\n",
    "shuttling_time_14 = np.array([1.05      , 1.07368421, 1.09736842, 1.12105263, 1.14473684,\n",
    "                            1.16842105, 1.19210526, 1.21578947, 1.23947368, 1.26315789,\n",
    "                            1.28684211, 1.31052632, 1.33421053, 1.35789474, 1.38157895,\n",
    "                            1.40526316, 1.42894737, 1.45263158, 1.47631579, 1.5       ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "phonon_number_15 = [14.303644720707341,\n",
    "                8.123262964216096,\n",
    "                4.550113912206718,\n",
    "                1.7715779668291562,\n",
    "                0.06542574863248608,\n",
    "                1.3336742424373118,\n",
    "                1.609921396344434,\n",
    "                0.8405368670945713,\n",
    "                1.7265657280517224,\n",
    "                3.1097152303331055,\n",
    "                5.163504234090552,\n",
    "                5.49155396841289,\n",
    "                1.855050510487784,\n",
    "                9.442146094503748,\n",
    "                36.26556079705349,\n",
    "                48.25401199884358,\n",
    "                21.046207410563675,\n",
    "                2.8340693671259762,\n",
    "                58.14776920559573,\n",
    "                176.9946765499624]\n",
    "shuttling_time_15 = np.array([1.73      , 1.74421053, 1.75842105, 1.77263158, 1.78684211,\n",
    "                        1.80105263, 1.81526316, 1.82947368, 1.84368421, 1.85789474,\n",
    "                        1.87210526, 1.88631579, 1.90052632, 1.91473684, 1.92894737,\n",
    "                        1.94315789, 1.95736842, 1.97157895, 1.98578947, 2.        ]) * 150 * (121 / 133.93969396934)\n",
    "\n",
    "merged_shuttling_time = np.concatenate((shuttling_time, shuttling_time_1, shuttling_time_2, shuttling_time_3, shuttling_time_4, shuttling_time_5, shuttling_time_6, shuttling_time_10))\n",
    "merged_phonon_number = np.concatenate((phonon_number, phonon_number_1, phonon_number_2, phonon_number_3, phonon_number_4, phonon_number_5, phonon_number_6, phonon_number_10))\n",
    "\n",
    "# combined = list(zip(merged_shuttling_time, merged_phonon_number))\n",
    "# sorted_combined = sorted(combined, key=lambda x: x[0])\n",
    "# merged_shuttling_time, merged_phonon_number = zip(*sorted_combined)\n",
    "\n",
    "merged_shuttling_time = merged_shuttling_time.tolist()\n",
    "merged_phonon_number = merged_phonon_number.tolist()\n",
    "\n",
    "sorted_pairs = sorted(zip(merged_shuttling_time, merged_phonon_number))\n",
    "merged_shuttling_time_sorted, merged_phonon_number_sorted = zip(*sorted_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9d9e6dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/73/s1xpd2ts0712_vwy8r1jbjyc0000gn/T/ipykernel_76654/2416290201.py:20: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k*\" (-> color='k'). The keyword argument will take precedence.\n",
      "  plt.plot(star_points['time'], star_points['heating'], 'k*', markersize=15, label='Experimental results', zorder=3, alpha=0.99, color='purple')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1275x980.769 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "star_points = {\n",
    "    'frequency': [6.38, 6.2, 6.02, 6.01, 6, 5.9, 4.8, 4.763, 4.762, 4.759, 3.846],     # x-coordinates in kHz\n",
    "    'heating': [277.126273436417, 182.607308118779, 166.496716134287, 124.756193618776, 122.344066677443, 213.952515403632, 190.2415382153003, 140.909568968540, 128.120610226197, 176.384550185347, 152.67749696743826]        # y-coordinates (phonon numbers)\n",
