Changeset 3528


Ignore:
Timestamp:
Nov 26, 2024, 11:20:48 AM (5 weeks ago)
Author:
mmaurice
Message:

Generic PCM:

Python postprocessing: cleaner implementation, add automatic network
import and export from and to various format (Generic PCM, vulcan) and
more practical tools to handle networks. Updated the python notebook
with additional examples of use.

MM

Location:
trunk/LMDZ.GENERIC/utilities/photochemistry
Files:
3 edited

Legend:

Unmodified
Added
Removed

  • trunk/LMDZ.GENERIC/utilities/photochemistry/Photochem_Visualizer.ipynb

    r3511 r3528  
    33  {
    44   "cell_type": "markdown",
    5    "id": "e95cb3d6-faab-4db6-84e0-3ff64cb9dfeb",
     5   "id": "99703d03-5db7-4850-add9-77033d4c6217",
    66   "metadata": {},
    77   "source": [
    … …  
    2323   "source": [
    2424    "## Loading simulation and calculating reaction rates"
     25   ]
     26  },
     27  {
     28   "cell_type": "markdown",
     29   "id": "50a0abe3-e224-4b07-95dc-a424dd35cdcc",
     30   "metadata": {},
     31   "source": [
     32    "If you do not have a simulation with photochemistry at hand, you can download an example file with:    \n",
     33    "```\n",
     34    "wget https://web.lmd.jussieu.fr/~mmaurice/photochem_example.zip\n",
     35    "unzip photochem_example.zip\n",
     36    "```"
    2537   ]
    2638  },
    … …  
    3547     "output_type": "stream",
    3648     "text": [
    37       "H2O2/3D/start_no_CO/diagfi61 loaded, simulations lasts 60.541668 sols\n"
     49      "./photochem_example/diagfi.nc loaded, simulations lasts 1.0 sols\n"
    3850     ]
    3951    }
    … …  
    4254    "import photochem_postproc as pcpp\n",
    4355    "\n",
    44     "sim_path        = 'H2O2/3D/start_no_CO'\n",
    45     "NetCDF_filename = 'diagfi61'\n",
     56    "sim_path        = './photochem_example'\n",
     57    "NetCDF_filename = 'diagfi.nc'\n",
    4658    "\n",
    4759    "# The simu class is just a wrapper for xr.Dataset\n",
    … …  
    5466   "metadata": {},
    5567   "source": [
    56     "Now let's try to calculate automatically the rates of all reactions found in the reactfile"
     68    "Now let's load the chemical network. Notice that unlike in this example, the reaction network is normally located in a ***chemnetwork/reactfile*** file."
    5769   ]
    5870  },
    … …  
    6072   "cell_type": "code",
    6173   "execution_count": 2,
    62    "id": "80f1c90a-7bfc-4e95-b614-c6e8432c53b3",
     74   "id": "355df458-41a8-4d48-a21c-49a427f94ea5",
    6375   "metadata": {},
    6476   "outputs": [
    … …  
    6880     "text": [
    6981      "reaction  no + hv -> n + o seems to be hard-coded. Add it manually if needed.\n",
    70       "reaction  co + oh -> co2 + h seems to be hard-coded. Add it manually if needed.\n",
    71       "['o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2o_vap', 'h', 'co2', 'co', 'ho2', 'h2']\n"
     82      "reaction  co + oh -> co2 + h seems to be hard-coded. Add it manually if needed.\n"
    7283     ]
    7384    }
    7485   ],
    7586   "source": [
    76     "my_sim = pcpp.compute_rates(my_sim)\n",
    77     "\n",
    78     "# We can see that species list have been added\n",
    79     "# to the simu object (as well as reactions dict)\n",
    80     "print(my_sim.species)"
     87    "my_sim.network = pcpp.network.from_file(my_sim.path+'/network.def')"
    8188   ]
    8289  },
    … …  
    9198  {
    9299   "cell_type": "code",
    93    "execution_count": 4,
    94    "id": "e1ed253d-9186-4871-bf93-5ba0fc5f6a66",
     100   "execution_count": 3,
     101   "id": "bf9f8c56-6615-4f9f-acae-c788a9543770",
    95102   "metadata": {},
    96103   "outputs": [],
    97104   "source": [
    98105    "# First load the parametrization for its rate\n",
    99     "from reaction_rate_lib import k_JPL_2015\n",
     106    "from reaction_rate_lib import k_CO_OH_to_CO2_H_JPL_2015\n",
    100107    "\n",
    101108    "# Then create the reaction objet (here for the reaction co + oh -> co2 + h):\n",
    102     "hard_coded_reaction = pcpp.reaction(['co','oh'],['co2','h'],k_JPL_2015)\n",
    103     "\n",
    104     "# Finally, add it to the reactions of my_sim\n",
    105     "my_sim = pcpp.compute_rates(my_sim,{'co + oh -> co2 + h':hard_coded_reaction})"
     109    "hard_coded_reaction = pcpp.reaction(['co','oh'],['co2','h'],k_CO_OH_to_CO2_H_JPL_2015)\n",
     110    "\n",
     111    "my_sim.network.append(hard_coded_reaction)"
     112   ]
     113  },
     114  {
     115   "cell_type": "markdown",
     116   "id": "5b193a59-9338-487e-977e-35556a7a405a",
     117   "metadata": {},
     118   "source": [
     119    "We are now ready to compute the rates (be careful with memory, as it will add 2 4D fields per chemical species, and 2 4D fields per reaction!)"
     120   ]
     121  },
     122  {
     123   "cell_type": "code",
     124   "execution_count": null,
     125   "id": "776388e6-0506-428b-a99c-f02283bad4d8",
     126   "metadata": {},
     127   "outputs": [],
     128   "source": [
     129    "my"
    106130   ]
    107131  },
    … …  
    125149  {
    126150   "cell_type": "code",
     151   "execution_count": 4,
     152   "id": "f34cbcfa-339b-46dd-b33e-4c699a118f60",
     153   "metadata": {},
     154   "outputs": [],
     155   "source": [
     156    "import matplotlib.pyplot as plt"
     157   ]
     158  },
     159  {
     160   "cell_type": "markdown",
     161   "id": "779d0619-582e-4628-8161-5c0d9d943261",
     162   "metadata": {},
     163   "source": [
     164    "#### Meridional slice"
     165   ]
     166  },
     167  {
     168   "cell_type": "code",
    127169   "execution_count": 5,
    128    "id": "f34cbcfa-339b-46dd-b33e-4c699a118f60",
    129    "metadata": {},
    130    "outputs": [],
    131    "source": [
    132     "import matplotlib.pyplot as plt"
    133    ]
    134   },
    135   {
    136    "cell_type": "markdown",
    137    "id": "779d0619-582e-4628-8161-5c0d9d943261",
    138    "metadata": {},
    139    "source": [
    140     "#### Meridional slice"
    141    ]
    142   },
    143   {
    144    "cell_type": "code",
    145    "execution_count": 6,
    146170   "id": "f54ea828-ea01-4f8c-9349-e5f051ef7043",
    147171   "metadata": {},
    … …  
    149173    {
    150174     "data": {
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    152176      "text/plain": [
    153177       "<Figure size 640x480 with 6 Axes>"
    … …  
    184208  {
    185209   "cell_type": "code",
    186    "execution_count": 7,
     210   "execution_count": 6,
    187211   "id": "2328745a-55e1-40d9-86c7-41f04bea872c",
    188212   "metadata": {},
    … …  
    190214    {
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CjEsvvbTIfPbo0cM4c+aM02VQmmwt7vOxsGPHjhmBgYHGQw895DD8+eefL5JJzl43ISHBaNasmcOwc5exs3kwjKLZk5+fb7Rs2dJISEgw8vPzHV63adOmxuWXX17ivBTn3HqmTp1qSDI+/PBD+7Dc3FwjLi7OCA4ONjIzMw3DOPuZWqdOHePIkSP2cT/77DNDkvHFF1/Yh7Vr185o2LChcfz4cfuw5cuXF/leYRhGkcx54YUXnC4fZ99fipvGwIEDjaCgIIf1tWXLFsPf398hC3fu3Gn4+/sbzzzzjMP0Nm/ebFSpUqXIcGdcef/l5+cbDRo0MAYPHuww3ty5cw1JxsqVKw3DMIzjx48bNWvWNIYPH+4w3v79+42wsDCH4QXfN8aOHetSTc6+V7m6njz1/Q8ojIwj4wpe15WMK26bNjw83Dh8+LB92KZNmww/Pz/j9ttvtw8r2C4q/J3fMAzj2muvNerUqWO/v379ekOS8cADDziMN3To0CK542y+27Rp4/T952zbztk0Dhw4YAQEBBj9+/d3WEb/93//Z0hyeB+6un1UnIJMOd/N2fwUdvDgQafbwoZhGCkpKYYkY+vWrUWes2bNGvv3DVReHP7sgmrVqtn/Pn78uA4dOqSePXvqxIkT2rp1q1dq2rdvn9LS0pSUlOTwq8Pll1+u1q1bO4w7b948hYWF6fLLL7efA+HQoUOKiYlRcHCw00NHXVF4uWRnZ+vQoUPq1q2bDMPQxo0bi4z/r3/9y+F+z549Ha4mtWjRIlWpUsW+V6D0z3ku7rvvvlLVt27dOh04cED33nuvw7mf+vfvr1atWunLL780XePixYtVtWpVDR8+3D7Mz8/Pbb/8nU/BCXBHjx7tMPzcvQ4Nw9Ann3yiAQMGyDAMh/WekJCgY8eOacOGDSW+VmBgoPz8/vmIyMvL0+HDhxUcHKwLL7zQ4bmDBg1SlSpVNGfOHPuwn3/+WVu2bNGQIUPsw+bNm6eePXuqVq1aDvXEx8crLy9PK1eudHj9wYMHq169eg7D/P397edVzM/P15EjR3TmzBl17tzZoSZX19ORI0f07bff6oYbbrD/bx86dEiHDx9WQkKCtm/fXuRQeaC0UlJStHTpUofbV199ZX/c3Z+BBQp/Vv/99986duyYevbsed7PAOmf//0lS5Zo4MCBDode1a9fXzfffLO+++47ZWZmOjxn+PDhLp2fyJ3ZWnB6hrlz58owDPvwOXPmqGvXroqOjnb6ugV71vTq1Ut//PGHjh07Zup1nUlLS9P27dt188036/Dhw/bPlezsbPXt21crV650yyFNixYtUmRkpG666Sb7sKpVq2r06NHKysrSihUrHMYfMmSIatWqZb/fs2dPSbJn3N69e7V582bdfvvtDkda9OrVS+3atStzveeTl5enr7/+WgMHDnRYXxdddJESEhIcxv3000+Vn5+vG264wSFPIiMj1bJlS5e+17jy/rPZbLr++uu1aNEiZWVl2cefM2eOGjRooB49ekiSli5dqqNHj+qmm25yqMff31+xsbFO6yn8f+6spuK+V5lZT576/gc4Q8YVXxcZV7yCbdqhQ4faD6uVpPbt2+vyyy93evEPZ9trhw8ftq+rxYsXS/pnr9LCyvpec9U333yj3Nxc3XfffQ6HSzs7Sszs9tG5HnnkkSL/d85uL730UonTKTjNhrOLDRVsR597Ko66desqNjZWISEhJU4bFR+HP7vgl19+0b///W99++23RYLFHR/OpfHnn39KktPzF5zb9Nm+fbuOHTum8PBwp9M6cOCApH/mpfCHRUBAgMOH+7l27dql8ePH6/PPPy9ybp5zl0tQUFCRBlGtWrUcnvfnn3+qfv36RQ4bv/DCC4utoSQFy8jZ81u1aqXvvvuu1DUWHKpUoLyubPfnn3/Kz8+vyGHT587jwYMHdfToUb311lt66623nE6rYL3v37/fYXhYWJiqVaum/Px8vfLKK/bzZRQ+B0zhq3/VrVvXfq6Np556StI/X3KqVKmiQYMG2cfbvn27fvrppyLL+Nx6Cji7gqAkvf/++3rppZe0detWnT592un4rq6nHTt2yDAMPf7443r88ceLret8h/4BrujSpUuJJ7F392dggYULF+rpp59WWlqacnJy7MOdnRfoXAcPHtSJEyec1nDRRRcpPz9fu3fvVps2bezDi/vfPZe7s3XIkCFasGCBVq9erW7duun333/X+vXrixw6tGrVKk2YMEGrV68ucg6mY8eOlXh4kCu2b98uSSUe0nfs2DGHBl9p/Pnnn2rZsqX9x58CF110kf3xwgpvdEqyv35BxhWM7yzPWrRo4dIGelkcPHhQJ0+eLPZ7TeENy+3bt8swjGLP4VRwSG9WVpZDM9Df39+eQa6+/4YMGaKpU6fq888/180336ysrCwtWrTIfhh5QT2SdNlllzmtJzQ01OF+lSpVnJ6qxpXvVWbWk6vf/wB3IOMckXGuZVxJ22sXXXSRvv766yIXsyopz0JDQ+3bS+euq/LcXpOKbqfXq1evyHIxu310rtatWxfZoag0CprRhf+HChQcXl24YQ0URlPxPI4ePapevXopNDRUTz75pJo3b66goCBt2LBBjz76qNtPoFpcAJbmpL4F8vPzFR4ero8++sjp4wUfYvfff7/DCd579epV5KS8heu5/PLLdeTIET