{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------\n", "Initializing structure...\n", "Working in 2D dimensions.\n", "Computational cell is 34.4 x 7.82 x 0 with resolution 50\n", " prism, center = (-9.09425,1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (-4,0.06,0)\n", " (-4.108,0.061,0)\n", " (-4.215,0.062,0)\n", " (-4.322,0.065,0)\n", " (-4.429,0.07,0)\n", " (-4.535,0.075,0)\n", " (-4.641,0.081,0)\n", " (-4.747,0.089,0)\n", " (-4.852,0.097,0)\n", " (-5.062,0.117,0)\n", " (-5.167,0.129,0)\n", " (-5.271,0.141,0)\n", " (-5.479,0.169,0)\n", " (-5.582,0.184,0)\n", " (-5.685,0.2,0)\n", " (-5.788,0.217,0)\n", " (-5.891,0.235,0)\n", " (-5.993,0.253,0)\n", " (-6.095,0.272,0)\n", " (-6.197,0.292,0)\n", " (-6.299,0.313,0)\n", " (-6.4,0.334,0)\n", " (-6.501,0.356,0)\n", " (-6.703,0.402,0)\n", " (-6.803,0.425,0)\n", " (-6.904,0.45,0)\n", " (-7.204,0.525,0)\n", " (-7.303,0.552,0)\n", " (-7.403,0.578,0)\n", " (-7.502,0.605,0)\n", " (-7.601,0.633,0)\n", " (-7.7,0.66,0)\n", " (-7.799,0.688,0)\n", " (-7.898,0.717,0)\n", " (-7.996,0.745,0)\n", " (-8.095,0.774,0)\n", " (-8.193,0.803,0)\n", " (-8.291,0.833,0)\n", " (-8.389,0.862,0)\n", " (-8.487,0.892,0)\n", " (-8.585,0.921,0)\n", " (-8.781,0.981,0)\n", " (-8.878,1.011,0)\n", " (-9.074,1.071,0)\n", " (-9.268,1.131,0)\n", " (-9.366,1.161,0)\n", " (-9.463,1.191,0)\n", " (-9.56,1.22,0)\n", " (-9.658,1.25,0)\n", " (-9.949,1.337,0)\n", " (-10.047,1.366,0)\n", " (-10.144,1.395,0)\n", " (-10.338,1.451,0)\n", " (-10.436,1.478,0)\n", " (-10.533,1.506,0)\n", " (-10.63,1.533,0)\n", " (-10.728,1.559,0)\n", " (-10.922,1.611,0)\n", " (-11.02,1.636,0)\n", " (-11.117,1.66,0)\n", " (-11.215,1.685,0)\n", " (-11.313,1.708,0)\n", " (-11.41,1.731,0)\n", " (-11.508,1.754,0)\n", " (-11.606,1.776,0)\n", " (-11.704,1.797,0)\n", " (-11.802,1.817,0)\n", " (-11.901,1.837,0)\n", " (-11.999,1.856,0)\n", " (-12.098,1.875,0)\n", " (-12.196,1.893,0)\n", " (-12.295,1.91,0)\n", " (-12.394,1.926,0)\n", " (-12.592,1.956,0)\n", " (-12.691,1.97,0)\n", " (-12.791,1.982,0)\n", " (-12.89,1.994,0)\n", " (-12.99,2.005,0)\n", " (-13.19,2.025,0)\n", " (-13.291,2.033,0)\n", " (-13.392,2.04,0)\n", " (-13.492,2.046,0)\n", " (-13.593,2.051,0)\n", " (-13.695,2.055,0)\n", " (-13.796,2.058,0)\n", " (-14,2.06,0)\n", " (-17.2,2.06,0)\n", " (-17.2,2.56,0)\n", " (-14,2.56,0)\n", " (-13.892,2.559,0)\n", " (-13.785,2.558,0)\n", " (-13.678,2.555,0)\n", " (-13.571,2.55,0)\n", " (-13.465,2.545,0)\n", " (-13.359,2.539,0)\n", " (-13.253,2.531,0)\n", " (-13.148,2.523,0)\n", " (-12.938,2.503,0)\n", " (-12.833,2.491,0)\n", " (-12.729,2.479,0)\n", " (-12.521,2.451,0)\n", " (-12.418,2.436,0)\n", " (-12.315,2.42,0)\n", " (-12.212,2.403,0)\n", " (-12.109,2.385,0)\n", " (-12.007,2.367,0)\n", " (-11.905,2.348,0)\n", " (-11.803,2.328,0)\n", " (-11.701,2.307,0)\n", " (-11.6,2.286,0)\n", " (-11.499,2.264,0)\n", " (-11.297,2.218,0)\n", " (-11.197,2.195,0)\n", " (-11.096,2.17,0)\n", " (-10.796,2.095,0)\n", " (-10.697,2.068,0)\n", " (-10.597,2.042,0)\n", " (-10.498,2.015,0)\n", " (-10.399,1.987,0)\n", " (-10.3,1.96,0)\n", " (-10.201,1.932,0)\n", " (-10.102,1.903,0)\n", " (-10.004,1.875,0)\n", " (-9.905,1.846,0)\n", " (-9.807,1.817,0)\n", " (-9.709,1.787,0)\n", " (-9.611,1.758,0)\n", " (-9.513,1.728,0)\n", " (-9.415,1.699,0)\n", " (-9.219,1.639,0)\n", " (-9.122,1.609,0)\n", " (-8.926,1.549,0)\n", " (-8.732,1.489,0)\n", " (-8.634,1.459,0)\n", " (-8.537,1.429,0)\n", " (-8.44,1.4,0)\n", " (-8.342,1.37,0)\n", " (-8.051,1.283,0)\n", " (-7.953,1.254,0)\n", " (-7.856,1.225,0)\n", " (-7.662,1.169,0)\n", " (-7.564,1.142,0)\n", " (-7.467,1.114,0)\n", " (-7.37,1.087,0)\n", " (-7.272,1.061,0)\n", " (-7.078,1.009,0)\n", " (-6.98,0.984,0)\n", " (-6.883,0.96,0)\n", " (-6.785,0.935,0)\n", " (-6.687,0.912,0)\n", " (-6.59,0.889,0)\n", " (-6.492,0.866,0)\n", " (-6.394,0.844,0)\n", " (-6.296,0.823,0)\n", " (-6.198,0.803,0)\n", " (-6.099,0.783,0)\n", " (-6.001,0.764,0)\n", " (-5.902,0.745,0)\n", " (-5.804,0.727,0)\n", " (-5.705,0.71,0)\n", " (-5.606,0.694,0)\n", " (-5.408,0.664,0)\n", " (-5.309,0.65,0)\n", " (-5.209,0.638,0)\n", " (-5.11,0.626,0)\n", " (-5.01,0.615,0)\n", " (-4.81,0.595,0)\n", " (-4.709,0.587,0)\n", " (-4.608,0.58,0)\n", " (-4.508,0.574,0)\n", " (-4.407,0.569,0)\n", " (-4.305,0.565,0)\n", " (-4.204,0.562,0)\n", " (-4,0.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (4,0.06,0)\n", " (4,0.56,0)\n", " (4.204,0.562,0)\n", " (4.305,0.565,0)\n", " (4.407,0.569,0)\n", " (4.508,0.574,0)\n", " (4.608,0.58,0)\n", " (4.709,0.587,0)\n", " (4.81,0.595,0)\n", " (5.01,0.615,0)\n", " (5.11,0.626,0)\n", " (5.209,0.638,0)\n", " (5.309,0.65,0)\n", " (5.408,0.664,0)\n", " (5.606,0.694,0)\n", " (5.705,0.71,0)\n", " (5.804,0.727,0)\n", " (5.902,0.745,0)\n", " (6.001,0.764,0)\n", " (6.099,0.783,0)\n", " (6.198,0.803,0)\n", " (6.296,0.823,0)\n", " (6.394,0.844,0)\n", " (6.492,0.866,0)\n", " (6.59,0.889,0)\n", " (6.687,0.912,0)\n", " (6.785,0.935,0)\n", " (6.883,0.96,0)\n", " (6.98,0.984,0)\n", " (7.078,1.009,0)\n", " (7.272,1.061,0)\n", " (7.37,1.087,0)\n", " (7.467,1.114,0)\n", " (7.564,1.142,0)\n", " (7.662,1.169,0)\n", " (7.856,1.225,0)\n", " (7.953,1.254,0)\n", " (8.051,1.283,0)\n", " (8.342,1.37,0)\n", " (8.44,1.4,0)\n", " (8.537,1.429,0)\n", " (8.634,1.459,0)\n", " (8.732,1.489,0)\n", " (8.926,1.549,0)\n", " (9.122,1.609,0)\n", " (9.219,1.639,0)\n", " (9.415,1.699,0)\n", " (9.513,1.728,0)\n", " (9.611,1.758,0)\n", " (9.709,1.787,0)\n", " (9.807,1.817,0)\n", " (9.905,1.846,0)\n", " (10.004,1.875,0)\n", " (10.102,1.903,0)\n", " (10.201,1.932,0)\n", " (10.3,1.96,0)\n", " (10.399,1.987,0)\n", " (10.498,2.015,0)\n", " (10.597,2.042,0)\n", " (10.697,2.068,0)\n", " (10.796,2.095,0)\n", " (11.096,2.17,0)\n", " (11.197,2.195,0)\n", " (11.297,2.218,0)\n", " (11.499,2.264,0)\n", " (11.6,2.286,0)\n", " (11.701,2.307,0)\n", " (11.803,2.328,0)\n", " (11.905,2.348,0)\n", " (12.007,2.367,0)\n", " (12.109,2.385,0)\n", " (12.212,2.403,0)\n", " (12.315,2.42,0)\n", " (12.418,2.436,0)\n", " (12.521,2.451,0)\n", " (12.729,2.479,0)\n", " (12.833,2.491,0)\n", " (12.938,2.503,0)\n", " (13.148,2.523,0)\n", " (13.253,2.531,0)\n", " (13.359,2.539,0)\n", " (13.465,2.545,0)\n", " (13.571,2.55,0)\n", " (13.678,2.555,0)\n", " (13.785,2.558,0)\n", " (13.892,2.559,0)\n", " (14,2.56,0)\n", " (17.2,2.56,0)\n", " (17.2,2.06,0)\n", " (14,2.06,0)\n", " (13.796,2.058,0)\n", " (13.695,2.055,0)\n", " (13.593,2.051,0)\n", " (13.492,2.046,0)\n", " (13.392,2.04,0)\n", " (13.291,2.033,0)\n", " (13.19,2.025,0)\n", " (12.99,2.005,0)\n", " (12.89,1.994,0)\n", " (12.791,1.982,0)\n", " (12.691,1.97,0)\n", " (12.592,1.956,0)\n", " (12.394,1.926,0)\n", " (12.295,1.91,0)\n", " (12.196,1.893,0)\n", " (12.098,1.875,0)\n", " (11.999,1.856,0)\n", " (11.901,1.837,0)\n", " (11.802,1.817,0)\n", " (11.704,1.797,0)\n", " (11.606,1.776,0)\n", " (11.508,1.754,0)\n", " (11.41,1.731,0)\n", " (11.313,1.708,0)\n", " (11.215,1.685,0)\n", " (11.117,1.66,0)\n", " (11.02,1.636,0)\n", " (10.922,1.611,0)\n", " (10.728,1.559,0)\n", " (10.63,1.533,0)\n", " (10