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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(12)\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 = 40\n",
    "bnds = [(0, 1)]*num_layers\n",
    "bnds = [(300, 400)] + bnds\n",
    "its_first = 0\n",
    "psnew = 500\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] = 500 \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)"
   ]
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   "metadata": {
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     "start_time": "2018-11-04T07:55:55.500769Z"
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   },
   "outputs": [],
   "source": [
    "np.savetxt(X=b, fname='b.txt')"
   ]
  }
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