| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "markdown", |
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| 5 | "id": "99703d03-5db7-4850-add9-77033d4c6217", |
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| 6 | "metadata": {}, |
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| 7 | "source": [ |
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| 8 | "# Generic PCM Photochemistry postprocessing and visualization demonstrator" |
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| 9 | ] |
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| 10 | }, |
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| 11 | { |
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| 12 | "cell_type": "markdown", |
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| 13 | "id": "a6b7b35c-fb19-4bda-81dd-ee03df1e4ef8", |
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| 14 | "metadata": {}, |
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| 15 | "source": [ |
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| 16 | "This Notebook will show you how to use the Generic PCM photochemistry postprocessing library and how to make interactive visualization with it. For it to work, you'll need to copy the *photochemistry_postprocessing.py* file along this notebook in the directory containing the output file (*diagfi.nc*) as well as the reaction network file chemnetwork/reactfile (to become *reaction.def*)." |
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| 17 | ] |
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| 18 | }, |
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| 19 | { |
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| 20 | "cell_type": "markdown", |
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| 21 | "id": "ed55c2f3-5fa8-481c-b9d8-8e31f93f7993", |
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| 22 | "metadata": {}, |
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| 23 | "source": [ |
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| 24 | "## Loading simulation and calculating reaction rates" |
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| 25 | ] |
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| 26 | }, |
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| 27 | { |
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| 28 | "cell_type": "markdown", |
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| 29 | "id": "50a0abe3-e224-4b07-95dc-a424dd35cdcc", |
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| 30 | "metadata": {}, |
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| 31 | "source": [ |
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| 32 | "If you do not have a simulation with photochemistry at hand, you can download an example file with: \n", |
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| 33 | "```\n", |
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| 34 | "wget https://web.lmd.jussieu.fr/~mmaurice/photochem_example.zip\n", |
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| 35 | "unzip photochem_example.zip\n", |
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| 36 | "```" |
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| 37 | ] |
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| 38 | }, |
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| 39 | { |
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| 40 | "cell_type": "code", |
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| 41 | "execution_count": 1, |
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| 42 | "id": "cd28bac5-65f6-464b-9c2b-6cba1bde9472", |
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| 43 | "metadata": {}, |
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| 44 | "outputs": [ |
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| 45 | { |
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| 46 | "name": "stdout", |
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| 47 | "output_type": "stream", |
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| 48 | "text": [ |
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| 49 | "./photochem_example/diagfi.nc loaded, simulations lasts 1.0 sols\n" |
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| 50 | ] |
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| 51 | } |
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| 52 | ], |
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| 53 | "source": [ |
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| 54 | "import photochem_postproc as pcpp\n", |
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| 55 | "\n", |
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| 56 | "sim_path = './photochem_example'\n", |
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| 57 | "NetCDF_filename = 'diagfi.nc'\n", |
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| 58 | "\n", |
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| 59 | "# The simu class is just a wrapper for xr.Dataset\n", |
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| 60 | "my_sim = pcpp.GPCM_simu(sim_path,NetCDF_filename)" |
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| 61 | ] |
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| 62 | }, |
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| 63 | { |
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| 64 | "cell_type": "markdown", |
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| 65 | "id": "703aaead-59d4-4a93-8bfd-25ad8478dee0", |
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| 66 | "metadata": {}, |
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| 67 | "source": [ |
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| 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." |
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| 69 | ] |
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| 70 | }, |
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| 71 | { |
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| 72 | "cell_type": "code", |
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| 73 | "execution_count": 2, |
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| 74 | "id": "355df458-41a8-4d48-a21c-49a427f94ea5", |
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| 75 | "metadata": {}, |
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| 76 | "outputs": [ |
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| 77 | { |
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| 78 | "name": "stdout", |
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| 79 | "output_type": "stream", |
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| 80 | "text": [ |
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| 81 | "reaction no + hv -> n + o seems to be hard-coded. Add it manually if needed.\n", |
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| 82 | "reaction co + oh -> co2 + h seems to be hard-coded. Add it manually if needed.\n" |
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| 83 | ] |
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| 84 | } |
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| 85 | ], |
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| 86 | "source": [ |
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| 87 | "my_sim.network = pcpp.network.from_file(my_sim.path+'/network.def')" |
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| 88 | ] |
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| 89 | }, |
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| 90 | { |
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| 91 | "cell_type": "markdown", |