.533,1.506,0)\n", " (10.436,1.478,0)\n", " (10.338,1.451,0)\n", " (10.144,1.395,0)\n", " (10.047,1.366,0)\n", " (9.949,1.337,0)\n", " (9.658,1.25,0)\n", " (9.56,1.22,0)\n", " (9.463,1.191,0)\n", " (9.366,1.161,0)\n", " (9.268,1.131,0)\n", " (9.074,1.071,0)\n", " (8.878,1.011,0)\n", " (8.781,0.981,0)\n", " (8.585,0.921,0)\n", " (8.487,0.892,0)\n", " (8.389,0.862,0)\n", " (8.291,0.833,0)\n", " (8.193,0.803,0)\n", " (8.095,0.774,0)\n", " (7.996,0.745,0)\n", " (7.898,0.717,0)\n", " (7.799,0.688,0)\n", " (7.7,0.66,0)\n", " (7.601,0.633,0)\n", " (7.502,0.605,0)\n", " (7.403,0.578,0)\n", " (7.303,0.552,0)\n", " (7.204,0.525,0)\n", " (6.904,0.45,0)\n", " (6.803,0.425,0)\n", " (6.703,0.402,0)\n", " (6.501,0.356,0)\n", " (6.4,0.334,0)\n", " (6.299,0.313,0)\n", " (6.197,0.292,0)\n", " (6.095,0.272,0)\n", " (5.993,0.253,0)\n", " (5.891,0.235,0)\n", " (5.788,0.217,0)\n", " (5.685,0.2,0)\n", " (5.582,0.184,0)\n", " (5.479,0.169,0)\n", " (5.271,0.141,0)\n", " (5.167,0.129,0)\n", " (5.062,0.117,0)\n", " (4.852,0.097,0)\n", " (4.747,0.089,0)\n", " (4.641,0.081,0)\n", " (4.535,0.075,0)\n", " (4.429,0.07,0)\n", " (4.322,0.065,0)\n", " (4.215,0.062,0)\n", " (4.108,0.061,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,0.31,0)\n", " height 0, axis (0,0,1), 4 vertices:\n", " (-4,0.06,0)\n", " (-4,0.56,0)\n", " (4,0.56,0)\n", " (4,0.06,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (-9.09425,-1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (-17.2,-2.56,0)\n", " (-17.2,-2.06,0)\n", " (-14,-2.06,0)\n", " (-13.796,-2.058,0)\n", " (-13.695,-2.055,0)\n", " (-13.593,-2.051,0)\n", " (-13.492,-2.046,0)\n", " (-13.392,-2.04,0)\n", " (-13.291,-2.033,0)\n", " (-13.19,-2.025,0)\n", " (-12.99,-2.005,0)\n", " (-12.89,-1.994,0)\n", " (-12.791,-1.982,0)\n", " (-12.691,-1.97,0)\n", " (-12.592,-1.956,0)\n", " (-12.394,-1.926,0)\n", " (-12.295,-1.91,0)\n", " (-12.196,-1.893,0)\n", " (-12.098,-1.875,0)\n", " (-11.999,-1.856,0)\n", " (-11.901,-1.837,0)\n", " (-11.802,-1.817,0)\n", " (-11.704,-1.797,0)\n", " (-11.606,-1.776,0)\n", " (-11.508,-1.754,0)\n", " (-11.41,-1.731,0)\n", " (-11.313,-1.708,0)\n", " (-11.215,-1.685,0)\n", " (-11.117,-1.66,0)\n", " (-11.02,-1.636,0)\n", " (-10.922,-1.611,0)\n", " (-10.728,-1.559,0)\n", " (-10.63,-1.533,0)\n", " (-10.533,-1.506,0)\n", " (-10.436,-1.478,0)\n", " (-10.338,-1.451,0)\n", " (-10.144,-1.395,0)\n", " (-10.047,-1.366,0)\n", " (-9.949,-1.337,0)\n", " (-9.658,-1.25,0)\n", " (-9.56,-1.22,0)\n", " (-9.463,-1.191,0)\n", " (-9.366,-1.161,0)\n", " (-9.268,-1.131,0)\n", " (-9.074,-1.071,0)\n", " (-8.878,-1.011,0)\n", " (-8.781,-0.981,0)\n", " (-8.585,-0.921,0)\n", " (-8.487,-0.892,0)\n", " (-8.389,-0.862,0)\n", " (-8.291,-0.833,0)\n", " (-8.193,-0.803,0)\n", " (-8.095,-0.774,0)\n", " (-7.996,-0.745,0)\n", " (-7.898,-0.717,0)\n", " (-7.799,-0.688,0)\n", " (-7.7,-0.66,0)\n", " (-7.601,-0.633,0)\n", " (-7.502,-0.605,0)\n", " (-7.403,-0.578,0)\n", " (-7.303,-0.552,0)\n", " (-7.204,-0.525,0)\n", " (-6.904,-0.45,0)\n", " (-6.803,-0.425,0)\n", " (-6.703,-0.402,0)\n", " (-6.501,-0.356,0)\n", " (-6.4,-0.334,0)\n", " (-6.299,-0.313,0)\n", " (-6.197,-0.292,0)\n", " (-6.095,-0.272,0)\n", " (-5.993,-0.253,0)\n", " (-5.891,-0.235,0)\n", " (-5.788,-0.217,0)\n", " (-5.685,-0.2,0)\n", " (-5.582,-0.184,0)\n", " (-5.479,-0.169,0)\n", " (-5.271,-0.141,0)\n", " (-5.167,-0.129,0)\n", " (-5.062,-0.117,0)\n", " (-4.852,-0.097,0)\n", " (-4.747,-0.089,0)\n", " (-4.641,-0.081,0)\n", " (-4.535,-0.075,0)\n", " (-4.429,-0.07,0)\n", " (-4.322,-0.065,0)\n", " (-4.215,-0.062,0)\n", " (-4.108,-0.061,0)\n", " (-4,-0.06,0)\n", " (-4,-0.56,0)\n", " (-4.204,-0.562,0)\n", " (-4.305,-0.565,0)\n", " (-4.407,-0.569,0)\n", " (-4.508,-0.574,0)\n", " (-4.608,-0.58,0)\n", " (-4.709,-0.587,0)\n", " (-4.81,-0.595,0)\n", " (-5.01,-0.615,0)\n", " (-5.11,-0.626,0)\n", " (-5.209,-0.638,0)\n", " (-5.309,-0.65,0)\n", " (-5.408,-0.664,0)\n", " (-5.606,-0.694,0)\n", " (-5.705,-0.71,0)\n", " (-5.804,-0.727,0)\n", " (-5.902,-0.745,0)\n", " (-6.001,-0.764,0)\n", " (-6.099,-0.783,0)\n", " (-6.198,-0.803,0)\n", " (-6.296,-0.823,0)\n", " (-6.394,-0.844,0)\n", " (-6.492,-0.866,0)\n", " (-6.59,-0.889,0)\n", " (-6.687,-0.912,0)\n", " (-6.785,-0.935,0)\n", " (-6.883,-0.96,0)\n", " (-6.98,-0.984,0)\n", " (-7.078,-1.009,0)\n", " (-7.272,-1.061,0)\n", " (-7.37,-1.087,0)\n", " (-7.467,-1.114,0)\n", " (-7.564,-1.142,0)\n", " (-7.662,-1.169,0)\n", " (-7.856,-1.225,0)\n", " (-7.953,-1.254,0)\n", " (-8.051,-1.283,0)\n", " (-8.342,-1.37,0)\n", " (-8.44,-1.4,0)\n", " (-8.537,-1.429,0)\n", " (-8.634,-1.459,0)\n", " (-8.732,-1.489,0)\n", " (-8.926,-1.549,0)\n", " (-9.122,-1.609,0)\n", " (-9.219,-1.639,0)\n", " (-9.415,-1.699,0)\n", " (-9.513,-1.728,0)\n", " (-9.611,-1.758,0)\n", " (-9.709,-1.787,0)\n", " (-9.807,-1.817,0)\n", " (-9.905,-1.846,0)\n", " (-10.004,-1.875,0)\n", " (-10.102,-1.903,0)\n", " (-10.201,-1.932,0)\n", " (-10.3,-1.96,0)\n", " (-10.399,-1.987,0)\n", " (-10.498,-2.015,0)\n", " (-10.597,-2.042,0)\n", " (-10.697,-2.068,0)\n", " (-10.796,-2.095,0)\n", " (-11.096,-2.17,0)\n", " (-11.197,-2.195,0)\n", " (-11.297,-2.218,0)\n", " (-11.499,-2.264,0)\n", " (-11.6,-2.286,0)\n", " (-11.701,-2.307,0)\n", " (-11.803,-2.328,0)\n", " (-11.905,-2.348,0)\n", " (-12.007,-2.367,0)\n", " (-12.109,-2.385,0)\n", " (-12.212,-2.403,0)\n", " (-12.315,-2.42,0)\n", " (-12.418,-2.436,0)\n", " (-12.521,-2.451,0)\n", " (-12.729,-2.479,0)\n", " (-12.833,-2.491,0)\n", " (-12.938,-2.503,0)\n", " (-13.148,-2.523,0)\n", " (-13.253,-2.531,0)\n", " (-13.359,-2.539,0)\n", " (-13.465,-2.545,0)\n", " (-13.571,-2.55,0)\n", " (-13.678,-2.555,0)\n", " (-13.785,-2.558,0)\n", " (-13.892,-2.559,0)\n", " (-14,-2.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,-1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (14,-2.56,0)\n", " (13.892,-2.559,0)\n", " (13.785,-2.558,0)\n", " (13.678,-2.555,0)\n", " (13.571,-2.55,0)\n", " (13.465,-2.545,0)\n", " (13.359,-2.539,0)\n", " (13.253,-2.531,0)\n", " (13.148,-2.523,0)\n", " (12.938,-2.503,0)\n", " (12.833,-2.491,0)\n", " (12.729,-2.479,0)\n", " (12.521,-2.451,0)\n", " (12.418,-2.436,0)\n", " (12.315,-2.42,0)\n", " (12.212,-2.403,0)\n", " (12.109,-2.385,0)\n", " (12.007,-2.367,0)\n", " (11.905,-2.348,0)\n", " (11.803,-2.328,0)\n", " (11.701,-2.307,0)\n", " (11.6,-2.286,0)\n", " (11.499,-2.264,0)\n", " (11.297,-2.218,0)\n", " (11.197,-2.195,0)\n", " (11.096,-2.17,0)\n", " (10.796,-2.095,0)\n", " (10.697,-2.068,0)\n", " (10.597,-2.042,0)\n", " (10.498,-2.015,0)\n", " (10.399,-1.987,0)\n", " (10.3,-1.96,0)\n", " (10.201,-1.932,0)\n", " (10.102,-1.903,0)\n", " (10.004,-1.875,0)\n", " (9.905,-1.846,0)\n", " (9.807,-1.817,0)\n", " (9.709,-1.787,0)\n", " (9.611,-1.758,0)\n", " (9.513,-1.728,0)\n", " (9.415,-1.699,0)\n", " (9.219,-1.639,0)\n", " (9.122,-1.609,0)\n", " (8.926,-1.549,0)\n", " (8.732,-1.489,0)\n", " (8.634,-1.459,0)\n", " (8.537,-1.429,0)\n", " (8.44,-1.4,0)\n", " (8.342,-1.37,0)\n", " (8.051,-1.283,0)\n", " (7.953,-1.254,0)\n", " (7.856,-1.225,0)\n", " (7.662,-1.169,0)\n", " (7.564,-1.142,0)\n", " (7.467,-1.114,0)\n", " (7.37,-1.087,0)\n", " (7.272,-1.061,0)\n", " (7.078,-1.009,0)\n", " (6.98,-0.984,0)\n", " (6.883,-0.96,0)\n", " (6.785,-0.935,0)\n", " (6.687,-0.912,0)\n", " (6.59,-0.889,0)\n", " (6.492,-0.866,0)\n", " (6.394,-0.844,0)\n", " (6.296,-0.823,0)\n", " (6.198,-0.803,0)\n", " (6.099,-0.783,0)\n", " (6.001,-0.764,0)\n", " (5.902,-0.745,0)\n", " (5.804,-0.727,0)\n", " (5.705,-0.71,0)\n", " (5.606,-0.694,0)\n", " (5.408,-0.664,0)\n", " (5.309,-0.65,0)\n", " (5.209,-0.638,0)\n", " (5.11,-0.626,0)\n", " (5.01,-0.615,0)\n", " (4.81,-0.595,0)\n", " (4.709,-0.587,0)\n", " (4.608,-0.58,0)\n", " (4.508,-0.574,0)\n", " (4.407,-0.569,0)\n", " (4.305,-0.565,0)\n", " (4.204,-0.562,0)\n", " (4,-0.56,0)\n", " (4,-0.06,0)\n", " (4.108,-0.061,0)\n", " (4.215,-0.062,0)\n", " (4.322,-0.065,0)\n", " (4.429,-0.07,0)\n", " (4.535,-0.075,0)\n", " (4.641,-0.081,0)\n", " (4.747,-0.089,0)\n", " (4.852,-0.097,0)\n", " (5.062,-0.117,0)\n", " (5.167,-0.129,0)\n", " (5.271,-0.141,0)\n", " (5.479,-0.169,0)\n", " (5.582,-0.184,0)\n", " (5.685,-0.2,0)\n", " (5.788,-0.217,0)\n", " (5.891,-0.235,0)\n", " (5.993,-0.253,0)\n", " (6.095,-0.272,0)\n", " (6.197,-0.292,0)\n", " (6.299,-0.313,0)\n", " (6.4,-0.334,0)\n", " (6.501,-0.356,0)\n", " (6.703,-0.402,0)\n", " (6.803,-0.425,0)\n", " (6.904,-0.45,0)\n", " (7.204,-0.525,0)\n", " (7.303,-0.552,0)\n", " (7.403,-0.578,0)\n", " (7.502,-0.605,0)\n", " (7.601,-0.633,0)\n", " (7.7,-0.66,0)\n", " (7.799,-0.688,0)\n", " (7.898,-0.717,0)\n", " (7.996,-0.745,0)\n", " (8.095,-0.774,0)\n", " (8.193,-0.803,0)\n", " (8.291,-0.833,0)\n", " (8.389,-0.862,0)\n", " (8.487,-0.892,0)\n", " (8.585,-0.921,0)\n", " (8.781,-0.981,0)\n", " (8.878,-1.011,0)\n", " (9.074,-1.071,0)\n", " (9.268,-1.131,0)\n", " (9.366,-1.161,0)\n", " (9.463,-1.191,0)\n", " (9.56,-1.22,0)\n", " (9.658,-1.25,0)\n", " (9.949,-1.337,0)\n", " (10.047,-1.366,0)\n", " (10.144,-1.395,0)\n", " (10.338,-1.451,0)\n", " (10.436,-1.478,0)\n", " (10.533,-1.506,0)\n", " (10.63,-1.533,0)\n", " (10.728,-1.559,0)\n", " (10.922,-1.611,0)\n", " (11.02,-1.636,0)\n", " (11.117,-1.66,0)\n", " (11.215,-1.685,0)\n", " (11.313,-1.708,0)\n", " (11.41,-1.731,0)\n", " (11.508,-1.754,0)\n", " (11.606,-1.776,0)\n", " (11.704,-1.797,0)\n", " (11.802,-1.817,0)\n", " (11.901,-1.837,0)\n", " (11.999,-1.856,0)\n", " (12.098,-1.875,0)\n", " (12.196,-1.893,0)\n", " (12.295,-1.91,0)\n", " (12.394,-1.926,0)\n", " (12.592,-1.956,0)\n", " (12.691,-1.97,0)\n", " (12.791,-1.982,0)\n", " (12.89,-1.994,0)\n", " (12.99,-2.005,0)\n", " (13.19,-2.025,0)\n", " (13.291,-2.033,0)\n", " (13.392,-2.04,0)\n", " (13.492,-2.046,0)\n", " (13.593,-2.051,0)\n", " (13.695,-2.055,0)\n", " (13.796,-2.058,0)\n", " (14,-2.06,0)\n", " (17.2,-2.06,0)\n", " (17.2,-2.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,-0.31,0)\n", " height 0, axis (0,0,1), 4 vertices:\n", " (-4,-0.56,0)\n", " (-4,-0.06,0)\n", " (4,-0.06,0)\n", " (4,-0.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "subpixel-averaging is 11.5011% done, 30.9631 s remaining\n", "subpixel-averaging is 20.4466% done, 15.8135 s remaining\n", "subpixel-averaging is 29.3921% done, 9.71826 s remaining\n", "subpixel-averaging is 58.5715% done, 2.83111 s remaining\n", "subpixel-averaging is 71.3508% done, 1.62492 s remaining\n", "subpixel-averaging is 80.2963% done, 0.998215 s remaining\n", "subpixel-averaging is 89.2418% done, 0.492406 s remaining\n", "subpixel-averaging is 11.5011% done, 30.9076 s remaining\n", "subpixel-averaging is 20.4466% done, 15.9068 s remaining\n", "subpixel-averaging is 29.3921% done, 9.80747 s remaining\n", "subpixel-averaging is 57.7195% done, 2.9313 s remaining\n", "subpixel-averaging is 71.1378% done, 1.66162 s remaining\n", "subpixel-averaging is 80.0833% done, 1.0166 s remaining\n", "subpixel-averaging is 88.8158% done, 0.504616 s remaining\n", "subpixel-averaging is 11.5011% done, 30.9943 s remaining\n", "subpixel-averaging is 20.4466% done, 15.8803 s remaining\n", "subpixel-averaging is 29.3921% done, 9.78632 s remaining\n", "subpixel-averaging is 56.8676% done, 3.03894 s remaining\n", "subpixel-averaging is 70.9248% done, 1.64887 s remaining\n", "subpixel-averaging is 79.8703% done, 1.02929 s remaining\n", "subpixel-averaging is 88.8158% done, 0.514541 s remaining\n", "time for set_epsilon = 102.547 s\n", "-----------\n", "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 12 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Meep progress: 5.5600000000000005/177.5 = 3.1% done in 4.0s, 123.7s to go\n", "on time step 556 (time=5.56), 0.00719569 s/step\n", "Meep progress: 10.59/177.5 = 6.0% done in 8.0s, 126.1s to go\n", "on time step 1059 (time=10.59), 0.00795647 s/step\n", "Meep progress: 15.25/177.5 = 8.6% done in 12.0s, 127.8s to go\n", "on time step 1525 (time=15.25), 0.0086014 s/step\n", "Meep progress: 19.76/177.5 = 11.1% done in 16.0s, 127.8s to go\n", "on time step 1976 (time=19.76), 0.00887541 s/step\n", "Meep progress: 24.02/177.5 = 13.5% done in 20.0s, 127.9s to go\n", "on time step 2402 (time=24.02), 0.00940361 s/step\n", "Meep progress: 29.82/177.5 = 16.8% done in 24.0s, 119.0s to go\n", "on time step 2982 (time=29.82), 0.00690337 s/step\n", "Meep progress: 35.64/177.5 = 20.1% done in 28.0s, 111.6s to go\n", "on time step 3564 (time=35.64), 0.00688025 s/step\n", "Meep progress: 41.43/177.5 = 23.3% done in 32.0s, 105.2s to go\n", "on time step 4143 (time=41.43), 0.00691613 s/step\n", "Meep progress: 47.25/177.5 = 26.6% done in 36.0s, 99.3s to go\n", "on time step 4725 (time=47.25), 0.0068737 s/step\n", "Meep progress: 52.99/177.5 = 29.9% done in 40.0s, 94.1s to go\n", "on time step 5299 (time=52.99), 0.0069787 s/step\n", "Meep progress: 58.68/177.5 = 33.1% done in 44.0s, 89.2s to go\n", "on time step 5868 (time=58.68), 0.00703139 s/step\n", "Meep progress: 64.46000000000001/177.5 = 36.3% done in 48.0s, 84.2s to go\n", "on time step 6446 (time=64.46), 0.0069234 s/step\n", "Meep progress: 70.27/177.5 = 39.6% done in 52.0s, 79.4s to go\n", "on time step 7027 (time=70.27), 0.00689556 s/step\n", "Meep progress: 76.01/177.5 = 42.8% done in 56.1s, 74.8s to go\n", "on time step 7601 (time=76.01), 0.00698002 s/step\n", "Meep progress: 81.79/177.5 = 46.1% done in 60.1s, 70.3s to go\n", "on time step 8179 (time=81.79), 0.00692945 s/step\n", "Meep progress: 87.48/177.5 = 49.3% done in 64.1s, 65.9s to go\n", "on time step 8748 (time=87.48), 0.00703933 s/step\n", "Meep progress: 93.25/177.5 = 52.5% done in 68.1s, 61.5s to go\n", "on time step 9325 (time=93.25), 0.00693846 s/step\n", "Meep progress: 99.02/177.5 = 55.8% done in 72.1s, 57.1s to go\n", "on time step 9902 (time=99.02), 0.00693694 s/step\n", "Meep progress: 104.81/177.5 = 59.0% done in 76.1s, 52.8s to go\n", "on time step 10481 (time=104.81), 0.00691733 s/step\n", "Meep progress: 110.57000000000001/177.5 = 62.3% done in 80.1s, 48.5s to go\n", "on time step 11057 (time=110.57), 0.00695527 s/step\n", "Meep progress: 116.32000000000001/177.5 = 65.5% done in 84.1s, 44.2s to go\n", "on time step 11632 (time=116.32), 0.00696429 s/step\n", "Meep progress: 122.05/177.5 = 68.8% done in 88.1s, 40.0s to go\n", "on time step 12205 (time=122.05), 0.00698096 s/step\n", "Meep progress: 127.81/177.5 = 72.0% done in 92.1s, 35.8s to go\n", "on time step 12781 (time=127.81), 0.00694492 s/step\n", "Meep progress: 133.59/177.5 = 75.3% done in 96.1s, 31.6s to go\n", "on time step 13359 (time=133.59), 0.00692692 s/step\n", "Meep progress: 139.34/177.5 = 78.5% done in 100.1s, 27.4s to go\n", "on time step 13934 (time=139.34), 0.00695738 s/step\n", "Meep progress: 145.11/177.5 = 81.8% done in 104.1s, 23.2s to go\n", "on time step 14511 (time=145.11), 0.00694059 s/step\n", "Meep progress: 150.94/177.5 = 85.0% done in 108.1s, 19.0s to go\n", "on time step 15094 (time=150.94), 0.00686312 s/step\n", "Meep progress: 156.67000000000002/177.5 = 88.3% done in 112.1s, 14.9s to go\n", "on time step 15667 (time=156.67), 0.00698277 s/step\n", "Meep progress: 162.42000000000002/177.5 = 91.5% done in 116.1s, 10.8s to go\n", "on time step 16242 (time=162.42), 0.00696221 s/step\n", "Meep progress: 168.18/177.5 = 94.7% done in 120.1s, 6.7s to go\n", "on time step 16818 (time=168.18), 0.00695355 s/step\n", "Meep progress: 173.85/177.5 = 97.9% done in 124.1s, 2.6s to go\n", "on time step 17385 (time=173.85), 0.00706013 s/step\n", "run 0 finished at t = 177.5 (17750 timesteps)\n" ] } ], "source": [ "import meep as mp\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "\n", "res = 50 # pixels/μm\n", "three_d = False # 3d calculation?\n", "d = 0.12 # branch separation\n", "\n", "gdsII_file = 'coupler.gds'\n", "CELL_LAYER = 0\n", "PORT1_LAYER = 1\n", "PORT2_LAYER = 2\n", "PORT3_LAYER = 3\n", "PORT4_LAYER = 4\n", "SOURCE_LAYER = 5\n", "UPPER_BRANCH_LAYER = 31\n", "LOWER_BRANCH_LAYER = 32\n", "default_d = 0.3\n", "\n", "t_oxide = 1.0\n", "t_Si = 0.22\n", "t_air = 0.78\n", "\n", "dpml = 1\n", "cell_thickness = dpml+t_oxide+t_Si+t_air+dpml\n", "si_zmin = 0\n", "\n", "oxide = mp.Medium(epsilon=2.25)\n", "silicon=mp.Medium(epsilon=12)\n", "\n", "lcen = 1.55\n", "fcen = 1/lcen\n", "df = 0.2*fcen\n", "\n", "cell_zmax = 0.5*cell_thickness if three_d else 0\n", "cell_zmin = -0.5*cell_thickness if three_d else 0\n", "si_zmax = t_Si if three_d else 0\n", "\n", "# read cell size, volumes for source region and flux monitors,\n", "# and coupler geometry from GDSII file\n", "upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax)\n", "lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax)\n", "\n", "cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax)\n", "p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax)\n", "p2 = mp.GDSII_vol(gdsII_file, PORT2_LAYER, si_zmin, si_zmax)\n", "p3 = mp.GDSII_vol(gdsII_file, PORT3_LAYER, si_zmin, si_zmax)\n", "p4 = mp.GDSII_vol(gdsII_file, PORT4_LAYER, si_zmin, si_zmax)\n", "src_vol = mp.GDSII_vol(gdsII_file, SOURCE_LAYER, si_zmin, si_zmax)\n", "\n", "# displace upper and lower branches of coupler (as well as source and flux regions)\n", "if d != default_d:\n", " delta_y = 0.5*(d-default_d)\n", " delta = mp.Vector3(y=delta_y)\n", " p1.center += delta\n", " p2.center -= delta\n", " p3.center += delta\n", " p4.center -= delta\n", " src_vol.center += delta\n", " cell.size += 2*delta\n", " for np in range(len(lower_branch)):\n", " lower_branch[np].center -= delta\n", " for nv in range(len(lower_branch[np].vertices)):\n", " lower_branch[np].vertices[nv] -= delta\n", " for np in range(len(upper_branch)):\n", " upper_branch[np].center += delta\n", " for nv in range(len(upper_branch[np].vertices)):\n", " upper_branch[np].vertices[nv] += delta\n", "\n", "geometry = upper_branch+lower_branch\n", "\n", "if three_d:\n", " oxide_center = mp.Vector3(z=-0.5*t_oxide)\n", " oxide_size = mp.Vector3(cell.size.x,cell.size.y,t_oxide)\n", " oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)]\n", " geometry = geometry+oxide_layer\n", "\n", "sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df),\n", " size=src_vol.size,\n", " center=src_vol.center,\n", " eig_band=1,\n", " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z,\n", " eig_match_freq=True)]\n", "\n", "sim = mp.Simulation(resolution=res,\n", " cell_size=cell.size,\n", " boundary_layers=[mp.PML(dpml)],\n", " sources=sources,\n", " geometry=geometry)\n", "\n", "mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1))\n", "mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2))\n", "mode3 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p3))\n", "mode4 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p4))\n", "\n", "sim.run(until_after_sources=100)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 12 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.088124,-0.000000,0.000000)\n", "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 12 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.088124,-0.000000,0.000000)\n", "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 13 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.088124,-0.000000,0.000000)\n", "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 14 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.088124,-0.000000,0.000000)\n", "trans:, 0.12, 0.000002, 0.557065, 0.426816\n" ] } ], "source": [ "# S parameters\n", "p1_coeff = sim.get_eigenmode_coefficients(mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", "p2_coeff = sim.get_eigenmode_coefficients(mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,1]\n", "p3_coeff = sim.get_eigenmode_coefficients(mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", "p4_coeff = sim.get_eigenmode_coefficients(mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", "\n", "# transmittance\n", "p2_trans = abs(p2_coeff)**2/abs(p1_coeff)**2\n", "p3_trans = abs(p3_coeff)**2/abs(p1_coeff)**2\n", "p4_trans = abs(p4_coeff)**2/abs(p1_coeff)**2\n", "\n", "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d,p2_trans,p3_trans,p4_trans))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Field time usage:\n", " connecting chunks: 0.149937 s\n", " time stepping: 116.36 s\n", " communicating: 8.63537 s\n", " Fourier transforming: 0.747245 s\n", " MPB: 0.0412967 s\n", " everything else: 0.820934 s\n", "\n", "-----------\n", "Initializing structure...\n", "Working in 2D dimensions.