{
 "cells": [
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   "cell_type": "code",
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   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category =RuntimeWarning)\n",
    "import numpy as np\n",
    "import makeqx as mkq\n",
    "import de2 as de\n",
    "import multiprocessing as mp\n",
    "    \n",
    "# def arc_0(qx, lam_min=400, lam_max=800, npoints=50):\n",
    "#     cthick = qx[0]\n",
    "#     q_x = qx[1:]\n",
    "#     Rs0, Rp0 = mkq.tmm_lam_parallel(q_x, cthick, 0, n_par=12, lam_low=lam_min, lam_high=lam_max, lam_pts=npoints, n_subs=1.52)\n",
    "#     return np.mean(Rs0)\n",
    "    \n",
    "# def arc_dig_0(qx, lam_min=400, lam_max=800, npoints=50):\n",
    "#     cthick = qx[0]\n",
    "#     q_x = mkq.digitize_qx(qx[1:], dlevels=2)\n",
    "#     Rs0, Rp0 = mkq.tmm_lam_parallel(q_x, cthick, 0, n_par=12, lam_low=lam_min, lam_high=lam_max, lam_pts=npoints, n_subs=1.52)\n",
    "#     return np.mean(Rs0)\n",
    "\n",
    "\n",
    "def tmm_wrapper2(arg):\n",
    "    args, kwargs = arg\n",
    "    return mkq.tmm_eval_wsweep(*args, **kwargs)\n",
    "\n",
    "\n",
    "def arc_par(pop, **kwargs):\n",
    "    jobs = []\n",
    "    pool=mp.Pool(60)\n",
    "    for indiv in pop:\n",
    "        indivi = mkq.digitize_qx(indiv[1:], dlevels=2)\n",
    "        jobs.append((indivi, indiv[0], 0))\n",
    "    arg = [(j, kwargs) for j in jobs]\n",
    "    answ = np.array(pool.map(tmm_wrapper2, arg))\n",
    "    pool.close()\n",
    "    return np.mean(answ, axis=1)\n",
    "\n",
    "    \n",
    "num_layers = 35 \n",
    "bnds = [(0, 1)]*num_layers\n",
    "bnds = [(300, 400)] + bnds\n",
    "its_first = 0\n",
    "psnew = 40\n",
    "its_second = 500\n",
    "\n",
    "pop = np.ones((psnew, 1+ num_layers))\n",
    "for indiv in range(psnew):\n",
    "    pop[indiv,1:] = mkq.make_qx(num_layers)\n",
    "    pop[indiv,0] = np.random.uniform(    bnds[0][0], bnds[0][1], 1 )\n",
    "    \n",
    "b, c, hstry = de.de_cp(fobj=arc_par, bounds=bnds, pop=pop, history=[], it_start=its_first, popsize=psnew, its=its_second, lam_low=400, lam_high=800, lam_pts=50)\n",
    "\n",
    "\n",
    "#m1 = mkq.tmm_eval2(qx=pop[0][1:], cthick=pop[0][0]) \n",
    "#m2 = mkq.tmm_eval(qx=pop[0][1:], cthick=pop[0][0]) \n",
    "\n",
    "#arc_par(pop, lam_low=400, lam_high=800, lam_pts=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-25T10:33:20.269270Z",
     "start_time": "2018-11-25T10:32:36.651677Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category =RuntimeWarning)\n",
    "import numpy as np\n",
    "import makeqx as mkq\n",
    "import de2 as de\n",
    "import multiprocessing as mp\n",
    "import qxnew as qn    \n",
    "# def arc_0(qx, lam_min=400, lam_max=800, npoints=50):\n",
    "#     cthick = qx[0]\n",
    "#     q_x = qx[1:]\n",
    "#     Rs0, Rp0 = mkq.tmm_lam_parallel(q_x, cthick, 0, n_par=12, lam_low=lam_min, lam_high=lam_max, lam_pts=npoints, n_subs=1.52)\n",
    "#     return np.mean(Rs0)\n",
    "    \n",
    "# def arc_dig_0(qx, lam_min=400, lam_max=800, npoints=50):\n",
    "#     cthick = qx[0]\n",
    "#     q_x = mkq.digitize_qx(qx[1:], dlevels=2)\n",
    "#     Rs0, Rp0 = mkq.tmm_lam_parallel(q_x, cthick, 0, n_par=12, lam_low=lam_min, lam_high=lam_max, lam_pts=npoints, n_subs=1.52)\n",
    "#     return np.mean(Rs0)\n",
    "\n",
    "\n",
    "def tmm_wrapper2(arg):\n",
    "    args, kwargs = arg\n",