    "}\n",
    "\n",
    "# Decrease the heating values by 0\n",
    "star_points['heating'] = [heating - 0 for heating in star_points['heating']]\n",
    "# Convert frequencies to times using the relationship: 8.25 kHz -> 275/2 μs\n",
    "conversion_factor = 275/2  # μs\n",
    "star_points['time'] = [(8.25/freq) * conversion_factor for freq in star_points['frequency']]\n",
    "\n",
    "# Plotting settings\n",
    "half_column_width_inches = 4.25 * 3\n",
    "aspect_ratio = 1.3  # Desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "\n",
    "plt.plot(merged_shuttling_time_sorted, merged_phonon_number_sorted, marker='o', linestyle='-', color='C0', zorder=0, alpha=0.3, label='$\\omega_{\\mathrm{COM}} = \\omega_{\\mathrm{0}}$')\n",
    "\n",
    "# Add stars at specified points\n",
    "plt.plot(star_points['time'], star_points['heating'], 'k*', markersize=15, label='Experimental results', zorder=3, alpha=0.99, color='purple')\n",
    "\n",
    "# Add vertical dashed lines at specified times\n",
    "for time in star_points['time']:\n",
    "    plt.axvline(x=time, color='black', linestyle='--', alpha=0.5)\n",
    "\n",
    "# plt.scatter(merged_shuttling_time_sorted, merged_phonon_number_sorted, marker='o', linestyle='-', color = \"C0\", zorder=2, alpha=0.5, label='$\\omega_{\\mathrm{COM}} = \\omega_{\\mathrm{0}}$')\n",
    "# plt.scatter(shuttling_time_0, phonon_number_0, c=\"black\", s=10, zorder=1)\n",
    "\n",
    "# plt.scatter(shuttling_time_8, phonon_number_8, c=\"black\", s=5, zorder=1)\n",
    "# plt.scatter(shuttling_time_9, phonon_number_9, c=\"black\", s=5, zorder=1)\n",
    "# plt.scatter(shuttling_time_10, phonon_number_10, c=\"red\", s=5, zorder=2)\n",
    "merged_shuttling_time_5 = np.concatenate((shuttling_time_11, shuttling_time_12, shuttling_time_13, shuttling_time_14, shuttling_time_15))\n",
    "merged_phonon_number_5 = np.concatenate((phonon_number_11, phonon_number_12, phonon_number_13, phonon_number_14, phonon_number_15))\n",
    "merged_shuttling_time_5 = merged_shuttling_time_5.tolist()\n",
    "merged_phonon_number_5 = merged_phonon_number_5.tolist()\n",
    "sorted_pairs_5 = sorted(zip(merged_shuttling_time_5, merged_phonon_number_5))\n",
    "merged_shuttling_time_sorted_5, merged_phonon_number_sorted_5 = zip(*sorted_pairs_5)\n",
    "merged_shuttling_time_sorted_5_1 = [time for time in merged_shuttling_time_sorted_5 if time < 215]\n",
    "merged_phonon_number_sorted_5_1 = [phonon for time, phonon in zip(merged_shuttling_time_sorted_5, merged_phonon_number_sorted_5) if time < 215]\n",
    "merged_shuttling_time_sorted_5_2 = [time for time in merged_shuttling_time_sorted_5 if time > 215]\n",
    "merged_phonon_number_sorted_5_2 = [phonon for time, phonon in zip(merged_shuttling_time_sorted_5, merged_phonon_number_sorted_5) if time > 215]\n",
    "# plt.plot(merged_shuttling_time_sorted_5, merged_phonon_number_sorted_5, marker='^', linestyle='-', c=\"red\", zorder=2, alpha=0.6, label='$\\omega_{\\mathrm{COM}} = 1.05 \\, \\omega_{\\mathrm{0}}$')\n",
    "plt.plot(merged_shuttling_time_sorted_5_1, merged_phonon_number_sorted_5_1, marker='^', linestyle='-', c=\"red\", zorder=2, alpha=0.3, label='$\\omega_{\\mathrm{COM}} = 1.05 \\, \\omega_{\\mathrm{0}}$')\n",
    "plt.plot(merged_shuttling_time_sorted_5_2, merged_phonon_number_sorted_5_2, marker='^', linestyle='-', c=\"red\", zorder=2, alpha=0.3)\n",