\n", "Computational cell is 34.4 x 7.82 x 0 with resolution 50\n", " prism, center = (-9.09425,1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (-4,0.06,0)\n", " (-4.108,0.061,0)\n", " (-4.215,0.062,0)\n", " (-4.322,0.065,0)\n", " (-4.429,0.07,0)\n", " (-4.535,0.075,0)\n", " (-4.641,0.081,0)\n", " (-4.747,0.089,0)\n", " (-4.852,0.097,0)\n", " (-5.062,0.117,0)\n", " (-5.167,0.129,0)\n", " (-5.271,0.141,0)\n", " (-5.479,0.169,0)\n", " (-5.582,0.184,0)\n", " (-5.685,0.2,0)\n", " (-5.788,0.217,0)\n", " (-5.891,0.235,0)\n", " (-5.993,0.253,0)\n", " (-6.095,0.272,0)\n", " (-6.197,0.292,0)\n", " (-6.299,0.313,0)\n", " (-6.4,0.334,0)\n", " (-6.501,0.356,0)\n", " (-6.703,0.402,0)\n", " (-6.803,0.425,0)\n", " (-6.904,0.45,0)\n", " (-7.204,0.525,0)\n", " (-7.303,0.552,0)\n", " (-7.403,0.578,0)\n", " (-7.502,0.605,0)\n", " (-7.601,0.633,0)\n", " (-7.7,0.66,0)\n", " (-7.799,0.688,0)\n", " (-7.898,0.717,0)\n", " (-7.996,0.745,0)\n", " (-8.095,0.774,0)\n", " (-8.193,0.803,0)\n", " (-8.291,0.833,0)\n", " (-8.389,0.862,0)\n", " (-8.487,0.892,0)\n", " (-8.585,0.921,0)\n", " (-8.781,0.981,0)\n", " (-8.878,1.011,0)\n", " (-9.074,1.071,0)\n", " (-9.268,1.131,0)\n", " (-9.366,1.161,0)\n", " (-9.463,1.191,0)\n", " (-9.56,1.22,0)\n", " (-9.658,1.25,0)\n", " (-9.949,1.337,0)\n", " (-10.047,1.366,0)\n", " (-10.144,1.395,0)\n", " (-10.338,1.451,0)\n", " (-10.436,1.478,0)\n", " (-10.533,1.506,0)\n", " (-10.63,1.533,0)\n", " (-10.728,1.559,0)\n", " (-10.922,1.611,0)\n", " (-11.02,1.636,0)\n", " (-11.117,1.66,0)\n", " (-11.215,1.685,0)\n", " (-11.313,1.708,0)\n", " (-11.41,1.731,0)\n", " (-11.508,1.754,0)\n", " (-11.606,1.776,0)\n", " (-11.704,1.797,0)\n", " (-11.802,1.817,0)\n", " (-11.901,1.837,0)\n", " (-11.999,1.856,0)\n", " (-12.098,1.875,0)\n", " (-12.196,1.893,0)\n", " (-12.295,1.91,0)\n", " (-12.394,1.926,0)\n", " (-12.592,1.956,0)\n", " (-12.691,1.97,0)\n", " (-12.791,1.982,0)\n", " (-12.89,1.994,0)\n", " (-12.99,2.005,0)\n", " (-13.19,2.025,0)\n", " (-13.291,2.033,0)\n", " (-13.392,2.04,0)\n", " (-13.492,2.046,0)\n", " (-13.593,2.051,0)\n", " (-13.695,2.055,0)\n", " (-13.796,2.058,0)\n", " (-14,2.06,0)\n", " (-17.2,2.06,0)\n", " (-17.2,2.56,0)\n", " (-14,2.56,0)\n", " (-13.892,2.559,0)\n", " (-13.785,2.558,0)\n", " (-13.678,2.555,0)\n", " (-13.571,2.55,0)\n", " (-13.465,2.545,0)\n", " (-13.359,2.539,0)\n", " (-13.253,2.531,0)\n", " (-13.148,2.523,0)\n", " (-12.938,2.503,0)\n", " (-12.833,2.491,0)\n", " (-12.729,2.479,0)\n", " (-12.521,2.451,0)\n", " (-12.418,2.436,0)\n", " (-12.315,2.42,0)\n", " (-12.212,2.403,0)\n", " (-12.109,2.385,0)\n", " (-12.007,2.367,0)\n", " (-11.905,2.348,0)\n", " (-11.803,2.328,0)\n", " (-11.701,2.307,0)\n", " (-11.6,2.286,0)\n", " (-11.499,2.264,0)\n", " (-11.297,2.218,0)\n", " (-11.197,2.195,0)\n", " (-11.096,2.17,0)\n", " (-10.796,2.095,0)\n", " (-10.697,2.068,0)\n", " (-10.597,2.042,0)\n", " (-10.498,2.015,0)\n", " (-10.399,1.987,0)\n", " (-10.3,1.96,0)\n", " (-10.201,1.932,0)\n", " (-10.102,1.903,0)\n", " (-10.004,1.875,0)\n", " (-9.905,1.846,0)\n", " (-9.807,1.817,0)\n", " (-9.709,1.787,0)\n", " (-9.611,1.758,0)\n", " (-9.513,1.728,0)\n", " (-9.415,1.699,0)\n", " (-9.219,1.639,0)\n", " (-9.122,1.609,0)\n", " (-8.926,1.549,0)\n", " (-8.732,1.489,0)\n", " (-8.634,1.459,0)\n", " (-8.537,1.429,0)\n", " (-8.44,1.4,0)\n", " (-8.342,1.37,0)\n", " (-8.051,1.283,0)\n", " (-7.953,1.254,0)\n", " (-7.856,1.225,0)\n", " (-7.662,1.169,0)\n", " (-7.564,1.142,0)\n", " (-7.467,1.114,0)\n", " (-7.37,1.087,0)\n", " (-7.272,1.061,0)\n", " (-7.078,1.009,0)\n", " (-6.98,0.984,0)\n", " (-6.883,0.96,0)\n", " (-6.785,0.935,0)\n", " (-6.687,0.912,0)\n", " (-6.59,0.889,0)\n", " (-6.492,0.866,0)\n", " (-6.394,0.844,0)\n", " (-6.296,0.823,0)\n", " (-6.198,0.803,0)\n", " (-6.099,0.783,0)\n", " (-6.001,0.764,0)\n", " (-5.902,0.745,0)\n", " (-5.804,0.727,0)\n", " (-5.705,0.71,0)\n", " (-5.606,0.694,0)\n", " (-5.408,0.664,0)\n", " (-5.309,0.65,0)\n", " (-5.209,0.638,0)\n", " (-5.11,0.626,0)\n", " (-5.01,0.615,0)\n", " (-4.81,0.595,0)\n", " (-4.709,0.587,0)\n", " (-4.608,0.58,0)\n", " (-4.508,0.574,0)\n", " (-4.407,0.569,0)\n", " (-4.305,0.565,0)\n", " (-4.204,0.562,0)\n", " (-4,0.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (4,0.06,0)\n", " (4,0.56,0)\n", " (4.204,0.562,0)\n", " (4.305,0.565,0)\n", " (4.407,0.569,0)\n", " (4.508,0.574,0)\n", " (4.608,0.58,0)\n", " (4.709,0.587,0)\n", " (4.81,0.595,0)\n", " (5.01,0.615,0)\n", " (5.11,0.626,0)\n", " (5.209,0.638,0)\n", " (5.309,0.65,0)\n", " (5.408,0.664,0)\n", " (5.606,0.694,0)\n", " (5.705,0.71,0)\n", " (5.804,0.727,0)\n", " (5.902,0.745,0)\n", " (6.001,0.764,0)\n", " (6.099,0.783,0)\n", " (6.198,0.803,0)\n", " (6.296,0.823,0)\n", " (6.394,0.844,0)\n", " (6.492,0.866,0)\n", " (6.59,0.889,0)\n", " (6.687,0.912,0)\n", " (6.785,0.935,0)\n", " (6.883,0.96,0)\n", " (6.98,0.984,0)\n", " (7.078,1.009,0)\n", " (7.272,1.061,0)\n", " (7.37,1.087,0)\n", " (7.467,1.114,0)\n", " (7.564,1.142,0)\n", " (7.662,1.169,0)\n", " (7.856,1.225,0)\n", " (7.953,1.254,0)\n", " (8.051,1.283,0)\n", " (8.342,1.37,0)\n", " (8.44,1.4,0)\n", " (8.537,1.429,0)\n", " (8.634,1.459,0)\n", " (8.732,1.489,0)\n", " (8.926,1.549,0)\n", " (9.122,1.609,0)\n", " (9.219,1.639,0)\n", " (9.415,1.699,0)\n", " (9.513,1.728,0)\n", " (9.611,1.758,0)\n", " (9.709,1.787,0)\n", " (9.807,1.817,0)\n", " (9.905,1.846,0)\n", " (10.004,1.875,0)\n", " (10.102,1.903,0)\n", " (10.201,1.932,0)\n", " (10.3,1.96,0)\n", " (10.399,1.987,0)\n", " (10.498,2.015,0)\n", " (10.597,2.042,0)\n", " (10.697,2.068,0)\n", " (10.796,2.095,0)\n", " (11.096,2.17,0)\n", " (11.197,2.195,0)\n", " (11.297,2.218,0)\n", " (11.499,2.264,0)\n", " (11.6,2.286,0)\n", " (11.701,2.307,0)\n", " (11.803,2.328,0)\n", " (11.905,2.348,0)\n", " (12.007,2.367,0)\n", " (12.109,2.385,0)\n", " (12.212,2.403,0)\n", " (12.315,2.42,0)\n", " (12.418,2.436,0)\n", " (12.521,2.451,0)\n", " (12.729,2.479,0)\n", " (12.833,2.491,0)\n", " (12.938,2.503,0)\n", " (13.148,2.523,0)\n", " (13.253,2.531,0)\n", " (13.359,2.539,0)\n", " (13.465,2.545,0)\n", " (13.571,2.55,0)\n", " (13.678,2.555,0)\n", " (13.785,2.558,0)\n", " (13.892,2.559,0)\n", " (14,2.56,0)\n", " (17.2,2.56,0)\n", " (17.2,2.06,0)\n", " (14,2.06,0)\n", " (13.796,2.058,0)\n", " (13.695,2.055,0)\n", " (13.593,2.051,0)\n", " (13.492,2.046,0)\n", " (13.392,2.04,0)\n", " (13.291,2.033,0)\n", " (13.19,2.025,0)\n", " (12.99,2.005,0)\n", " (12.89,1.994,0)\n", " (12.791,1.982,0)\n", " (12.691,1.97,0)\n", " (12.592,1.956,0)\n", " (12.394,1.926,0)\n", " (12.295,1.91,0)\n", " (12.196,1.893,0)\n", " (12.098,1.875,0)\n", " (11.999,1.856,0)\n", " (11.901,1.837,0)\n", " (11.802,1.817,0)\n", " (11.704,1.797,0)\n", " (11.606,1.776,0)\n", " (11.508,1.754,0)\n", " (11.41,1.731,0)\n", " (11.313,1.708,0)\n", " (11.215,1.685,0)\n", " (11.117,1.66,0)\n", " (11.02,1.636,0)\n", " (10.922,1.611,0)\n", " (10.728,1.559,0)\n", " (10.63,1.533,0)\n", " (10.533,1.506,0)\n", " (10.436,1.478,0)\n", " (10.338,1.451,0)\n", " (10.144,1.395,0)\n", " (10.047,1.366,0)\n", " (9.949,1.337,0)\n", " (9.658,1.25,0)\n", " (9.56,1.22,0)\n", " (9.463,1.191,0)\n", " (9.366,1.161,0)\n", " (9.268,1.131,0)\n", " (9.074,1.071,0)\n", " (8.878,1.011,0)\n", " (8.781,0.981,0)\n", " (8.585,0.921,0)\n", " (8.487,0.892,0)\n", " (8.389,0.862,0)\n", " (8.291,0.833,0)\n", " (8.193,0.803,0)\n", " (8.095,0.774,0)\n", " (7.996,0.745,0)\n", " (7.898,0.717,0)\n", " (7.799,0.688,0)\n", " (7.7,0.66,0)\n", " (7.601,0.633,0)\n", " (7.502,0.605,0)\n", " (7.403,0.578,0)\n", " (7.303,0.552,0)\n", " (7.204,0.525,0)\n", " (6.904,0.45,0)\n", " (6.803,0.425,0)\n", " (6.703,0.402,0)\n", " (6.501,0.356,0)\n", " (6.4,0.334,0)\n", " (6.299,0.313,0)\n", " (6.197,0.292,0)\n", " (6.095,0.272,0)\n", " (5.993,0.253,0)\n", " (5.891,0.235,0)\n", " (5.788,0.217,0)\n", " (5.685,0.2,0)\n", " (5.582,0.184,0)\n", " (5.479,0.169,0)\n", " (5.271,0.141,0)\n", " (5.167,0.129,0)\n", " (5.062,0.117,0)\n", " (4.852,0.097,0)\n", " (4.747,0.089,0)\n", " (4.641,0.081,0)\n", " (4.535,0.075,0)\n", " (4.429,0.07,0)\n", " (4.322,0.065,0)\n", " (4.215,0.062,0)\n", " (4.108,0.061,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,0.31,0)\n", " height 