    "    return qn.tmm_eval_wsweep(*args, **kwargs)\n",
    "\n",
    "\n",
    "def arc_par(pop, **kwargs):\n",
    "    jobs = []\n",
    "    pool=mp.Pool(90)\n",
    "    for indiv in pop:\n",
    "        indiv = indiv.reshape((int(indiv.size/2), 2))\n",
    "        indiv[:,1] = mkq.digitize_qx(indiv[:,1], dlevels=2)\n",
    "        indiv = indiv.flatten()\n",
    "        jobs.append((indiv, 0.5, 8 , 0))\n",
    "    arg = [(j, kwargs) for j in jobs]\n",
    "    answ = np.array(pool.map(tmm_wrapper2, arg))\n",
    "    pool.close()\n",
    "    return np.mean(answ, axis=1)\n",
    "\n",
    "    \n",
    "num_layers = 30\n",
    "d_min = 0.5\n",
    "d_max = 8\n",
    "\n",
    "bnds = [(d_min, d_max), (0,1) ]*num_layers\n",
    "#bnds = [(300, 400)] + bnds\n",
    "its_first = 0\n",
    "psnew = 160\n",
    "its_second = 500\n",
    "\n",
    "pop = np.ones((psnew, 2*num_layers))\n",
    "for indiv in range(psnew):\n",
    "    pop[indiv] = qn.make_qxn(num_layers, dlevels=2)\n",
    "    #pop[indiv][::2] = (d_max - d_min)*pop[indiv][::2] + d_min\n",
    "    pop[indiv,0] = np.random.uniform(    bnds[0][0], bnds[0][1], 1 )\n",
    "    \n",
    "b, c, hstry = de.de_cp(fobj=arc_par, bounds=bnds, pop=pop, history=[], it_start=its_first, popsize=psnew, its=its_second, lam_low=400, lam_high=800, lam_pts=50)\n",
    "\n",
    "np.sum(b[::2]), b[::2], b[1::2], c\n",
    "\n",
    "#m1 = mkq.tmm_eval2(qx=pop[0][1:], cthick=pop[0][0]) \n",
    "#m2 = mkq.tmm_eval(qx=pop[0][1:], cthick=pop[0][0]) \n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-27T02:07:24.693765Z",
     "start_time": "2018-11-26T12:51:52.517421Z"
    }
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category =RuntimeWarning)\n",
    "import numpy as np\n",
    "import de2 as de\n",
    "import multiprocessing as mp\n",
    "import makeqx as mkq\n",
    "import qxnew as qn    \n",
    "\n",
    "def tmm_wrapper2(arg):\n",
    "    args, kwargs = arg\n",
    "    return qn.tmm_eval_wsweep(*args, **kwargs)\n",
    "\n",
    "def arc_par(pop, **kwargs):\n",
    "    jobs = []\n",
    "    pool=mp.Pool(90)\n",
    "    for indiv in pop:\n",
    "        indiv = indiv.reshape((int(indiv.size/2), 2))\n",
    "        indiv[:,1] = mkq.digitize_qx(indiv[:,1], dlevels=2)\n",
    "        indiv = indiv.flatten()\n",
    "        jobs.append((indiv, 0, 1, 0))\n",
    "    arg = [(j, kwargs) for j in jobs]\n",
    "    answ = np.array(pool.map(tmm_wrapper2, arg))\n",
    "    pool.close()\n",
    "    return np.mean(answ, axis=1)\n",
    "\n",
    "    \n",
    "num_layers = 50\n",
    "d_min = 0.5\n",
    "d_max = 15\n",
    "\n",
    "bnds = [(d_min, d_max), (0,1)]*num_layers\n",
    "#bnds = [(300, 400)] + bnds\n",
    "its_first = 0\n",
    "psnew = 40\n",
    "islands = 10 \n",
    "its_second = 500\n",
    "numgens = 10 \n",
    "\n",
    "isl = np.ones((psnew*islands, 2*num_layers))\n",
    "for indiv in range(psnew*islands):\n",
    "    isl[indiv] = qn.make_qxn(num_layers, dlevels=2)\n",
    "poplist = np.split(isl, islands)\n",
    "\n",
    "bids = np.zeros(islands, dtype=int)\n",
    "bfits = np.zeros(islands)\n",
    "bests = isl[0:islands]\n",
    "\n",
    "\n",
    "for gen in range(numgens):\n",
    "    print(\"==============================\")\n",
    "    print(\"Epoch #:\" + str(gen + 1))\n",
    "    print(\"==============================\")\n",
    "    for isln in range(islands):\n",
