HajimeKawahara/exojax

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elower grid error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tqdm\n",
    "from exojax.spec.lbderror import single_tilde_line_strength_zeroth\n",
    "from exojax.spec.lbderror import worst_tilde_line_strength_first\n",
    "from exojax.spec.lbderror import worst_tilde_line_strength_second\n",
    "from exojax.spec.lbderror import evaluate_trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [02:50<00:00,  5.86it/s]\n"
     ]
    }
   ],
   "source": [
    "#N=10\n",
    "N = 1000\n",
    "Tarr = np.logspace(np.log10(100.), np.log10(5000.), N)\n",
    "Twtarr = np.logspace(np.log10(100.), np.log10(2000.), N) + 0.0001\n",
    "Tref = 400.\n",
    "dE_0th = 1500.\n",
    "dE_1st = dE_0th * 2\n",
    "dE_2nd = dE_0th * 3\n",
    "\n",
    "arr0 = []\n",
    "arr1 = []\n",
    "arr2 = []\n",
    "trange0 = []\n",
    "trange1 = []\n",
    "trange2 = []\n",
    "crit = 0.01\n",
    "\n",
    "#Twtarr = np.array([1000.0])\n",
    "for iT, Twt in enumerate(tqdm.tqdm(Twtarr)):\n",
    "    x = single_tilde_line_strength_zeroth(1./Tarr, 1.0/Twt, 1.0/Tref, dE_0th)\n",
    "    Tl, Tu = evaluate_trange(Tarr, x, crit, Twt)\n",
    "    trange0.append([Tl, Twt, Tu])\n",
    "    arr0.append(x)\n",
    "\n",
    "    x = worst_tilde_line_strength_first(Tarr, Twt, Tref, dE_1st)\n",
    "    Tl, Tu = evaluate_trange(Tarr, x, crit, Twt)\n",
    "    trange1.append([Tl, Twt, Tu])\n",
    "    arr1.append(x)\n",
    "\n",
    "    x = worst_tilde_line_strength_second(Tarr, Twt, Tref, dE_2nd)\n",
    "    Tl, Tu = evaluate_trange(Tarr, x, crit, Twt)\n",
    "    trange2.append([Tl, Twt, Tu])\n",
    "    arr2.append(x)\n",
    "    \n",
    "if True:\n",
    "    arr0 = np.array(arr0).reshape(N, N)\n",
    "    trange0 = np.array(trange0)\n",
    "    arr1 = np.array(arr1).reshape(N, N)\n",
    "    trange1 = np.array(trange1)\n",
    "    arr2 = np.array(arr2).reshape(N, N)\n",
    "    trange2 = np.array(trange2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14fa358b5310>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,5))\n",
    "ax = fig.add_subplot(131)\n",
    "co=plt.contourf(Tarr, Twtarr, arr0, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "\n",
    "plt.plot(trange0[:,0], trange0[:,1],color=\"black\",ls=\"dashed\")\n",
    "plt.plot(trange0[:,2], trange0[:,1],color=\"black\")\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "\n",
    "ax = fig.add_subplot(132)\n",
    "co=plt.contourf(Tarr, Twtarr, arr1, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "\n",
    "plt.plot(trange1[:,0], trange1[:,1],color=\"black\",ls=\"dashed\")\n",
    "plt.plot(trange1[:,2], trange1[:,1],color=\"black\")\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "\n",
    "ax = fig.add_subplot(133)\n",
    "#plt.imshow(arr,cmap=\"bwr\")\n",
    "co=plt.contourf(Tarr, Twtarr, arr2, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "plt.axvline(Tref,color=\"magenta\",lw=0.5, ls=\"dashed\")\n",
    "#plt.axhline(1000)\n",
    "plt.plot(trange2[:,0], trange2[:,1],color=\"black\",ls=\"dashed\")\n",
    "plt.plot(trange2[:,2], trange2[:,1],color=\"black\")\n",
    "\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "#plt.title(\"2nd, dE = \"+str(dE_2nd)+\"cm-1\")\n",
    "#plt.colorbar(co)\n",
    "#plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1992.1057806860729, 2007.76896591621)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tl, Tu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.imshow(arr,cmap=\"bwr\")\n",
    "co=plt.contourf(Tarr, Twtarr, arr2, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "plt.axvline(Tref,color=\"magenta\",lw=0.5, ls=\"dashed\")\n",
    "#plt.axhline(1000)\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "plt.title(\"2nd, dE = \"+str(dE_2nd)+\"cm-1\")\n",
    "plt.colorbar(co)\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.imshow(arr,cmap=\"bwr\")\n",
    "co=plt.contourf(Tarr, Twtarr, arr1, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "plt.axvline(Tref,color=\"magenta\",lw=0.5, ls=\"dashed\")\n",
    "#plt.axhline(1000)\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "plt.title(\"1st, dE = \"+str(dE_1st)+\"cm-1\")\n",
    "plt.colorbar(co)\n",
    "plt.ylabel(\"$T_{wt}$ (K)\")\n",
    "plt.xlabel(\"$T$ (K)\")\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "co=plt.contourf(Tarr, Twtarr, np.abs(arr0), levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "plt.axvline(Tref,color=\"magenta\",lw=0.5, ls=\"dashed\")\n",
    "#plt.axhline(1000)\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "plt.title(\"0th, dE = \"+str(dE_0th)+\"cm-1\")\n",
    "plt.colorbar(co)\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [01:44<00:00,  9.55it/s]\n"
     ]
    }
   ],
   "source": [
    "N=1000\n",
    "Twtarr = np.logspace(np.log10(500.1),np.log10(2000.1),N)\n",
    "Tarr = np.logspace(np.log10(100.), np.log10(4000.), N)\n",
    "\n",
    "Tref=500.\n",
    "dE_2nd=1500.\n",
    "\n",
    "arr2 = []\n",
    "for Twt in tqdm.tqdm(Twtarr):\n",
    "    x = worst_tilde_line_strength_second(Tarr, Twt, Tref, dE_2nd)\n",
    "    arr2.append(x)\n",
    "\n",
    "arr2 = np.array(arr2).reshape(N, N)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.imshow(arr,cmap=\"bwr\")\n",
    "co=plt.contourf(Tarr, Twtarr, arr2, levels=[0.0, 0.005,0.01,0.03,0.05],cmap=\"CMRmap_r\")\n",
    "co.clabel(fmt='%1.2f', fontsize=14)\n",
    "plt.axvline(Tref,color=\"magenta\",lw=0.5, ls=\"dashed\")\n",
    "#plt.axhline(1000)\n",
    "plt.plot(Twtarr,Twtarr,color=\"cyan\",lw=0.5, ls=\"dashed\")\n",
    "plt.title(\"2nd, dE = \"+str(dE_2nd)+\"cm-1\")\n",
    "plt.colorbar(co)\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.125"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "500/160."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.,  200.,  300.,  400.,  500.,  600.,  700.,  800.,  900.,\n",
       "       1000., 1100., 1200., 1300., 1400., 1500.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L=15\n",
    "dEarr = np.linspace(100,1500,L)\n",