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| 92 | "id": "63d2fe39-aedc-45b9-bfc8-d7670336a876", |
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| 93 | "metadata": {}, |
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| 94 | "source": [ |
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| 95 | "Some reactions' rates are hard-coded and need to be added manually (you should find their rates in *reaction_rate_lib.py*). To do that we first need to define a new reaction and call again **compute_rates** with the new reaction as second argument:" |
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| 96 | ] |
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| 97 | }, |
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| 98 | { |
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| 99 | "cell_type": "code", |
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| 100 | "execution_count": 3, |
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| 101 | "id": "bf9f8c56-6615-4f9f-acae-c788a9543770", |
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| 102 | "metadata": {}, |
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| 103 | "outputs": [], |
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| 104 | "source": [ |
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| 105 | "# First load the parametrization for its rate\n", |
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| 106 | "from reaction_rate_lib import k_CO_OH_to_CO2_H_JPL_2015\n", |
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| 107 | "\n", |
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| 108 | "# Then create the reaction objet (here for the reaction co + oh -> co2 + h):\n", |
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| 109 | "hard_coded_reaction = pcpp.reaction(['co','oh'],['co2','h'],k_CO_OH_to_CO2_H_JPL_2015)\n", |
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| 110 | "\n", |
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| 111 | "my_sim.network.append(hard_coded_reaction)" |
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| 112 | ] |
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| 113 | }, |
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| 114 | { |
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| 115 | "cell_type": "markdown", |
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| 116 | "id": "5b193a59-9338-487e-977e-35556a7a405a", |
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| 117 | "metadata": {}, |
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| 118 | "source": [ |
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| 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!)" |
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| 120 | ] |
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| 121 | }, |
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| 122 | { |
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| 123 | "cell_type": "code", |
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| 124 | "execution_count": 4, |
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| 125 | "id": "776388e6-0506-428b-a99c-f02283bad4d8", |
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| 126 | "metadata": {}, |
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| 127 | "outputs": [], |
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| 128 | "source": [ |
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| 129 | "my_sim.compute_rates()" |
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| 130 | ] |
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| 131 | }, |
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| 132 | { |
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| 133 | "cell_type": "markdown", |
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| 134 | "id": "042e4416-5ad3-4d1d-bbe7-896619253146", |
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| 135 | "metadata": {}, |
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| 136 | "source": [ |
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| 137 | "## Now let's do some visualization" |
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| 138 | ] |
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| 139 | }, |
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| 140 | { |
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| 141 | "cell_type": "markdown", |
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| 142 | "id": "d5e0e556-863a-4f39-b6be-ebe1402a5f95", |
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| 143 | "metadata": {}, |
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| 144 | "source": [ |
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| 145 | "### Static visualization\n", |
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| 146 | "Here we use the built-in visualization methods of the *simu* class." |
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| 147 | ] |
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| 148 | }, |
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| 149 | { |
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| 150 | "cell_type": "code", |
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| 151 | "execution_count": 5, |
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| 152 | "id": "f34cbcfa-339b-46dd-b33e-4c699a118f60", |
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| 153 | "metadata": {}, |
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| 154 | "outputs": [], |
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| 155 | "source": [ |
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| 156 | "import matplotlib.pyplot as plt" |
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| 157 | ] |
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| 158 | }, |
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| 159 | { |
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| 160 | "cell_type": "markdown", |
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| 161 | "id": "779d0619-582e-4628-8161-5c0d9d943261", |
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| 162 | "metadata": {}, |
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| 163 | "source": [ |
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| 164 | "#### Meridional slice" |
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| 165 | ] |
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| 166 | }, |
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| 167 | { |
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| 168 | "cell_type": "code", |
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| 169 | "execution_count": 6, |
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| 170 | "id": "f54ea828-ea01-4f8c-9349-e5f051ef7043", |
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| 171 | "metadata": {}, |
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| 172 | "outputs": [ |
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| 173 | { |
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| 174 | "data": { |