366KNq1aqVatSooT179mjo0KFFlosvXFXLmzW6e70XLP9bb7212NBv3769pH9+jS1sxowZGjp0qCZNmqTHH39cd9xxh5566inVrl1bfn5+euCBB4qs3xtvvFHDhg1TWlqaOnbsqLlz56pv377282oV1HT55ZfrkUcecVrPBRdc4HDfWXB9+OGHGjp0qAYOHKiHH35Y4eHh8vf31+TJk/X777+fZ6kUVTAfY8aMKbInTIHy+gICmGGz2Rz2Vihw7mfG//73P1199dW69NJL9cYbb6h+/fqqWrWqZsyYoVmzZnmkNle+dHoiWwcMGKDq1atr7ty56tatm+bOnSs/Pz+Hc+H9/vvv6tu3r1q1aqUpU6aoUaNGCggI0KJFi/Tyyy+X+Lqufk4XTOOFF16wn+f1XMWdc9mTiss4Z+8jd/LU9xqbzaavvvrK6XwVLN8XX3zR4UJijRs31s6dO029/7p27aomTZpo7ty5uvnmm/XFF1/o5MmTDnviF4z/wQcfKDIyskg9Vao4ft0ufCRAAbPfq1xdTq58/wOshowriow7q6LlmZnto3Odu1NQcc63s1Dt2rUVGBioffv2FXmsYFhUVNR5XweVE03F81i+fLkOHz6sTz/9VJdeeql9eHp6epFxXflF7HzjFvx6ce4Vj8/d46Bx48aSzv5aVNi2bdsc7jdv3lzffPONunfvXmIQPvLII7r11luL1OLM5s2b9dtvv+n999/X7bffbh++dOnSYp9zPo0bN1ZqaqqysrIcwujc+TEzvYLnn7v3wLZt2+yPm53msmXLdOLECYe94Hbs2OHS80ta786ucu1svefn5+v33393+EXv3GVUcGXovLw8xcfHl1jTueus4NfYjz/+WH369NG7777r8PjRo0cdmoWSNHDgQI0YMcJ+CPRvv/2mcePGOYzTvHlzZWVlnbeeknz88cdq1qyZPv30U4dlee5FYlxdTwWHulStWrVMdQHuYOYzsFatWg6nZihw7mfGJ598oqCgIH399dcOh7TMmDGjyHOdfT7Vq1dP1atXd1rD1q1b5efnp0aNGpU8Y06YyVZX1ahRQ1dddZXmzZunKVOmaM6cOerZs6fDl+AvvvhCOTk5+vzzzx32dHDlMFBX87lgT/LQ0FCPfq40btxYP/30k/Lz8x0aVAWH1ZnNuILxneWZKxlX1u819erVU7Vq1Vz+XmMYhpo2bVriBtftt99uP0RZOtsMMPv+u+GGG/TKK68oMzNTc+bMUZMmTdS1a1eHeiQpPDy81Ovc1e9VZtaTq9//gPJAxpFxzhTeXjvX1q1bVbduXYe9FF2dZn5+vtLT0x32FnTH9pr0zzIqfDGwkrbTCx9Wf/DgwSJ7oZd1++jcnYKKU9LOQtI/p4lq166d1q1bV+SxH374Qc2aNeMwZxSLcyqeR8EvIYV/+cjNzdUbb7xRZNwaNWq4vDt7wYfjuR/cjRs3lr+/f5HzJ5z7evXr11fHjh31/vvvO7zm0qVLtWXLFodxb7jhBuXl5dkPTS3szJkz9hpat26t+Ph4+y0mJqbY+p0tF8Mw9MorrxT7nPPp16+fzpw5o2nTptmH5eXl6bXXXivV9Dp37qzw8HBNnz7dYVfur776Sr/++qtLVwA7V0JCgk6fPq23337bPiw/P18pKSkuPb9GjRpOm4fNmzfXsWPH9NNPP9mH7du3T/Pnz3cYLzExUZL06quvOgw/99AHf39/DR48WJ988ol+/vnnIq9X+Gqchdd5fHy8fc9Ff3//Ir/4zZs3z+k5BmvWrKmEhATNnTtXs2fPVkBAgAYOHOgwzg033KDVq1fr66+/LvL8o0eP6syZM0WGn8vZ++6HH37Q6tWrHcZzdT2Fh4erd+/eevPNN53+MmfmqqVAWZn5DGzevLm2bt3q8B7dtGmTVq1a5TCev7+/bDabw6/oO3fu1IIFC4pM09nnk7+/v6644gp99tln2rlzp314RkaGZs2apR49ehQ5tNMVZrLVjCFDhmjv3r165513tGnTJoe9yYp73WPHjjndAD1XwYZU4XzOy8srcoqJmJgYNW/eXC+++KLDobcF3PW50q9fP+3fv9/hfLZnzpzRa6+9puDgYPXq1cvU9KKiotS2bVv95z//cah7xYoV2rx583mfX9z3mtDQUNWtW/e832v8/f2VkJCgBQsWaNeuXfbhv/76a5HcGDRokPz9/fXEE08UySnDMHT48GFJ//xwVDjfunfvbn+tgnELlPT+GzJkiHJycvT+++9r8eLFuuGGGxweT0hIUGhoqCZNmuRwWo4CrqxzV79XmVlPrn7/A8oDGUfGOVN4m7bw+vn555+1ZMkS9evXz9T0JNmPPjp3ebu6TVnS9prkuIyys7OLNPXi4+NVtWpVvfbaaw7rwtmVnMu6feSucypK0nXXXacff/zRobG4bds2ffvttw57xALnYk/F8+jWrZtq1aqlpKQkjR49WjabTR988IHT3atjYmI0Z84cJScn65JLLlFwcLAGDBjgdLoFDbvRo0crISFB/v7+uvHGGxUWFqbrr79er732mmw2m5o3b66FCxc6PZ/C5MmT1b9/f/Xo0UN33HGHjhw5otdee01t2rRx+JDv1auXRowYocmTJystLU1XXHGFqlatqu3bt2vevHl65ZVXdN1115laLq1atVLz5s01ZswY7dmzR6Ghofrkk0+K/PpixoABA9S9e3eNHTtWO3fuVOvWrfXpp5+W+ryVVatW1XPPPadhw4apV69euummm5SRkaFXXnlFTZo00YMPPmh6mgMHDlSXLl300EMPaceOHWrVqpU+//xzHTlyRNL591aNiYnRN998oylTpigqKkpNmzZVbGysbrzxRj366KO69tprNXr0aJ04cULTpk3TBRdc4HB+pI4dO+qmm27SG2+8oWPHjqlbt25KTU11+svbs88+q2XLlik2NlbDhw9X69atdeTIEW3YsEHffPONvebiXHXVVXryySc1bNgwdevWTZs3b9ZHH33k8ItbYUOGDNGtt96qN954QwkJCQ6/4EnSww8/rM8//1xXXXWVhg4dqpiYGGVnZ2vz5s36+OOPtXPnziJ7QDqr6dNPP9W1116r/v37Kz09XdOnT1fr1q0d3vNm1lNKSop69Oihdu3aafjw4WrWrJkyMjK0evVq/fXXX9q0aVOJNQGu+uqrr5yenL1bt25q1qyZqc/AO+64Q1OmTFFCQoLuvPNOHThwQNOnT1ebNm0czt3Uv39/TZkyRVdeeaVuvvlmHThwQCkpKWrRooXDjxhS8Z9PTz/9tJYuXaoePXro3nvvVZUqVfTmm28qJydHzz//fKmWhZlsNaNfv34KCQnRmDFj7D+uFHbFFVcoICBAAwYM0IgRI5SVlaW3335b4eHhTn9YKKxNmzbq2rWrxo0bpyNHjqh27dqaPXt2kS/8fn5+euedd5SYmKg2bdpo2LBhatCggfbs2aNly5YpNDRUX3zxhX18m8123j0InLn77rv15ptvaujQoVq/fr2aNGmijz/+WKtWrdLUqVNLtUfBpEmTdM0116h79+4aNmyY/v77b73++utq27at043Hwgq+1zz22GO68cYbVbVqVQ0YMEA1atTQXXfdpWeffVZ33XWXOnfurJUrV+q3334rMo0nnnhCixcvVs+ePXXvvffam6Rt2rRxeL82b95cTz/9tMaNG6edO3dq4MCBCgkJUXp6uubPn6+7775bY8aMKbZWs++/iy++WC1atNBjjz2mnJycIhvyoaGhmjZtmm677TZdfPHFuvHGG1WvXj3t2rVLX375pbp3767XX3+9xOVn5nuVq+vJE9//gOKQcY7zTMa57oUXXlBiYqLi4uJ055136uTJk3rttdcUFhamiRMnmpqW9M+6Hjx4sKZOnarDhw+ra9euWrFihT13XNlemzZtmp5++mm1aNFC4eHhuuyyy3TFFVcoOjpad955px5++GH5+/vrvffes3/eF6hXr57GjBmjyZMn66qrrlK/fv20ceNGffXVV0W2dcq6feSucypK/1zY5u2331b//v01ZswYVa1aVVOmTFFERIQeeught7wGKijPX2Datzi7rPyqVauMrl27GtWqVTOioqKMRx55xPj6668NScayZcvs42VlZRk333yzUbNmTUOS0bhxY8MwDCM9Pd2QZMyYMcM+7pkzZ4z77rvPqFevnmGz2RwuT3/w4EFj8ODBRvXq1Y1atWoZI0aMMH7++eci0zAMw/jkk0+Miy66yAgMDDRat25tfPrpp0ZSUpL9tQt76623jJiYGKNatWpGSEiI0a5dO+ORRx4x9u7dW6pltWXLFiM+Pt4IDg426tatawwfPtzYtGlTkTqTkpKMGjVqFHn+hAkTjHPfgocPHzZuu+02IzQ01AgLCzNuu+02Y+PGjU7n/VzLli0rsk4MwzDmzJljdOrUyQgMDDRq165t3HLLLcZff/3lMI6ZGg8ePGjcfPPNRkhIiBEWFmYMHTrUWLVqlSHJmD17donP3bp1q3HppZca1apVMyQZSUlJ9seWLFlitG3b1ggICDAuvPBC48MPP3Q6jZMnTxqjR4826tSpY9SoUcMYMGCAsXv3bkOSMWHCBIdxMzIyjJEjRxqNGjUyqlatakRGRhp9+/Y13nrrrRKXpWEYxqlTp4yHHnrIqF+/vlGtWjWje/fuxurVq41evXoZvXr1KjJ+Zmamfb4+/PBDp9M8fvy4MW7cOKNFixZGQECAUbduXaNbt27Giy++aOTm5hqGcfb/5YUXXijy/Pz8fGPSpElG48aNjcDAQKNTp07GwoULnb7nXV1PhmEYv//+u3H77bcbkZGRRtWqVY0GDRoYV111lfHxxx+fdzkB51OQK8XdCn+2mfkM/PDDD41mzZoZAQEBRseOHY2vv/7a6f/Cu+++a7Rs2dIIDAw0WrVqZcyYMcP059OGDRuMhIQEIzg42KhevbrRp08f4/vvv3c6nz/++GOxy6A02VpcphXnlltuMSQZ8fHxTh///PPPjfbt2xtBQUFGkyZNjOeee8547733itTn7LPu999/N+Lj443AwEAjIiLC+L//+z9j6dKlTrNn48aNxqBBg4w6deoYgYGBRuPGjY0bbrjBSE1NtY9z/PhxQ5Jx4403nne+nNWTkZFhDBs2zKhbt64REBBgtGvXrsj7pKTPVGe5MXv2bKNVq1ZGYGCg0bZtW+Pzzz83Bg8ebLRq1eq8z33qqaeMBg0aGH5+fg7L88SJE8add95phIWFGSEhIcYNN9xgHDhwwOk0VqxYYcTExBgBAQFGs2bNjOnTpzt9vxrGP9+BevToYdSoUcOoUaOG0apVK2PkyJHGtm3bil2OBVx9/xV47LHHDElGixYtip3msmXLjISEBCMsLMwICgoymjdvbgwdOtRYt26dfZzivm8YhuvfqwzD9fVkGO7//gcURsaRccVlnDPOtosNwzC++eYbo3v37ka1atWM0NBQY8CAAcaWLVscxilYrwcPHnQY7mz5Z2dnGyNHjjRq165tBAcHGwMHDjS2bdtmSDKeffbZEp+7f/9+o3///kZISIghyWE5rV+/3oiNjTUCAgKM6OhoY8qUKU6nkZeXZzzxxBP27ajevXsbP//8s9G4cWOH955huLZ9VF52795tXHfddUZoaKgRHBxsXHXVVcb27dvLtQb4HpthePiMpkAlsGDBAl177bX67rvv7IdXwXpYT/BFO3fuVNOmTe0XUULFsWjRIl111VXatGmT2rVr5+1yitWxY0fVq1evTOdNhuexnuCLyDiUl7S0NHXq1EkffvihbrnlFm+XA1QYnFMRMOncK2wVnA8mNDRUF198sZeqwrlYTwCsbtmyZbrxxhst01A8ffp0kUPdli9frk2bNql3797eKQpFsJ4AoGTOrog8depU+fn5OVw8B0DZcU5FwKT77rtPJ0+eVFxcnHJycvTpp5/q+++/16RJk7i6ooWwnqxl9+7duu2223TgwAFVqVJFjz/+OCd9RqX3wgsveLsEB3v27FF8fLxuvfVWRUVFaevWrZo+fboiIyP1r3/9y9vl4f9jPbkX+QRUPM8//7zWr1+vPn36qEqVKvrqq6/01Vdf6e677y7VFb2B8uZL2URTETDpsssu00svvaSFCxfq1KlTatGihV577TWNGjXK26WhENaTtVSpUkVTp05Vx44dtX//fsXExKhfv372K8YC8L5atWopJiZG77zzjg4ePKgaNWqof//+evbZZ1WnTh1vl4f/j/XkXuQTUPF069ZNS5cu1VNPPaWsrCxFR0dr4sSJeuyxx7xdGuASX8omzqkIACh3HTp00MKFC/m1GABgKeQTAMBqrJxNnFMRAKCVK1dqwIABioqKks1m04IFC4qMk5KSoiZNmigoKEixsbFau3ZtqV5r/fr1ysvLs2QoAgCshXwCAFgN2XQWTUUAgLKzs9WhQwelpKQ4fXzOnDlKTk7WhAkTtGHDBnXo0EEJCQk6cOCAfZyOHTuqbdu2RW579+61j3PkyBHdfvvteuuttzw+TwAA30c+AQCshmw6q8If/pyfn6+9e/cqJCRENpvN2+UAqIAMw9Dx48cVFRUlP7+y/VZz6tQp5ebmuqWmcz/zAgMDFRgYeN7n2mw2zZ8/XwMHDrQPi42N1SWXXKLXX39d0j+frY0aNdJ9992nsWPHulRTTk6OLr/8cg0fPly33Xab6zNTAZFNAMqDu/LJXdlUUBP5ZF3kEwBPY9upYmVThb9Qy969ey27myiAimX37t1q2LBhqZ9/6tQpRUfX0MGD+WWuJTg4WFlZWQ7DJkyYoIkTJ5qeVm5urtavX69x48bZh/n5+Sk+Pl6rV692aRqGYWjo0KG67LLLLB2K5YVsAlCeypJP7swmiXyyOvIJQHlh26l4vpRNFb6pGBISIknqXfd2VfEL8HI1ACqiM/m5Wn7oP/bPm9LKzc3VwYP5Wv5DuIKDS793QFaWod6xB7R7926Fhobah7vyS5szhw4dUl5eniIiIhyGR0REaOvWrS5NY9WqVZozZ47at29vP+fIBx98oHbt2pWqJl9HNgEoD+7IJ3dlk0Q++QLyCYCnse10fr6UTRW+qViwC2sVvwCCEYBHueswoeBgm4JDynIowD+/1oWGhjoEozf16NFD+fnu2culIiCbAJQnd+RT2bNJIp+sj3wCUF7YdiqeL2UTF2oBAJSobt268vf3V0ZGhsPwjIwMRUZGeqkqAEBlRz4BAKymsmUTTUUAQIkCAgIUExOj1NRU+7D8/HylpqYqLi7Oi5UBACoz8gkAYDWVLZsq/OHPAIDzy8rK0o4dO+z309P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    193217      "text/plain": [
    194218       "<Figure size 640x480 with 6 Axes>"
    … …  
    225249  {
    226250   "cell_type": "code",
    227    "execution_count": 8,
    228    "id": "e2aa2f5b-c527-44f8-98a5-ebde0e62f01f",
    229    "metadata": {},
    230    "outputs": [
    231     {
    232      "data": {
    233       "text/plain": [
    234        "'kg/kg'"
    235       ]
    236      },
    237      "execution_count": 8,
    238      "metadata": {},
    239      "output_type": "execute_result"
    240     }
    241    ],
    242    "source": [
    243     "profile = my_sim.get_subset('h2o_vap',)\n",
    244     "profile.units"
    245    ]
    246   },
    247   {
    248    "cell_type": "code",
    249    "execution_count": 9,
     251   "execution_count": 7,
    250252   "id": "4f544843-012d-41a6-8d7a-c4b40f45a5e1",
    251253   "metadata": {},
    … …  
    253255    {
    254256     "data": {
    255       "image/png": 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",
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",
    256258      "text/plain": [
    257259       "<Figure size 640x480 with 1 Axes>"
    … …  
    291293  {
    292294   "cell_type": "code",
    293    "execution_count": 10,
     295   "execution_count": 8,
    294296   "id": "56b07967-3900-4454-ac0d-29cfae7cc1f9",
    295297   "metadata": {},
    … …  
    302304    "w_lon = widgets.FloatSlider(min=-180, max=180, step=1, description=\"longitude\")\n",
    303305    "w_alt = widgets.FloatSlider(min=0, max=max(my_sim.data[\"altitude\"]), step=1, description=\"altitude\")\n",
    304     "w_time = widgets.FloatSlider(min=0, max=max(my_sim[\"Time\"]), step=1, description=\"time\")\n",
     306    "w_time = widgets.FloatSlider(min=0, max=max(my_sim[\"Time\"]), step=0.1, description=\"time\")\n",
    305307    "\n",
    306308    "# Fields\n",
    307     "w_single_sp   = widgets.Select(options=my_sim.species, value=\"h2o_vap\", description=\"species\")\n",
    308     "w_multiple_sp = widgets.SelectMultiple(options=my_sim.species, value=[\"h2o_vap\"], description=\"species\")\n",