    "# plt.scatter(merged_shuttling_time_sorted_5, merged_phonon_number_sorted_5, marker='^', color = \"red\", zorder=2, alpha=0.5, label='$\\omega_{\\mathrm{COM}} = 1.05 \\, \\omega_{\\mathrm{0}}$')\n",
    "# plt.plot(shuttling_time_1, phonon_number_1, marker='o', linestyle='-', color='C0')\n",
    "\n",
    "merged_shuttling_time_10 = np.concatenate((shuttling_time_m1, shuttling_time_m2, shuttling_time_m3, shuttling_time_m4))\n",
    "merged_phonon_number_10 = np.concatenate((phonon_number_m1, phonon_number_m2, phonon_number_m3, phonon_number_m4))\n",
    "merged_shuttling_time_10 = merged_shuttling_time_10.tolist()\n",
    "merged_phonon_number_10 = merged_phonon_number_10.tolist()\n",
    "sorted_pairs_10 = sorted(zip(merged_shuttling_time_10, merged_phonon_number_10))\n",
    "merged_shuttling_time_sorted_10, merged_phonon_number_sorted_10 = zip(*sorted_pairs_10)\n",
    "merged_shuttling_time_sorted_10_1 = [time for time in merged_shuttling_time_sorted_10 if time < 215]\n",
    "merged_phonon_number_sorted_10_1 = [phonon for time, phonon in zip(merged_shuttling_time_sorted_10, merged_phonon_number_sorted_10) if time < 215]\n",
    "merged_shuttling_time_sorted_10_2 = [time for time in merged_shuttling_time_sorted_10 if time > 215]\n",
    "merged_phonon_number_sorted_10_2 = [phonon for time, phonon in zip(merged_shuttling_time_sorted_10, merged_phonon_number_sorted_10) if time > 215]\n",
    "# plt.plot(merged_shuttling_time_sorted_10, merged_phonon_number_sorted_10, marker=\"D\", linestyle='-', c=\"green\", zorder=2, alpha=0.7, label='$\\omega_{\\mathrm{COM}} = 1.10 \\, \\omega_{\\mathrm{0}}$')\n",
    "plt.plot(merged_shuttling_time_sorted_10_1, merged_phonon_number_sorted_10_1, marker=\"D\", linestyle='-', c=\"green\", zorder=2, alpha=0.3, label='$\\omega_{\\mathrm{COM}} = 1.10 \\, \\omega_{\\mathrm{0}}$')\n",
    "plt.plot(merged_shuttling_time_sorted_10_2, merged_phonon_number_sorted_10_2, marker=\"D\", linestyle='-', c=\"green\", zorder=2, alpha=0.3)\n",
    "# plt.scatter(merged_shuttling_time_sorted_10, merged_phonon_number_sorted_10, marker='D', color = \"green\", zorder=2, alpha=0.5, label='$\\omega_{\\mathrm{COM}} = 1.10 \\, \\omega_{\\mathrm{0}}$')\n",
    "\n",
    "# time = 150 * (121 / 133.93969396934)\n",
    "# start = time * 0.99\n",
    "# stop = time * 1.01\n",
    "# plt.axvspan(start, stop, color='red', alpha=0.3)\n",
    "\n",
    "time = 150 * (121 / 133.93969396934) * 2\n",
    "start = time * 0.995\n",
    "stop = time * 1.005\n",
    "# plt.axvspan(start, stop, color='red', alpha=0.3)\n",
    "# plt.axvline(start/2+stop/2, color='black', linestyle='--', alpha=1)\n",
    "\n",
    "# First average curve (130-230 μs)\n",
    "# Extracting the datasets for interpolation\n",
    "time1 = np.array(merged_shuttling_time_sorted)\n",
    "phonon1 = np.array(merged_phonon_number_sorted)\n",
    "time2 = np.array(merged_shuttling_time_sorted_5_1)\n",
    "phonon2 = np.array(merged_phonon_number_sorted_5_1)\n",
    "time3 = np.array(merged_shuttling_time_sorted_10_1)\n",
    "phonon3 = np.array(merged_phonon_number_sorted_10_1)\n",
    "# Computing the overlapping time range\n",
    "t_min = max(time1.min(), time2.min(), time3.min())\n",
    "t_max = min(time1.max(), time2.max(), time3.max())\n",
    "# Creating common time points for interpolation\n",
    "common_time = np.linspace(t_min, t_max, num=1000)\n",
    "# Interpolating the phonon numbers onto the common time points\n",
    "phonon1_interp = np.interp(common_time, time1, phonon1)\n",
    "phonon2_interp = np.interp(common_time, time2, phonon2)\n",