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| 175 | "image/png": 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evSfq2qasSwCBxM7ZJDGmeqrmdmU9Agh23sozxktz3FnvrFPYFU1F2Jbdd9JqaqjeYA0BX20jq7d9sG4vAE1n9fjkicZqDrYxL5SzqWaNlUcC7/0KwD98OaZVX3YwZYyZdVbzdTd1fVc9vrH16enz+Gs7HepcSTYFGZqKNsA34SdY/R92XwmWbRys26cms6+TYESwC8UvTWoK5vEv0DMqmLdNdaHyOoEq7jZR0Dgrx49g2I6erD9ff8FVfX1647nqW0ZTthu5FRpoKvqRux8qX3747DqYh9qAEwjhGmrbBIB1/2m261gYquPgoc6VttsmobotgFBU8/Pe1DGpKUfNNeXINCvZccwMhP2fmuy4Hqv4qzYz283O6wu+Q1PRx+z2wfLFNxCestu6sYJVPw1g3QOQ7DMWuFuHP3+aE+qs2PljvQOhraExwJPGYl3Lq+vIbG+NPb74f30wjot2/OKqpmBc701V33ZjXSFkmoqHO1UqZp9/nisQP1jeajYG4mu3i6buwLHugcBzuFOlwqL895/rQB8nzO6wmWlWBvq68RVfNBdZ14D9+TOfzP6ay5vjvz9/ourJ44OVXRuLwb7em4r1g7qETFNR8t1ResH84Qrm12ZX7uw0s12A4OLLC2kE63jhzdcVrOvIm5qyg8z6BQKXr3/l5OlpNwLl/8hmx0671R8qWO+A50KqqVgfBhHYFe9NBJMnnnhCCxYskMPh0JQpU3T99ddbXVLAYCyAXfj66pOAFcgn85p62gpvXQk3UHDQQG12OFox1NY5AkugZBNNRQCAz3311VdatGiR8vPzZRiGBg8erEsvvVStW7e2ujQAHqjaGWSHDIGOfPKtupppoT5uhPrrtxrrH4EgkLLJficyAAAEnW+//Vbp6emKiopSdHS0UlJStHz5cqvLAtAE7JghGJBP/nOocyXjBixR9d7j/YdAEUjZRFMRAKBVq1ZpxIgRSkpKksPh0NKlS2vNk5OTo27duikqKkppaWlat26d28vv27evVq5cqQMHDujnn3/WypUrtXPnTi++AgBAMCKfgOBFkw+Bimw6gZ8/AwBUVlamlJQU3XjjjRo1alSt+xcvXqzs7Gzl5uYqLS1N8+bNU2ZmprZs2aIOHTpIklJTU3X8+PFaj/3oo4/Uu3dv3X777RoyZIji4uJ01llnKTw83OevCwAQ2MgnAE1B4xK+QDadQFMRAIJUSUmJy+3IyEhFRkbWOe+wYcM0bNiwepc1Z84cjR8/XuPGjZMk5ebmatmyZZo/f76mTJkiSSooKGiwnltuuUW33HKLJOn3v/+9TjnlFHdfCgAgiJBPAKr48oItNBRhBtnkGZqKAGAzrbYdVrNmhsePP378iCQpOTnZZfr06dM1Y8YM08srLy9Xfn6+pk6d6pwWFhamjIwMrVmzxu3l7NmzRx06dNCWLVu0bt065ebmmq4FAGCNpmaTRD4B8B8aiqGDfSdr0VQEgCC1Y8cOxcbGOm/X901bY/bt26eKigolJCS4TE9ISNDmzZvdXs7ll1+ugwcPqmXLllqwYIGaNSOCACAUkU8AfImGIjxBNnnGnlUBAJosNjbWJRitZuabOQBA8CKfAFTnzZ9A01CEp8gmz9BURFBolnTIZ8s+vquFz5YNBIJ27dopPDxcxcXFLtOLi4uVmJhoUVVA4PBVRpFPCHXkE+B9TcmspuRSKDcD61vn3sz5qufg/w6+F2rZRFMRtubLZqG3arDzwGyH9RcKKg4dsboEn4qIiFD//v2Vl5enkSNHSpIqKyuVl5eniRMnWlscYAG7jK3kExpDPiGQ2akJYqdavM2b43HNZQXb+vJ3dtX1fA2tU3fqM/Mamrr96nsusim40FT0sUD9T7OVARBo68yK8Ay0dQT7Ky0t1datW523CwsLVVBQoPj4eHXp0kXZ2dnKysrSgAEDNHDgQM2bN09lZWXOK5ohsAT6GGJFRgXiOvPHkQ/uPifgKfLJPjz5fHsy3tR8nmZJh/w+7tf3WqtPD/SGmT/GayvWlzuvyxvvS6vUbHD7si4znz27rB9/IZtOoKmo0PsAuKOp6ySQB+qm8mWTMVjWEexn/fr1Gjx4sPN2dna2JCkrK0sLFy7UmDFjtHfvXk2bNk1FRUVKTU3V8uXLa52AGN7D571+/vppVrBuA28fcROs6wn2QD5ZxxufbTP/L27o+TwZtxpaXvXlePo6/dEw88UR6VaN2f442tPd12amFrtmnL/qamxd2XX9+BrZdELINBXDEw8pvEXonqfB3xo7OiKUBp+mBmgorStYZ9CgQTIMo8F5Jk6cGJSH7FuJbPK/xn5KFEpjblPyKZTWE6xFPlkjPPGQpCivL7epY4c7R055+yeg7vDFQQVmXkcgNcd81Vz05PU11hi2yzqzg7o+e6G8fsimE0KmqQh7COWBx2yAhvK6AgB/CvXxlp83AQgk1ccsu45JZs+F587j3Zmfpo/nWFeNC4TPHvyPpiLgZxxCDgCwG7IJQCAJxDHJ3aMZm/LaAmm9ePNcmYH0ugMd6xo10VQELMKADACwG7IJAPyj5k9vGX89w3oDrBVmdQEAAAAAAISqUG2MeeMcmwCsRVMRAAAAAAAEDBqKgD3QVAQAAAAAAH7nyUVpaCgC9sE5FQEAAAAAgO3QQATsjSMVAQAAAACAJepqHHJEIhAYaCoCAAAAAABboJkIBA5+/gwAAAAAACxDIxEITBypCAAAAAAAAMAUjlRE0OqVWOz2vN8WJfiwEuuZWRf1CfZ1BAD+5O64HKxjL7kEAPZkdnxmLPaMr9cz2xH+QlMRAcUbOyFmlmvnwdVX68IuzxdIjpWV639WFwHAMr4cHxtath0zyp9ZQS41jnxCIKv+GbdqvAu0Mdgsb4yjdthO1TX2mppSo5W509h6bmptNR/vyyYm2RRcaCr6UDD9Z9eOQW7V81sZllavDwCBK1jHD3+PyXZaj03dAfAmO60XAN7n7c94Q+NVXc/VK7G4yWOct1+Dv8dgb+2X+OOLMDvvO3raBLVTzvmjFnfWk53WCaxDU1F8GNzhz8On7b49/P1tnN3XBwDf4fPfOH/kU6BsB/IJgLeclrBXzVtG+Gz5dY1XjY0pZhqLVoxP3m6omWmQufOcVhxJ7s/moievz906Qz3vQv31o2Eh01T0dTDCldlv7gJ1oPLlN5SBuk4AuI9s8j93G2+BPgb7ssEY6OsGgL2YGVPqayzaaVyqr1Hlzn6Dp6/DruvFG0eY+kNDzUWr1yFgd5Ze/XnVqlUaMWKEkpKS5HA4tHTpUpf7DcPQtGnT1LFjR0VHRysjI0PfffedNcWiSXolFjf4Fyy88ZqCbZ0AgYh8Cg01x+xgzCXJe7kSjOsGCCRk068CZV+isRq9/ToCZb34grdea83lhNI6BDxlaVOxrKxMKSkpysnJqfP+xx57TE899ZRyc3P15ZdfqmXLlsrMzNSRI0f8XClgntkwD7XwB+yMfAo9oTD+erqjST4B9kA2IRD5Oj98ca5Mcg9wn6U/fx42bJiGDRtW532GYWjevHn6v//7P11++eWSpL/+9a9KSEjQ0qVLdc011/izVMBjjR32T2AB9kM+Idi5+5M0MgqwD7IJcEVGAdaz9EjFhhQWFqqoqEgZGRnOaXFxcUpLS9OaNWvqfdzRo0dVUlLi8gdYraGfPQAILJ7kE9kEO2oogzhKAwgs7Dsh1JBRgD3YtqlYVFQkSUpIcP0WPSEhwXlfXWbPnq24uDjnX3Jysk/rBMyoed4uAIHHk3wim2BXdeUR+QQEHvadYGe++IkyAHuwbVPRU1OnTtXBgwedfzt27LC6JMAFIQiEHrIJdsc5pIDQRD4h0JBTgL3YtqmYmJgoSSoudh00iouLnffVJTIyUrGxsS5/AAB4iyf5RDYBAHyJfSfYnbeubg3AXmzbVOzevbsSExOVl5fnnFZSUqIvv/xS6enpFlYGAAhl5BMAwG7IJgQzmomAfVl69efS0lJt3brVebuwsFAFBQWKj49Xly5dNGnSJM2cOVOnnHKKunfvrvvvv19JSUkaOXKkdUUDAIIe+QQAsBuyCaGIhiJgb5Y2FdevX6/Bgwc7b2dnZ0uSsrKytHDhQt1zzz0qKyvTzTffrAMHDujcc8/V8uXLFRUVZVXJAIAQQD4BAOyGbEKg65VYrG+LEhqfUTQTgUBhaVNx0KBBMgyj3vsdDocefPBBPfjgg36sCgAQ6sgnAIDdkE0IBTQTgcBi23MqAgCCyxVXXKE2bdroyiuvrHXf+++/r9NOO02nnHKK/vKXv1hQHQAgVJFPgP/U1zTkQiyAq0DJJkuPVARgvSHtNtc5/Z/7evq5EgS7O+64QzfeeKNefvlll+nHjx9Xdna2VqxYobi4OPXv319XXHGF2rZta1GlAKxCJsEK5BPgX8HWPKyeXfXlVX35VhdfZF7V85OngSNQsommIoJKY4N1qA2iZsKrscf6at3VVaOdt1Nd9R6JOq5/WFBLoBk0aJBWrlxZa/q6devUp08fderUSZI0bNgwffTRR7r22mv9XCHgH4E27nmbJ9nk73UWaNso0Oq1G/LJvoa028x7GU3iTsPP0+V5cr+7z+Gt933NemguBo5AySaaighYTd0pCcaB1BshZsWyG3ququ1k1bd7/nzdVlq1apUef/xx5efna/fu3Xr77bdrXS0yJydHjz/+uIqKipSSkqKnn35aAwcObPJz79q1yxmKktSpUyft3LmzycsFrOKtppkUHFnlq3G0Zlb4erxu6P8Q7jy3r3YQG5ovGL70Ip9CV9V73S6NRTvVUcUO9dhNQ2NkU7ehFftDntbrbvPT31/QScFxQAbZdAJNRQQcbw3mwRTIwdz48mXzOJjXm1llZWVKSUnRjTfeqFGjRtW6f/HixcrOzlZubq7S0tI0b948ZWZmasuWLerQoYMkKTU1VcePH6/12I8++khJSUk+fw3wvmAaJ/3BF2NKIG8Dq76MsuvzmdmW5NMJ5FNostvRVXZocNY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|
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| 176 | "text/plain": [ |
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| 177 | "<Figure size 640x480 with 6 Axes>" |
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| 178 | ] |
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| 179 | }, |
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| 180 | "metadata": {}, |
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| 181 | "output_type": "display_data" |
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| 182 | } |
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| 183 | ], |
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| 184 | "source": [ |
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| 185 | "plt.subplot(131)\n", |
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| 186 | "my_sim.plot_meridional_slice('h2o_vap',logcb=True)\n", |
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| 187 | "plt.title('Time- and longitude-average')\n", |
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| 188 | "\n", |
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| 189 | "plt.subplot(132)\n", |
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| 190 | "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0)\n", |
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| 191 | "plt.title('Time = 0 and longitude-average')\n", |
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| 192 | "\n", |
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| 193 | "plt.subplot(133)\n", |
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| 194 | "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0,lon=0)\n", |
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| 195 | "plt.title('Time = 0 and longitude = 0°')\n", |
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| 196 | "\n", |
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| 197 | "plt.subplots_adjust(right=2)" |
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| 198 | ] |
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| 199 | }, |
|---|
| 200 | { |
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| 201 | "cell_type": "markdown", |
|---|
| 202 | "id": "3d691c4b-a690-4644-a00a-1ee77c85658a", |
|---|
| 203 | "metadata": {}, |
|---|
| 204 | "source": [ |
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| 205 | "#### Time evolution" |
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| 206 | ] |
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| 207 | }, |
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| 208 | { |
|---|
| 209 | "cell_type": "code", |
|---|
| 210 | "execution_count": 7, |
|---|
| 211 | "id": "2328745a-55e1-40d9-86c7-41f04bea872c", |
|---|
| 212 | "metadata": {}, |
|---|
| 213 | "outputs": [ |
|---|
| 214 | { |
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| 215 | "data": { |