    309     "w_reactions   = widgets.SelectMultiple(options=my_sim.reactions.keys(), value=[\"co2 + hv -> co + o\"], description=\"reactions\")\n",
     309    "w_single_sp   = widgets.Select(options=my_sim.network.species, value=\"h2o_vap\", description=\"species\")\n",
     310    "w_multiple_sp = widgets.SelectMultiple(options=my_sim.network.species, value=[\"h2o_vap\"], description=\"species\")\n",
     311    "w_reactions   = widgets.SelectMultiple(options=my_sim.network.reactions.keys(), value=[\"co2 + hv -> co + o\"], description=\"reactions\")\n",
    310312    "\n",
    311313    "# Miscelaneous\n",
    … …  
    324326  {
    325327   "cell_type": "code",
    326    "execution_count": 11,
     328   "execution_count": 9,
    327329   "id": "56e81d82-fd55-464c-a746-66cf23822957",
    328330   "metadata": {},
    … …  
    331333     "data": {
    332334      "application/vnd.jupyter.widget-view+json": {
    333        "model_id": "6d5cd66506d34879ac2252b9a7c3e026",
     335       "model_id": "09d636f9d1544e818b3d65401146cc63",
    334336       "version_major": 2,
    335337       "version_minor": 0
    … …  
    339341      ]
    340342     },
    341      "execution_count": 11,
     343     "execution_count": 9,
    342344     "metadata": {},
    343345     "output_type": "execute_result"
    … …  
    367369  {
    368370   "cell_type": "code",
    369    "execution_count": 12,
     371   "execution_count": 10,
    370372   "id": "fd7b4103-0436-4bda-bb39-96666c39f332",
    371373   "metadata": {},
    … …  
    374376     "data": {
    375377      "application/vnd.jupyter.widget-view+json": {
    376        "model_id": "36798865efc74c7aba687cf67cb26d54",
     378       "model_id": "c11050a864054709a98c962bcc721bb9",
    377379       "version_major": 2,
    378380       "version_minor": 0
    379381      },
    380382      "text/plain": [
    381        "VBox(children=(FloatSlider(value=0.0, description='time', max=60.54166793823242, step=1.0), FloatSlider(value=…"
    382       ]
    383      },
    384      "execution_count": 12,
     383       "VBox(children=(FloatSlider(value=0.0, description='time', max=1.0), FloatSlider(value=0.0, description='altitu…"
     384      ]
     385     },
     386     "execution_count": 10,
    385387     "metadata": {},
    386388     "output_type": "execute_result"
    … …  
    410412  {
    411413   "cell_type": "code",
    412    "execution_count": 13,
     414   "execution_count": 11,
    413415   "id": "e4691cae-637b-4555-ac87-d556521a4c3f",
    414416   "metadata": {},
    … …  
    417419     "data": {
    418420      "application/vnd.jupyter.widget-view+json": {
    419        "model_id": "acdd52eb1fd8445c8b970c414fe2d10e",
     421       "model_id": "8df03ed7eb3d46609f51c8c87fb3ff24",
    420422       "version_major": 2,
    421423       "version_minor": 0
    422424      },
    423425      "text/plain": [
    424        "HBox(children=(VBox(children=(FloatSlider(value=55.0, description='time', max=60.54166793823242, step=1.0), Fl…"
    425       ]
    426      },
    427      "execution_count": 13,
     426       "HBox(children=(VBox(children=(FloatSlider(value=0.0, description='time', max=1.0), FloatSlider(value=0.0, desc…"
     427      ]
     428     },
     429     "execution_count": 11,
    428430     "metadata": {},
    429431     "output_type": "execute_result"
    … …  
    457459  {
    458460   "cell_type": "code",
    459    "execution_count": 15,
     461   "execution_count": 12,
    460462   "id": "e4db2d8b-6183-4fbe-8ca1-940ef15aaa28",
    461463   "metadata": {},
    … …  
    464466     "data": {
    465467      "application/vnd.jupyter.widget-view+json": {
    466        "model_id": "6089ba159a1d4814b8807b78a65fa19e",
     468       "model_id": "5b689b68c4f34343989f2921d6600425",
    467469       "version_major": 2,
    468470       "version_minor": 0
    469471      },
    470472      "text/plain": [
    471        "HBox(children=(VBox(children=(Select(description='species', index=3, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    472       ]
    473      },
    474      "execution_count": 15,
     473       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     474      ]
     475     },
     476     "execution_count": 12,
    475477     "metadata": {},
    476478     "output_type": "execute_result"
    … …  
    510512  {
    511513   "cell_type": "code",
    512    "execution_count": 16,
     514   "execution_count": 13,
    513515   "id": "9dd59a1c-58c4-41f0-9730-9e97d2607c6a",
    514516   "metadata": {},
    … …  
    517519     "data": {
    518520      "application/vnd.jupyter.widget-view+json": {
    519        "model_id": "e3d87cbaec0147bca8596046cdaad974",
     521       "model_id": "b3c1d194f8d1489180e781b65a599d19",
    520522       "version_major": 2,
    521523       "version_minor": 0
    … …  
    525527      ]
    526528     },
    527      "execution_count": 16,
     529     "execution_count": 13,
    528530     "metadata": {},
    529531     "output_type": "execute_result"
    … …  
    560562  {
    561563   "cell_type": "code",
    562    "execution_count": 17,
     564   "execution_count": 14,
    563565   "id": "ff21a3dc-d44a-4f0e-9aa4-211741bb592d",
    564566   "metadata": {},
    … …  
    567569     "data": {
    568570      "application/vnd.jupyter.widget-view+json": {
    569        "model_id": "525673a453824ea4bcdc5591d796dcb5",
     571       "model_id": "70c2af3118d84892a85767a0ee9eb9ac",
    570572       "version_major": 2,
    571573       "version_minor": 0
    572574      },
    573575      "text/plain": [
    574        "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    575       ]
    576      },
    577      "execution_count": 17,
     576       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     577      ]
     578     },
     579     "execution_count": 14,
    578580     "metadata": {},
    579581     "output_type": "execute_result"
    … …  
    583585    "def make_reaction_rate_viz(sp,t):\n",
    584586    "\n",
    585     "    for r in my_sim.reactions:\n",
    586     "        if sp in my_sim.reactions[r].products:\n",
    587     "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
    588     "        elif sp in my_sim.reactions[r].reactants:\n",
    589     "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
     587    "    for r in my_sim.network.get_subnetwork({'species':[sp]}):\n",
     588    "        if sp in r.products:\n",
     589    "            my_sim.plot_profile('rate ('+r.formula+')',t=t,logx=True,label=r.formula)\n",
     590    "        elif sp in r.reactants:\n",
     591    "            my_sim.plot_profile('rate ('+r.formula+')',t=t,logx=True,ls='--',label=r.formula)\n",
    590592    "\n",
    591593    "    plt.legend()\n",
    … …  
    608610  {
    609611   "cell_type": "code",
    610    "execution_count": 18,
     612   "execution_count": 15,
    611613   "id": "b5b73171-0101-4656-b2ce-7e398070ebbb",
    612614   "metadata": {},
    … …  
    615617     "data": {
    616618      "application/vnd.jupyter.widget-view+json": {
    617        "model_id": "4ac76b3211434eb5b46298f1de86d554",
     619       "model_id": "e7ddfcdad6234de69570d4d3e44cec4a",
    618620       "version_major": 2,
    619621       "version_minor": 0
    620622      },
    621623      "text/plain": [
    622        "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
    623       ]
    624      },
    625      "execution_count": 18,
     624       "HBox(children=(VBox(children=(SelectMultiple(description='species', index=(6,), options=('o2', 'o', 'o1d', 'o3…"
     625      ]
     626     },
     627     "execution_count": 15,
    626628     "metadata": {},
    627629     "output_type": "execute_result"
    … …  
    629631   ],
    630632   "source": [
    631     "def make_sp_prof_atlas(sp,t,lon,lat,avg):\n",
     633    "def make_sp_prof_atlas(sps,t,lon,lat,avg):\n",
    632634    "\n",
    633635    "    plt.subplot(121) # Vertical profile\n",
    634     "    for r in my_sim.reactions:\n",
    635     "        if sp in my_sim.reactions[r].products:\n",
    636     "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
    637     "        elif sp in my_sim.reactions[r].reactants:\n",
    638     "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
     636    "    for i,sp in enumerate(sps):\n",
     637    "        my_sim.plot_profile(sp,t=t,lon=lon,lat=lat,logx=True,label=sp,c=cmap(i))\n",
     638    "        if avg:\n",
     639    "            my_sim.plot_profile(sp,t=t,logx=True,c=cmap(i),ls='--')\n",
     640    "    if avg: # just for the legend\n",
     641    "        plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
     642    "        plt.plot([],[],ls='--',c='k',label='average')\n",
    639643    "\n",
    640644    "    plt.legend()\n",
    … …  
    647651    "    plt.subplots_adjust(right=2)\n",
    648652    "\n",
    649     "out = widgets.interactive_output(make_sp_prof_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    650     "\n",
    651     "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
     653    "out = widgets.interactive_output(make_sp_prof_atlas,{'sps':w_multiple_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
     654    "\n",
     655    "widgets.HBox([widgets.VBox([w_multiple_sp,w_time,w_lon,w_lat,w_average]),out])"
    652656   ]