    "dEarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 30\n",
      "1 / 30\n",
      "2 / 30\n",
      "3 / 30\n",
      "4 / 30\n",
      "5 / 30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:50, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4241/792253124.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworst_tilde_line_strength_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTwt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdE_1st\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0miTwt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0miTref\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworst_tilde_line_strength_second\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTwt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdE_2nd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m             \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0miTwt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0miTref\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/exojax/src/exojax/spec/lbderror.py\u001b[0m in \u001b[0;36mworst_tilde_line_strength_second\u001b[0;34m(T, Ttyp, Tref, dE)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0mff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0mparr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mjnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/api.py\u001b[0m in \u001b[0;36mvmap_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1581\u001b[0m                   _mapped_axis_size(in_tree, args_flat, in_axes_flat, \"vmap\",\n\u001b[1;32m   1582\u001b[0m                                     kws=True))\n\u001b[0;32m-> 1583\u001b[0;31m     out_flat = batching.batch(\n\u001b[0m\u001b[1;32m   1584\u001b[0m         \u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_size_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_axes_flat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1585\u001b[0m         \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mflatten_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vmap out_axes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_axes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/linear_util.py\u001b[0m in \u001b[0;36mcall_wrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m       \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m       \u001b[0;31m# Some transformations yield from inside context managers, so we have to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/exojax/src/exojax/spec/lbderror.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m    152\u001b[0m     \"\"\"\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m         return single_tilde_line_strength_second(1 / T, 1 / Ttyp, 1 / Tref, dE,\n\u001b[0m\u001b[1;32m    155\u001b[0m                                                 p)\n\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/exojax/src/exojax/spec/lbderror.py\u001b[0m in \u001b[0;36msingle_tilde_line_strength_second\u001b[0;34m(t, twp, tref, dE, p)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     w1 = weight_point1_dE(twp, tref, dE,\n\u001b[0;32m--> 113\u001b[0;31m                           p) + dfw1(twp, tref, dE, p) * (t - twp) + ddfw1(twp, tref, dE, p) * (t - twp)**2/2.0\n\u001b[0m\u001b[1;32m    114\u001b[0m     w2 = weight_point2_dE(twp, tref, dE,\n\u001b[1;32m    115\u001b[0m                           p) + dfw2(twp, tref, dE, p) * (t - twp) + ddfw2(twp, tref, dE, p) * (t - twp)**2/2.0\n",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/api.py\u001b[0m in \u001b[0;36mgrad_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1001\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mapi_boundary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1002\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mgrad_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue_and_grad_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1004\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/api.py\u001b[0m in \u001b[0;36mvalue_and_grad_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1077\u001b[0m       \u001b[0m_check_input_dtype_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mholomorphic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleaf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1078\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1079\u001b[0;31m       \u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvjp_py\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_vjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdyn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce_axes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1080\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m       ans, vjp_py, aux = _vjp(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/api.py\u001b[0m in \u001b[0;36m_vjp\u001b[0;34m(fun, has_aux, reduce_axes, *primals)\u001b[0m\n\u001b[1;32m   2495\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2496\u001b[0m     \u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten_fun_nokwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2497\u001b[0;31m     out_primal, out_vjp = ad.vjp(\n\u001b[0m\u001b[1;32m   2498\u001b[0m         flat_fun, primals_flat, reduce_axes=reduce_axes)\n\u001b[1;32m   2499\u001b[0m     \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/ad.py\u001b[0m in \u001b[0;36mvjp\u001b[0;34m(traceable, primals, has_aux, reduce_axes)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     \u001b[0mout_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0mout_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/ad.py\u001b[0m in \u001b[0;36mlinearize\u001b[0;34m(traceable, *primals, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m   \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m   \u001b[0mjvpfun_flat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjvpfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m   \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_pvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_to_jaxpr_nounits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjvpfun_flat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_pvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m   \u001b[0mout_primals_pvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tangents_pvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_unflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_pvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m   \u001b[0;32massert\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_primal_pval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_known\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mout_primal_pval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mout_primals_pvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/profiler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mTraceAnnotation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdecorator_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/partial_eval.py\u001b[0m in \u001b[0;36mtrace_to_jaxpr_nounits\u001b[0;34m(fun, pvals, instantiate)\u001b[0m\n\u001b[1;32m    767\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_main\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJaxprTrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_stack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurrent_name_stack\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m     \u001b[0mfun\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrace_to_subjaxpr_nounits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstantiate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 769\u001b[0;31m     \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout_pvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    770\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    771\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/linear_util.py\u001b[0m in \u001b[0;36mcall_wrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m       \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m       \u001b[0;31m# Some transformations yield from inside context managers, so we have to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/exojax/src/exojax/spec/lbderror.py\u001b[0m in \u001b[0;36mweight_point1_dE\u001b[0;34m(t, tref, dE, p)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mweight\u001b[0m \u001b[0mat\u001b[0m \u001b[0mpoint\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \"\"\"\n\u001b[0;32m---> 42\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mweight_point2_dE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36m__rsub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    577\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_radd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m   \u001b[0;32mdef\u001b[0m \u001b[0m__rsub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rsub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    580\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__mul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__rmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py\u001b[0m in \u001b[0;36mdeferring_binary_op\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4656\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mswap\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4657\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_accepted_binop_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4658\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mbinary_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4659\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_rejected_binop_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4660\u001b[0m       raise TypeError(f\"unsupported operand type(s) for {opchar}: \"\n",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/api.py\u001b[0m in \u001b[0;36mcache_miss\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m       \u001b[0min_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_lambda_input_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs_flat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m       \u001b[0mflat_fun\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m     out_flat = xla.xla_call(\n\u001b[0m\u001b[1;32m    529\u001b[0m         \u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs_flat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflat_fun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1962\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1963\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall_bind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mget_bind_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mcall_bind\u001b[0;34m(primitive, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1977\u001b[0m   \u001b[0mtracers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_raise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m   \u001b[0mfun_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1979\u001b[0;31m   \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprimitive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtracers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1980\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_lower\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_todos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv_trace_todo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/ad.py\u001b[0m in \u001b[0;36mprocess_call\u001b[0;34m(self, call_primitive, f, tracers, params)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[0mupdate_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_param_updaters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_primitive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[0mnew_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich_nz\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mupdate_params\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m     result = call_primitive.bind(_update_annotation(f_jvp, f.in_type, which_nz),\n\u001b[0m\u001b[1;32m    