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| 216 | "image/png": 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0MOfOncv3OShMthb0/nihEydOGG9vb/PEE084jL/88st5Mim/x42JiTENGjRwGLv4Oc5vHYzJmz05OTmmcePGJiYmxuTk5Dg8bv369c1NN910yXUpyMX1JCQkGEnm448/to9lZWWZqKgo4+vra9LS0owx599Tq1evbo4dO2Zf9osvvjCSzFdffWUfa9mypbnqqqvMyZMn7WPLly/P87nCGJMnc1555ZV8n5/8Pr8UNEefPn2Mj4+Pw+u1detW4+np6ZCFu3btMp6enubFF190mG/Lli2mQoUKecbz48zfX05Ojqldu7bp16+fw3Jz5841kszKlSuNMcacPHnSVK1a1QwZMsRhuYMHD5qAgACH8dzPG6NGjXKqpvw+Vzn7OhXX5z/gQmQcGZf7uM5kXEHbtEFBQebo0aP2sc2bNxsPDw/zwAMP2Mdyt4su/MxvjDG33367qV69uv36hg0bjCTz2GOPOSwXFxeXJ3fyW+/mzZvn+/eX37ZdfnMcOnTIeHl5mZ49ezo8R//617+MJIe/Q2e3jwqSmymXu+S3Phc6fPhwvtvCxhiTmJhoJJlt27bluc/atWvtnzdQfvHzZydUqlTJ/u+TJ0/qyJEj6tixo06dOqVt27a5pKYDBw4oOTlZsbGxDt863HTTTWrWrJnDsvPmzVNAQIBuuukm+zEQjhw5ooiICPn6+ub701FnXPi8ZGRk6MiRI2rXrp2MMdq0aVOe5f/xj384XO/YsaPD2aQWL16sChUq2PcKlP4+zsUjjzxSqPrWr1+vQ4cO6eGHH3Y49lPPnj3VpEkTLVq0yHKNS5YsUcWKFTVkyBD7mIeHR5F983c5uQfAHTFihMP4xXsdGmP02WefqXfv3jLGOLzuMTExOnHihDZu3HjJx/L29paHx99vEdnZ2Tp69Kh8fX11zTXXONy3b9++qlChgubMmWMf+/nnn7V161YNGDDAPjZv3jx17NhR1apVc6gnOjpa2dnZWrlypcPj9+vXTzVr1nQY8/T0tB9XMScnR8eOHdO5c+fUunVrh5qcfZ2OHTum77//Xv3797f/3z5y5IiOHj2qmJgY7dixI89P5YHCSkxM1Lfffutw+frrr+23F/V7YK4L36v/+usvnThxQh07drzse4D09//9pUuXqk+fPg4/vapVq5buuecerVq1SmlpaQ73GTJkiFPHJyrKbM09PMPcuXNljLGPz5kzR23btlWdOnXyfdzcPWs6deqkP/74QydOnLD0uPlJTk7Wjh07dM899+jo0aP295WMjAx169ZNK1euLJKfNC1evFghISG6++677WMVK1bUiBEjlJ6erhUrVjgsP2DAAFWrVs1+vWPHjpJkz7j9+/dry5YteuCBBxx+adGpUye1bNnyiuu9nOzsbH3zzTfq06ePw+vVtGlTxcTEOCz7+eefKycnR/3793fIk5CQEDVu3NipzzXO/P3ZbDbdeeedWrx4sdLT0+3Lz5kzR7Vr11aHDh0kSd9++62OHz+uu+++26EeT09PRUZG5lvPhf/P86upoM9VVl6n4vr8B+SHjCu4LjKuYLnbtHFxcfaf1UrStddeq5tuuinfk3/kt7129OhR+2u1ZMkSSX/vVXqhK/1bc9Z3332nrKwsPfLIIw4/l87vV2JWt48u9tRTT+X5f5ff5bXXXrvkPLmH2cjvZEO529EXH4qjRo0aioyMlJ+f3yXnRtnHz5+d8Msvv+j//u//9P333+cJlqJ4cy6MP//8U5LyPX7BxU2fHTt26MSJEwoKCsp3rkOHDkn6e10ufLPw8vJyeHO/2O7duzVmzBh9+eWXeY7Nc/Hz4uPjk6dBVK1aNYf7/fnnn6pVq1aen41fc801BdZwKbnPUX73b9KkiVatWlXoGnN/qpSrpM5s9+eff8rDwyPPz6YvXsfDhw/r+PHjeu+99/Tee+/lO1fu637w4EGH8YCAAFWqVEk5OTl644037MfLuPAYMBee/atGjRr2Y208//zzkv7+kFOhQgX17dvXvtyOHTv0v//9L89zfHE9ufI7g6Akffjhh3rttde0bds2nT17Nt/lnX2ddu7cKWOMnn32WT377LMF1nW5n/4BzmjTps0lD2Jf1O+BuRYuXKgXXnhBycnJyszMtI/nd1ygix0+fFinTp3Kt4amTZsqJydHe/bsUfPmze3jBf3fvVhRZ+uAAQO0YMECrVmzRu3atdPvv/+uDRs25Pnp0OrVqzV27FitWbMmzzGYTpw4ccmfBzljx44dknTJn/SdOHHCocFXGH/++acaN25s//InV9OmTe23X+jCjU5J9sfPzbjc5fPLs0aNGjm1gX4lDh8+rNOnTxf4uebCDcsdO3bIGFPgMZxyf9Kbnp7u0Az09PS0Z5Czf38DBgxQQkKCvvzyS91zzz1KT0/X4sWL7T8jz61Hkrp27ZpvPf7+/g7XK1SokO+hapz5XGXldXL28x9QFMg4R2Sccxl3qe21pk2b6ptvvslzMqtL5Zm/v799e+ni16okt9ekvNvpNWvWzPO8WN0+ulizZs3y7FBUGLnN6Av/D+XK/Xn1hQ1r4EI0FS/j+PHj6tSpk/z9/TV+/Hg1bNhQPj4+2rhxo55++ukiP4BqQQFYmIP65srJyVFQUJA++eSTfG/PfRN79NFHHQ7w3qlTpzwH5b2wnptuuknHjh3T008/rSZNmqhKlSrat2+f4uLi8jwvpeGsWq6ssahf99zn/7777isw9K+99lpJf38be6EZM2YoLi5OEyZM0LPPPqsHH3xQzz//vAIDA+Xh4aHHHnssz+t71113aeDAgUpOTlZ4eLjmzp2rbt262Y+rlVvTTTfdpKeeeirfeq6++mqH6/kF18cff6y4uDj16dNHTz75pIKCguTp6amJEyfq999/v8yzklfueowcOTLPnjC5SuoDCGCFzWZz2Fsh18XvGf/9739166236sYbb9SUKVNUq1YtVaxYUTNmzNCsWbOKpTZnPnQWR7b27t1blStX1ty5c9WuXTvNnTtXHh4eDsfC+/3339WtWzc1adJEkydPVlhYmLy8vLR48WK9/vrrl3xcZ9+nc+d45ZVX7Md5vVhBx1wuTgVlXH5/R0WpuD7X2Gw2ff311/muV+7z++qrrzqcSKxu3bratWuXpb+/tm3bql69epo7d67uueceffXVVzp9+rTDnvi5y3/00UcKCQnJU0+FCo4fty/8JUAuq5+rnH2enPn8B7gbMi4vMu68spZnVraPLnbxTkEFudzOQoGBgfL29taBAwfy3JY7FhoaetnHQflEU/Eyli9frqNHj+rzzz/XjTfeaB9PSUnJs6wz34hdbtncby8uPuPxxXsc1K1bV9L5b4sutH37dofrDRs21Hfffaf27dtfMgifeuop3XfffXlqyc+WLVv022+/6cMPP9QDDzxgH//2228LvM/l1K1bV0lJSUpPT3cIo4vXx8p8ufe/eO+B7du322+3OueyZct06tQph73gdu7c6dT9L/W653eW6/xe95ycHP3+++8O3+hd/Bzlnhk6Oztb0dHRl6zp4tcs99vYTz/9VF26dNG0adMcbj9+/LhDs1CS+vTpo6FDh9p/Av3bb79p9OjRDss0bNhQ6enpl63nUj799FM1aNBAn3/+ucNzefFJYpx9nXJ/6lKxYsUrqgsoClbeA6tVq+ZwaIZcF79nfPbZZ/Lx8dE333zj8JOWGTNm5Llvfu9PNWvWVOXKlfOtYdu2bfLw8FBYWNilVywfVrLVWVWqVFGvXr00b948TZ48WXPmzFHHjh0dPgR/9dVXyszM1Jdffumwp4MzPwN1Np9z9yT39/cv1veVunXr6n//+59ycnIcGlS5P6uzmnG5y+eXZ85k3JV+rqlZs6YqVark9OcaY4zq169/yQ2uBx54wP4TZel8M8Dq31///v31xhtvKC0tTXPmzFG9evXUtm1bh3okKSgoqNCvubOfq6y8Ts5+/gNKAhlHxuXnwu21i23btk01atRw2EvR2TlzcnKUkpLisLdgUWyvSX8/RxeeDOxS2+kX/qz+8OHDefZCv9Lto4t3CirIpXYWkv4+TFTLli21fv36PLf9+OOPatCgAT9zRoE4puJl5H4TcuE3H1lZWZoyZUqeZatUqeL07uy5b44Xv3HXrVtXnp6eeY6fcPHj1apVS+Hh4frwww8dHvPbb7/V1q1bHZbt37+/srOz7T9NvdC5c+fsNTRr1kzR0dH2S0RERIH15/e8GGP0xhtvFHify+nRo4fOnTund955xz6WnZ2tt956q1DztW7dWkFBQZo6darDrtxff/21fv31V6fOAHaxmJgYnT17Vu+//759LCcnR4mJiU7dv0qVKvk2Dxs2bKgTJ07of//7n33swIEDmj9/vsNy3bt3lyS9+eabDuMX//TB09NT/fr102effaaff/45z+NdeDbOC1/z6Oho+56Lnp6eeb7xmzdvXr7HGKxatapiYmI0d+5czZ49W15eXurTp4/DMv3799eaNWv0zTff5Ln/8ePHde7cuTzjF8vv7+7HH3/UmjVrHJZz9nUKCgpS586d9e677+b7zZyVs5YCV8rKe2DDhg21bds2h7/RzZs3a/Xq1Q7LeXp6ymazOXyLvmvXLi1YsCDPnPm9P3l6eurmm2/WF198oV27dtnHU1NTNWvWLHXo0CHPTzudYSVbrRgwYID279+vDz74QJs3b3bYm6ygxz1x4kS+G6AXy92QujCfs7Oz8xxiIiIiQg0bNtSrr77q8NPbXEX1vtKjRw8dPHjQ4Xi2586d01tvvSVfX1916tTJ0nyhoaFq0aKF/v3vfzvUvWLFCm3ZsuWy9y/oc42/v79q1Khx2c81np6eiomJ0YIFC7R79277+K+//ponN/r27StPT08999xzeXLKGKOjR49K+vuLowvzrX379vbHyl0216X+/gYMGKDMzEx9+OGHWrJkifr37+9we0xMjPz9/TVhwgSHw3LkcuY1d/ZzlZXXydnPf0BJIOPIuPxcuE1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|
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| 217 | "text/plain": [ |
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| 218 | "<Figure size 640x480 with 6 Axes>" |
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| 219 | ] |
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| 220 | }, |
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| 221 | "metadata": {}, |
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| 222 | "output_type": "display_data" |
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| 223 | } |
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| 224 | ], |
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| 225 | "source": [ |
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| 226 | "plt.subplot(131)\n", |
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| 227 | "my_sim.plot_time_evolution('h2o_vap',logcb=True)\n", |
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| 228 | "plt.title('latitude- and longitude-average')\n", |
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| 229 | "\n", |
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| 230 | "plt.subplot(132)\n", |
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| 231 | "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0)\n", |
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| 232 | "plt.title('Equatorial value, longitude-average')\n", |
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| 233 | "\n", |
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| 234 | "plt.subplot(133)\n", |
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| 235 | "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0,lon=0)\n", |
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| 236 | "plt.title('Equatorial value longitude = 0°')\n", |
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| 237 | "\n", |
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| 238 | "plt.subplots_adjust(right=2)" |
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| 239 | ] |
|---|
| 240 | }, |
|---|
| 241 | { |
|---|
| 242 | "cell_type": "markdown", |
|---|
| 243 | "id": "f287c71d-83eb-4bad-a11e-dfb34e679adf", |
|---|
| 244 | "metadata": {}, |
|---|
| 245 | "source": [ |
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| 246 | "#### Vertical profiles" |
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| 247 | ] |
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| 248 | }, |
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| 249 | { |
|---|
| 250 | "cell_type": "code", |
|---|
| 251 | "execution_count": 8, |
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| 252 | "id": "4f544843-012d-41a6-8d7a-c4b40f45a5e1", |
|---|
| 253 | "metadata": {}, |
|---|
| 254 | "outputs": [ |
|---|
| 255 | { |
|---|
| 256 | "data": { |
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| 257 | "image/png": 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", |