    653657  },
    … …  
    662666  {
    663667   "cell_type": "code",
    664    "execution_count": 19,
     668   "execution_count": 16,
    665669   "id": "c849fda1-3969-4709-bb17-fdcaff5cbf86",
    666670   "metadata": {},
    … …  
    669673     "data": {
    670674      "application/vnd.jupyter.widget-view+json": {
    671        "model_id": "7e6bc22e2111489c88cbae0c2b6eea97",
     675       "model_id": "69b988d5c7954448b2cd33e27d568329",
    672676       "version_major": 2,
    673677       "version_minor": 0
    674678      },
    675679      "text/plain": [
    676        "HBox(children=(VBox(children=(Select(description='species', options=('o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2…"
    677       ]
    678      },
    679      "execution_count": 19,
     680       "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
     681      ]
     682     },
     683     "execution_count": 16,
    680684     "metadata": {},
    681685     "output_type": "execute_result"
     686    },
     687    {
     688     "data": {
     689      "image/png": 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0CKi3t1etra0KhUJf30BqqkKhkFpaWs7qNk6cOKFTp07poosuGvKc6upqZWZmJo9gMOhlTAAThKcAdXV1qa+vT4FAoN96IBBQJBI5q9tYvXq1ZsyY0S9i31RZWalYLJY8Ojs7vYwJYIKYNJYX27x5s3bt2qXm5malp6cPeZ7f75ff7x/DyQBY8BSgrKwspaWlKRqN9luPRqPKycn51r1btmzR5s2b9fbbb2vevHneJwVwzvH0FMzn86mgoEBNTU3JtUQioaamJhUVFQ2574knntDGjRvV2NioBQsWDH9aAOcUz0/BwuGwysvLtWDBAi1cuFA1NTXq7u7WypUrJUllZWWaOXOmqqurJUm///3vtX79er344ovKy8tLvlZ04YUX6sILLxzBXwXAROM5QKWlpTp69KjWr1+vSCSi/Px8NTY2Jl+Y7ujoUGrq1w+snnrqKfX29uoXv/hFv9upqqrSI4888v2mBzChef4ckIXR+gwCgLMzLj4HBAAjiQABMEOAAJghQADMECAAZggQADMECIAZAgTADAECYIYAATBDgACYIUAAzBAgAGYIEAAzBAiAGQIEwAwBAmCGAAEwQ4AAmCFAAMwQIABmCBAAMwQIgBkCBMAMAQJghgABMEOAAJghQADMECAAZggQADMECIAZAgTADAECYIYAATBDgACYIUAAzBAgAGYIEAAzBAiAGQIEwAwBAmCGAAEwQ4AAmCFAAMwQIABmCBAAMwQIgBkCBMAMAQJghgABMEOAAJghQADMECAAZggQADMECIAZAgTADAECYIYAATBDgACYIUAAzBAgAGYIEAAzBAiAmWEFqLa2Vnl5eUpPT1dhYaH27Nnzref/9a9/1WWXXab09HRdffXVamhoGNawAM4tngNUX1+vcDisqqoq7d27V/Pnz1dxcbGOHDky6PkffPCBbr31Vt1+++3at2+fli1bpmXLlumjjz763sMDmNhSnHPOy4bCwkJde+212rZtmyQpkUgoGAzqvvvu05o1awacX1paqu7ubr3xxhvJtZ/85CfKz8/Xjh07Br1GT0+Penp6kj/HYjHNmjVLnZ2dysjI8DIugBEQj8cVDAZ17NgxZWZmjtwNOw96enpcWlqae/XVV/utl5WVuZtvvnnQPcFg0P3pT3/qt7Z+/Xo3b968Ia9TVVXlJHFwcIyz49NPP/WSjO80SR50dXWpr69PgUCg33ogENCBAwcG3ROJRAY9PxKJDHmdyspKhcPh5M/Hjh3TxRdfrI6OjpGt7yg681+MifSojZnHxkSc+cyzkIsuumhEb9dTgMaK3++X3+8fsJ6ZmTlh/oGdkZGRwcxjgJnHRmrqyL5x7unWsrKylJaWpmg02m89Go0qJydn0D05OTmezgdw/vAUIJ/Pp4KCAjU1NSXXEomEmpqaVFRUNOieoqKifudL0ltvvTXk+QDOI15fNNq1a5fz+/1u586d7uOPP3Z33XWXmzZtmotEIs4551asWOHWrFmTPP/99993kyZNclu2bHH79+93VVVVbvLkye7DDz8862uePHnSVVVVuZMnT3od1wwzjw1mHhujNbPnADnn3JNPPulmzZrlfD6fW7hwofvHP/6R/N+WLl3qysvL+53/0ksvublz5zqfz+euvPJKt3v37u81NIBzg+fPAQHASOFvwQCYIUAAzBAgAGYIEAAz4yZAE/ErPrzMXFdXpyVLlmj69OmaPn26QqHQd/6Oo8Hr/89n7Nq1SykpKVq2bNnoDjgIrzMfO3ZMFRUVys3Nld/v19y5c8f83w+vM9fU1OjSSy/VlClTFAwGtWrVKp08eXKMppXeffddlZSUaMaMGUpJSdFrr732nXuam5t1zTXXyO/365JLLtHOnTu9X9j6bTjn/vfZIp/P55599ln3z3/+0915551u2rRpLhqNDnr++++/79LS0twTTzzhPv74Y/fwww97/mzRWM+8fPlyV1tb6/bt2+f279/vfvWrX7nMzEz3r3/9a9zOfMbhw4fdzJkz3ZIlS9zPf/7zsRn2//E6c09Pj1uwYIG78cYb3XvvvecOHz7smpubXVtb27id+YUXXnB+v9+98MIL7vDhw+7NN990ubm5btWqVWM2c0NDg1u7dq175ZVXnKQBf3D+Te3t7e6CCy5w4XDYffzxx+7JJ590aWlprrGx0dN1x0WAFi5c6CoqKpI/9/X1uRkzZrjq6upBz7/lllvcTTfd1G+tsLDQ/frXvx7VOf9/Xmf+ptOnT7upU6e6559/frRGHGA4M58+fdotWrTI/fnPf3bl5eVjHiCvMz/11FNu9uzZrre3d6xGHMDrzBUVFe6nP/1pv7VwOOwWL148qnMO5WwC9OCDD7orr7yy31ppaakrLi72dC3zp2C9vb1qbW1VKBRKrqWmpioUCqmlpWXQPS0tLf3Ol6Ti4uIhzx9pw5n5m06cOKFTp06N+F8XD2W4Mz/66KPKzs7W7bffPhZj9jOcmV9//XUVFRWpoqJCgUBAV111lTZt2qS+vr5xO/OiRYvU2tqafJrW3t6uhoYG3XjjjWMy83CM1H3Q/K/hx+orPkbScGb+ptWrV2vGjBkD/iGOluHM/N577+mZZ55RW1vbGEw40HBmbm9v19///nfddtttamho0KFDh3Tvvffq1KlTqqqqGpczL1++XF1dXbruuuvknNPp06d1991366GHHhr1eYdrqPtgPB7XV199pSlTppzV7Zg/Ajofbd68Wbt27dKrr76q9PR063EGdfz4ca1YsUJ1dXXKysqyHuesJRIJZWdn6+mnn1ZBQYFKS0u1du3aIb99czxobm7Wpk2btH37du3du1evvPKKdu/erY0bN1qPNurMHwFNxK/4GM7MZ2zZskWbN2/W22+/rXnz5o3mmP14nfnTTz/VZ599ppKSkuRaIpGQJE2aNEkHDx7UnDlzxtXMkpSbm6vJkycrLS0tuXb55ZcrEomot7dXPp9v3M28bt06rVixQnfccYck6eqrr1Z3d7fuuusurV27dsS/g2ckDHUfzMjIOOtHP9I4eAQ0Eb/iYzgzS9ITTzyhjRs3qrGxUQsWLBiLUZO8znzZZZfpww8/VFtbW/K4+eabdf3116utrU3BYHDczSxJixcv1qFDh5KxlKRPPvlEubm5ox6f4c584sSJAZE5E1A3Tv9Uc8Tug95eHx8dFl/xMdYzb9682fl8Pvfyyy+7f//738nj+PHj43bmb7J4F8zrzB0dHW7q1KnuN7/5jTt48KB74403XHZ2tnvsscfG7cxVVVVu6tSp7i9/+Ytrb293f/vb39ycOXPcLbfcMmYzHz9+3O3bt8/t27fPSXJbt251+/btc59//rlzzrk1a9a4FStWJM8/8zb87373O7d//35XW1s7cd+Gd25ifsWHl5kvvvjiQb/ku6qqatzO/E0WAXLO+8wffPCBKywsdH6/382ePds9/vjj7vTp0+N25lOnTrlHHnnEzZkzx6Wnp7tgMOjuvfde95///GfM5n3nnXcG/ffzzJzl5eVu6dKlA/bk5+c7n8/nZs+e7Z577jnP1+XrOACYMX8NCMD5iwABMEOAAJghQADMECAAZggQADMECIAZAgTADAECYIYAATBDgACY+T+wEjtFJei6JAAAAABJRU5ErkJggg==",
     690      "text/plain": [
     691       "<Figure size 640x480 with 1 Axes>"
     692      ]
     693     },
     694     "metadata": {},
     695     "output_type": "display_data"
    682696    }
    683697   ],
    684698   "source": [
    685699    "def make_sp_rate_atlas(sp,t,lon,lat,avg):\n",
    686     "\n",
     700    "        \n",
    687701    "    plt.subplot(121) # Vertical profile\n",
    688     "    for r in my_sim.reactions:\n",
    689     "        if sp in my_sim.reactions[r].products:\n",
    690     "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,label=r)\n",
    691     "        elif sp in my_sim.reactions[r].reactants:\n",
    692     "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,ls='--',label=r)\n",
     702    "    for r in my_sim.network:\n",
     703    "        if sp in r.products:\n",
     704    "            my_sim.plot_profile('rate ('+r.formula+')',t=t,lon=lon if not avg else 'avg',lat=lat if not avg else 'avg',logx=True,label=r.formula)\n",
     705    "        elif sp in r.reactants:\n",
     706    "            my_sim.plot_profile('rate ('+r.formula+')',t=t,lon=lon if not avg else 'avg',lat=lat if not avg else 'avg',logx=True,ls='--',label=r.formula)\n",
    693707    "        \n",
    694708    "    plt.legend()\n",
    … …  
    699713    "    plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n",
    700714    "\n",
    701     "    plt.subplots_adjust(right=2)\n",
    702     "\n",
    703     "out = widgets.interactive_output(make_sp_prof_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
     715    "    plt.subplots_adjust(right=1.5)\n",
     716    "\n",
     717    "out = widgets.interactive_output(make_sp_rate_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    704718    "\n",
    705719    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
     720   ]
     721  },
     722  {
     723   "cell_type": "markdown",
     724   "id": "ff6ad79c-d07b-4d81-83d8-fc02e24ba6d4",
     725   "metadata": {},
     726   "source": [
     727    "## Chemical network processing"
     728   ]
     729  },
     730  {
     731   "cell_type": "markdown",
     732   "id": "da719183-d81b-4dfd-948a-a275e41ae32a",
     733   "metadata": {},
     734   "source": [
     735    "### Working with other formats"
     736   ]
     737  },
     738  {
     739   "cell_type": "markdown",
     740   "id": "fd6c578a-7a59-4f1f-b180-bc514d4482a4",
     741   "metadata": {},
     742   "source": [
     743    "We can import and export chemical network from and into various formats, not only Generic-PCM style."
     744   ]
     745  },
     746  {
     747   "cell_type": "code",
     748   "execution_count": 17,
     749   "id": "e25768d8-914b-4409-947d-b3aa3f3d9c87",
     750   "metadata": {},
     751   "outputs": [
     752    {
     753     "name": "stdout",
     754     "output_type": "stream",
     755     "text": [
     756      "co + oh -> co2 + h has a custom reaction constant: add it manually\n",
     757      "o2 + hv -> o + o is a photolysis: you need to add the branching ratio manually\n",
     758      "o2 + hv -> o + o1d is a photolysis: you need to add the branching ratio manually\n",
     759      "o3 + hv -> o2 + o1d is a photolysis: you need to add the branching ratio manually\n",
     760      "o3 + hv -> o2 + o is a photolysis: you need to add the branching ratio manually\n",
     761      "h2o2 + hv -> oh + oh is a photolysis: you need to add the branching ratio manually\n",
     762      "h2o_vap + hv -> h + oh is a photolysis: you need to add the branching ratio manually\n",
     763      "co2 + hv -> co + o is a photolysis: you need to add the branching ratio manually\n",
     764      "co2 + hv -> co + o1d is a photolysis: you need to add the branching ratio manually\n",
     765      "ho2 + hv -> oh + o is a photolysis: you need to add the branching ratio manually\n"
     766     ]
     767    }
     768   ],
     769   "source": [
     770    "# Let's export our simulation's network into a file readable by the VULCAN model\n",
     771    "my_sim.network.to_file('photochem_example/VULCAN_network.txt',format='vulcan')"
     772   ]
     773  },
     774  {
     775   "cell_type": "markdown",
     776   "id": "e12f6270-65ba-44d3-8ec7-28292fb50cce",
     777   "metadata": {},
     778   "source": [
     779    "Some notifications have been issued because one has to manually add some extra informations for some reactions. But you the file ***photochem_example/VULCAN_network.txt*** should have been correctly written, and you can use it to run a VULCAN simulation once you have added the required informations."