0, axis (0,0,1), 4 vertices:\n", " (-4,0.06,0)\n", " (-4,0.56,0)\n", " (4,0.56,0)\n", " (4,0.06,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (-9.09425,-1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (-17.2,-2.56,0)\n", " (-17.2,-2.06,0)\n", " (-14,-2.06,0)\n", " (-13.796,-2.058,0)\n", " (-13.695,-2.055,0)\n", " (-13.593,-2.051,0)\n", " (-13.492,-2.046,0)\n", " (-13.392,-2.04,0)\n", " (-13.291,-2.033,0)\n", " (-13.19,-2.025,0)\n", " (-12.99,-2.005,0)\n", " (-12.89,-1.994,0)\n", " (-12.791,-1.982,0)\n", " (-12.691,-1.97,0)\n", " (-12.592,-1.956,0)\n", " (-12.394,-1.926,0)\n", " (-12.295,-1.91,0)\n", " (-12.196,-1.893,0)\n", " (-12.098,-1.875,0)\n", " (-11.999,-1.856,0)\n", " (-11.901,-1.837,0)\n", " (-11.802,-1.817,0)\n", " (-11.704,-1.797,0)\n", " (-11.606,-1.776,0)\n", " (-11.508,-1.754,0)\n", " (-11.41,-1.731,0)\n", " (-11.313,-1.708,0)\n", " (-11.215,-1.685,0)\n", " (-11.117,-1.66,0)\n", " (-11.02,-1.636,0)\n", " (-10.922,-1.611,0)\n", " (-10.728,-1.559,0)\n", " (-10.63,-1.533,0)\n", " (-10.533,-1.506,0)\n", " (-10.436,-1.478,0)\n", " (-10.338,-1.451,0)\n", " (-10.144,-1.395,0)\n", " (-10.047,-1.366,0)\n", " (-9.949,-1.337,0)\n", " (-9.658,-1.25,0)\n", " (-9.56,-1.22,0)\n", " (-9.463,-1.191,0)\n", " (-9.366,-1.161,0)\n", " (-9.268,-1.131,0)\n", " (-9.074,-1.071,0)\n", " (-8.878,-1.011,0)\n", " (-8.781,-0.981,0)\n", " (-8.585,-0.921,0)\n", " (-8.487,-0.892,0)\n", " (-8.389,-0.862,0)\n", " (-8.291,-0.833,0)\n", " (-8.193,-0.803,0)\n", " (-8.095,-0.774,0)\n", " (-7.996,-0.745,0)\n", " (-7.898,-0.717,0)\n", " (-7.799,-0.688,0)\n", " (-7.7,-0.66,0)\n", " (-7.601,-0.633,0)\n", " (-7.502,-0.605,0)\n", " (-7.403,-0.578,0)\n", " (-7.303,-0.552,0)\n", " (-7.204,-0.525,0)\n", " (-6.904,-0.45,0)\n", " (-6.803,-0.425,0)\n", " (-6.703,-0.402,0)\n", " (-6.501,-0.356,0)\n", " (-6.4,-0.334,0)\n", " (-6.299,-0.313,0)\n", " (-6.197,-0.292,0)\n", " (-6.095,-0.272,0)\n", " (-5.993,-0.253,0)\n", " (-5.891,-0.235,0)\n", " (-5.788,-0.217,0)\n", " (-5.685,-0.2,0)\n", " (-5.582,-0.184,0)\n", " (-5.479,-0.169,0)\n", " (-5.271,-0.141,0)\n", " (-5.167,-0.129,0)\n", " (-5.062,-0.117,0)\n", " (-4.852,-0.097,0)\n", " (-4.747,-0.089,0)\n", " (-4.641,-0.081,0)\n", " (-4.535,-0.075,0)\n", " (-4.429,-0.07,0)\n", " (-4.322,-0.065,0)\n", " (-4.215,-0.062,0)\n", " (-4.108,-0.061,0)\n", " (-4,-0.06,0)\n", " (-4,-0.56,0)\n", " (-4.204,-0.562,0)\n", " (-4.305,-0.565,0)\n", " (-4.407,-0.569,0)\n", " (-4.508,-0.574,0)\n", " (-4.608,-0.58,0)\n", " (-4.709,-0.587,0)\n", " (-4.81,-0.595,0)\n", " (-5.01,-0.615,0)\n", " (-5.11,-0.626,0)\n", " (-5.209,-0.638,0)\n", " (-5.309,-0.65,0)\n", " (-5.408,-0.664,0)\n", " (-5.606,-0.694,0)\n", " (-5.705,-0.71,0)\n", " (-5.804,-0.727,0)\n", " (-5.902,-0.745,0)\n", " (-6.001,-0.764,0)\n", " (-6.099,-0.783,0)\n", " (-6.198,-0.803,0)\n", " (-6.296,-0.823,0)\n", " (-6.394,-0.844,0)\n", " (-6.492,-0.866,0)\n", " (-6.59,-0.889,0)\n", " (-6.687,-0.912,0)\n", " (-6.785,-0.935,0)\n", " (-6.883,-0.96,0)\n", " (-6.98,-0.984,0)\n", " (-7.078,-1.009,0)\n", " (-7.272,-1.061,0)\n", " (-7.37,-1.087,0)\n", " (-7.467,-1.114,0)\n", " (-7.564,-1.142,0)\n", " (-7.662,-1.169,0)\n", " (-7.856,-1.225,0)\n", " (-7.953,-1.254,0)\n", " (-8.051,-1.283,0)\n", " (-8.342,-1.37,0)\n", " (-8.44,-1.4,0)\n", " (-8.537,-1.429,0)\n", " (-8.634,-1.459,0)\n", " (-8.732,-1.489,0)\n", " (-8.926,-1.549,0)\n", " (-9.122,-1.609,0)\n", " (-9.219,-1.639,0)\n", " (-9.415,-1.699,0)\n", " (-9.513,-1.728,0)\n", " (-9.611,-1.758,0)\n", " (-9.709,-1.787,0)\n", " (-9.807,-1.817,0)\n", " (-9.905,-1.846,0)\n", " (-10.004,-1.875,0)\n", " (-10.102,-1.903,0)\n", " (-10.201,-1.932,0)\n", " (-10.3,-1.96,0)\n", " (-10.399,-1.987,0)\n", " (-10.498,-2.015,0)\n", " (-10.597,-2.042,0)\n", " (-10.697,-2.068,0)\n", " (-10.796,-2.095,0)\n", " (-11.096,-2.17,0)\n", " (-11.197,-2.195,0)\n", " (-11.297,-2.218,0)\n", " (-11.499,-2.264,0)\n", " (-11.6,-2.286,0)\n", " (-11.701,-2.307,0)\n", " (-11.803,-2.328,0)\n", " (-11.905,-2.348,0)\n", " (-12.007,-2.367,0)\n", " (-12.109,-2.385,0)\n", " (-12.212,-2.403,0)\n", " (-12.315,-2.42,0)\n", " (-12.418,-2.436,0)\n", " (-12.521,-2.451,0)\n", " (-12.729,-2.479,0)\n", " (-12.833,-2.491,0)\n", " (-12.938,-2.503,0)\n", " (-13.148,-2.523,0)\n", " (-13.253,-2.531,0)\n", " (-13.359,-2.539,0)\n", " (-13.465,-2.545,0)\n", " (-13.571,-2.55,0)\n", " (-13.678,-2.555,0)\n", " (-13.785,-2.558,0)\n", " (-13.892,-2.559,0)\n", " (-14,-2.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,-1.32149,0)\n", " height 0, axis (0,0,1), 174 vertices:\n", " (14,-2.56,0)\n", " (13.892,-2.559,0)\n", " (13.785,-2.558,0)\n", " (13.678,-2.555,0)\n", " (13.571,-2.55,0)\n", " (13.465,-2.545,0)\n", " (13.359,-2.539,0)\n", " (13.253,-2.531,0)\n", " (13.148,-2.523,0)\n", " (12.938,-2.503,0)\n", " (12.833,-2.491,0)\n", " (12.729,-2.479,0)\n", " (12.521,-2.451,0)\n", " (12.418,-2.436,0)\n", " (12.315,-2.42,0)\n", " (12.212,-2.403,0)\n", " (12.109,-2.385,0)\n", " (12.007,-2.367,0)\n", " (11.905,-2.348,0)\n", " (11.803,-2.328,0)\n", " (11.701,-2.307,0)\n", " (11.6,-2.286,0)\n", " (11.499,-2.264,0)\n", " (11.297,-2.218,0)\n", " (11.197,-2.195,0)\n", " (11.096,-2.17,0)\n", " (10.796,-2.095,0)\n", " (10.697,-2.068,0)\n", " (10.597,-2.042,0)\n", " (10.498,-2.015,0)\n", " (10.399,-1.987,0)\n", " (10.3,-1.96,0)\n", " (10.201,-1.932,0)\n", " (10.102,-1.903,0)\n", " (10.004,-1.875,0)\n", " (9.905,-1.846,0)\n", " (9.807,-1.817,0)\n", " (9.709,-1.787,0)\n", " (9.611,-1.758,0)\n", " (9.513,-1.728,0)\n", " (9.415,-1.699,0)\n", " (9.219,-1.639,0)\n", " (9.122,-1.609,0)\n", " (8.926,-1.549,0)\n", " (8.732,-1.489,0)\n", " (8.634,-1.459,0)\n", " (8.537,-1.429,0)\n", " (8.44,-1.4,0)\n", " (8.342,-1.37,0)\n", " (8.051,-1.283,0)\n", " (7.953,-1.254,0)\n", " (7.856,-1.225,0)\n", " (7.662,-1.169,0)\n", " (7.564,-1.142,0)\n", " (7.467,-1.114,0)\n", " (7.37,-1.087,0)\n", " (7.272,-1.061,0)\n", " (7.078,-1.009,0)\n", " (6.98,-0.984,0)\n", " (6.883,-0.96,0)\n", " (6.785,-0.935,0)\n", " (6.687,-0.912,0)\n", " (6.59,-0.889,0)\n", " (6.492,-0.866,0)\n", " (6.394,-0.844,0)\n", " (6.296,-0.823,0)\n", " (6.198,-0.803,0)\n", " (6.099,-0.783,0)\n", " (6.001,-0.764,0)\n", " (5.902,-0.745,0)\n", " (5.804,-0.727,0)\n", " (5.705,-0.71,0)\n", " (5.606,-0.694,0)\n", " (5.408,-0.664,0)\n", " (5.309,-0.65,0)\n", " (5.209,-0.638,0)\n", " (5.11,-0.626,0)\n", " (5.01,-0.615,0)\n", " (4.81,-0.595,0)\n", " (4.709,-0.587,0)\n", " (4.608,-0.58,0)\n", " (4.508,-0.574,0)\n", " (4.407,-0.569,0)\n", " (4.305,-0.565,0)\n", " (4.204,-0.562,0)\n", " (4,-0.56,0)\n", " (4,-0.06,0)\n", " (4.108,-0.061,0)\n", " (4.215,-0.062,0)\n", " (4.322,-0.065,0)\n", " (4.429,-0.07,0)\n", " (4.535,-0.075,0)\n", " (4.641,-0.081,0)\n", " (4.747,-0.089,0)\n", " (4.852,-0.097,0)\n", " (5.062,-0.117,0)\n", " (5.167,-0.129,0)\n", " (5.271,-0.141,0)\n", " (5.479,-0.169,0)\n", " (5.582,-0.184,0)\n", " (5.685,-0.2,0)\n", " (5.788,-0.217,0)\n", " (5.891,-0.235,0)\n", " (5.993,-0.253,0)\n", " (6.095,-0.272,0)\n", " (6.197,-0.292,0)\n", " (6.299,-0.313,0)\n", " (6.4,-0.334,0)\n", " (6.501,-0.356,0)\n", " (6.703,-0.402,0)\n", " (6.803,-0.425,0)\n", " (6.904,-0.45,0)\n", " (7.204,-0.525,0)\n", " (7.303,-0.552,0)\n", " (7.403,-0.578,0)\n", " (7.502,-0.605,0)\n", " (7.601,-0.633,0)\n", " (7.7,-0.66,0)\n", " (7.799,-0.688,0)\n", " (7.898,-0.717,0)\n", " (7.996,-0.745,0)\n", " (8.095,-0.774,0)\n", " (8.193,-0.803,0)\n", " (8.291,-0.833,0)\n", " (8.389,-0.862,0)\n", " (8.487,-0.892,0)\n", " (8.585,-0.921,0)\n", " (8.781,-0.981,0)\n", " (8.878,-1.011,0)\n", " (9.074,-1.071,0)\n", " (9.268,-1.131,0)\n", " (9.366,-1.161,0)\n", " (9.463,-1.191,0)\n", " (9.56,-1.22,0)\n", " (9.658,-1.25,0)\n", " (9.949,-1.337,0)\n", " (10.047,-1.366,0)\n", " (10.144,-1.395,0)\n", " (10.338,-1.451,0)\n", " (10.436,-1.478,0)\n", " (10.533,-1.506,0)\n", " (10.63,-1.533,0)\n", " (10.728,-1.559,0)\n", " (10.922,-1.611,0)\n", " (11.02,-1.636,0)\n", " (11.117,-1.66,0)\n", " (11.215,-1.685,0)\n", " (11.313,-1.708,0)\n", " (11.41,-1.731,0)\n", " (11.508,-1.754,0)\n", " (11.606,-1.776,0)\n", " (11.704,-1.797,0)\n", " (11.802,-1.817,0)\n", " (11.901,-1.837,0)\n", " (11.999,-1.856,0)\n", " (12.098,-1.875,0)\n", " (12.196,-1.893,0)\n", " (12.295,-1.91,0)\n", " (12.394,-1.926,0)\n", " (12.592,-1.956,0)\n", " (12.691,-1.97,0)\n", " (12.791,-1.982,0)\n", " (12.89,-1.994,0)\n", " (12.99,-2.005,0)\n", " (13.19,-2.025,0)\n", " (13.291,-2.033,0)\n", " (13.392,-2.04,0)\n", " (13.492,-2.046,0)\n", " (13.593,-2.051,0)\n", " (13.695,-2.055,0)\n", " (13.796,-2.058,0)\n", " (14,-2.06,0)\n", " (17.2,-2.06,0)\n", " (17.2,-2.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,-0.31,0)\n", " height 0, axis (0,0,1), 4 vertices:\n", " (-4,-0.56,0)\n", " (-4,-0.06,0)\n", " (4,-0.06,0)\n", " (4,-0.56,0)\n", " dielectric constant epsilon diagonal = (12,12,12)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "subpixel-averaging is 11.5011% done, 30.8183 s remaining\n", "subpixel-averaging is 20.4466% done, 15.7497 s remaining\n", "subpixel-averaging is 29.6051% done, 9.5833 s remaining\n", "subpixel-averaging is 61.1273% done, 2.54648 s remaining\n", "subpixel-averaging is 71.3508% done, 1.63014 s remaining\n", "subpixel-averaging is 80.2963% done, 0.999069 s remaining\n", "subpixel-averaging is 89.2418% done, 0.489546 s remaining\n", "subpixel-averaging is 11.5011% done, 30.8424 s remaining\n", "subpixel-averaging is 20.4466% done, 15.9006 s remaining\n", "subpixel-averaging is 29.3921% done, 9.75747 s remaining\n", "subpixel-averaging is 57.9325% done, 2.90513 s remaining\n", "subpixel-averaging is 71.1378% done, 1.65849 s remaining\n", "subpixel-averaging is 80.0833% done, 1.01553 s remaining\n", "subpixel-averaging is 88.8158% done, 0.504138 s remaining\n", "subpixel-averaging is 11.5011% done, 30.9702 s remaining\n", "subpixel-averaging is 20.4466% done, 15.7172 s remaining\n", "subpixel-averaging is 29.3921% done, 9.76 s remaining\n", "subpixel-averaging is 57.9325% done, 2.90604 s remaining\n", "subpixel-averaging is 71.1378% done, 1.66284 s remaining\n", "subpixel-averaging is 80.0833% done, 1.01205 s remaining\n", "subpixel-averaging is 89.0288% done, 0.504221 s remaining\n", "time for set_epsilon = 102.322 s\n", "-----------\n", "MPB solved for omega_1(2.2349,0,0) = 0.686107 after 12 iters\n", "MPB solved for omega_1(2.08827,0,0) = 0.645201 after 7 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 4 iters\n", "MPB solved for omega_1(2.08812,0,0) = 0.645161 after 1 iters\n", "Meep progress: 5.41/400.0 = 1.4% done in 4.0s, 292.0s to go\n", "on time step 541 (time=5.41), 0.00739947 s/step\n", "Meep progress: 10.450000000000001/400.0 = 2.6% done in 8.0s, 298.4s to go\n", "on time step 1045 (time=10.45), 0.0079413 s/step\n", "Meep progress: 15.11/400.0 = 3.8% done in 12.0s, 305.9s to go\n", "on time step 1511 (time=15.11), 0.0085874 s/step\n", "Meep progress: 19.61/400.0 = 4.9% done in 16.0s, 310.6s to go\n", "on time step 1961 (time=19.61), 0.00890556 s/step\n", "Meep progress: 23.82/400.0 = 6.0% done in 20.0s, 316.2s to go\n", "on time step 2382 (time=23.82), 0.00951234 s/step\n", "Meep progress: 29.55/400.0 = 7.4% done in 24.0s, 301.1s to go\n", "on time step 2955 (time=29.55), 0.00698203 s/step\n", "Meep progress: 35.21/400.0 = 8.8% done in 28.0s, 290.4s to go\n", "on time step 3521 (time=35.21), 0.0070767 s/step\n", "Meep progress: 40.86/400.0 = 10.2% done in 32.0s, 281.5s to go\n", "on time step 4086 (time=40.86), 0.00709053 s/step\n", "Meep progress: 46.53/400.0 = 11.6% done in 36.0s, 273.8s to go\n", "on time step 4653 (time=46.53), 0.00706554 s/step\n", "Meep progress: 52.230000000000004/400.0 = 13.1% done in 40.0s, 266.6s to go\n", "on time step 5223 (time=52.23), 0.00702252 s/step\n", "Meep progress: 57.980000000000004/400.0 = 14.5% done in 44.0s, 259.8s to go\n", "on time step 5798 (time=57.98), 0.00695708 s/step\n", "Meep progress: 63.74/400.0 = 15.9% done in 48.0s, 253.5s to go\n", "on time step 6374 (time=63.74), 0.00695095 s/step\n", "Meep progress: 69.53/400.0 = 17.4% done in 52.0s, 247.4s to go\n", "on time step 6953 (time=69.53), 0.00690956 s/step\n", "Meep progress: 75.17/400.0 = 18.8% done in 56.0s, 242.2s to go\n", "on time step 7517 (time=75.17), 0.00709654 s/step\n", "Meep progress: 80.95/400.0 = 20.2% done in 60.0s, 236.7s to go\n", "on time step 8095 (time=80.95), 0.00692188 s/step\n", "Meep progress: 86.69/400.0 = 21.7% done in 64.1s, 231.5s to go\n", "on time step 8669 (time=86.69), 0.00697913 s/step\n", "Meep progress: 92.39/400.0 = 23.1% done in 68.1s, 226.6s to go\n", "on time step 9239 (time=92.39), 0.00702692 s/step\n", "Meep progress: 98.15/400.0 = 24.5% done in 72.1s, 221.6s to go\n", "on time step 9815 (time=98.15), 0.00695458 s/step\n", "Meep progress: 104.01/400.0 = 26.0% done in 76.1s, 216.5s to go\n", "on time step 10401 (time=104.01), 0.00682702 s/step\n", "Meep progress: 109.67/400.0 = 27.4% done in 80.1s, 212.0s to go\n", "on time step 10967 (time=109.67), 0.00707033 s/step\n", "Meep progress: 115.27/400.0 = 28.8% done in 84.1s, 207.7s to go\n", "on time step 11527 (time=115.27), 0.0071473 s/step\n", "Meep progress: 120.97/400.0 = 30.2% done in 88.1s, 203.2s to go\n", "on time step 12097 (time=120.97), 0.00702199 s/step\n", "Meep progress: 126.67/400.0 = 31.7% done in 92.1s, 198.7s to go\n", "on time step 12667 (time=126.67), 0.00702586 s/step\n", "Meep progress: 132.41/400.0 = 33.1% done in 96.1s, 194.2s to go\n", "on time step 13241 (time=132.41), 0.00697363 s/step\n", "Meep progress: 138.11/400.0 = 34.5% done in 100.1s, 189.8s to go\n", "on time