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| 258 | "text/plain": [ |
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| 259 | "<Figure size 640x480 with 1 Axes>" |
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| 260 | ] |
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| 261 | }, |
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| 262 | "metadata": {}, |
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| 263 | "output_type": "display_data" |
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| 264 | } |
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| 265 | ], |
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| 266 | "source": [ |
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| 267 | "my_sim.plot_profile('h2o_vap',logx=True,label='Time- and grid-averaged')\n", |
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| 268 | "my_sim.plot_profile('h2o_vap',t=0,logx=True,label='Time=0, grid-averaged')\n", |
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| 269 | "my_sim.plot_profile('h2o_vap',t=0,lat=0,logx=True,label='Time=0, equatorial value zonally-averaged')\n", |
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| 270 | "my_sim.plot_profile('h2o_vap',t=0,lat=0,lon=0,logx=True,label='Time=0, equatorial value, lon = 0°')\n", |
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| 271 | "plt.legend()\n", |
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| 272 | "plt.grid()" |
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| 273 | ] |
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| 274 | }, |
|---|
| 275 | { |
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| 276 | "cell_type": "markdown", |
|---|
| 277 | "id": "e73804b8-98f3-4283-8c5d-153465db88e7", |
|---|
| 278 | "metadata": {}, |
|---|
| 279 | "source": [ |
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| 280 | "## Interactive visualization\n", |
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| 281 | "Now we go fancy!" |
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| 282 | ] |
|---|
| 283 | }, |
|---|
| 284 | { |
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| 285 | "cell_type": "markdown", |
|---|
| 286 | "id": "7f745af6-fe37-4075-b433-abb03ad3492e", |
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| 287 | "metadata": {}, |
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| 288 | "source": [ |
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| 289 | "#### Define widgets\n", |
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| 290 | "Let's define some widgets (see [here](https://ipywidgets.readthedocs.io/en/latest/) for documenttion on jupyter's widgets):" |
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| 291 | ] |
|---|
| 292 | }, |
|---|
| 293 | { |
|---|
| 294 | "cell_type": "code", |
|---|
| 295 | "execution_count": 9, |
|---|
| 296 | "id": "56b07967-3900-4454-ac0d-29cfae7cc1f9", |
|---|
| 297 | "metadata": {}, |
|---|
| 298 | "outputs": [], |
|---|
| 299 | "source": [ |
|---|
| 300 | "import ipywidgets as widgets\n", |
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| 301 | "\n", |
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| 302 | "# Coordinates\n", |
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| 303 | "w_lat = widgets.FloatSlider(min=-90, max=90, step=1, description=\"latitude\")\n", |
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| 304 | "w_lon = widgets.FloatSlider(min=-180, max=180, step=1, description=\"longitude\")\n", |
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| 305 | "w_alt = widgets.FloatSlider(min=0, max=max(my_sim.data[\"altitude\"]), step=1, description=\"altitude\")\n", |
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| 306 | "w_time = widgets.FloatSlider(min=0, max=max(my_sim[\"Time\"]), step=0.1, description=\"time\")\n", |
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| 307 | "\n", |
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| 308 | "# Fields\n", |
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| 309 | "w_single_sp = widgets.Select(options=my_sim.network.species, value=\"h2o_vap\", description=\"species\")\n", |
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| 310 | "w_multiple_sp = widgets.SelectMultiple(options=my_sim.network.species, value=[\"h2o_vap\"], description=\"species\")\n", |
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| 311 | "w_reactions = widgets.SelectMultiple(options=my_sim.network.reactions.keys(), value=[\"co2 + hv -> co + o\"], description=\"reactions\")\n", |
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| 312 | "\n", |
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| 313 | "# Miscelaneous\n", |
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| 314 | "w_average = widgets.Checkbox(description='show average')" |
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| 315 | ] |
|---|
| 316 | }, |
|---|
| 317 | { |
|---|
| 318 | "cell_type": "markdown", |
|---|
| 319 | "id": "ddeec0ba-12c2-4bd9-a38f-98a0e1949f3b", |
|---|
| 320 | "metadata": {}, |
|---|
| 321 | "source": [ |
|---|
| 322 | "#### OH meridional slice at various longitudes\n", |
|---|
| 323 | "OH is a photolysis product with a short lifetime, so it exist only on the dayside. Scrolling through the longitudes will exhibit this dichotomy." |
|---|
| 324 | ] |
|---|
| 325 | }, |
|---|
| 326 | { |
|---|
| 327 | "cell_type": "code", |
|---|
| 328 | "execution_count": 10, |
|---|
| 329 | "id": "56e81d82-fd55-464c-a746-66cf23822957", |
|---|
| 330 | "metadata": {}, |
|---|
| 331 | "outputs": [ |
|---|
| 332 | { |
|---|
| 333 | "data": { |
|---|
| 334 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 335 | "model_id": "14ac96bee52944c99e53df9d8fd6ed33", |
|---|
| 336 | "version_major": 2, |
|---|
| 337 | "version_minor": 0 |
|---|
| 338 | }, |
|---|
| 339 | "text/plain": [ |
|---|
| 340 | "VBox(children=(FloatSlider(value=0.0, description='longitude', max=180.0, min=-180.0, step=1.0), Output()))" |
|---|
| 341 | ] |
|---|
| 342 | }, |
|---|
| 343 | "execution_count": 10, |
|---|
| 344 | "metadata": {}, |
|---|
| 345 | "output_type": "execute_result" |
|---|
| 346 | } |
|---|
| 347 | ], |
|---|
| 348 | "source": [ |
|---|
| 349 | "# Slice plotting unction\n", |
|---|
| 350 | "def make_slice(lon):\n", |
|---|
| 351 | " my_sim.plot_meridional_slice('oh',t=0,lon=lon,logcb=True)\n", |
|---|
| 352 | "\n", |
|---|
| 353 | "# Define interactive output\n", |
|---|
| 354 | "out = widgets.interactive_output(make_slice,{'lon':w_lon})\n", |
|---|
| 355 | "\n", |
|---|
| 356 | "# Build the output frame\n", |
|---|
| 357 | "widgets.VBox([w_lon,out])" |
|---|
| 358 | ] |
|---|
| 359 | }, |
|---|
| 360 | { |
|---|
| 361 | "cell_type": "markdown", |
|---|
| 362 | "id": "3379c31a-95c6-4ab3-b178-20b706eed1f3", |
|---|
| 363 | "metadata": {}, |
|---|
| 364 | "source": [ |
|---|
| 365 | "#### Water vapor atlas\n", |
|---|
| 366 | "We can have several action widgets" |
|---|
| 367 | ] |
|---|
| 368 | }, |
|---|
| 369 | { |
|---|
| 370 | "cell_type": "code", |
|---|
| 371 | "execution_count": 11, |
|---|
| 372 | "id": "fd7b4103-0436-4bda-bb39-96666c39f332", |
|---|
| 373 | "metadata": {}, |
|---|
| 374 | "outputs": [ |
|---|
| 375 | { |
|---|
| 376 | "data": { |
|---|
| 377 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 378 | "model_id": "7931087537c2471289e1e968f1d99fc2", |
|---|
| 379 | "version_major": 2, |
|---|
| 380 | "version_minor": 0 |
|---|
| 381 | }, |
|---|
| 382 | "text/plain": [ |
|---|
| 383 | "VBox(children=(FloatSlider(value=0.0, description='time', max=1.0), FloatSlider(value=0.0, description='altitu…" |
|---|
| 384 | ] |
|---|
| 385 | }, |
|---|
| 386 | "execution_count": 11, |
|---|
| 387 | "metadata": {}, |
|---|