     780   ]
     781  },
     782  {
     783   "cell_type": "markdown",
     784   "id": "0b258f20-f574-4382-b394-afcf171d1993",
     785   "metadata": {},
     786   "source": [
     787    "### Making subnetwork"
     788   ]
     789  },
     790  {
     791   "cell_type": "markdown",
     792   "id": "819b6abf-0ec5-4b53-8af5-31da362f6b86",
     793   "metadata": {},
     794   "source": [
     795    "We can also use these tools to design and export subnetworks:"
     796   ]
     797  },
     798  {
     799   "cell_type": "code",
     800   "execution_count": 18,
     801   "id": "1e1585ff-dc78-44ae-9e38-3025fde45a59",
     802   "metadata": {},
     803   "outputs": [
     804    {
     805     "name": "stdout",
     806     "output_type": "stream",
     807     "text": [
     808      "Photolysis sub-network:\n",
     809      "=======================\n",
     810      "o2 + hv -> o + o\n",
     811      "o2 + hv -> o + o1d\n",
     812      "o3 + hv -> o2 + o1d\n",
     813      "o3 + hv -> o2 + o\n",
     814      "h2o2 + hv -> oh + oh\n",
     815      "h2o_vap + hv -> h + oh\n",
     816      "co2 + hv -> co + o\n",
     817      "co2 + hv -> co + o1d\n",
     818      "ho2 + hv -> oh + o\n",
     819      "\n",
     820      "HOx sub-network:\n",
     821      "=================\n",
     822      "h2o2 + hv -> oh + oh\n",
     823      "h2o_vap + hv -> h + oh\n",
     824      "ho2 + hv -> oh + o\n",
     825      "o1d + h2o_vap -> oh + oh\n",
     826      "o1d + h2 -> oh + h\n",
     827      "o + h2 -> oh + h\n",
     828      "o + ho2 -> oh + o2\n",
     829      "o + oh -> o2 + h\n",
     830      "h + o3 -> oh + o2\n",
     831      "h + ho2 -> oh + oh\n",
     832      "h + ho2 -> h2 + o2\n",
     833      "h + ho2 -> h2o_vap + o\n",
     834      "oh + ho2 -> h2o_vap + o2\n",
     835      "oh + h2o2 -> h2o_vap + ho2\n",
     836      "oh + h2 -> h2o_vap + h\n",
     837      "o + h2o2 -> oh + ho2\n",
     838      "oh + o3 -> ho2 + o2\n",
     839      "ho2 + o3 -> oh + o2 + o2\n",
     840      "ho2 + ho2 -> h2o2 + o2\n",
     841      "oh + oh -> h2o_vap + o\n",
     842      "h + o2 + M -> ho2 + M\n",
     843      "h + oh + M -> h2o_vap + M\n",
     844      "oh + oh + M -> h2o2 + M\n",
     845      "h + h + M -> h2 + M\n",
     846      "ho2 + ho2 + M -> h2o2 + o2 + M\n",
     847      "co + oh -> co2 + h\n",
     848      "\n",
     849      "C sub-network:\n",
     850      "===============\n",
     851      "co2 + hv -> co + o\n",
     852      "co2 + hv -> co + o1d\n",
     853      "o1d + co -> o + co\n",
     854      "o1d + co2 -> o + co2\n",
     855      "co + o + M -> co2 + M\n",
     856      "co + oh -> co2 + h\n"
     857     ]
     858    }
     859   ],
     860   "source": [
     861    "# Select by type:\n",
     862    "print('Photolysis sub-network:')\n",
     863    "print('=======================')\n",
     864    "for r in my_sim.network.get_subnetwork({'type':[pcpp.photolysis]}):\n",
     865    "    print(r.formula)\n",
     866    "\n",
     867    "# Select by species:\n",
     868    "print('')\n",
     869    "print('HOx sub-network:')\n",
     870    "print('=================')\n",
     871    "for r in my_sim.network.get_subnetwork({'species':['h','oh','ho2']}):\n",
     872    "    print(r.formula)\n",
     873    "\n",
     874    "# Select by element:\n",
     875    "print('')\n",
     876    "print('C sub-network:')\n",
     877    "print('===============')\n",
     878    "for r in my_sim.network.get_subnetwork({'elements':['c']}):\n",
     879    "    print(r.formula)"
     880   ]
     881  },
     882  {
     883   "cell_type": "markdown",
     884   "id": "4ea46279-9234-4a53-800d-891efa4ec9d1",
     885   "metadata": {},
     886   "source": [
     887    "### Exporting custom subnetwork\n",
     888    "You can make your own custom chemical network (ideally selecting reactions from a large database - here we just work with our original simulation's reduced network). You may want to save the associated ***traceur.def*** file (notice that you'll have to add manually parameters, but at least you won't forget tracers.)"
     889   ]
     890  },
     891  {
     892   "cell_type": "code",
     893   "execution_count": 21,
     894   "id": "513f6d4e-a0e2-4095-9a7c-acafc79a465b",
     895   "metadata": {},
     896   "outputs": [
     897    {
     898     "data": {
     899      "application/vnd.jupyter.widget-view+json": {
     900       "model_id": "d69273658f144d8f952fe3dc40004e67",
     901       "version_major": 2,
     902       "version_minor": 0
     903      },
     904      "text/plain": [
     905       "HBox(children=(SelectMultiple(layout=Layout(height='400px'), options=('o2 + hv -> o + o', 'o2 + hv -> o + o1d'…"
     906      ]
     907     },
     908     "execution_count": 21,
     909     "metadata": {},
     910     "output_type": "execute_result"
     911    }
     912   ],
     913   "source": [
     914    "from ipywidgets import Layout\n",
     915    "\n",
     916    "def select_reactions(reactions,net_filename,trac_filename,format):\n",
     917    "    save_network_button = widgets.Button(description='Save reactfile')\n",
     918    "    save_tracers_button = widgets.Button(description='Save tracfile')\n",
     919    "    \n",
     920    "    def on_save_network_button_clicked(b):\n",
     921    "        to_save = pcpp.network()\n",
     922    "        for r in reactions:\n",
     923    "            to_save.append(my_sim.network[r])\n",
     924    "        to_save.to_file(net_filename,format=format)\n",
     925    "        \n",
     926    "    def on_save_tracers_button_clicked(b):\n",
     927    "        to_save = pcpp.network()\n",
     928    "        for r in reactions:\n",
     929    "            to_save.append(my_sim.network[r])\n",
     930    "        to_save.save_traceur_file(trac_filename)\n",
     931    "        \n",
     932    "    save_network_button.on_click(on_save_network_button_clicked)\n",
     933    "    save_tracers_button.on_click(on_save_tracers_button_clicked)\n",
     934    "    display(save_network_button, widgets.Output())\n",
     935    "    display(save_tracers_button, widgets.Output())\n",
     936    "    \n",
     937    "w_reactions         = widgets.SelectMultiple(options=my_sim.network.reactions.keys(),layout=Layout(height='400px'))\n",
     938    "w_network_filename  = widgets.Text(value='photochem_example/my_custom_network.def',description='network filename:',disabled=False)\n",
     939    "w_tracers_filename  = widgets.Text(value='photochem_example/my_custom_traceur.def',description='tracers filename:',disabled=False)\n",
     940    "w_format            = widgets.Select(options=['GPCM','vulcan'],value='GPCM',description='format:')\n",
     941    "\n",
     942    "out = widgets.interactive_output(select_reactions,{'reactions':w_reactions,'net_filename':w_network_filename,\n",
     943    "                                                   'trac_filename':w_tracers_filename,'format':w_format})\n",
     944    "widgets.HBox([w_reactions,widgets.VBox([w_format,w_network_filename,w_tracers_filename,out])])"
    706945   ]
    707946  }

  • trunk/LMDZ.GENERIC/utilities/photochemistry/photochem_postproc.py

    r3511 r3528  
    4545
    4646    """
    47     def __init__(self,path,datafilename='diagfi',verbose=False):
     47    def __init__(self,path,filename='diagfi.c',verbose=False):
    4848        """
    4949        Parameters
    … …  
    5858        self.path = path
    5959        try:
    60             self.data = xr.open_dataset(path+'/'+datafilename+'.nc',decode_times=False)
    61             print(path+'/'+datafilename,'loaded, simulations lasts',self.data['Time'].values[-1],'sols')
     60            self.data = xr.open_dataset(path+'/'+filename,decode_times=False)
     61            print(path+'/'+filename,'loaded, simulations lasts',self.data['Time'].values[-1],'sols')
    6262        except:
    6363            raise Exception('Data not found')
    … …  
    101101            data_subset = self[field]
    102102           
    103         if 't' in kw:
     103        if 't' in kw and 'Time' in data_subset.dims:
    104104            if kw['t'] == 'avg':
    105105                data_subset = data_subset.mean(dim='Time')
    106106            else:
    107107                data_subset = data_subset.sel(Time=kw['t'],method='nearest')
    108         if 'lat' in kw:
     108        if 'lat' in kw and 'latitude' in data_subset.dims:
    109109            if kw['lat'] == 'avg':
    110110                data_subset = self.__area_weight__(data_subset).mean(dim='latitude')
    111111            else:
    112112                data_subset = data_subset.sel(latitude=kw['lat'],method='nearest')
    113         if 'lon' in kw:
     113        if 'lon' in kw and 'longitude' in data_subset.dims:
    114114            if kw['lon'] == 'avg':
    115115                data_subset = data_subset.mean(dim='longitude')
    116116            else:
    117117                data_subset = data_subset.sel(longitude=kw['lon'],method='nearest')
    118         if 'alt' in kw:
     118        if 'alt' in kw and 'altitude' in data_subset.dims:
    119119            if kw['alt'] == 'avg':
    120120                data_subset = data_subset.mean(dim='altitude')
    … …  
    272272        plt.xlabel(field+' ['+self[field].units+']')
    273273        plt.ylabel('altitude [km]')
     274
     275    def read_tracfile(self,filename='traceur.def'):
     276        """ Read the traceurs of a simulation
     277   
     278        Parameters
     279        ----------
     280        filename : string (optional)
     281            Name of the tracer file. Default: 'traceur.def'
     282        """
     283        self.tracers = {}
     284        self.M       = {}
     285        with open(self.path+'/'+filename) as tracfile:
     286           
     287            for iline,line in enumerate(tracfile):
     288               
     289                # First line
     290                if iline == 0:
     291                    if not '#ModernTrac-v1' in line:
     292                        raise Exception('Can only read modern traceur.def')
     293                    continue
     294                   
     295                # Second line (number of tracers)
     296                elif iline == 1:
     297                    continue
     298               
     299                # Empty line
     300                elif len(line.split()) == 0:
     301                    continue
     302                   
     303                # Commented line
     304                elif line[0] == '!':
     305                    continue
     306   
     307                # Regular entry
     308                else:
     309                    line       = line.replace('=',' ').split()
     310                    tracparams = {line[2*i+1]:float(line[2*i+2]) for i in range(int(len(line)/2))}
     311                    self.tracers = self.tracers | {line[0]:tracparams}
     312
     313    def compute_rates(self):
     314        """ Computes reaction rates for a simulation
     315   
     316        Parameters
     317        ----------
     318        s : GPCM_simu
     319            Simulation object
     320        reactions : dict (optional)
     321            Dictionnary of reactions whose rate to compute as returned by read_reactfile
     322            If nothing passed, call read_reactfile to identify reactions
     323   
     324        Returns
     325        -------
     326        GPCM_simu
     327            Simulation object with reactions rates, rates constants, species vmr and densities as new fields
     328        """
     329   
     330        # self.network     = network.from_file(self.path+'/chemnetwork/reactfile')
     331        self.read_tracfile() # we need to read the traceur.def to know the molar mass of species
     332        reactions = self.network.reactions
     333        if not set(self.network.species) <= set(list(self.tracers.keys())):
     334            raise Exception('Chemical network contains species that are not in the traceur.def file')
     335               
     336        densities = {}
     337   
     338        # Background density
     339        self['total density']     = (self['p'] / R / self['temp'] / 1e6 * N_A).assign_attrs({'units':'cm^-3.s^-1'}) # 1e6 converts m³ to cm^3
     340   
     341        for sp in self.network.species:
     342            # volume mixing ratios
     343            self[sp+' vmr'] = (self[sp] * self.tracers[background]['mmol'] / self.tracers[sp]['mmol']).assign_attrs({'units':'m^3/m^3'})
     344            # molecular densities
     345            self[sp+' density'] = (self[sp+' vmr'] * self['p'] / R / self['temp'] / 1e6 * N_A).assign_attrs({'units':'cm^-3.s^-1'}) # 1e6 converts m³ to cm^3
     346            densities[sp] = self[sp+' density']
     347   
     348        for r in reactions:
     349   
     350            # Photolysis
     351            if type(reactions[r]) == photolysis:
     352   
     353                # Cases with branching ratios
     354                if reactions[r].reactants[0] == 'co2':
     355                    if 'o1d' in reactions[r].products:
     356                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jco2_o1d'])
     357                    else:
     358                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jco2_o'])
     359                elif reactions[r].reactants[0] == 'o2':
     360                    if 'o1d' in reactions[r].products:
     361                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jo2_o1d'])
     362                    else:
     363                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jo2_o'])
     364                elif reactions[r].reactants[0] == 'o3':
     365                    if 'o1d' in reactions[r].products:
     366                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jo3_o1d'])
     367                    else:
     368                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jo3_o'])
     369                elif reactions[r].reactants[0] == 'ch2o':
     370                    if 'cho' in reactions[r].products:
     371                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jch2o_cho'])
     372                    else:
     373                        self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jch2o_co'])
     374                elif reactions[r].reactants[0] == 'h2o_vap':
     375                    self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['jh2o'])
     376                else:
     377                    # General case
     378                    self['rate ('+reactions[r].formula+')'] = reactions[r].rate(densities,j=self['j'+reactions[r].reactants[0]])
     379            else:
     380                self['k ('+reactions[r].formula+')'] = reactions[r].constant(self['temp'],densities[background])
     381                self['rate ('+reactions[r].formula+')'] = reactions[r].rate(self['temp'],densities)
     382   
     383                # 3-body reaction
     384                if type(reactions[r]) == termolecular_reaction:
     385                    self['k ('+reactions[r].formula+')'] = self['k ('+reactions[r].formula+')'].assign_attrs({'units':'cm^6.s^-1'})
     386   
     387                # 2-body reaction
     388                else:
     389                    self['k ('+reactions[r].formula+')'] = self['k ('+reactions[r].formula+')'].assign_attrs({'units':'cm^3.s^-1'})
     390                   
     391            self['rate ('+reactions[r].formula+')'] = self['rate ('+reactions[r].formula+')'].assign_attrs({'units':'cm^-3.s^-1'})
    274392
    275393    def to_chempath(self,t,dt,filename_suffix='_chempath',lon='avg',lat='avg',alt='avg'):
    … …  
    296414        # Save pecies list in chempath format
    297415        with open(self.path+'/species'+filename_suffix+'.txt', 'w') as fp:
    298             for sp in self.species:
     416            for sp in self.network.species:
    299417                fp.write("%s\n" % sp)
    300418
    301419        # Save reactions list in chempath format
    302420        with open(self.path+'/reactions'+filename_suffix+'.txt', 'w') as fp:
    303             for r in self.reactions:
    304                 current_formula = self.reactions[r].formula
    305                 current_formula = current_formula.replace(' ','')
    306                 current_formula = current_formula.replace('->','=')
    307                 fp.write("%s\n" % current_formula)
     421            for r in self.network:
     422                fp.write("%s\n" % r.to_string(format='chempath'))
    308423
    309424        # Save rates, concentrations and times in chempath format
    … …  
    339454    products : list
    340455        Produced molecules formulae (e.g. ["C", "D"])
    341     constant : fun
     456    constant : callable
    342457        Reaction rate constant, function of potentially temperature and densities
    343458
    … …  
    346461    rate(T,densities)
    347462        Reaction rate for given temperature and densities
     463    from_string(line,format)
     464        Set up from an ASCII string
     465    to_string(format)
     466        Return an ASCII line readable by a photochemical model
    348467    """
    349    
    350468    def __init__(self,reactants,products,constant):
    351469        """
    352470        Parameters
    353471        ----------
    354         reactants : list
     472        reactants : list(string)
    355473            Reacting molecules formulae (e.g. ["A", "B"])
    356         products : list
     474        products : list(string)
    357475            Produced molecules formulae (e.g. ["C", "D"])
    358476        constant : fun
    … …  
    383501        return self.constant(T,densities[background])*densities[self.reactants[0]]*densities[self.reactants[1]]
    384502
     503    @classmethod
     504    def from_string(cls,line,format='GPCM',high_pressure_term=False):
     505        """ Set up from an ASCII string
     506
     507        Format
     508        ------
     509        GPCM
     510            A               B (... to col. 50) B               C (... to col. 100) + cst string
     511        vulcan
     512            [ A + B -> C + D (... to col. 40) ] + cst string
     513
     514        Parameter
     515        ---------
     516        line : string
     517            ASCII string (usually formula and rate constant parameter)
     518        format : string (optional)
     519            Model format to write in (default: GPCM, options: GPCM, vulcan)
     520        high_pressure_term : bool (optional)
     521            Does the rate constant include a high-pressure term? (default: False)
     522        """
     523        if format == 'GPCM':
     524
     525            reactants          = line[:50].split()
     526            products           = line[50:100].split()
     527            cst_params         = line[101:]
     528
     529            if 'hv' in reactants:
     530                reactants.pop(reactants.index('hv'))
     531                if int(line[100]) == 0:
     532                    # photolysis calculated with cross sections
     533                    return cls(reactants,products)
     534            else:
     535                high_pressure_term = int(line[100]) == 2
     536
     537        elif format == 'vulcan':
     538
     539            reactants  = line[line.index('[')+1:line.index('->')].split()[::2]
     540            products   = line[line.index('->')+2:line.index(']')].split()[::2]
     541            cst_params = line[line.index(']')+1:]
     542
     543            if cst_params.split()[0][0].isalpha():
     544                # photolysis calculated with cross sections
     545                return cls(reactants,products,0.)
