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     "end_time": "2018-12-21T07:58:17.675598Z",
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   "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 oldqx as oqx\n",
    "\n",
    "\n",
    "def rndtop5(x):\n",
    "    return np.round(x*2.0)/2\n",
    "\n",
    "def tmm_wrapper2(arg):\n",
    "    args, kwargs = arg\n",
    "    return oqx.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 = rndtop5(indiv)\n",
    "        #indiv = indiv.flatten()\n",
    "        jobs.append((indiv, 0))\n",
    "    arg = [(j, kwargs) for j in jobs]\n",
    "    answ = np.array(pool.map(tmm_wrapper2, arg))\n",
    "    pool.close()\n",
    "    return answ\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
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    }
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   "outputs": [],
   "source": [
    "dataset_size = 100000\n",
    "\n",
    "# parameters of the dataset\n",
    "num_layers = 16\n",
    "num_lpoints = 256\n",
    "lam_min = 300\n",
    "lam_max = 1200\n",
    "d_min = 0\n",
    "d_max = 200\n",
    "\n",
    "dataset_X = np.random.uniform(0,1,num_layers*dataset_size).astype(float).reshape(dataset_size, num_layers)\n",
    "lams = np.linspace(lam_min, lam_max, num_lpoints, endpoint=True)\n",
    "dataset_Y = np.zeros((dataset_size,num_lpoints))\n",
    "\n",
    "dataset_Y = arc_par(dataset_X,  lam_low=lam_min, lam_high=lam_max, lam_pts = num_lpoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2018-12-21T07:58:56.988277Z",
     "start_time": "2018-12-21T07:58:56.966718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 256)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "h5f = h5py.File('./datasets/s16_siti.h5', 'w')\n",
    "h5f.create_dataset('sizes', data=dataset_X)\n",
    "h5f.create_dataset('spectrum', data=dataset_Y)\n",
    "h5f.close()"
   ]
  }
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