    "phonon3_interp = np.interp(common_time, time3, phonon3)\n",
    "# Computing the average phonon number at each time point\n",
    "average_phonon = (phonon1_interp + phonon2_interp + phonon3_interp) / 3\n",
    "# Calculate the standard deviation at each point\n",
    "std_dev = np.std([phonon1_interp, phonon2_interp, phonon3_interp], axis=0)\n",
    "# Plotting the average curve\n",
    "plt.plot(\n",
    "    common_time,\n",
    "    average_phonon,\n",
    "    label=\"Average of simulation\",\n",
    "    color=\"purple\",\n",
    "    linestyle=\"--\",\n",
    "    linewidth=4,\n",
    "    alpha=0.7\n",
    ")\n",
    "plt.fill_between(\n",
    "    common_time,\n",
    "    average_phonon - std_dev,\n",
    "    average_phonon + std_dev,\n",
    "    color='purple',\n",
    "    alpha=0.0\n",
    ")\n",
    "\n",
    "# Second average curve (230-270 μs)\n",
    "time1_2 = np.array(merged_shuttling_time_sorted)\n",
    "phonon1_2 = np.array(merged_phonon_number_sorted)\n",
    "time2_2 = np.array(merged_shuttling_time_sorted_5_2)\n",
    "phonon2_2 = np.array(merged_phonon_number_sorted_5_2)\n",
    "time3_2 = np.array(merged_shuttling_time_sorted_10_2)\n",
    "phonon3_2 = np.array(merged_phonon_number_sorted_10_2)\n",
    "# Filter data points between 230 and 270 μs\n",
    "mask1 = (time1_2 >= 230) & (time1_2 <= 270)\n",
    "mask2 = (time2_2 >= 230) & (time2_2 <= 270)\n",
    "mask3 = (time3_2 >= 230) & (time3_2 <= 270)\n",
    "time1_2 = time1_2[mask1]\n",
    "phonon1_2 = phonon1_2[mask1]\n",
    "time2_2 = time2_2[mask2]\n",
    "phonon2_2 = phonon2_2[mask2]\n",
    "time3_2 = time3_2[mask3]\n",
    "phonon3_2 = phonon3_2[mask3]\n",
    "# Computing the overlapping time range for the second region\n",
    "t_min_2 = max(time1_2.min(), time2_2.min(), time3_2.min())\n",
    "t_max_2 = min(time1_2.max(), time2_2.max(), time3_2.max())\n",
    "common_time_2 = np.linspace(t_min_2, t_max_2, num=1000)\n",
    "# Interpolating the phonon numbers onto the common time points\n",
    "phonon1_interp_2 = np.interp(common_time_2, time1_2, phonon1_2)\n",
    "phonon2_interp_2 = np.interp(common_time_2, time2_2, phonon2_2)\n",
    "phonon3_interp_2 = np.interp(common_time_2, time3_2, phonon3_2)\n",
    "# Computing the average phonon number at each time point\n",
    "average_phonon_2 = (phonon1_interp_2 + phonon2_interp_2 + phonon3_interp_2) / 3\n",
    "# Calculate the standard deviation for the second region\n",
    "std_dev_2 = np.std([phonon1_interp_2, phonon2_interp_2, phonon3_interp_2], axis=0)\n",
    "# Plotting the second average curve\n",
    "plt.plot(\n",
    "    common_time_2,\n",
    "    average_phonon_2,\n",
    "    color=\"purple\",\n",
    "    linestyle=\"--\",\n",
    "    linewidth=4,\n",
    "    alpha=0.7\n",
    ")\n",
    "plt.fill_between(\n",
    "    common_time_2,\n",
    "    average_phonon_2 - std_dev_2,\n",
    "    average_phonon_2 + std_dev_2,\n",
    "    color='purple',\n",
    "    alpha=0.0\n",
    ")\n",
    "\n",
    "plt.xlabel('Shuttling time (μs)', fontsize=12)\n",
    "plt.ylabel('COM mode phonon number', fontsize=12)\n",
    "plt.title('Coherent excitation vs shuttling time', fontsize=12)\n",
    "plt.yscale('log')\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.xlim(100, 225)\n",
    "# plt.ylim(50, 500)\n",
    "\n",
    "pdf_filename = './outputs/Phonon_number_vs_shuttling_time.pdf'\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "645e63d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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