     546     
     547        if 'M' in reactants:
     548            reactants.pop(reactants.index('M'))
     549        if 'M' in products:
     550            products.pop(products.index('M'))
     551
     552        if high_pressure_term:
     553            return cls(reactants,products,reaction_constant_dens_dep.from_string(cst_params,format))
     554        else:
     555            return cls(reactants,products,reaction_constant.from_string(cst_params,format))
     556
     557    def to_string(self,format='GPCM'):
     558        """ Return an ASCII line readable by a photochemical model
     559
     560        Format
     561        ------
     562        GPCM
     563            A               B (... to col. 50) B               C (... to col. 100) + cst string
     564        vulcan
     565            [ A + B -> C + D (... to col. 40) ] + cst string
     566        chempath
     567            A+B=C+D
     568
     569        Parameter
     570        ---------
     571        format : string (optional)
     572            Model format to write in (default: GPCM, options: GPCM, vulcan, chempath)
     573
     574        Returns
     575        -------
     576        string
     577            ASCII line readable by a photochemical model
     578        """
     579
     580        if format == 'GPCM':
     581            line = ''
     582            # reactants (characters 1 to 50)
     583            for molecule in self.reactants:
     584                line += molecule.lower().ljust(16,' ')
     585            line = line.ljust(50,' ')
     586           
     587            # products (characters 51 to 100)
     588            for molecule in self.products:
     589                line += molecule.lower().ljust(16,' ')
     590            line = line.ljust(100,' ')
     591           
     592        elif format == 'vulcan':
     593            # formula
     594            line = '[ '
     595            for molecule in self.reactants:
     596                line = line + molecule[:-4].upper() + ' + ' if '_vap' in molecule else line + molecule.upper() + ' + '
     597            line = line[:-2] + '-> '
     598            for molecule in self.products:
     599                line = line + molecule[:-4].upper() + ' + ' if '_vap' in molecule else line + molecule.upper() + ' + '
     600            line = line[:-2]
     601            line = line.ljust(40,' ') + ' ]   '
     602
     603        elif format == 'chempath':
     604            line = r.formula
     605            line = line.replace(' ','')
     606            line = line.replace('->','=')
     607            return line
     608           
     609        # constant
     610        if type(self.constant ) in [reaction_constant,reaction_constant_dens_dep]:
     611            line += self.constant.to_string(format)
     612        else:
     613            print(self.formula,'has a custom reaction constant: add it manually')
     614        return line
     615           
     616
    385617class termolecular_reaction(reaction):
    386 
     618    """ Instantiates a three-body reaction
     619   
     620    Attributes
     621    ----------
     622    formula : str
     623        Reaction formula (e.g. "A + B -> C + D")
     624    reactants : list
     625        Reactanting molecules formulae (e.g. ["A", "B"])
     626    products : list
     627        Produced molecules formulae (e.g. ["C", "D"])
     628    constant : callable
     629        Reaction rate constant, function of potentially temperature and densities
     630
     631    Methods
     632    -------
     633    rate(T,densities)
     634        Reaction rate for given temperature and densities
     635    from_string(line,format)
     636        Set up from an ASCII string
     637    to_string(format)
     638        Return an ASCII line readable by a photochemical model
     639    """
    387640    def __init__(self,reactants,products,constant):
    388641
    … …  
    392645        self.constant  = constant
    393646
     647    @classmethod
     648    def from_string(cls,line,format='GPCM',high_pressure_term=False):
     649        """ Set up from an ASCII string
     650
     651        Format
     652        ------
     653        GPCM
     654            A               B (... to col. 50) B               C (... to col. 100) + cst string
     655        vulcan
     656            [ A + B -> C + D (... to col. 40) ] + cst string
     657
     658        Parameter
     659        ---------
     660        line : string
     661            ASCII string (usually formula and rate constant parameter)
     662        format : string (optional)
     663            Model format to write in (default: GPCM, options: GPCM, vulcan)
     664        high_pressure_term : bool (optional)
     665            Does the rate constant include a high-pressure term? (default: False)
     666        """
     667        new_instance = super().from_string(line,format,high_pressure_term)
     668        if not high_pressure_term:
     669            # In case we have a 3-body reaction without
     670            # a high pressure term, enforce density dependence
     671            new_instance.constant.params['d'] = 1.
     672        return new_instance
     673
     674    def to_string(self,format='GPCM'):
     675        """ Return an ASCII line readable by a photochemical model
     676
     677        Format
     678        ------
     679        GPCM
     680            A         B         M (... to col. 50) B         C (... to col. 100) + cst string
     681        vulcan
     682            [ A + B + M -> C + D + M (... to col. 40) ] + cst string
     683        chempath
     684            A+B=C+D
     685
     686        Parameter
     687        ---------
     688        format : string (optional)
     689            Model format to write in (default: GPCM, options: GPCM, vulcan, chempath)
     690
     691        Returns
     692        -------
     693        string
     694            ASCII line readable by a photochemical model
     695        """
     696        if format == 'GPCM':
     697            line = ''
     698            # reactants (characters 1 to 50)
     699            for molecule in self.reactants:
     700                line += molecule.lower().ljust(16,' ')
     701            line += 'M'
     702            line = line.ljust(50,' ')
     703           
     704            # products (characters 51 to 100)
     705            for molecule in self.products:
     706                line += molecule.lower().ljust(16,' ')
     707            line = line.ljust(100,' ')
     708           
     709            # constant (characters 101 to end of line)
     710            line += self.constant.to_string(format)
     711            return line
     712           
     713        elif format == 'vulcan':
     714            # formula
     715            line = '[ '
     716            for molecule in self.reactants:
     717                line += molecule[:-4].upper() + ' + ' if '_vap' in molecule else molecule.upper() + ' + '
     718            line += 'M -> '
     719            for molecule in self.products:
     720                line += molecule[:-4].upper() + ' + ' if '_vap' in molecule else molecule.upper() + ' + '
     721            line += 'M'
     722            line = line.ljust(40,' ') + ' ]   '
     723           
     724            # constant
     725            line += self.constant.to_string(format)
     726            return line
     727
     728        elif format == 'chempath':
     729            line = r.formula
     730            line = line.replace(' ','')
     731            line = line.replace('->','=')
     732            return line
     733
    394734class photolysis(reaction):
    395735
    396     def __init__(self,reactants,products):
     736    def __init__(self,reactants,products,constant=None):
    397737
    398738        self.formula   = ''.join([r_+' + ' for r_ in reactants[:-1]])+reactants[-1]+' + hv -> '+''.join([r_+' + ' for r_ in products[:-1]])+products[-1]
    399739        self.products  = products
    400740        self.reactants = reactants
    401 
    402     def rate(self,j,densities):
     741        self.constant  = constant
     742
     743    def rate(self,densities,**kw):
    403744        """ Computes reaction rate
    404745
    … …  
    416757        """
    417758
    418         return j*densities[self.reactants[0]]
     759        if 'j' in kw:
     760            return kw['j']*densities[self.reactants[0]]
     761        else:
     762            # if a photolysis is prescribed with an Arrhenius constant, it is a trick to give
     763            # it a constant rate. We can simply call the constant with an arbitrary temperature.
     764            return self.constant(1.,densities[background])*densities[self.reactants[0]]
     765
     766    def to_string(self,format='GPCM'):
     767        """ Return an ASCII line readable by a photochemical model
     768
     769        Format
     770        ------
     771        GPCM
     772            A         hv (... to col. 50) B         C (... to col. 100) + cst string
     773        vulcan
     774            [ A -> C + D (... to col. 40) ]             A    br (to add manually)
     775        chemapth
     776            A=C+D (to check)
     777
     778        Parameter
     779        ---------
     780        format : string (optional)
     781            Model format to write in (default: GPCM, options: GPCM, vulcan)
     782
     783        Returns
     784        -------
     785        string
     786            ASCII line readable by a photochemical model
     787        """
     788
     789        if format == 'GPCM':
     790            line = ''
     791            # reactants (characters 1 to 50)
     792            for molecule in self.reactants:
     793                line = line + molecule.lower().ljust(16,' ')
     794            line += 'hv'
     795            line = line.ljust(50,' ')
     796           
     797            # products (characters 51 to 100)
     798            for molecule in self.products:
     799                line += molecule.lower().ljust(16,' ')
     800            line = line.ljust(100,' ')
     801           
     802            # constant (characters 101 to end of line)
     803            if self.constant == None:
     804                line += '0'
     805                print(self.formula,'is a photolysis: you need to add the cross section files manually')
     806            else:
     807                line += self.constant.to_string(format)
     808            return line
     809           
     810        elif format == 'vulcan':
     811            # formula
     812            line = '[ '
     813            for molecule in self.reactants:
     814                line += molecule[:-4].upper() + ' + ' if '_vap' in molecule else molecule.upper() + ' + '
     815            line = line[:-2] + '->'
     816            for molecule in self.products:
     817                line += molecule[:-4].upper() + ' + ' if '_vap' in molecule else molecule.upper() + ' + '
     818            line = line[:-2]
     819            line = line.ljust(40,' ') + ' ]   '
     820            molecule = self.reactants[0]
     821            line += molecule[:-4].upper() + ' + ' if '_vap' in molecule else molecule.upper()
     822            print(self.formula,'is a photolysis: you need to add the branching ratio manually')
     823            return line
     824
     825        elif format == 'chempath':
     826            line = r.formula
     827            line = line.replace(' ','')
     828            line = line.replace('hv','')
     829            line = line.replace('->','=')
     830            return line
    419831
    420832class reaction_constant:
    … …  
    460872        return self.params['a']*(T/self.params['T0'])**self.params['c']*np.exp(-self.params['b']/T)*density**self.params['d']
    461873
    462 class reaction_constant_type2(reaction_constant):
    463     """ Type 2 reaction rate constant
     874    @classmethod
     875    def from_string(cls,line,format='GPCM'):
     876        """ Creates an instance from an ASCII string in a variety of formats
     877
     878        Currently read formats: Generic PDM, vulcan
     879
     880        Parameters
     881        ----------
     882        line : string
     883            Rate constant parameters
     884        format : string (optional)
     885            Format in which parameters are writtenn (default: Generic PCM)
     886
     887        Returns
     888        -------
     889        reaction_constant
     890            The instance of reaction_constant created
     891
     892        """
     893        if format == 'GPCM':
     894            cst_param = line.split()
     895            return cls({'a':float(cst_param[0]),'b':float(cst_param[1]),
     896                        'c':float(cst_param[2]),'T0':float(cst_param[3]),
     897                        'd':float(cst_param[4])})
     898
     899        elif format == 'vulcan':
     900            cst_param = line.split()