    "        print(\"----------------- I-\" + str(isln + 1)+ \"---------------------\")\n",
    "        poplist[isln], bids[isln], bfits[isln], bests[isln] = de.de_cp(\n",
    "            fobj=arc_par, \n",
    "            bounds=bnds, \n",
    "            pop=poplist[isln], \n",
    "            history=[], \n",
    "            it_start=its_first, \n",
    "            popsize=psnew, \n",
    "            its=its_second, \n",
    "            lam_low=400, \n",
    "            lam_high=800, \n",
    "            lam_pts=50)\n",
    "        print(np.sum(bests[isln][::2]))\n",
    "    if gen < (numgens - 1):\n",
    "        print(\"Round robin best migration\")\n",
    "        stmp = poplist[islands-1][bids[islands-1]] \n",
    "        for isln in range(1, islands):\n",
    "            poplist[isln][bids[isln]] = poplist[isln-1][bids[isln-1]]\n",
    "        poplist[0][bids[0]] = stmp \n",
    "\n",
    "print(bfits) \n",
    "print(bests[np.argmin(bfits)][::2])\n",
    "#np.sum(b[::2]), b[::2], b[1::2], c\n",
    "\n",
    "#m1 = mkq.tmm_eval2(qx=pop[0][1:], cthick=pop[0][0]) \n",
    "#m2 = mkq.tmm_eval(qx=pop[0][1:], cthick=pop[0][0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-27T04:39:19.806762Z",
     "start_time": "2018-11-27T04:39:19.740844Z"
    }
   },
   "outputs": [],
   "source": [
    "print(np.sum(bests[np.argmin(bfits)][::2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-25T08:46:16.654004Z",
     "start_time": "2018-11-25T08:46:15.567261Z"
    }
   },
   "outputs": [],
   "source": [
    "isl = np.split(pop,8)\n",
    "isl[0].shape\n",
    "arc_par(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-26T10:52:44.968511Z",
     "start_time": "2018-11-26T10:52:44.892890Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import qxnew as qn\n",
    "import makeqx as mkq\n",
    "\n",
    "qn.make_qxn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-26T10:52:28.147240Z",
     "start_time": "2018-11-26T10:52:28.076907Z"
    }
   },
   "outputs": [],
   "source": [
    "qx = qn.make_qxn(40,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T09:33:50.326789Z",
     "start_time": "2018-11-24T09:33:50.307053Z"
    }
   },
   "outputs": [],
   "source": [
    "qx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T07:07:44.202457Z",
     "start_time": "2018-11-24T07:07:44.180095Z"
    }
   },
   "outputs": [],
   "source": [
    "qn.make_nxdx(qx, d_min=2, d_max=10, wavelen=550, n_substrate=1.52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T07:07:45.345103Z",
     "start_time": "2018-11-24T07:07:45.322470Z"
    }
   },
   "outputs": [],
   "source": [
    "qn.tmm_eval_wbk(qx, 2, 10, 0, 550, 1.52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T07:07:47.293093Z",
     "start_time": "2018-11-24T07:07:47.155314Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from jupyterthemes import jtplot\n",
    "# currently installed theme will be used to\n",
    "# set plot style if no arguments provided\n",
    "jtplot.style()\n",
    "Rs = qn.tmm_eval_wsweep(qx, d_min=2 , d_max=10, inc_ang=0, lam_low=400, lam_high=800, lam_pts=50, n_subs=1.52)  # in nm\n",
    "lams = np.linspace(400, 800, endpoint=True, num=50)\n",
    "plt.plot(lams, Rs )\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.ylabel('Reflectance (%)')\n",
    "plt.ylim([0,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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