| 388 | "output_type": "execute_result" |
|---|
| 389 | } |
|---|
| 390 | ], |
|---|
| 391 | "source": [ |
|---|
| 392 | "# Atlas plotting function\n", |
|---|
| 393 | "def make_atlas(t,alt):\n", |
|---|
| 394 | " my_sim.plot_atlas('h2o_vap',t=t,alt=alt)\n", |
|---|
| 395 | " plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n", |
|---|
| 396 | "\n", |
|---|
| 397 | "# Define interactive output\n", |
|---|
| 398 | "out = widgets.interactive_output(make_atlas,{'t':w_time,'alt':w_alt})\n", |
|---|
| 399 | "\n", |
|---|
| 400 | "# Build the output frame\n", |
|---|
| 401 | "widgets.VBox([w_time,w_alt,out])" |
|---|
| 402 | ] |
|---|
| 403 | }, |
|---|
| 404 | { |
|---|
| 405 | "cell_type": "markdown", |
|---|
| 406 | "id": "4b6bb2f8-1aff-4872-9580-7c4bd9598c31", |
|---|
| 407 | "metadata": {}, |
|---|
| 408 | "source": [ |
|---|
| 409 | "#### Temperature profile at various times and locations" |
|---|
| 410 | ] |
|---|
| 411 | }, |
|---|
| 412 | { |
|---|
| 413 | "cell_type": "code", |
|---|
| 414 | "execution_count": 12, |
|---|
| 415 | "id": "e4691cae-637b-4555-ac87-d556521a4c3f", |
|---|
| 416 | "metadata": {}, |
|---|
| 417 | "outputs": [ |
|---|
| 418 | { |
|---|
| 419 | "data": { |
|---|
| 420 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 421 | "model_id": "be3fdd341c364ae6a89147d4941723b6", |
|---|
| 422 | "version_major": 2, |
|---|
| 423 | "version_minor": 0 |
|---|
| 424 | }, |
|---|
| 425 | "text/plain": [ |
|---|
| 426 | "HBox(children=(VBox(children=(FloatSlider(value=0.0, description='time', max=1.0), FloatSlider(value=0.0, desc…" |
|---|
| 427 | ] |
|---|
| 428 | }, |
|---|
| 429 | "execution_count": 12, |
|---|
| 430 | "metadata": {}, |
|---|
| 431 | "output_type": "execute_result" |
|---|
| 432 | } |
|---|
| 433 | ], |
|---|
| 434 | "source": [ |
|---|
| 435 | "# Profile plotting unction\n", |
|---|
| 436 | "def make_prof(t,lon,lat,avg):\n", |
|---|
| 437 | " my_sim.plot_profile('temp',t=t,lon=lon,lat=lat,label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n", |
|---|
| 438 | " if avg:\n", |
|---|
| 439 | " my_sim.plot_profile('temp',t=t,label='average')\n", |
|---|
| 440 | " plt.legend()\n", |
|---|
| 441 | " plt.grid()\n", |
|---|
| 442 | "\n", |
|---|
| 443 | "# Define interactive output\n", |
|---|
| 444 | "out = widgets.interactive_output(make_prof,{'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n", |
|---|
| 445 | "\n", |
|---|
| 446 | "# Build the output frame\n", |
|---|
| 447 | "widgets.HBox([widgets.VBox([w_time,w_lon,w_lat,w_average]),out])" |
|---|
| 448 | ] |
|---|
| 449 | }, |
|---|
| 450 | { |
|---|
| 451 | "cell_type": "markdown", |
|---|
| 452 | "id": "9ad5a23a-7ed6-4f56-abbf-3cf16daa087a", |
|---|
| 453 | "metadata": {}, |
|---|
| 454 | "source": [ |
|---|
| 455 | "#### Extensive species visualizer\n", |
|---|
| 456 | "Combining the above examples for arbitrary species" |
|---|
| 457 | ] |
|---|
| 458 | }, |
|---|
| 459 | { |
|---|
| 460 | "cell_type": "code", |
|---|
| 461 | "execution_count": 13, |
|---|
| 462 | "id": "e4db2d8b-6183-4fbe-8ca1-940ef15aaa28", |
|---|
| 463 | "metadata": {}, |
|---|
| 464 | "outputs": [ |
|---|
| 465 | { |
|---|
| 466 | "data": { |
|---|
| 467 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 468 | "model_id": "ec9ab05df80e4da2964b3ee2dc436940", |
|---|
| 469 | "version_major": 2, |
|---|
| 470 | "version_minor": 0 |
|---|
| 471 | }, |
|---|
| 472 | "text/plain": [ |
|---|
| 473 | "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …" |
|---|
| 474 | ] |
|---|
| 475 | }, |
|---|
| 476 | "execution_count": 13, |
|---|
| 477 | "metadata": {}, |
|---|
| 478 | "output_type": "execute_result" |
|---|
| 479 | } |
|---|
| 480 | ], |
|---|
| 481 | "source": [ |
|---|
| 482 | "def make_visualizer(sp,t,alt):\n", |
|---|
| 483 | " \n", |
|---|
| 484 | " plt.subplot(131) # zonal average\n", |
|---|
| 485 | " my_sim.plot_meridional_slice(sp,t=t,logcb=True)\n", |
|---|
| 486 | " plt.plot([-90,90],[alt,alt],ls='--',lw=3,c='white')\n", |
|---|
| 487 | "\n", |
|---|
| 488 | " plt.subplot(132) # atlas\n", |
|---|
| 489 | " my_sim.plot_atlas(sp,t=t,alt=alt)\n", |
|---|
| 490 | " plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n", |
|---|
| 491 | "\n", |
|---|
| 492 | " plt.subplot(133) # profile\n", |
|---|
| 493 | " my_sim.plot_profile('temp',t=t)\n", |
|---|
| 494 | " plt.grid()\n", |
|---|
| 495 | "\n", |
|---|
| 496 | " plt.subplots_adjust(right=2)\n", |
|---|
| 497 | "\n", |
|---|
| 498 | "out = widgets.interactive_output(make_visualizer,{'sp':w_single_sp,'t':w_time,'alt':w_alt})\n", |
|---|
| 499 | "\n", |
|---|
| 500 | "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_alt]),out])" |
|---|
| 501 | ] |
|---|
| 502 | }, |
|---|
| 503 | { |
|---|
| 504 | "cell_type": "markdown", |
|---|
| 505 | "id": "b8d39b59-07f2-4988-8e5c-558221c825a1", |
|---|
| 506 | "metadata": {}, |
|---|
| 507 | "source": [ |
|---|
| 508 | "#### Multi-species profiles\n", |
|---|
| 509 | "Shift+click to select multiple species (Command+click on Mac)" |
|---|
| 510 | ] |
|---|
| 511 | }, |
|---|
| 512 | { |
|---|
| 513 | "cell_type": "code", |
|---|
| 514 | "execution_count": 14, |
|---|
| 515 | "id": "9dd59a1c-58c4-41f0-9730-9e97d2607c6a", |
|---|
| 516 | "metadata": {}, |
|---|
| 517 | "outputs": [ |
|---|
| 518 | { |
|---|
| 519 | "data": { |
|---|
| 520 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 521 | "model_id": "07740f3642a44b72bbd6bf3777dad80f", |
|---|
| 522 | "version_major": 2, |
|---|
| 523 | "version_minor": 0 |
|---|
| 524 | }, |
|---|
| 525 | "text/plain": [ |
|---|
| 526 | "HBox(children=(VBox(children=(SelectMultiple(description='species', index=(6,), options=('o2', 'o', 'o1d', 'o3…" |
|---|
| 527 | ] |
|---|
| 528 | }, |
|---|
| 529 | "execution_count": 14, |
|---|
| 530 | "metadata": {}, |
|---|
| 531 | "output_type": "execute_result" |
|---|
| 532 | } |
|---|
| 533 | ], |
|---|
| 534 | "source": [ |
|---|
| 535 | "cmap = plt.get_cmap(\"tab10\")\n", |
|---|
| 536 | "def make_sp_prof(sps,t,lon,lat,avg):\n", |
|---|
| 537 | "\n", |
|---|
| 538 | " for i,sp in enumerate(sps):\n", |
|---|
| 539 | " my_sim.plot_profile(sp,t=t,lon=lon,lat=lat,logx=True,label=sp,c=cmap(i))\n", |
|---|
| 540 | " if avg:\n", |
|---|
| 541 | " my_sim.plot_profile(sp,t=t,logx=True,c=cmap(i),ls='--')\n", |
|---|
| 542 | " if avg: # just for the legend\n", |
|---|
| 543 | " plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n", |
|---|
| 544 | " plt.plot([],[],ls='--',c='k',label='average')\n", |
|---|
| 545 | " \n", |
|---|
| 546 | " plt.legend()\n", |
|---|
| 547 | " plt.grid()\n", |
|---|
| 548 | "\n", |
|---|
| 549 | "out = widgets.interactive_output(make_sp_prof,{'sps':w_multiple_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n", |
|---|
| 550 | "\n", |
|---|
| 551 | "widgets.HBox([widgets.VBox([w_multiple_sp,w_time,w_lon,w_lat,w_average]),out])" |
|---|
| 552 | ] |
|---|
| 553 | }, |
|---|
| 554 | { |
|---|
| 555 | "cell_type": "markdown", |
|---|
| 556 | "id": "5fb2c987-345d-4a74-b4bf-fb368eeaac2b", |
|---|
| 557 | "metadata": {}, |
|---|
| 558 | "source": [ |
|---|
| 559 | "#### Species-specific reaction rates" |
|---|
| 560 | ] |
|---|
| 561 | }, |
|---|
| 562 | { |
|---|
| 563 | "cell_type": "code", |
|---|
| 564 | "execution_count": 15, |
|---|
| 565 | "id": "ff21a3dc-d44a-4f0e-9aa4-211741bb592d", |
|---|
| 566 | "metadata": {}, |
|---|
| 567 | "outputs": [ |
|---|
| 568 | { |
|---|
| 569 | "data": { |
|---|
| 570 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 571 | "model_id": "42b9e7928f514fed9754edfcbce8e897", |
|---|
| 572 | "version_major": 2, |
|---|
| 573 | "version_minor": 0 |
|---|
| 574 | }, |
|---|
| 575 | "text/plain": [ |
|---|
| 576 | "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …" |
|---|
| 577 | ] |
|---|
| 578 | }, |
|---|
| 579 | "execution_count": 15, |
|---|
| 580 | "metadata": {}, |
|---|
| 581 | "output_type": "execute_result" |
|---|
| 582 | } |
|---|
| 583 | ], |
|---|
| 584 | "source": [ |
|---|
| 585 | "def make_reaction_rate_viz(sp,t):\n", |
|---|
| 586 | "\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", |
|---|
| 592 | "\n", |
|---|
| 593 | " plt.legend()\n", |
|---|
| 594 | " plt.grid()\n", |
|---|
| 595 | "\n", |
|---|
| 596 | "out=widgets.interactive_output(make_reaction_rate_viz,{'sp':w_single_sp,'t':w_time})\n", |
|---|
| 597 | "\n", |
|---|
| 598 | "widgets.HBox([widgets.VBox([w_single_sp,w_time]),out])" |
|---|
| 599 | ] |
|---|
| 600 | }, |
|---|
| 601 | { |
|---|
| 602 | "cell_type": "markdown", |
|---|
| 603 | "id": "7e0b725d-58f5-4b4e-b170-125b4707df03", |
|---|
| 604 | "metadata": {}, |
|---|
| 605 | "source": [ |
|---|
| 606 | "#### Profile with atlas locator\n", |
|---|
| 607 | "Here the atlas shows the column mass" |
|---|
| 608 | ] |
|---|
| 609 | }, |
|---|
| 610 | { |
|---|
| 611 | "cell_type": "code", |
|---|
| 612 | "execution_count": 16, |
|---|
| 613 | "id": "b5b73171-0101-4656-b2ce-7e398070ebbb", |
|---|
| 614 | "metadata": {}, |
|---|
| 615 | "outputs": [ |
|---|
| 616 | { |
|---|
| 617 | "data": { |
|---|
| 618 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 619 | "model_id": "71f016cdee32419b92bbaee7409e9925", |
|---|
| 620 | "version_major": 2, |
|---|
| 621 | "version_minor": 0 |
|---|
| 622 | }, |
|---|
| 623 | "text/plain": [ |
|---|
| 624 | "HBox(children=(VBox(children=(SelectMultiple(description='species', index=(6,), options=('o2', 'o', 'o1d', 'o3…" |
|---|
| 625 | ] |
|---|
| 626 | }, |
|---|
| 627 | "execution_count": 16, |
|---|
| 628 | "metadata": {}, |
|---|
| 629 | "output_type": "execute_result" |
|---|
| 630 | } |
|---|
| 631 | ], |
|---|
| 632 | "source": [ |
|---|
| 633 | "def make_sp_prof_atlas(sps,t,lon,lat,avg):\n", |
|---|
| 634 | "\n", |
|---|
| 635 | " plt.subplot(121) # Vertical profile\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", |
|---|
| 643 | "\n", |
|---|
| 644 | " plt.legend()\n", |
|---|
| 645 | " plt.grid()\n", |
|---|
| 646 | "\n", |
|---|
| 647 | " plt.subplot(122) # Atlas\n", |
|---|
| 648 | " my_sim.plot_atlas(sp+'_col',t=t)\n", |
|---|
| 649 | " plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n", |
|---|
| 650 | "\n", |
|---|
| 651 | " plt.subplots_adjust(right=2)\n", |
|---|
| 652 | "\n", |
|---|
| 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])" |
|---|
| 656 | ] |
|---|
| 657 | }, |
|---|
| 658 | { |
|---|
| 659 | "cell_type": "markdown", |
|---|
| 660 | "id": "239adb94-d3b7-4a9a-81fc-297e72e63639", |
|---|
| 661 | "metadata": {}, |
|---|
| 662 | "source": [ |
|---|
| 663 | "#### Species-specific reaction rates with atlas locator" |
|---|
| 664 | ] |
|---|
| 665 | }, |
|---|
| 666 | { |
|---|
| 667 | "cell_type": "code", |
|---|
| 668 | "execution_count": 17, |
|---|
| 669 | "id": "c849fda1-3969-4709-bb17-fdcaff5cbf86", |
|---|
| 670 | "metadata": {}, |
|---|
| 671 | "outputs": [ |
|---|
| 672 | { |
|---|
| 673 | "data": { |
|---|
| 674 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 675 | "model_id": "5066b8d918a54391973628bb36173d9e", |
|---|
| 676 | "version_major": 2, |
|---|
| 677 | "version_minor": 0 |
|---|
| 678 | }, |
|---|
| 679 | "text/plain": [ |
|---|
| 680 | "HBox(children=(VBox(children=(Select(description='species', index=6, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …" |
|---|
| 681 | ] |
|---|
| 682 | }, |
|---|
| 683 | "execution_count": 17, |
|---|
| 684 | "metadata": {}, |
|---|
| 685 | "output_type": "execute_result" |
|---|
| 686 | } |
|---|
| 687 | ], |
|---|
| 688 | "source": [ |
|---|
| 689 | "def make_sp_rate_atlas(sp,t,lon,lat,avg):\n", |
|---|
| 690 | " \n", |
|---|
| 691 | " plt.subplot(121) # Vertical profile\n", |
|---|
| 692 | " for r in my_sim.network:\n", |
|---|
| 693 | " if sp in r.products:\n", |
|---|
| 694 | " 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", |
|---|
| 695 | " elif sp in r.reactants:\n", |
|---|
| 696 | " 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", |
|---|
| 697 | " \n", |
|---|
| 698 | " plt.legend()\n", |
|---|
| 699 | " plt.grid()\n", |
|---|
| 700 | "\n", |
|---|
| 701 | " plt.subplot(122) # Atlas\n", |
|---|
| 702 | " my_sim.plot_atlas(sp+'_col',t=t)\n", |
|---|
| 703 | " plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n", |
|---|
| 704 | "\n", |
|---|
| 705 | " plt.subplots_adjust(right=1.5)\n", |
|---|
| 706 | "\n", |
|---|
| 707 | "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", |
|---|
| 708 | "\n", |
|---|
| 709 | "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])" |
|---|
| 710 | ] |
|---|
| 711 | }, |
|---|
| 712 | { |
|---|
| 713 | "cell_type": "markdown", |
|---|
| 714 | "id": "ff6ad79c-d07b-4d81-83d8-fc02e24ba6d4", |
|---|
| 715 | "metadata": {}, |
|---|
| 716 | "source": [ |
|---|
| 717 | "## Chemical network processing" |
|---|
| 718 | ] |
|---|
| 719 | }, |
|---|
| 720 | { |
|---|
| 721 | "cell_type": "markdown", |
|---|
| 722 | "id": "da719183-d81b-4dfd-948a-a275e41ae32a", |
|---|
| 723 | "metadata": {}, |
|---|
| 724 | "source": [ |
|---|
| 725 | "### Working with other formats" |
|---|
| 726 | ] |
|---|
| 727 | }, |
|---|
| 728 | { |
|---|
| 729 | "cell_type": "markdown", |
|---|
| 730 | "id": "fd6c578a-7a59-4f1f-b180-bc514d4482a4", |
|---|
| 731 | "metadata": {}, |
|---|
| 732 | "source": [ |
|---|
| 733 | "We can import and export chemical network from and into various formats, not only Generic-PCM style." |
|---|
| 734 | ] |
|---|
| 735 | }, |
|---|
| 736 | { |
|---|
| 737 | "cell_type": "code", |
|---|
| 738 | "execution_count": 18, |
|---|
| 739 | "id": "e25768d8-914b-4409-947d-b3aa3f3d9c87", |
|---|
| 740 | "metadata": {}, |
|---|
| 741 | "outputs": [ |
|---|
| 742 | { |
|---|
| 743 | "name": "stdout", |
|---|
| 744 | "output_type": "stream", |
|---|
| 745 | "text": [ |
|---|
| 746 | "co + oh -> co2 + h has a custom reaction constant: add it manually\n", |
|---|
| 747 | "o2 + hv -> o + o is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 748 | "o2 + hv -> o + o1d is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 749 | "o3 + hv -> o2 + o1d is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 750 | "o3 + hv -> o2 + o is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 751 | "h2o2 + hv -> oh + oh is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 752 | "h2o_vap + hv -> h + oh is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 753 | "co2 + hv -> co + o is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 754 | "co2 + hv -> co + o1d is a photolysis: you need to add the branching ratio manually\n", |
|---|
| 755 | "ho2 + hv -> oh + o is a photolysis: you need to add the branching ratio manually\n" |
|---|
| 756 | ] |
|---|
| 757 | } |
|---|
| 758 | ], |
|---|
| 759 | "source": [ |
|---|
| 760 | "# Let's export our simulation's network into a file readable by the VULCAN model\n", |
|---|
| 761 | "my_sim.network.to_file('photochem_example/VULCAN_network.txt',format='vulcan')" |
|---|
| 762 | ] |
|---|
| 763 | }, |
|---|
| 764 | { |
|---|
| 765 | "cell_type": "markdown", |
|---|
| 766 | "id": "e12f6270-65ba-44d3-8ec7-28292fb50cce", |
|---|
| 767 | "metadata": {}, |
|---|
| 768 | "source": [ |
|---|
| 769 | "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." |
|---|
| 770 | ] |
|---|
| 771 | }, |
|---|
| 772 | { |
|---|
| 773 | "cell_type": "markdown", |
|---|
| 774 | "id": "0b258f20-f574-4382-b394-afcf171d1993", |
|---|
| 775 | "metadata": {}, |
|---|
| 776 | "source": [ |
|---|
| 777 | "### Making subnetwork" |
|---|
| 778 | ] |
|---|
| 779 | }, |
|---|
| 780 | { |
|---|
| 781 | "cell_type": "markdown", |
|---|
| 782 | "id": "819b6abf-0ec5-4b53-8af5-31da362f6b86", |
|---|
| 783 | "metadata": {}, |
|---|
| 784 | "source": [ |
|---|
| 785 | "We can also use these tools to design and export subnetworks:" |
|---|
| 786 | ] |
|---|
| 787 | }, |
|---|
| 788 | { |
|---|
| 789 | "cell_type": "code", |
|---|
| 790 | "execution_count": 19, |
|---|
| 791 | "id": "1e1585ff-dc78-44ae-9e38-3025fde45a59", |
|---|
| 792 | "metadata": {}, |
|---|
| 793 | "outputs": [ |
|---|
| 794 | { |
|---|
| 795 | "name": "stdout", |
|---|
| 796 | "output_type": "stream", |
|---|
| 797 | "text": [ |
|---|
| 798 | "Photolysis sub-network:\n", |
|---|
| 799 | "=======================\n", |
|---|
| 800 | "o2 + hv -> o + o\n", |
|---|
| 801 | "o2 + hv -> o + o1d\n", |
|---|
| 802 | "o3 + hv -> o2 + o1d\n", |
|---|
| 803 | "o3 + hv -> o2 + o\n", |
|---|
| 804 | "h2o2 + hv -> oh + oh\n", |
|---|
| 805 | "h2o_vap + hv -> h + oh\n", |
|---|
| 806 | "co2 + hv -> co + o\n", |
|---|
| 807 | "co2 + hv -> co + o1d\n", |
|---|
| 808 | "ho2 + hv -> oh + o\n", |
|---|
| 809 | "\n", |
|---|
| 810 | "HOx sub-network:\n", |
|---|
| 811 | "=================\n", |
|---|
| 812 | "h2o2 + hv -> oh + oh\n", |
|---|
| 813 | "h2o_vap + hv -> h + oh\n", |
|---|
| 814 | "ho2 + hv -> oh + o\n", |
|---|
| 815 | "o1d + h2o_vap -> oh + oh\n", |
|---|
| 816 | "o1d + h2 -> oh + h\n", |
|---|
| 817 | "o + h2 -> oh + h\n", |
|---|
| 818 | "o + ho2 -> oh + o2\n", |
|---|
| 819 | "o + oh -> o2 + h\n", |
|---|
| 820 | "h + o3 -> oh + o2\n", |
|---|
| 821 | "h + ho2 -> oh + oh\n", |
|---|
| 822 | "h + ho2 -> h2 + o2\n", |
|---|
| 823 | "h + ho2 -> h2o_vap + o\n", |
|---|
| 824 | "oh + ho2 -> h2o_vap + o2\n", |
|---|
| 825 | "oh + h2o2 -> h2o_vap + ho2\n", |
|---|
| 826 | "oh + h2 -> h2o_vap + h\n", |
|---|
| 827 | "o + h2o2 -> oh + ho2\n", |
|---|
| 828 | "oh + o3 -> ho2 + o2\n", |
|---|
| 829 | "ho2 + o3 -> oh + o2 + o2\n", |
|---|
| 830 | "ho2 + ho2 -> h2o2 + o2\n", |
|---|
| 831 | "oh + oh -> h2o_vap + o\n", |
|---|
| 832 | "h + o2 + M -> ho2 + M\n", |
|---|
| 833 | "h + oh + M -> h2o_vap + M\n", |
|---|
| 834 | "oh + oh + M -> h2o2 + M\n", |
|---|
| 835 | "h + h + M -> h2 + M\n", |
|---|
| 836 | "ho2 + ho2 + M -> h2o2 + o2 + M\n", |
|---|
| 837 | "co + oh -> co2 + h\n", |
|---|
| 838 | "\n", |
|---|
| 839 | "C sub-network:\n", |
|---|
| 840 | "===============\n", |
|---|
| 841 | "co2 + hv -> co + o\n", |
|---|
| 842 | "co2 + hv -> co + o1d\n", |
|---|
| 843 | "o1d + co -> o + co\n", |
|---|
| 844 | "o1d + co2 -> o + co2\n", |
|---|
| 845 | "co + o + M -> co2 + M\n", |
|---|
| 846 | "co + oh -> co2 + h\n" |
|---|
| 847 | ] |
|---|
| 848 | } |
|---|
| 849 | ], |
|---|
| 850 | "source": [ |
|---|
| 851 | "# Select by type:\n", |
|---|
| 852 | "print('Photolysis sub-network:')\n", |
|---|
| 853 | "print('=======================')\n", |
|---|
| 854 | "for r in my_sim.network.get_subnetwork({'type':[pcpp.photolysis]}):\n", |
|---|
| 855 | " print(r.formula)\n", |
|---|
| 856 | "\n", |
|---|
| 857 | "# Select by species:\n", |
|---|
| 858 | "print('')\n", |
|---|
| 859 | "print('HOx sub-network:')\n", |
|---|
| 860 | "print('=================')\n", |
|---|
| 861 | "for r in my_sim.network.get_subnetwork({'species':['h','oh','ho2']}):\n", |
|---|
| 862 | " print(r.formula)\n", |
|---|
| 863 | "\n", |
|---|
| 864 | "# Select by element:\n", |
|---|
| 865 | "print('')\n", |
|---|
| 866 | "print('C sub-network:')\n", |
|---|
| 867 | "print('===============')\n", |
|---|
| 868 | "for r in my_sim.network.get_subnetwork({'elements':['c']}):\n", |
|---|
| 869 | " print(r.formula)" |
|---|
| 870 | ] |
|---|
| 871 | }, |
|---|
| 872 | { |
|---|
| 873 | "cell_type": "markdown", |
|---|
| 874 | "id": "4ea46279-9234-4a53-800d-891efa4ec9d1", |
|---|
| 875 | "metadata": {}, |
|---|
| 876 | "source": [ |
|---|
| 877 | "### Exporting custom subnetwork\n", |
|---|
| 878 | "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.)" |
|---|
| 879 | ] |
|---|
| 880 | }, |
|---|
| 881 | { |
|---|
| 882 | "cell_type": "code", |
|---|
| 883 | "execution_count": 20, |
|---|
| 884 | "id": "513f6d4e-a0e2-4095-9a7c-acafc79a465b", |
|---|
| 885 | "metadata": {}, |
|---|
| 886 | "outputs": [ |
|---|
| 887 | { |
|---|
| 888 | "data": { |
|---|
| 889 | "application/vnd.jupyter.widget-view+json": { |
|---|
| 890 | "model_id": "93b44294c8a743239d62dda010335241", |
|---|
| 891 | "version_major": 2, |
|---|
| 892 | "version_minor": 0 |
|---|
| 893 | }, |
|---|
| 894 | "text/plain": [ |
|---|
| 895 | "HBox(children=(SelectMultiple(layout=Layout(height='400px'), options=('o2 + hv -> o + o', 'o2 + hv -> o + o1d'…" |
|---|
| 896 | ] |
|---|
| 897 | }, |
|---|
| 898 | "execution_count": 20, |
|---|
| 899 | "metadata": {}, |
|---|
| 900 | "output_type": "execute_result" |
|---|
| 901 | } |
|---|
| 902 | ], |
|---|
| 903 | "source": [ |
|---|
| 904 | "from ipywidgets import Layout\n", |
|---|
| 905 | "\n", |
|---|
| 906 | "def select_reactions(reactions,net_filename,trac_filename,format):\n", |
|---|
| 907 | " save_network_button = widgets.Button(description='Save reactfile')\n", |
|---|
| 908 | " save_tracers_button = widgets.Button(description='Save tracfile')\n", |
|---|
| 909 | " \n", |
|---|
| 910 | " def on_save_network_button_clicked(b):\n", |
|---|
| 911 | " to_save = pcpp.network()\n", |
|---|
| 912 | " for r in reactions:\n", |
|---|
| 913 | " to_save.append(my_sim.network[r])\n", |
|---|
| 914 | " to_save.to_file(net_filename,format=format)\n", |
|---|
| 915 | " \n", |
|---|
| 916 | " def on_save_tracers_button_clicked(b):\n", |
|---|
| 917 | " to_save = pcpp.network()\n", |
|---|
| 918 | " for r in reactions:\n", |
|---|
| 919 | " to_save.append(my_sim.network[r])\n", |
|---|
| 920 | " to_save.save_traceur_file(trac_filename)\n", |
|---|
| 921 | " \n", |
|---|
| 922 | " save_network_button.on_click(on_save_network_button_clicked)\n", |
|---|
| 923 | " save_tracers_button.on_click(on_save_tracers_button_clicked)\n", |
|---|
| 924 | " display(save_network_button, widgets.Output())\n", |
|---|
| 925 | " display(save_tracers_button, widgets.Output())\n", |
|---|
| 926 | " \n", |
|---|
| 927 | "w_reactions = widgets.SelectMultiple(options=my_sim.network.reactions.keys(),layout=Layout(height='400px'))\n", |
|---|
| 928 | "w_network_filename = widgets.Text(value='photochem_example/my_custom_network.def',description='network filename:',disabled=False)\n", |
|---|
| 929 | "w_tracers_filename = widgets.Text(value='photochem_example/my_custom_traceur.def',description='tracers filename:',disabled=False)\n", |
|---|
| 930 | "w_format = widgets.Select(options=['GPCM','vulcan'],value='GPCM',description='format:')\n", |
|---|
| 931 | "\n", |
|---|
| 932 | "out = widgets.interactive_output(select_reactions,{'reactions':w_reactions,'net_filename':w_network_filename,\n", |
|---|
| 933 | " 'trac_filename':w_tracers_filename,'format':w_format})\n", |
|---|
| 934 | "widgets.HBox([w_reactions,widgets.VBox([w_format,w_network_filename,w_tracers_filename,out])])" |
|---|
| 935 | ] |
|---|
| 936 | } |
|---|
| 937 | ], |
|---|
| 938 | "metadata": { |
|---|
| 939 | "kernelspec": { |
|---|
| 940 | "display_name": "Python 3 (ipykernel)", |
|---|
| 941 | "language": "python", |
|---|
| 942 | "name": "python3" |
|---|
| 943 | }, |
|---|
| 944 | "language_info": { |
|---|
| 945 | "codemirror_mode": { |
|---|
| 946 | "name": "ipython", |
|---|
| 947 | "version": 3 |
|---|
| 948 | }, |
|---|
| 949 | "file_extension": ".py", |
|---|
| 950 | "mimetype": "text/x-python", |
|---|
| 951 | "name": "python", |
|---|
| 952 | "nbconvert_exporter": "python", |
|---|
| 953 | "pygments_lexer": "ipython3", |
|---|
| 954 | "version": "3.11.7" |
|---|
| 955 | } |
|---|
| 956 | }, |
|---|
| 957 | "nbformat": 4, |
|---|
| 958 | "nbformat_minor": 5 |
|---|
| 959 | } |
|---|