     901            T0 = 300.
     902            return cls({'a':float(cst_param[0])*T0**float(cst_param[1]),'b':float(cst_param[1]),
     903                        'c':float(cst_param[2]),'T0':T0,'d':0.})
     904
     905    def to_string(self,format='GPCM'):
     906        """ Return an ASCII line readable by a photochemical model
     907
     908        Format
     909        ------
     910        GPCM
     911            1    a           b           c           T0          d
     912        vulcan
     913            A            B        C
     914
     915        Parameter
     916        ---------
     917        format : string (optional)
     918            Model format to write in (default: GPCM, options: GPCM, vulcan)
     919
     920        Returns
     921        -------
     922        string
     923            ASCII line readable by a photochemical model
     924        """
     925
     926        if format == 'GPCM':
     927            return '1    '+'{:1.2e}'.format(self.params['a']).ljust(12,' ')+str(self.params['T0']).ljust(12,' ') \
     928                          +str(self.params['c']).ljust(12,' ')+str(self.params['b']).ljust(12,' ')  \
     929                          +str(self.params['d'])
     930        elif format == 'vulcan':
     931           
     932            return '{:1.2e}'.format(self.params['a']/self.params['T0']**self.params['c']).ljust(12,' ')  \
     933                  +str(self.params['c']).ljust(12,' ')+str(self.params['b'])
     934
     935class reaction_constant_dens_dep(reaction_constant):
     936    """ Type 2 reaction rate constant (Arrhenius with high pressure term)
    464937   
    465938    Instantiates type 2 rate constant for a particular reaction
    … …  
    495968        return self.params['g']*np.exp(-self.params['h']/T)+num*density**self.params['dup']/(1+num/den*density**self.params['ddown'])*self.params['fc']**(1/(1+np.log10(num/den*density)**2))
    496969
    497 # TODO: implement type 3 reaction constant
    498 
    499 def read_reactfile(path):
    500     """ Reads the reactfile formatted for simulations with the Generic PCM
    501    
    502     Parameters
    503     ----------
    504     path : str
    505         Path to the reactfile (to become reaction.def)
    506 
    507     Returns
    508     -------
    509     dict
    510         Keys are reactions formulae, items are reactions instances
    511     """
    512 
    513     reactions = {}
    514     with open(path+'/chemnetwork/reactfile') as reactfile:
    515         for line in reactfile:
    516             # Commented line
    517             if line[0] == '!':
    518                 # Hard-coded reaction
    519                 if 'hard' in line and 'coded' in line:
    520                     hard_coded_reaction = reaction(line[1:51].split(),line[51:101].split(),None)
    521                     print('reaction ',hard_coded_reaction.formula,'seems to be hard-coded. Add it manually if needed.')
    522                 continue
    523             else:
    524                 reactants = line[:50].split()
    525                 products  = line[50:100].split()
    526                
    527                 # Photolysis
    528                 if 'hv' in line:
    529                     reactants.pop(reactants.index('hv'))
    530                     new_reaction = photolysis(reactants,products)
    531                    
    532                 # Other reactions
    533                 else:
    534                     cst_params = [float(val) for val in line[101:].split()]
    535                    
    536                     # three-body reaction
    537                     if 'M' in line:
    538                        
    539                         # if third body is not the background gas
    540                         if line[line.index('M')+2] != ' ':
    541                             third_body = reactants[reactants.index('M')+1]
    542                         else:
    543                             third_body = 'background'
    544                         reactants.pop(reactants.index('M'))
    545                         try:
    546                             products.pop(reactants.index('M'))
    547                         except:
    548                             pass
    549                         if int(line[100]) == 1:
    550                             rate_constant = reaction_constant({param_key:cst_params[i] for i,param_key in enumerate(['a','b','c','T0','d'])})
    551                             # if the third body is not the background gas, we treat it as a two-body reaction
    552                             if third_body != 'background':
    553                                 products.append(third_body)
    554                                 rate_constant.params['d'] = 0
    555                         elif int(line[100]) == 2:
    556                             rate_constant = reaction_constant_type2({param_key:cst_params[i] for i,param_key in enumerate(['k0','n','a0','kinf','m','b0','T0','fc','g','h','dup','ddown'])})
    557                             if third_body != 'background':
    558                                 raise Exception('Dont know how to handle three body reaction with type 2 constant when third body is not the background gas.')
    559                         else:
    560                             raise Exception('rate constant parameterization type ',line[100],' not recognized.')
    561                         new_reaction = termolecular_reaction(reactants,products,rate_constant)
    562 
    563                     # two-body reaction
    564                     else:
    565                         rate_constant = reaction_constant({param_key:cst_params[i] for i,param_key in enumerate(['a','b','c','T0','d'])})
    566                         new_reaction = reaction(reactants,products,rate_constant)
    567                
    568                 reactions[new_reaction.formula] = new_reaction
    569 
    570     return reactions
    571                
    572 
    573 def density(P,T,VMR=1):
    574     """ Computes molecular density using the perfect gas law
    575 
    576     Parameters
    577     ----------
    578     P : float
    579         Pressure [Pa]
    580     T : float
    581         Temperature [K]
    582     VMR : float (optional)
    583         Volume mixing ratio [cm^3/cm^3]
    584 
    585     Returns
    586     -------
    587     float
    588         Molecular density [cm^-3]
    589     """
    590    
    591     m3_to_cm3 = 1e6
    592     return VMR * P / R / T / m3_to_cm3 * N_A
    593 
    594 def compute_rates(s,reactions='read'):
    595     """ Computes reaction rates for a simulation
    596 
    597     Parameters
    598     ----------
    599     s : GPCM_simu
    600         Simulation object
    601     reactions : dict or 'read' (optional)
    602         Dictionnary of reactions whose rate to compute as returned by read_reactfile
    603         If nothing passed, call read_reactfile to identify reactions
    604 
    605     Returns
    606     -------
    607     GPCM_simu
    608         Simulation object with reactions rates, rates constants, species vmr and densities as new fields
    609     """
    610 
    611     if reactions == 'read':
    612         reactions      = read_reactfile(s.path)
    613         s.tracers   = read_traceurs(s.path)
    614         s.species   = []
    615         s.reactions = {} # reactions dict will be merged at the end
    616 
    617     # Register new species
    618     for r in reactions:
    619         for sp in reactions[r].reactants:
    620             if not sp in s.tracers or not s.tracers[sp].is_chim:
    621                 raise Warning(sp, 'is not recorded as a chimical tracer')
    622             if not sp in s.species:
    623                 s.species.append(sp)
    624         for sp in reactions[r].products:
    625             if not sp in s.tracers or not s.tracers[sp].is_chim:
    626                 raise Warning(sp, 'is not recorded as a chimical tracer')
    627             if not sp in s.species:
    628                 s.species.append(sp)
    629        
    630     densities = {}
    631 
    632     # Background density
    633     s['total density']     = density(s['p'],s['temp']).assign_attrs({'units':'cm^-3.s^-1'})
    634 
    635     for sp in s.species:
    636         # volume mixing ratios
    637         s[sp+' vmr'] = s[sp] * s.tracers['co2'].M / s.tracers[sp].M
    638         s[sp+' vmr'] = s[sp+' vmr'].assign_attrs({'units':'m^3/m^3'})
    639         # molecular densities
    640         s[sp+' density'] = density(s['p'],s['temp'],VMR=s[sp+' vmr'])
    641         s[sp+' density'] = s[sp+' density'].assign_attrs({'units':'cm^-3'})
    642         densities[sp] = s[sp+' density']
    643 
    644     for r in reactions:
    645 
    646         # Photolysis
    647         if type(reactions[r]) == photolysis:
    648 
    649             # Cases with branching ratios
    650             if reactions[r].reactants[0] == 'co2':
    651                 if 'o1d' in reactions[r].products:
    652                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jco2_o1d'],densities)
    653                 else:
    654                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jco2_o'],densities)
    655             elif reactions[r].reactants[0] == 'o2':
    656                 if 'o1d' in reactions[r].products:
    657                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jo2_o1d'],densities)
    658                 else:
    659                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jo2_o'],densities)
    660             elif reactions[r].reactants[0] == 'o3':
    661                 if 'o1d' in reactions[r].products:
    662                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jo3_o1d'],densities)
    663                 else:
    664                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jo3_o'],densities)
    665             elif reactions[r].reactants[0] == 'ch2o':
    666                 if 'cho' in reactions[r].products:
    667                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jch2o_cho'],densities)
    668                 else:
    669                     s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jch2o_co'],densities)
    670             elif reactions[r].reactants[0] == 'h2o_vap':
    671                 s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['jh2o'],densities)
    672             else:
    673                 # General case
    674                 s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['j'+reactions[r].reactants[0]],densities)
    675         else:
    676             s['k ('+reactions[r].formula+')'] = reactions[r].constant(s['temp'],densities[background])
    677             s['rate ('+reactions[r].formula+')'] = reactions[r].rate(s['temp'],densities)
    678 
    679             # Termolecular reaction
    680             if type(reactions[r]) == termolecular_reaction:
    681                 s['k ('+reactions[r].formula+')'] = s['k ('+reactions[r].formula+')'].assign_attrs({'units':'cm^6.s^-1'})
    682 
    683             # Bimolecular reaction
    684             else:
    685                 s['k ('+reactions[r].formula+')'] = s['k ('+reactions[r].formula+')'].assign_attrs({'units':'cm^3.s^-1'})
    686                
    687         s['rate ('+reactions[r].formula+')'] = s['rate ('+reactions[r].formula+')'].assign_attrs({'units':'cm^-3.s^-1'})
    688                
    689     s.reactions = s.reactions | reactions
    690    
    691     return s
    692 
    693 class trac:
    694     """ Tracer class
    695    
    696     A class to store useful informations about simulations tracers
     970    @classmethod
     971    def from_string(cls,line,format='GPCM'):
     972        """ Creates an instance from an ASCII string in a variety of formats
     973
     974        Currently read formats: Generic PDM, vulcan
     975
     976        Parameters
     977        ----------
     978        line : string
     979            Rate constant parameters
     980        format : string (optional)
     981            Format in which parameters are writtenn (default: Generic PCM)
     982
     983        Returns
     984        -------
     985        reaction_constant
     986            The instance of reaction_constant created
     987
     988        """
     989        if format == 'GPCM':
     990            cst_param = line.split()
     991            return cls({'k0':float(cst_param[0]),  'n':float(cst_param[1]),   'a0':float(cst_param[2]),
     992                        'kinf':float(cst_param[3]),'m':float(cst_param[4]),   'b0':float(cst_param[5]),
     993                        'T0':float(cst_param[6]),  'fc':float(cst_param[7]),  'g':float(cst_param[8]),
     994                        'h':float(cst_param[9]),   'dup':float(cst_param[10]),'ddown':float(cst_param[10])})
     995
     996        elif format == 'vulcan':
     997            cst_param = line.split()
     998            T0 = 300.
     999            return cls({'k0':float(cst_param[0])*T0**float(cst_param[1]),  'n':float(cst_param[1]),'a0':float(cst_param[2]),
     1000                        'kinf':float(cst_param[3])*T0**float(cst_param[4]),'m':float(cst_param[4]),'b0':float(cst_param[5]),
     1001                        'T0':T0,'fc':0.6,'g':0.,'h':0.,'dup':1,'ddown':1})
     1002
     1003    def to_string(self,format='GPCM'):
     1004        """ Return an ASCII line readable by a photochemical model
     1005
     1006        Format
     1007        ------
     1008        GPCM
     1009            1    k0          n           a0          kinf        m           b0          T0          fc          g           h           dup         ddown
     1010        vulcan
     1011            A_0         B_0          C_0      A_inf       B_int        C_inf
     1012
     1013        Parameter
     1014        ---------
     1015        format : string (optional)
     1016            Model format to write in (default: GPCM, options: GPCM, vulcan)
     1017
     1018        Returns
     1019        -------
     1020        string
     1021            ASCII line readable by a photochemical model
     1022        """
     1023        if format == 'GPCM':
     1024            return '2    '+'{:1.2e}'.format(self.params['k0']).ljust(12,' ') +str(self.params['T0']).ljust(12,' ')  +str(self.params['n']).ljust(12,' ')\
     1025                          +str(self.params['a0']).ljust(12,' ') +'{:1.2e}'.format(self.params['kinf']).ljust(12,' ')+str(self.params['m']).ljust(12,' ') \
     1026                          +str(self.params['b0']).ljust(12,' ') +str(self.params['g']).ljust(12,' ')  +str(self.params['h']).ljust(12,' ')   \
     1027                          +str(self.params['dup']).ljust(12,' ')+str(self.params['ddown']).ljust(12,' ')+str(self.params['fc'])
     1028        elif format == 'vulcan':
     1029            return '{:1.2e}'.format(self.params['k0']/self.params['T0']**self.params['n']).ljust(12,' ')+str(self.params['n']).ljust(12,' ')\
     1030                  +str(self.params['a0']).ljust(12,' ')+'{:1.2e}'.format(self.params['kinf']/self.params['T0']**self.params['m']).ljust(12,' ') \
     1031                  +str(self.params['m']).ljust(12,' ')+str(self.params['b0'])
     1032
     1033class network:
     1034    """ Reaction network object
    6971035   
    6981036    Attributes
    6991037    ----------
    700     name : string
    701         Tracer's name
    702     M : float
    703         Tracer's molar mass [g/mol]
    704     is_chim : bool
    705         Is it a photochemical tracer?
     1038    reactions : dict
     1039        Chemical reactions in the network
     1040    species : list(string)
     1041        Chemical species in the network
     1042
     1043    Methods
     1044    -------
     1045    append(to_append)
     1046        Computes the reaction rate for given temperature and density
     1047    update_species()
     1048        Update list of species from list of reactions
     1049    from_file(path,format)
     1050        Instantiates a network from a file
     1051    to_file(path,format)
     1052        Save the network into a file
     1053    get_subnetwork(criteria)
     1054        Generate a subnetwork based on a dictionnary of given criteria
    7061055    """
    707     def __init__(self,name,M,is_chim):
    708         self.name    = name
    709         self.M       = M
    710         self.is_chim = is_chim
    711 
    712 def read_traceurs(path):
    713     """ Read the traceurs of a simulation
    714 
    715     Parameters
    716     ----------
    717     path : string
    718         path to simulation
    719 
    720     Returns
    721     -------
    722     dict
    723         dictionnaries of all tracers
    724     """
    725     tracdict = {}
    726     with open(path+'/traceur.def') as tracfile:
     1056    def __init__(self,reactions={}):
     1057
     1058        self.reactions = reactions
     1059        self.species   = []
     1060        if reactions != {}:
     1061            self.update_species()
     1062
     1063    def __getitem__(self,formula):
     1064
     1065        return self.reactions[formula]
     1066
     1067    def __iter__(self):
     1068
     1069        self.current = list(self.reactions.keys())[0]
     1070        return self
     1071
     1072    def __next__(self):
     1073
     1074        if self.current == 'finished':
     1075            raise StopIteration
     1076        elif self.current == list(self.reactions.keys())[-1]:
     1077            current = self.current
     1078            self.current = 'finished'
     1079        else:
     1080            current = self.current
     1081            self.current = list(self.reactions.keys())[list(self.reactions.keys()).index(self.current)+1]
     1082           
     1083        return self.reactions[current]
     1084   
     1085    def append(self,to_append):
     1086        """ Append a reaction to the network
     1087
     1088        Updates list of species to account for new species
     1089
     1090        Parameter
     1091        ---------
     1092        to_append : reaction or network
     1093            Reaction or network of reactions to append
     1094        """
     1095        if type(to_append) == network:
     1096            for r in to_append:
     1097                self.append(r)
     1098        else:
     1099            self.reactions = self.reactions | {to_append.formula:to_append}
     1100           
     1101        self.update_species()
     1102
     1103    def update_species(self):
     1104        """ Update list of species from list of reactions
    7271105       
    728         for iline,line in enumerate(tracfile):
    729            
    730             # Empty line
    731             if len(line.split()) == 0:
    732                 continue
     1106        """
     1107        if self.reactions == {}:
     1108            raise Exception('Network empty')
     1109           
     1110        for r in self:
     1111            for sp in r.reactants:
     1112                if not sp in self.species:
     1113                    self.species.append(sp)
     1114            for sp in r.products:
     1115                if not sp in self.species:
     1116                    self.species.append(sp)
     1117
     1118    @classmethod
     1119    def from_file(cls,path,format='GPCM'):
     1120        """ Instantiates a network from a file
     1121
     1122        Currently read formats: Generic PCM, VULCAN
     1123       
     1124        Parameters
     1125        ----------
     1126        path : str
     1127            Path to the network file
     1128        format : string (optional)
     1129            Format of the network file to read. Default: GPCM
     1130   
     1131        Returns
     1132        -------
     1133        network
     1134            The network instance created
     1135        """
     1136        reactions = {}
     1137
     1138        if format == 'GPCM':
     1139            with open(path) as reactfile:
     1140                for line in reactfile:
     1141                    # Commented line
     1142                    if line[0] == '!':
     1143                        # Hard-coded reaction
     1144                        if 'hard' in line and 'coded' in line:
     1145                            hard_coded_reaction = reaction(line[1:51].split(),line[51:101].split(),None)
     1146                            print('reaction ',hard_coded_reaction.formula,'seems to be hard-coded. Add it manually if needed.')
     1147                        continue
     1148                    else:
     1149                        # Photolysis
     1150                        if 'hv' in line:
     1151                            new_reaction = photolysis.from_string(line,format)
     1152                        # Other reactions
     1153                        else:
     1154                            # three-body reaction
     1155                            if 'M' in line:
     1156                                if line[line.index('M')+2] != ' ':
     1157                                    # if third body is not the background gas, treat it as a simple reaction
     1158                                    new_reaction = reaction.from_string(line.replace('M',' '),format)
     1159                                else:
     1160                                    new_reaction = termolecular_reaction.from_string(line,format)
     1161                            # two-body reaction
     1162                            else:
     1163                                new_reaction = reaction.from_string(line,format)
     1164                        reactions[new_reaction.formula] = new_reaction
     1165
     1166        elif format == 'vulcan':
     1167           
     1168            with open(path) as reactfile:
     1169                re_tri     = False
     1170                re_tri_k0  = False
     1171                special_re = False
     1172                conden_re  = False
     1173                recomb_re  = False
     1174                photo_re   = False
     1175                ion_re     = False
     1176                for line in reactfile:
     1177                    if line.startswith("# 3-body"):
     1178                        re_tri = True
     1179                       
     1180                    if line.startswith("# 3-body reactions without high-pressure rates"):
     1181                        re_tri_k0 = True
     1182                       
     1183                    elif line.startswith("# special"):
     1184                        re_tri = False
     1185                        re_tri_k0 = False
     1186                        special_re = True # switch to reactions with special forms (hard coded) 
     1187                   
     1188                    elif line.startswith("# condensation"):
     1189                        re_tri = False
     1190                        re_tri_k0 = False
     1191                        special_re = False
     1192                        conden_re = True
     1193                   
     1194                    elif line.startswith("# radiative"):
     1195                        re_tri = False
     1196                        re_tri_k0 = False
     1197                        special_re = False
     1198                        conden_re = False
     1199                        recomb_re = True
     1200                       
     1201                    elif line.startswith("# photo"):
     1202                        re_tri = False
     1203                        re_tri_k0 = False
     1204                        special_re = False # turn off reading in the special form
     1205                        conden_re = False
     1206                        recomb_re = False
     1207                        photo_re = True
     1208                         
     1209                    elif line.startswith("# ionisation"):
     1210                        re_tri = False
     1211                        re_tri_k0 = False
     1212                        special_re = False # turn off reading in the special form
     1213                        conden_re = False
     1214                        recomb_re = False
     1215                        photo_re = False
     1216                        ion_re = True
     1217
     1218                    if line.startswith("#") or line.split() == []:
     1219                        continue
     1220                    else:
     1221                        line = line[line.index('['):]
     1222
     1223                    if re_tri:
     1224                        new_reaction = termolecular_reaction.from_string(line,format,False)
     1225                    elif re_tri_k0:
     1226                        new_reaction = termolecular_reaction.from_string(line,format,True)
     1227                    elif special_re:
     1228                        print('special reaction :',line[line.index('[')+1:line.index(']')])
     1229                    elif conden_re:
     1230                        print('condensation reaction :',line[line.index('[')+1:line.index(']')])
     1231                    elif recomb_re:
     1232                        print('recombination reaction :',line[line.index('[')+1:line.index(']')])
     1233                    elif photo_re:
     1234                        new_reaction = photolysis.from_string(line,format)
     1235                    elif ion_re:
     1236                        print('ionisation reaction :',line[line.index('[')+1:line.index(']')])
     1237                    else:
     1238                        new_reaction = reaction.from_string(line,format)
     1239
     1240                    reactions[new_reaction.formula] = new_reaction
     1241       
     1242        return cls(reactions)
     1243
     1244    def to_file(self,path,format='GPCM'):
     1245        """ Save the network into a file
     1246
     1247        Currently read formats: Generic PCM, VULCAN
     1248       
     1249        Parameters
     1250        ----------
     1251        path : str
     1252            Path to the network file to create
     1253        format : string (optional)
     1254            Format of the network file to read. Default: GPCM
     1255        """
     1256        if format == 'GPCM':
     1257            with open(path, 'w') as reactfile:
     1258                for i,r in enumerate(self):
     1259                    reactfile.write(f'# Reaction {str(i+1)}: {r.formula}\n')
     1260                    reactfile.write(r.to_string(format)+'\n')
     1261                   
     1262        elif format == 'vulcan':
     1263            with open(path, 'w') as reactfile:
    7331264               
    734             # First line
    735             elif iline == 0:
    736                 if not '#ModernTrac-v1' in line:
    737                     raise Exception('Can only read modern traceur.def')
    738                 continue
     1265                # header
     1266                reactfile.write('# VULCAN photochemical network\n')
     1267                reactfile.write('# Generated by the photochemical tools of the Generic PCM\n')
     1268                reactfile.write('#########################################################\n')
     1269                reactfile.write('# in the form of k = A T^B exp(-C/T)\n')
     1270                # 2-body reactions
     1271                reactfile.write('# Two-body Reactions\n')
     1272                reactfile.write('# id   Reactions                                    A           B           C\n')
     1273                reactfile.write('\n')
     1274                n = 1
     1275                for r in self.reactions:
     1276                    if type(self.reactions[r]) == reaction:
     1277                        reactfile.write(' '+str(n).ljust(7,' ')+self.reactions[r].to_string(format)+'\n')
     1278                        n += 1
     1279
     1280                # 3-body reactions with high-pressure term
     1281                reactfile.write('\n')
     1282                reactfile.write('# 3-body and Disscoiation Reactions\n')
     1283                reactfile.write('# id   # Reactions                                  A_0         B_0         C_0         A_inf       B_inf       C_inf\n')
     1284                reactfile.write('\n')
     1285                for r in self.reactions:
     1286                    if type(self.reactions[r]) == termolecular_reaction and type(self.reactions[r].constant) == reaction_constant_dens_dep:
     1287                        reactfile.write(' '+str(n).ljust(7,' ')+self.reactions[r].to_string(format)+'\n')
     1288                        n += 1
     1289
     1290                # 3-body reactions without high-pressure term
     1291                reactfile.write('\n')
     1292                reactfile.write('# 3-body reactions without high-pressure rates\n')
     1293                reactfile.write('# id   # Reactions                                  A_0         B_0         C_0\n')
     1294                reactfile.write('\n')
     1295                for r in self.reactions:
     1296                    if type(self.reactions[r]) == termolecular_reaction and type(self.reactions[r].constant) == reaction_constant:
     1297                        reactfile.write(' '+str(n).ljust(7,' ')+self.reactions[r].to_string(format)+'\n')
     1298                        n += 1
     1299
     1300                # photolysis
     1301                reactfile.write('\n')
     1302                reactfile.write('# reverse stops\n')
     1303                reactfile.write('# photo disscoiation (no reversals)                        # use sp to link br_index to RXXX\n')
     1304                reactfile.write('# id   # Reactions                                  sp     br_index #(starting from 1)\n')
     1305                reactfile.write('\n')
     1306                for r in self.reactions:
     1307                    if type(self.reactions[r]) == photolysis:
     1308                        reactfile.write(' '+str(n).ljust(7,' ')+self.reactions[r].to_string(format)+'\n')
     1309                        n += 1
     1310
     1311    def save_traceur_file(self,path):
     1312
     1313        with open(path, 'w') as tracfile:
     1314            tracfile.write('#ModernTrac-v1\n')
     1315            tracfile.write(str(len(self.species))+'\n')
     1316            for sp in self.species:
     1317                tracfile.write(sp.ljust(24,' ')+'mmol='.ljust(10,' ')+'is_chim=1\n')
     1318
     1319    def get_subnetwork(self,criteria):
     1320        """ Generate a subnetwork based on a dictionnary of given criteria
     1321
     1322        Parameter
     1323        ---------
     1324        criteria : dict
     1325            Selection criteria to include reactions in the subnetwork.
     1326            Criteria are: - 'species' : list of any of the species from the network
     1327                          - 'element' : list of elements to include
     1328                          - 'type'    : reaction, termolecular_reaction, photolysis
     1329
     1330        """
     1331        subnetwork = network()
     1332       
     1333        for r in self.reactions:
     1334           
     1335            keep = False
     1336           
     1337            if 'type' in criteria:
     1338                if type(self.reactions[r]) in criteria['type']:
     1339                    keep = True
     1340                   
     1341            if 'species' in criteria:
     1342                for sp in criteria['species']:
     1343                    if sp in self.reactions[r].reactants + self.reactions[r].products:
     1344                        keep = True
     1345                       
     1346            if 'elements' in criteria:
     1347                for elem in criteria['elements']:
     1348                    for sp in self.reactions[r].reactants + self.reactions[r].products:
     1349                        if elem in sp:
     1350                            keep = True
     1351                           
     1352            if keep:
     1353                subnetwork.append(self.reactions[r])
    7391354               
    740             # Second line (number of tracers)
    741             elif iline == 1:
    742                 ntrac = int(line)
    743                 continue
    744            
    745             # Commented line
    746             elif line[0] == '!':
    747                 continue
    748 
    749             # Regular entry
    750             else:
    751                 line    = line.split()
    752                 name    = line[0]
    753                 is_chim = False
    754                
    755                 for param in line[1:]:
    756                     if param[:4] == 'mmol':
    757                         M = float(param[5:])
    758                     elif param[:7] == 'is_chim':
    759                         is_chim = bool(float(param[8:]))
    760                        
    761                 tracdict[name] = trac(name,M,is_chim)
    762                
    763     if len(tracdict) != ntrac:
    764         raise Exception('Mismatch between announced an read number of tracers')
    765        
    766     return tracdict
     1355        return subnetwork

  • trunk/LMDZ.GENERIC/utilities/photochemistry/reaction_rate_lib.py

    r3431 r3528  
    11import numpy as np
    22
    3 def k_JPL_2015(T,dens):
     3def k_CO_OH_to_CO2_H_JPL_2015(T,dens):
    44    """ Computes rate for CO + OH -> CO2 + H from JPL 2015 """
    55    rate1 = 1.15e-13*T/300.
    … …  
    1010    return rate1 + rate2 * 0.6**xpo2
    1111
    12 def k_Joshi_2006(T,dens):
    13     """ Computes rate for CO + OH -> CO2 + H from Joshi et al 2006 """
     12def k_CO_OH_to_CO2_H_Joshi_2006(T,dens):
     13    """ Computes rate for CO + OH -> CO2 + H from Joshi and Wang 2006 """
    1414    k1a0 = 1.34*2.5*dens                                  \
    1515         * 1/(1/(3.62e-26*T**(-2.739)*np.exp(-20./T))  \
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