340\u001b[0m                                  *args, **new_params)\n\u001b[1;32m    341\u001b[0m     \u001b[0mprimal_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtangent_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_unflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1962\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1963\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall_bind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mget_bind_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mcall_bind\u001b[0;34m(primitive, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1977\u001b[0m   \u001b[0mtracers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_raise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m   \u001b[0mfun_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1979\u001b[0;31m   \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprimitive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtracers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1980\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_lower\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_todos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv_trace_todo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/partial_eval.py\u001b[0m in \u001b[0;36mprocess_call\u001b[0;34m(self, primitive, f, tracers, params)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;31m# Run the call, getting known out vals and aux data used for staged-out call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     out = primitive.bind(_update_annotation_known(f_, f.in_type, in_knowns),\n\u001b[0m\u001b[1;32m    268\u001b[0m                          *in_consts, **const_params)\n\u001b[1;32m    269\u001b[0m     \u001b[0mfwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_knowns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maux\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1962\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1963\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall_bind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mget_bind_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mcall_bind\u001b[0;34m(primitive, fun, *args, **params)\u001b[0m\n\u001b[1;32m   1975\u001b[0m   fun_, env_trace_todo = process_env_traces_call(\n\u001b[1;32m   1976\u001b[0m       fun, primitive, top_trace and top_trace.level, tuple(params.items()))\n\u001b[0;32m-> 1977\u001b[0;31m   \u001b[0mtracers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_raise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1978\u001b[0m   \u001b[0mfun_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1979\u001b[0m   \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_trace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprimitive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtracers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/util.py\u001b[0m in \u001b[0;36msafe_map\u001b[0;34m(f, *args)\u001b[0m\n\u001b[1;32m     47\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'length mismatch: {list(map(len, args))}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0munzip2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/core.py\u001b[0m in \u001b[0;36mfull_raise\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m    414\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mfull_raise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTracer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTracer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m     \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assert_live\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/interpreters/batching.py\u001b[0m in \u001b[0;36mpure\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mpure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mBatchTracer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnot_mapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource_info_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mlift\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/jax/_src/source_info_util.py\u001b[0m in \u001b[0;36mcurrent\u001b[0;34m()\u001b[0m\n\u001b[1;32m    150\u001b[0m   \u001b[0msource_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_source_info_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msource_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m     \u001b[0msource_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msource_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxla_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0msource_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tqdm\n",
    "from exojax.spec.lbderror import single_tilde_line_strength_zeroth\n",
    "from exojax.spec.lbderror import worst_tilde_line_strength_first\n",
    "from exojax.spec.lbderror import worst_tilde_line_strength_second\n",
    "\n",
    "N=30\n",
    "Twtarr = np.logspace(np.log10(100.1),np.log10(2000.1),N)\n",
    "Trefarr = np.logspace(np.log10(100.1),np.log10(2000.1),N)\n",
    "M=100\n",
    "Tarr = np.logspace(np.log10(100.), np.log10(3000.), M)\n",
    "L=15\n",
    "dEarr = np.linspace(100,1500,L)\n",
    "\n",
    "dE_0th=500.\n",
    "dE_1st=1000.\n",
    "dE_2nd=1500.\n",
    "\n",
    "arr=np.zeros((M,N,N,L,3))\n",
    "\n",
    "for idE, dE in tqdm.tqdm(enumerate(dEarr)):\n",
    "    for iTref, Tref in enumerate(Trefarr):\n",
    "        print(iTref,\"/\",N)\n",
    "        for iTwt, Twt in enumerate(Twtarr):\n",
    "            x = single_tilde_line_strength_zeroth(1./Tarr, 1.0/Twt, 1.0/Tref, dE_0th)\n",
    "            arr[:,iTwt,iTref,idE,0]=x\n",
    "            x = worst_tilde_line_strength_first(Tarr, Twt, Tref, dE_1st)\n",
    "            arr[:,iTwt,iTref,idE,1]=x\n",
    "            x = worst_tilde_line_strength_second(Tarr, Twt, Tref, dE_2nd)\n",
    "            arr[:,iTwt,iTref,idE,2]=x\n",
    "\n",
    "np.savez(\"elower_grid_arr.npz\",arr,Tarr,Twtarr,Trefarr,dEarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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