{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma comparison ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.data is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.material is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n"
     ]
    }
   ],
   "source": [
    "from tardis.simulation import Simulation\n",
    "from tardis.io.configuration.config_reader import Configuration\n",
    "from IPython.display import FileLinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The example tardis_example can be downloaded here**\n",
    "\n",
    "[tardis_example.yml](tardis_example.yml)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
      "  AstropyDeprecationWarning)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mtardis.plasma.standard_plasmas\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Reading Atomic Data from kurucz_cd23_chianti_H_He.h5 (\u001b[1mstandard_plasmas.py\u001b[0m:74)\n",
      "[\u001b[1mtardis.io.atom_data.util\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Atom Data kurucz_cd23_chianti_H_He.h5 not found in local path. Exists in TARDIS Data repo /Users/wkerzend/projects/tardis/tardis-data/kurucz_cd23_chianti_H_He.h5 (\u001b[1mutil.py\u001b[0m:29)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3185: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  if (yield from self.run_code(code, result)):\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Read Atom Data with UUID=6f7b09e887a311e7a06b246e96350010 and MD5=864f1753714343c41f99cb065710cace. (\u001b[1mbase.py\u001b[0m:184)\n",
      "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Non provided atomic data: synpp_refs, photoionization_data (\u001b[1mbase.py\u001b[0m:187)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
      "  AstropyDeprecationWarning)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/python/tardis/tardis/plasma/properties/ion_population.py:63: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  partition_function.index].dropna())\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/equivalencies.py:90: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (si.m, si.Hz, lambda x: _si.c.value / x),\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
      "  AstropyDeprecationWarning)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n"
     ]
    }
   ],
   "source": [
    "config = Configuration.from_yaml('tardis_example.yml')\n",
    "sim = Simulation.from_config(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing the plasma states ####\n",
    "In this example, we are accessing Si and also the unionized number density (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "                       0\n",
       "ion_number              \n",
       "0           3.406539e-13\n",
       "1           9.307653e-04\n",
       "2           2.293418e+04\n",
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All Si ionization states\n",
    "sim.plasma.ion_number_density.loc[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>14</th>\n",
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       "                       0\n",
       "ion_number              \n",
       "0           3.055157e-22\n",
       "1           8.347575e-13\n",
       "2           2.056853e-05\n",
       "3           2.214038e-01\n",
       "4           7.785756e-01\n",
       "5           0.000000e+00\n",
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       "8           0.000000e+00\n",
       "9           0.000000e+00\n",
       "10          0.000000e+00\n",
       "11          0.000000e+00\n",
       "12          0.000000e+00\n",
       "13          0.000000e+00\n",
       "14          0.000000e+00"
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing by si number density\n",
    "sim.plasma.ion_number_density.loc[14] / sim.plasma.number_density.loc[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000931\n",
       "Name: (14, 1), dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the first ionization state\n",
    "\n",
    "sim.plasma.ion_number_density.loc[14, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plasma.update(density=[1e-13])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">20</th>\n",
       "      <th>0</th>\n",
       "      <td>2.144981e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.601330e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.507800e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.833649e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0\n",
       "atomic_number ion_number              \n",
       "8             0           1.288175e+04\n",
       "              1           7.151416e+08\n",
       "              2           1.598774e+03\n",
       "              3           6.741210e-14\n",
       "              4           0.000000e+00\n",
       "              5           0.000000e+00\n",
       "              6           0.000000e+00\n",
       "              7           0.000000e+00\n",
       "              8           0.000000e+00\n",
       "12            0           3.800017e-05\n",
       "              1           6.545794e+03\n",
       "              2           7.432405e+07\n",
       "              3           0.000000e+00\n",
       "              4           0.000000e+00\n",
       "              5           0.000000e+00\n",
       "              6           0.000000e+00\n",
       "              7           0.000000e+00\n",
       "              8           0.000000e+00\n",
       "              9           0.000000e+00\n",
       "              10          0.000000e+00\n",
       "              11          0.000000e+00\n",
       "              12          0.000000e+00\n",
       "14            0           3.172104e-02\n",
       "              1           1.332867e+06\n",
       "              2           1.113664e+09\n",
       "              3           1.601707e+04\n",
       "              4           5.800053e-08\n",
       "              5           0.000000e+00\n",
       "              6           0.000000e+00\n",
       "              7           0.000000e+00\n",
       "...                                ...\n",
       "18            10          0.000000e+00\n",
       "              11          0.000000e+00\n",
       "              12          0.000000e+00\n",
       "              13          0.000000e+00\n",
       "              14          0.000000e+00\n",
       "              15          0.000000e+00\n",
       "              16          0.000000e+00\n",
       "              17          0.000000e+00\n",
       "              18          0.000000e+00\n",
       "20            0           2.144981e-07\n",
       "              1           1.601330e+02\n",
       "              2           4.507800e+07\n",
       "              3           1.833649e-06\n",
       "              4           0.000000e+00\n",
       "              5           0.000000e+00\n",
       "              6           0.000000e+00\n",
       "              7           0.000000e+00\n",
       "              8           0.000000e+00\n",
       "              9           0.000000e+00\n",
       "              10          0.000000e+00\n",
       "              11          0.000000e+00\n",
       "              12          0.000000e+00\n",
       "              13          0.000000e+00\n",
       "              14          0.000000e+00\n",
       "              15          0.000000e+00\n",
       "              16          0.000000e+00\n",
       "              17          0.000000e+00\n",
       "              18          0.000000e+00\n",
       "              19          0.000000e+00\n",
       "              20          0.000000e+00\n",
       "\n",
       "[94 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.plasma.ion_number_density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Updating the plasma state ####\n",
    "\n",
    "It is possible to update the plasma state with different temperatures or dilution factors (as well as different densities.). We are updating the radiative temperatures and plotting the evolution of the ionization state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /Users/wkerzend/python/tardis/tardis/plasma/properties/ion_population.py:63: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  partition_function.index].dropna())\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n"
     ]
    }
   ],
   "source": [
    "si_ionization_state = None\n",
    "for cur_t_rad in range(1000, 20000, 100):\n",
    "    sim.plasma.update(t_rad=[cur_t_rad])\n",
    "    if si_ionization_state is None:\n",
    "        si_ionization_state = sim.plasma.ion_number_density.loc[14].copy()\n",
    "        si_ionization_state.columns = [cur_t_rad]\n",
    "    else:\n",
    "        si_ionization_state[cur_t_rad] = sim.plasma.ion_number_density.loc[14].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number density [1/cm$^3$]')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "fig = figure(0, figsize=(10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "si_ionization_state.T.iloc[:, :3].plot(ax=ax)\n",
    "xlabel('radiative Temperature [K]')\n",
    "ylabel('Number density [1/cm$^3$]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}