step 13811 (time=138.11), 0.0070254 s/step\n", "Meep progress: 143.69/400.0 = 35.9% done in 104.1s, 185.7s to go\n", "on time step 14369 (time=143.69), 0.0071739 s/step\n", "Meep progress: 149.27/400.0 = 37.3% done in 108.1s, 181.6s to go\n", "on time step 14927 (time=149.27), 0.00717841 s/step\n", "Meep progress: 154.48/400.0 = 38.6% done in 112.1s, 178.2s to go\n", "on time step 15448 (time=154.48), 0.0076782 s/step\n", "Meep progress: 160.01/400.0 = 40.0% done in 116.1s, 174.1s to go\n", "on time step 16001 (time=160.01), 0.00724479 s/step\n", "Meep progress: 165.47/400.0 = 41.4% done in 120.1s, 170.2s to go\n", "on time step 16547 (time=165.47), 0.00733126 s/step\n", "Meep progress: 171.12/400.0 = 42.8% done in 124.1s, 166.0s to go\n", "on time step 17112 (time=171.12), 0.00708435 s/step\n", "Meep progress: 176.81/400.0 = 44.2% done in 128.1s, 161.7s to go\n", "on time step 17681 (time=176.81), 0.00703244 s/step\n", "Meep progress: 182.48/400.0 = 45.6% done in 132.1s, 157.5s to go\n", "on time step 18248 (time=182.48), 0.0070563 s/step\n", "Meep progress: 188.13/400.0 = 47.0% done in 136.1s, 153.3s to go\n", "on time step 18813 (time=188.13), 0.00708801 s/step\n", "Meep progress: 193.89000000000001/400.0 = 48.5% done in 140.1s, 149.0s to go\n", "on time step 19389 (time=193.89), 0.00695529 s/step\n", "Meep progress: 199.69/400.0 = 49.9% done in 144.1s, 144.6s to go\n", "on time step 19969 (time=199.69), 0.00690079 s/step\n", "Meep progress: 205.42000000000002/400.0 = 51.4% done in 148.1s, 140.3s to go\n", "on time step 20542 (time=205.42), 0.00698182 s/step\n", "Meep progress: 211.15/400.0 = 52.8% done in 152.1s, 136.1s to go\n", "on time step 21115 (time=211.15), 0.00698866 s/step\n", "Meep progress: 216.79/400.0 = 54.2% done in 156.1s, 131.9s to go\n", "on time step 21679 (time=216.79), 0.00709759 s/step\n", "Meep progress: 222.52/400.0 = 55.6% done in 160.1s, 127.7s to go\n", "on time step 22252 (time=222.52), 0.00699233 s/step\n", "Meep progress: 228.21/400.0 = 57.1% done in 164.1s, 123.6s to go\n", "on time step 22821 (time=228.21), 0.00703425 s/step\n", "Meep progress: 233.92000000000002/400.0 = 58.5% done in 168.1s, 119.4s to go\n", "on time step 23392 (time=233.92), 0.00701093 s/step\n", "Meep progress: 239.55/400.0 = 59.9% done in 172.2s, 115.3s to go\n", "on time step 23955 (time=239.55), 0.00711595 s/step\n", "Meep progress: 245.11/400.0 = 61.3% done in 176.2s, 111.3s to go\n", "on time step 24511 (time=245.11), 0.00720156 s/step\n", "Meep progress: 250.84/400.0 = 62.7% done in 180.2s, 107.1s to go\n", "on time step 25084 (time=250.84), 0.00699008 s/step\n", "Meep progress: 256.51/400.0 = 64.1% done in 184.2s, 103.0s to go\n", "on time step 25651 (time=256.51), 0.00706033 s/step\n", "Meep progress: 262.24/400.0 = 65.6% done in 188.2s, 98.8s to go\n", "on time step 26224 (time=262.24), 0.00699052 s/step\n", "Meep progress: 267.97/400.0 = 67.0% done in 192.2s, 94.7s to go\n", "on time step 26797 (time=267.97), 0.00699247 s/step\n", "Meep progress: 273.61/400.0 = 68.4% done in 196.2s, 90.6s to go\n", "on time step 27361 (time=273.61), 0.00710412 s/step\n", "Meep progress: 279.32/400.0 = 69.8% done in 200.2s, 86.5s to go\n", "on time step 27932 (time=279.32), 0.00700535 s/step\n", "Meep progress: 285.02/400.0 = 71.3% done in 204.2s, 82.4s to go\n", "on time step 28502 (time=285.02), 0.00702477 s/step\n", "Meep progress: 290.72/400.0 = 72.7% done in 208.2s, 78.3s to go\n", "on time step 29072 (time=290.72), 0.0070208 s/step\n", "Meep progress: 296.42/400.0 = 74.1% done in 212.2s, 74.1s to go\n", "on time step 29642 (time=296.42), 0.00702695 s/step\n", "Meep progress: 302.17/400.0 = 75.5% done in 216.2s, 70.0s to go\n", "on time step 30217 (time=302.17), 0.00696356 s/step\n", "Meep progress: 307.86/400.0 = 77.0% done in 220.2s, 65.9s to go\n", "on time step 30786 (time=307.86), 0.00704203 s/step\n", "Meep progress: 313.53000000000003/400.0 = 78.4% done in 224.2s, 61.8s to go\n", "on time step 31353 (time=313.53), 0.00706128 s/step\n", "Meep progress: 319.3/400.0 = 79.8% done in 228.2s, 57.7s to go\n", "on time step 31930 (time=319.3), 0.00694399 s/step\n", "Meep progress: 325.0/400.0 = 81.2% done in 232.2s, 53.6s to go\n", "on time step 32500 (time=325), 0.00702027 s/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Meep progress: 330.76/400.0 = 82.7% done in 236.2s, 49.4s to go\n", "on time step 33076 (time=330.76), 0.00694491 s/step\n", "Meep progress: 336.46/400.0 = 84.1% done in 240.2s, 45.4s to go\n", "on time step 33646 (time=336.46), 0.00702158 s/step\n", "Meep progress: 342.17/400.0 = 85.5% done in 244.2s, 41.3s to go\n", "on time step 34217 (time=342.17), 0.0070114 s/step\n", "Meep progress: 347.92/400.0 = 87.0% done in 248.2s, 37.2s to go\n", "on time step 34792 (time=347.92), 0.00696299 s/step\n", "Meep progress: 353.64/400.0 = 88.4% done in 252.2s, 33.1s to go\n", "on time step 35364 (time=353.64), 0.00699623 s/step\n", "Meep progress: 359.28000000000003/400.0 = 89.8% done in 256.2s, 29.0s to go\n", "on time step 35928 (time=359.28), 0.00710347 s/step\n", "Meep progress: 365.03000000000003/400.0 = 91.3% done in 260.2s, 24.9s to go\n", "on time step 36503 (time=365.03), 0.00696683 s/step\n", "Meep progress: 370.73/400.0 = 92.7% done in 264.2s, 20.9s to go\n", "on time step 37073 (time=370.73), 0.00701992 s/step\n", "Meep progress: 376.49/400.0 = 94.1% done in 268.2s, 16.8s to go\n", "on time step 37649 (time=376.49), 0.0069514 s/step\n", "Meep progress: 382.23/400.0 = 95.6% done in 272.2s, 12.7s to go\n", "on time step 38223 (time=382.23), 0.00697308 s/step\n", "Meep progress: 387.86/400.0 = 97.0% done in 276.2s, 8.6s to go\n", "on time step 38786 (time=387.86), 0.00710584 s/step\n", "Meep progress: 393.52/400.0 = 98.4% done in 280.3s, 4.6s to go\n", "on time step 39352 (time=393.52), 0.00706898 s/step\n", "Meep progress: 399.28000000000003/400.0 = 99.8% done in 284.3s, 0.5s to go\n", "on time step 39928 (time=399.28), 0.00694658 s/step\n", "run 0 finished at t = 400.0 (40000 timesteps)\n" ] } ], "source": [ "sim.reset_meep()\n", "\n", "sources = [mp.EigenModeSource(src=mp.ContinuousSource(fcen,fwidth=df),\n", " size=src_vol.size,\n", " center=src_vol.center,\n", " eig_band=1,\n", " eig_parity=mp.EVEN_Y+mp.ODD_Z,\n", " eig_match_freq=True)]\n", "\n", "sim = mp.Simulation(resolution=res,\n", " cell_size=cell.size,\n", " boundary_layers=[mp.PML(dpml)],\n", " sources=sources,\n", " geometry=geometry)\n", "\n", "sim.run(until=400) # arbitrary long run time to ensure that fields have reached steady state" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "eps_data = sim.get_epsilon()\n", "ez_data = numpy.real(sim.get_efield_z())\n", "\n", "plt.figure(dpi=200)\n", "plt.imshow(numpy.transpose(eps_data), interpolation='spline36', cmap='binary')\n", "plt.imshow(numpy.flipud(numpy.transpose(ez_data)), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", "plt.axis('off')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }