diff --git a/module3/exo3/carbon_dioxyde_concentration.ipynb b/module3/exo3/carbon_dioxyde_concentration.ipynb index 05aa6d7b575028aa6164ff16fa599ae9c1a2b03c..200a497bfc144faafe213df58367b8cc463cec61 100644 --- a/module3/exo3/carbon_dioxyde_concentration.ipynb +++ b/module3/exo3/carbon_dioxyde_concentration.ipynb @@ -15,14 +15,13 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ " %matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek\n", "import os\n", "import urllib\n", "import numpy as np\n", @@ -39,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -80,14 +79,14 @@ "7 | fit | Relevés originaux (C02) lissés | ppm\n", "8 | seasonally adjusted fit | Relevés lissés ajustés saisonalement | ppm\n", "9 | CO2 filled | Relevés originaux (CO2) complétés par les résultats lissés (fit) | ppm\n", - "10 | seasonally adjusted fit filled | Relevés ajustés saisonalement (seasonally sdjusted) complétés par les résultats lissés et ajustés (seasonally adjusted fit) | ppm\n", + "10 | seasonally adjusted fit filled | Relevés ajustés saisonalement (seasonally adjusted) complétés par les résultats lissés et ajustés (seasonally adjusted fit) | ppm\n", "\n", "Les 55 premières lignes contiennent les explications ci-dessus, donc nous ne les traiterons pas avec les autres données. Nous renommons également les colonnes pour plus de concision et de clarté." ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -1052,7 +1051,7 @@ "[780 rows x 10 columns]" ] }, - "execution_count": 121, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -1073,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -1225,7 +1224,7 @@ "779 -99.99 " ] }, - "execution_count": 122, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1244,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -2209,7 +2208,7 @@ "[773 rows x 10 columns]" ] }, - "execution_count": 123, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -2228,7 +2227,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -3193,7 +3192,7 @@ "[773 rows x 10 columns]" ] }, - "execution_count": 146, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -3219,16 +3218,16 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 150, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, @@ -3258,16 +3257,16 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 152, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, @@ -3292,21 +3291,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nous observons des variations saisonales de la concentration de C02. En ajustant ces fluctuations, (colonne `SAFitFilled`), nous obtenons un graphe plus lisse témoignant de l'augmentation de la concentration de C02 de ces 60 dernières années." + "Nous observons des variations saisonales de la concentration de C02. En ôtant ces fluctuations saisonales des données, (colonne `SAFitFilled`), nous obtenons un graphe témoignant uniquement de l'augmentation systématique de la concentration de C02 de ces 60 dernières années." ] }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 157, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, @@ -3336,7 +3335,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 85, "metadata": { "scrolled": true }, @@ -3344,10 +3343,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 178, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, @@ -3370,7 +3369,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 86, "metadata": { "scrolled": true }, @@ -3378,10 +3377,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 179, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, @@ -3413,16 +3412,16 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 165, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" }, @@ -3454,22 +3453,22 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 289, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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iQIsZ9yzPKt23l9BI5RJwfKJcBBr/4ap0P0XtHPUnUd2VC1Abd/9TXaVyvQyeCcFh/u276wDoPcL0ereOcT9/REXK+HuJ5sSeCSWn4HiOMf13VE7vhvP5/ouWaIg9E87uUy5Bjf/4diOUFRqr92Ob4OJJY/pvBdYx7s7c6t4qr9VWBs9UroEmiulqfIQjG0SQ/11xTlxLdPO/YLMUjmwIDoerrjOmf+ck0sQ1Haxj3B3Zqvxdt4Es2XacCQtWk/jEp0xYsJol2477vv/wLso1oKMn/MveLBUGFxVrTP/RvSF+TEBET1iGmhr1nA2arnK+GEHcYIhNMvWXujWMe/FplYrTnsGSbcd5cvEujheVI4HjReU8uXiXfwy8PQOKjsCpXb7vSwNn90OBw7U0N+RLHVT/p3Yq95DG95zYCsUnXBvWDNV7/gYoLfRPf23EGsY99xNAwpBMFi7Po7yy/pbw8spqFi73g0806QblIjD5ixbLUCc6yvAvdTB99IRlcGSpHC+DZxqr9yGZIKth3zLf99UOrGHcHdnQ/SqIS+ZEkfsKOU0d9yqd4qD/NaZeqlkKRzb0HQVd4o39Uu+WCL1S9Je6P3BGRyVOgoiuxuq913CI6W9al1zgG/eycyr1am0u5z4x7vO3N3Xc6wzJVHknCvb5p7+OiqvSvVqaG/qlDkqOo99A8Sn/9NdRObNXbR6qXS0ZqnchlN4PrYFLjXIlGk7gG/d9y1W5u1plz5+ZRERI/UINESE25s9M8o88zqiNXD2L8ymu6Cild8O/1O0ZgDR19IQl2JsFCEhSz5kp9F59Gfav8E9/bSDwjbsjC6LjXZXu56X25YWbUugbE4EA+sZE8MJNKcxL9VP9kC7xylVg0qWaZXBkQ4+h0H0QYIIv9bhk5RrUrhnf4shWdRQ69wRMoPf4MdCppyldsX7KtuMjKkpUys/0u+uV15qX2td/xtwd9gxY+axyHcT0M04Oq+KsdD/5cdchp74XLs/jRFE5fWIimD8zyX9/B84l+ob/Vq7CyG7+6bcjUXgQzuyBmS+4Dhmu96Agtddhx7+gstx3ZT3bQWAb9wOfqzJ3Ru1Sawp7pjLujmwY/6DR0liPJirdm+JLff0fIW8ZpP7AODmsinNV1GCjoin0nvNXOLjauM10bghst4yr0v04oyWpT/dBymWgl+i+YW8WdBsIPYcaLUl9+qQqF6HWu29wZEHvkSpCxUwkXAvhMabTe+Aa98pLvq907wn2DJX/ouSM0ZJYi/IiOOzjSvftRQgl18HVUFFstDTW4sIxldrZX5W22oItRO1xyVsK1ZVGS+MicI37obWqvJ0ZlQ11oic+NVoSa+GKjjKp3odkKlfh/s+NlsRaOJ8jM+v90gWVY94kBK5xd2RDWBdImGS0JO7pOVS5Dkz4Fj2gcWRB5z6u6CjT0W8sRMWZboke8DiyVURS7NVGS+KegVMhJMpUeg9M415dpcraJfmh0n17cS7RD69TVZo0nuOsdG/P8H2l+/YSZFOuwv0rlOtQ4zmlZ1UBdLMFTtQlJBwGz1ArjJrqls/3AyZ9QlrgyAZlMM2sbFBLyJoq5UrQeM6Blf6tdN9e7BnKZXhojdGSWIPcT0HWmNcl48SeAaVn4OgmoyUBAtW4O7IgJNJ/le7bS5805UIw0VItoHFkQ2R3tYnFzCRMUimgtd69gyMbYgao/D1m5uoZYAszjSvWK8ZdCPE3IcQZIcRub7TXLDU1KvveVX6sdN9egoLUt/mBlWrDlab9OCvdJ/mx0n17CQ5V9VVNFj0RkFy6oIInzBgd1ZCwzjBommlqOnhr5v4mMMtLbTXP8RxVzs7sSzQn9gyouqQMvKb9HPpCVbofcqPRkrQOe4ZyHeavN1qSwGbfcqipDKzn/cJROLndaEm8Y9yllOuAc95oq0UcWbWV7mf4pTuP6T9euRL0Et0zHFkQFq1SvQYCg6Yp16HWu2c4sqBTL1X4PhBImg3CZgq9+83nLoS4TwiRI4TIKSgoaF8jzlzOg6b6v9J9e7EFK1fCvuXKtaBpO9VVysVhRKX79hIaqVyHuZ8oV6Km7Vwug/0rVboBs0ZHNSSym9qxujfLcNeM3+6YlPJ1KWW6lDI9Li6ufY2c2mVspfv2MuRGuFysXAuatmN0pfv2MuRGKDkNxzYbLUlgcnBVYERHNWRIJhTuhwI/FAxphgD5OqzFWek+6QajJWkbiZOUS8HxsdGSBCaOLAiOMK7SfXu5egbYQk0TPRFwOLIhoqsqgB5IJM8FhOGumYAx7ku2HefpjdW8UTmLCa/u9F8RXG8QHKZcCrlLlYuhFsMK+wYQS7Ye5cymf7O8YigT/vhNYN2j8GgYOEUZ9zpLdK33lvl4y2GKd2azqDiFCQu/DKx71LkX9BvT6Evd33r3Vijke8BGIEkIcUwI8WNvtOvEWQT37eJRPF91u3+L4HoLewaUn4NvvwIwtrBvgLBk23He+2gJPShkWfWYwLxH9gwo+hZO7QS03lvDkm3H+WTJ+3SmjGU1Aaz3Uzvh3GHAGL17K1rmNillbylliJQyXkr5V2+068TQIrje4qrrlGuhdqlmiTH5mIXL85gqv6FS2lhdkwoE4D1KmqNciVrvrUbp/WtKZDgbaoYBAXiPnO8JassuGqH3gHDLGF782BuERsFV09VDXlNjjTH5mBNFZcwK2sRXNUO5SFSd4wF0j6K6K59xrXHXem+ZU0WlzLBtYU3NSCq4kjsqoO5R1wToNdxQvQeEcTe8CK63sGdC8Uk4vsU6Y/IhE6PPkBB0mmU1Y+odD7h7ZM+Eglwo2Kf13gpmRR8mVlxkWbUF9H70Gyg+ZYjeA8K4G14E11sMnqk2YDmyrDMmH/LLxP3USMHn1aNcxwLyHjnLwmm9t4qfx+dRIUNYWzPSdSwg75HTNePINkTvAWHc56X25YWbUugbE4EA+sZE8MJNKcbWTWwPETEwcDI4spk3so81xuRDks9/wbnYUYTH9ArsexTdR+2wdGRb52/ZV0jJoLNrONf7WrrGdA3sexSXBN2vNkzvQhqwiyo9PV3m5OT4vV9TsOVNyP4Z/GQD9BpmtDTmpfAgvJoGsxbAuAeMlsZzNvw3fP40PLrLfDVAzcTxLfCXaTDvzzDy+0ZL4zmrnoP1L8P8A2r3qocIIbZIKdNbc25AzNwtRdIc1AYHvbGlWZz3J9B2JzZFstM184mxcpgdRzYEBcNg/+Qh9Dn2DJDVkLfM711r4+5vOsXBgGsM371mehzZKh9+l3ijJfEO3QdBz2H6S705pFQ5WRImemWWawp6j4Qu/QzRuzbuRmDPhDN74ewBoyUxJ85K91aZtTuxZ8K3X0PxaaMlMSdnHHDuoLX07iy3eXA1VBT7tWtt3I3AGT2Rq2fvbnG6LgIlh3drsWcAUtX/1TTGkQ2IKy4sq2DPhOrLqq6uH9HG3Qi6xCuXw169RHeLIxt6DIHYq4yWxLv0sEO3Qdol1xSObOg/Djr3NFoS79JvDET18LvetXE3CnsGnNgKRUeNlsRclBSo/DtWWpo7cS7RD69TVZo0Vzh3CE7vsqbeg2yQPAf2rYBK/+2y1cbdKJwuh1y9RK9H3tLaSvcWfMhB6b2mCvI+M1oSc+Gc1VrNJePEngGVpXBwjd+61MbdKGKvUq4HvUSvjyNb5eXoadE9AH3TILqv1ntDHNnQewR0HWC0JL4hYaKqHudHvWvjbiT2DOWCKGln2UGrUV5UW+k+0/yV7tuLK3piFVSUGC2NObh4QlWrsupqDSA4VBUZylsK1ZV+6VIbdyOxZygXhI6eUOxfEViV7tuLPQOqLsGBz42WxBw4XZP2G42Vw9fYM+BSEeR/6ZfutHE3kp7DoGuiXqI7cWRB597Qd1TL5wYy/cdDZKzWuxNHFsQmQdxgoyXxLYOmwfW/gbhkv3SnjbuROJfoh75QLomOjLPSfXIAVbpvL67oieVQecloaYyltBDyN1jbJeMkJAIm/FQlkvMDwX7pRdM09kz46hXlkhj+H0ZL4zeWbDvOwuV5nCgqp09MBH9M+ZaxgVjpvr3YM2HrW3D4C5UKuoPQUO+vJu8mTVZ3HL37EYtPkQKAvqOUK2Lvx0ZL4jfc1ZM8/c0iKkJjAq/SfXtJnARh0R0q14w7vV/c9hGlkX1VpIzGq3irQPYsIUSeEOKAEOIJb7TZYQgKUrOWA6vgcqnR0viFhvUkQ6hiitjCiupRYOsgi8ngUJX5MHcpVFcZLY1faKj3TpQxnp1kVYyybnSUgXhs3IUQNuA1YDYwBLhNCDHE03Y7FPYMqCpXBr4D0LBu5DVBe4gW5XxUnmaQRAYxJBPKz8GRDUZL4hca6n1q0HbCRBX/Lks1SCJr442Z+xjggJTykJTyMvAvwOIxTV6m/zUQ0a3DRE80rBs5K2gTxTKCQ51HGySRQQyaDsERHVfvtk2ckTGcih5ukETWxhvGvS9QN0HKsdpj9RBC3CeEyBFC5BQU6E079bAFQ/INsO8zqLpstDQ+p249ySBqmGHL4QuZxqOzLLortSlCI+Hq6yD3E6ipMVoan1NX72FcZmrQDlbJ0Tw2y26wZNbEG8bdnbOsUe0+KeXrUsp0KWV6XFycF7q1GPZMqLiooicsTt16kmNEHt1FMT3GfjfwamR6A3smFJ+E49YvO1lX75ODdhIpKug/4XsdU+9+wBvG/RjQr87v8cAJL7TbsRg4BUI7d5joiXmpfdnwxDT+NakAgsMZc13HCQOtx+CZEBTS4fT++uiTEB7DhOvmGS2SZfGGcd8MXC2ESBRChAK3Ah3jL9WbBIepBz33U6ipbvl8K1BTo/zNg6ZDWCejpTGG8C7qi92RrcrMdQSqK1WOlaQbwBZitDSWxWPjLqWsAh4GlgMO4AMp5R5P2+2Q2DOgrBC+3Wi0JP7hxDa4eFxFjXRk7BlwPh9O7zZaEv9weB1cuqD17mO8EucupVwqpRwspRwkpfytN9rskFx1HQSHd5wKTY6s2kr3HWeHpluS54AI6jBRMziyISQKBk41WhJLo3eomomwTsrAO7KtHz0hpTLuCRMhoqvR0hhLVKzamdsRvtRrqpXrcfAMCAk3WhpLo4272bBnQPEJ5bKwMmf2qtJqemmusGdAgQPO7jdaEt9ydBOUntG5ZPyANu5mY/BM5aqwevSEVSvdt5fkOepfq7tmHNlgC4OrZxgtieXRxt1sRHRVSaUcWdaOnnBkq7zmnXoYLYk56BKvkshZ2bhLWRsdNQ3COhstjeXRxt2M2DOVy+LMXqMl8Q2FB1VkiF6a18eeCSe2QtHRls8NRE5uhwvfar37CW3czUjyHEBYdxbnHJddu2Tq4TR6uZ8YK4evcGSDsEHSbKMl6RBo425GOvVQLgsrG/feIyGmv9GSmIvug6DHUGvrPXEiRHYzWpIOgTbuZsWeoVwXhQeNlsS7XDiu8qjoKBn32DPgyFdQcsZoSbzLmVw4u0+7ZPyINu5mxfkQWG0W56p0r427W4ZkAvLKfbIKOjrK72jjblZi+kGfVOsZd0eWqv4ee7XRkpiTHkOg20Br6r3fGOjcy2hJOgzauJsZe4ZyYVw4brQk3qH0rKo6pJfmTSOEuj+Hv4DyIqOl8Q7n8+HUTr1a8zPauJsZ58NglSV63lKQNfohbwl7JtRUwb7lRkviHXR0lCFo425mYq9WLgyr7FZ1ZEPMAOiVYrQk5qZPGnTuYy299xoOXROMlqRDoY272bFnKldG6VmjJfGMSxfg0FrlctCV7psnKEjdpwMr4XKp0dJ4RvEpOPqNXq0ZgDbuZseeoVwZeUuNlsQz9q2A6sv6IW8t9gyouqQMfCDj3JCl37P4HW3czU6vFOXKCPToCUcWdOoF8aONliQw6D8eIrsHvt73ZkHsYOiRbLQkHQ5t3M2OM3ri0Frl2ghELpepGah9rnI5aFrGFqzK0O1bDlUVRkvTPsrOQf56PWs3CP2kBQJDblQujX0rjJakWZZsO86EBatJfOJTJixYzZJttSGcB1dDZZl+yNvKkBuh4iIc+sJoSZqlSb3nLQNZrfVuENq4BwJ905VLw/Gx0ZI0yZJtx3ly8S6OF5UjgeNF5Ty5eJd60B3ZKpXxgAlGixlYJE6CsGhTR820qPcu/VWIJEpgAAAgAElEQVQeIY3f8ci4CyFuEULsEULUCCHSvSWUpgFBQcqlcWCVcnGYkIXL8yivrK53rLyympc+261mcLrSfdsJDlPFW3I/heoqo6VxS1N6f+2z7WrFpqOjDMPTmftu4CZgnRdk0TSHPUO5Ng6uNloSt5woKnd7PKF4C1Rc0FEy7cWeAeXn4NuvjJbELU3pPal4I1RXaJeMgXhk3KWUDillnreE0TTDgGuVa8OkS/Q+MRFuj38nYhuEdoKBU/wqj2W46joIjjBt1EyTeg/fClE9VD4ZjSH4zecuhLhPCJEjhMgpKCjwV7fWwRYMSXMg7zOoumy0NI2YPzOJiBBbvWNRIYJZwVtUvUxd6b59hEbBVdPB8QnU1BgtTSPc6b1LSDWTxVZVdCbI1sSVGl/TonEXQqwUQux283NjWzqSUr4upUyXUqbHxcW1X+KOjD1DuTjyzecFm5falxduSqFvTAQC6BsTwf9OriS8olAvzT3FngnFJ1QJPpPhTu//N/4CwdXlOme/wQS3dIKU8jp/CKJpBQOnKBeHI1st103GvNS+zEvte+XAsidqK91fb5xQVmDwTAgKhr0fQ7z54hYa6f2jByC8CyRMNE4ojQ6FDChCwq9ET9RUt3y+kTgr3V81XVe695SIGEicrO6nlEZL0zzVlSpVho6OMhxPQyG/I4Q4BowHPhVCWCRHqYmxZ0BpAXz7tdGSNM+JbXDxmHbJeIshmXD+MJzeY7QkzZO/Hi4Vab2bAE+jZT6SUsZLKcOklD2llDO9JZimCa66Xrk6TBo94cJZ6X7wLKMlsQZJcwARGHoPiYJB04yWpMOj3TKBRlin2ugJEy/RpVQhm7rSvffoFAcDrjG3ca+pUVkgr74eQtyHSGr8hzbugYg9Q7k8TmwzWhL3FORC4QG9NPc29gw4swcKDxotiXuObYKS01rvJkEb90Bk8CwVPWHSDU260r2PcBpNM+vdFqr2NWgMRxv3QCSymwoz25tlTteMIwv6jdWV7r1Nl3hVgs+MrhmnK27QNAiPNloaDdq4By72DDh3ULlAzMS5w3Bql16a+wp7BhzfAheOGS1JfU7ugKJvtd5NhDbugUryXEwZPaEr3fsWZwI2xyfGytEQV3TUbKMl0dSijXug0rkn9B+nXDNmwpENvUfoSve+IvYqiLOb80s9YQJEdTdaEk0t2rgHMvYMOL0Lzh0yWhLFxZMqYkIvzb1CkxWOhmSqFMAlJknAV5AHZ/N0WmeToY17ANDkQ+6MRjHLEt1V6V4/5J7SbIUjewbIGrXN3ww4o3d0dJSp0Mbd5DT7kHcdoFwgZlmiO2or3cclGS1JwNNUhaOFy/Og5zDl9jJLSKQjG+LHQHRvoyXR1EEbd5PT7EMOapZ8bBNcPGGAdHUoLYT8DXrW7iWaqnB0oqhcla2zZ6jC2eVFfpasAeePqEgZ7YozHdq4m5xmH3K4YkxzP/WTRE2wT1e69yZNVThyHbdnQk0l7F/hR6nc4HLFab2bDW3cTU6LD3ncYIhNMn6J7qp0P8JYOSyCuwpHESE25s+sdXn1TYfOvY3X+94s6JUC3RKNlUPTCG3cTU6LDzmoWVP+BuUaMYJLF3Wley/jrsLRCzelXCmKERSkXmDuXwmXS40RsvgUHP1Gu+JMSouVmDTG4nyYFy7P40RROX1iIpg/M6l+5ZshmfDliyp6Iu0O/wu5fwVUX9ZLcy/TqMJRQ+wZsPkvcGCVMSXtcj8FpNa7SdHGPQBo8SHvNRxi+ivXiBHG3ZENnXqqfDIa/zFgAkR0VfffCOPuyIbuV0Fcsv/71rSIdstYASHU0vjQGuUi8SeV5bD/89pK9/rPya/YglURj32fQdVl//Zddg7yv9SuOBOjn0arYM9QrhF/R08cXA2VpXppbhRDMqHiIhz+wr/97vsMaqq0v93EaONuFeLHKNeIvzc0ObIhPEZXujeKxMkQ2tn/UTOObIiOhz6p/u1X02o8LZC9UAiRK4TYKYT4SAgR4y3BNG3EFT3xuXKV+ANd6d54QsJh8Az1crOmuuXzvUFFiXqJq10ypsbTmfvnwDAp5XBgH/Ck5yJp2o09Q7lIDq72T3/5X8KlC9olYzT2DCgrhG83+qe//SugukLr3eR4ZNyllCuklFW1v34NxHsukqbdJFyrXCT+cs3szaqtdD/VP/1p3HPV9RAc7r/0z45siIpTKac1psWbPve7gWVNfSiEuE8IkSOEyCkoMEmqUqthC1FRK3lLlcvEl9RUK1eArnRvPGGdYNB0ZXRranzbV+UlNXNPngNBtpbP1xhGi8ZdCLFSCLHbzc+Ndc55CqgC/tFUO1LK16WU6VLK9Li4OO9Ir2mMPUO5Sg6v820/RzdB6Rlj4qs1jbFnQPEJOLHVt/0cWgOXS7RLJgBocROTlPK65j4XQtwJzAWmS2nGas0djIFTIbSTmsVdNd13/ehK9+YiaRYEBdem3033XT+ObAjrAgmTfNeHxit4Gi0zC3gcyJRSlnlHJI1HhIQrV4kvoyekVA/5oGkQ1tk3fWjaRkRXFY7qyFL68QWu6KhZEBzqmz40XsNTn/ufgM7A50KI7UKI//WCTBpPsWcql8nRTb5p/+R2uKAr3ZuOIZmq5OKZvb5p/8gGKD+vNy4FCB7llpFSXuUtQTRe5OrrwRamZnEDxnvc3JJtx+slLvtbv2UkCZuKb9eYh6Q58MnP1aqq51CPm2uo93d6vc/AkEi1YtOYHr1D1YqEdVYPoCPb4yW6uzJ/wfs+4Uz30RDZzTvyarxD554qPNELobAN9X6iqJTOhz/jeOy1EBrpuawan6ONu1WxZ8CFo8qF4gENy/xdJY4xSJzg7aLhnkqo8QX2DDi9GwoPetRMQ72nigPEiSL+ctbzFYHGP2jjblWSZoOweTyLa1jmb1bQZgAWleiKS6bE+R7E23q3beaytLG4WBv3QEEbd6sS2Q0SJ6pdix64ZhqW+Ztl20xOzWCCY/p4KqHGF8T0h94jPTbu9fUumR20ifU1KXSO6e6ZfBq/oY27lbFnQOF+KMhrdxN1y/zFizMMC8pnFWPql/nTmAt7BhzPgQvH291EXb0PFUfoF1TAajFW6z2A0MbdyiTPBYRHs7i6tTxn17pkUmfc0XxlKI2xOEMVcz9tdxN19T7LtolqgrjmBq33QEIYsak0PT1d5uTk+L3fDslfZ6gUwD/50kttlcFP1nvelsa3vDZWJfe66xNztaXxCCHEFillq7Yg65m71bFnwqmdcD7fs3Z0pfvAwp6hNh2VnvWsnYJ9UJCrN6wFINq4Wx37XPWvp7HPubWzNm3cAwN7BsgalS7AE5wVnpLnei6Txq9o4251uiZAr+GeG3dHNnS/GuL0C7WAoNdwFTnjDb33TYcu2tceaGjj3hGwZyqXSvGp9l1/8SQc1pXuAwohlN4PrlEpoNvD2f1qE5x2yQQkpnmhWllZybFjx7h06ZLf5bE81ZVQfFJlDmxPFseyQrhcBtG9VVpZTWBQVQElpyGyO4RGtf360gLVRufelijMER4eTnx8PCEhgVvvty0vVE3zpB47dozOnTuTkJCA0LND73MmGIJCIPbqtl1XUQKFl6DTQIjWG5cCCilVKoLQKOg2sG3XXroA5y5BdBJ06ukb+fyIlJLCwkKOHTtGYmKi0eL4BdO4ZS5dukT37t21YfcV4TGqgk51VcvnOpESLhxTXwoWeMA7HEIovVcUty23v6xRG6BsYSoE0gIIIejevXuH8gyYxrgD2rD7kvAY9W9FG/yvZWehqly9TLPAsrxDEhGjjHVFceuvKTkD1RXQJR6EqUyER3Q0+2IdzWmaJyRClcUrL2rd+dVV6kVqaKcrXwwm4Y033qCoqJXj6OiERqkEcpdaq/fLyk8f3gXCo30rm8anaONeB5vNxsiRI10/+fn5rF27lrlz2xfju2TJEvbubX9VnPz8fP75z3+6fs/JyeGnP/1p+xoTQj2wrV2iF58EWV07e2s84/FIljaSlZXFggULXL9PnTqVhx9+mPLy8mau0gBq5h3eBS5dVDP4lrh4QrnjonXoY6BjmheqbaVhlZj5M5M8znsRERHB9u3185/n5+e3X8YlS5g7dy5Dhgxp1/VO4/79738fgPT0dNLTPSh+HB6jIiAqLqrImaa4XKZcMlFxasbvBo9laQOZmZlkZl7ZPDVo0CDefffdJs+vqqoiODhg/7S9T0QMlJ9TL8ebm41XlKgyep16QXCY/+TT+ISAnLm7qw705OJdLNnW/ix4reHZZ5/lxRdfdP0+bNgwl/F/++23GT58OCNGjOCOO+7gq6++Iisri/nz5zNy5EgOHjzIlClTcIaAnj17loSEBEAZ8YkTJ5KWlkZaWhpfffUVAE888QRffvklI0eO5KWXXqq3ijh37hzz5s1j+PDhjBs3jp07d7pkvPvuu5kyZQoDBw7klVdeAaC0tJQ5N32PEdffyrC0cbz//vuNxvfKK68wZMgQhqemcuuDv4TOvdi0aRPXXHMNqampXHPNNeTlqQyTdWX54osvXKud1NRUiouLkVIyf/58hg0bRkpKiqu/tWvXMmXKFL773e+SnJzMD37wA5zhuAkJCTzzzDOkpaWRkpJCbm4uAG+++SYPP/wwAAUFBdx8882kp6czevRoNmzY4Br3fffdx4wZM/jhD39IdXU18+fPZ/To0QwfPpz/+7//A+DkyZNMmjSJkSNHMmzYML780gs5d8xOaGc1g2/ONVPv5XkP/8mm8RkeTW+EEL8BbgRqgDPAXVLKE94QrDkaVokBKK+sZuHyPI9m7+Xl5YwcORKAxMREPvroo1Zdt2fPHn7729+yYcMGYmNjOXfuHN26dSMzM5O5c+fy3e9+t9nre/Toweeff054eDj79+/ntttuIycnhwULFvDiiy/yySdq6//atWtd1zzzzDOkpqayZMkSVq9ezQ9/+EPXqiM3N5c1a9ZQXFxMUlISDzzwAJ999hl9+vTh03/8L5Sf40J4/0ZyLFiwgMN7thBWfooiGQ1BwSQnJ7Nu3TqCg4NZuXIlv/zlL/n3v/9d77oXX3yR1157jQkTJlBSUkJ4eDiLFy9m+/bt7Nixg7NnzzJ69GgmTZoEwLZt29izZw99+vRhwoQJbNiwgWuvvRaA2NhYtm7dyv/8z//w4osv8sYbb9Tr62c/+xmPPvooEydOJD8/n5kzZ7q+cLZs2cL69euJiIjg9ddfp0uXLmzevJmKigomTJjAjBkzWLx4MTNnzuSpp56iurqasrKyVuk4oAkKgrBoFd4opfuNaM6X510T9Mtzi+Dp2nWhlPJXAEKInwJPAz/xWKoWaFglpqXjrcWdW6Y1rF69mu9+97vExsYC0K1b22qLVlZW8vDDD7N9+3ZsNhv79u1r8Zr169e7jOy0adMoLCzkwgUVCTNnzhzCwsIICwujR48enD59mpSUFB577DEe/00Uc69NYeKshEZtDk9J4Qd33Mm8G65n3h1KjRcuXODOO+9k//79CCGorKxsdN2ECRP4+c9/zg9+8ANuuukm4uPjWb9+Pbfddhs2m42ePXsyefJkNm/eTHR0NGPGjCE+Ph7A9W7DadxvuukmAEaNGsXixYsb9bVy5UpOnLgyf6iurqakpARQ7puICOVGWrFiBTt37uTDDz90jWP//v2MHj2au+++m8rKSubNm+f6Mrc8ETFq5n65FMI61f/MxC/PNe3HI7eMlPJinV+jAL9sd21YHail494iODiYmporL6WcMbNSylaFWdW9vm687UsvvUTPnj3ZsWMHOTk5XL58ucW23O0sdsoQFnbFX2qz2aiqqmLw4MFs2bKFlJFpPPnCn3juuV83uv7Tf/2Fh+78LltyjzAqPZ2qqip+9atfMXXqVHbv3k12drbbOOEnnniCN954g/LycsaNG0dubq5b+Zy4k6/hZw2PO6mpqWHZsmWsXbuWtWvXcuDAATp1UsYqKurKLkwpJa+++irbt29n+/btHD58mBkzZjBp0iTWrVtH3759ueOOO3j77beblNNShEUDwr1rpoWX55rAxGOfuxDit0KIo8APUDN3n1O3SoyTiBCbz6vEJCQksHXrVgC2bt3K4cOHAZg+fToffPABhYWFgPKHA3Tu3Jni4uJ612/ZsgXANaMENavs3bs3QUFBvPPOO1RXV7u9vi6TJk3iH//4B6DcNbGxsURHN/2y7MSJE0RGRnL7HT/ksZ8+wNZt2+tFT9RUlHF0326mXjeTP/zXSxQVFVFSUsKFCxfo21e5ut588023bR88eJCUlBQef/xx0tPTyc3NZdKkSbz//vtUV1dTUFDAunXrGDNmTJPytZYZM2bw6quvun533s+GzJw5kz//+c+ulca+ffsoLS3lyJEj9OjRg3vvvZcf//jHLn1aniCbSj1RXlS/7GIrXp5rApMW3TJCiJVALzcfPSWl/FhK+RTwlBDiSeBh4Jkm2rkPuA+gf//G/t624PSreztapiVuvvlm3n77bUaOHMno0aMZPHgwAEOHDuWpp55i8uTJ2Gw2UlNTefPNN7n11lu59957eeWVV/jwww957LHH+I//+A/eeecdpk2b5mr3wQcf5Oabb2bRokVMnTrVNQMdPnw4wcHBjBgxgrvuuovU1FTXNc8++yw/+tGPGD58OJGRkbz11lvNyr5r1y7mz59PUFAQIbYg/vz8/7sSPSEl1eePcPsjT3GhvBIp4T//8z+JiYnhF7/4BXfeeSd//OMf68lcl5dffpk1a9Zgs9kYMmQIs2fPJjQ0lI0bNzJixAiEEPzhD3+gV69erpek7eWVV17hoYceYvjw4VRVVTFp0iRGjRrV6Lx77rmH/Px80tLSkFISFxfHkiVLWLt2LQsXLiQkJIROnTp1nJk7KNdMxUVVcCU0Shn5i8dUvqDO7h5xTSDjtcRhQogBwKdSymEtnesucZjD4cBut3tFFk0L1NTA6V0Q0Q1i+qnZ3PnDKrZZR0pYl+oqpfdOPVWeoLJzUHQEuvSDqFijpfMLgW5n/FaJSQhRNwtVJuDZtEzjH1zRE0VqQ9PF4xAc3mEe8A6LLViFRZY79X4CQiJV1kiN5fA0WmaBECIJFQp5BD9Eymi8RHgXZdzP56st592vslQeEU0TRHRR8eznj0BNJXRL1C9RLYpHxl1KebO3BNH4mfAugFA+2PCY9uV51wQe4THKuFdcUG659uR51wQEeqrWUXFGTxCk84h0JGwhV5KJ6fz8lkYn4OjIxPRTL9mCQ42WRONPYgYon7stcCsSaVpGz9x9zL59+/j444+NFsM9tlAIjTRaCo2/CQ4zrd43bdrEF198YbQYlkAb9zoIIbjjjjtcv1dVVREXF9fmlL8JCQmcPXsWgMGDB7N9+/ZGeWrqntMaXn755WbzoNxzzz0epRduK3fddVe9jVjuePrpp1m5cqXX+mzrPXNHQUEBY8eOJTU1lS+//LLdbTZMx9weGur0hhtuME2een/Kcs0117j+P2rUKBYtWsTGjRv90reV0W6ZOkRFRbF7927Ky8uJiIjg888/d+3O9IRnnnG7r6tNvPzyy9x+++1ERjaecVVXVzdKsGUGnnvuObfHq6ursdmMSU61atUqkpOTW9z01RIN0zG3h4Y6Xbp0qUcyeRN/yuLMggoq7cSf/vSnJs+VUiKlJChIz0tbQt+hBsyePZtPP/0UgPfee4/bbrvN9VlTaXYLCwuZMWMGqamp3H///fXyqrz77ruMGTOGESNGcP/997tSC9TFec7IkSPdnvPKK69w4sQJpk6dytSpUwHo1KkTTz/9NGPHjmXjxo310gk/8MADpKenM3To0HpfLE2l1C0oKOD6668nLS2N+++/nwEDBnD27Fny8/MZNuzKnrQXX3yRZ599tpH8zz33HKNHj2bYsGHcd999rvHXnd0nJCTw3HPPce2117Jo0SIOHjzIrFmzGDVqFBMnTnS7c7U197XuPauuruauu+5ypRl+6aWX6rW3fft2fvGLX7B06VJGjhxZr9hHc2M9cOAA1113HSNGjCAtLY2DBw82SsdcNy0xwNy5c11ZPN3pw51O664i/vjHPzJs2DCGDRvGyy+/7JLRbrdz7733MnToUGbMmOEagytd8/Dh3HrrrY3u5Z49e1z3a/jw4ezfvx+AefPmMWrUKIYOHcrrr7/uOt8pS2lpKXPmzGHEiBEMGzbMlbp51apVpKamkpKSwt13301FRYXrOnd/Y02logZcuYEAFi5c6ErT7LxXznE/+OCDpKWlcfToUVasWMH48eNJS0vjlltucSWPe+KJJ1z34bHHHmt0HzoUzm9Cf/6MGjVKNmTv3r1Xfln6uJR/u8G7P0sfb9RnQ6KiouSOHTvkzTffLMvLy+WIESPkmjVr5Jw5c6SUUj788MPy2WeflVJKuWrVKjlixAgppZSPPPKI/PWvfy2llPKTTz6RgCwoKJB79+6Vc+bMkZcvX5ZSSnnffffJN998U0op5YABA1znzJ0713XOAw88IN96661GsjnPdwLI999/3/X75MmT5ebNm+W50gq5cc9huePoebn76Dk5YeIkuWPHDlcbr7zyipRSytdee03++Mc/llJK+dBDD8nf/e53Ukoply1b5pL/8OHDcujQoa4+Fi5cKJ955hkppZR33nmnXLRokZRSysLCQtc5t99+u8zKymp0zoABA+Tvf/9713nTpk2T+/btk1JK+fXXX8upU6c2GnNz99XdPcvJyZHXXXed6/rz5883avPvf/+7fOihhxrd1+bGOmbMGLl48WIppZTl5eWytLS03t+Fu3bnzJkj16xZU+/+VFVVycmTJ9fTR12dOn/PycmRw4YNkyUlJbK4uFgOGTJEbt26VR4+fFjabDa5bds2KaWUt9xyi3znnXeklFL26tVb7jh8Ru44el5+4zgiz5VW1Bv3ww8/LN99910ppZQVFRWyrKysnmxlZWVy6NCh8uzZs/Vk+fDDD+U999zjaqeoqEiWl5fL+Ph4mZeXJ6WU8o477pAvvfSS6zp3f2PPPPOMHD9+vLx06ZIsKCiQ3bp1c+kvKipKSinl8uXL5T333CNrampkVVWVnD17tly7dq08fPiwFELIjRs3SimlLCgokBMnTpQlJSVSSikXLFggf/3rX8vCwkI5ePBgWVNTI6V0r/96diYAAXJkK+2snrk3YPjw4eTn5/Pee+9xww031Pts/fr1Lp983TS769at4/bbbwdUut2uXVWVo1WrVuFwOLj++uuZMmUKW7Zs4ejRo/XaXLVqFVu2bGH06NGMHDmSVatWcejQoRbltNls3Hxz/W0GFy9Vcvx8OZ98vJjvzZ7Md66/lr179rBp207XOXVT6joLjaxfv94125s1a5ZL/tayZs0axo4dS0pKCqtXr2bPnj1uz/ve974HQElJCV999RW33HKLa+Z98uTJRuc3d1/d3bOBAwdy6NAhHnnkET777LNmE6m1luLiYo4fP853vvMdAMLDw926xprjgw8+IC0tjdTUVPbs2dPiu5H169fzne98h6ioKDp16sRNN93kKiqSmJjoSlPs1OH5sssMShrC/3voHj5Z/D41Iojj58s5X3Ylu+j48eP53e9+x+9//3uOHDniSo38yiuvMGLECMaNG8fRo0ddM3onKSkprFy5kscff5wvv/ySLl26kJeXR2Jioiu30p133sm6detc17j7G4MrqahjY2NdqajrsmLFCr766iumTp3K9OnTOXHihCs534ABAxg3bhwAX3/9NXv37mXChAmMHDmSt956iyNHjhAdHU14eDj33HMPixcvbrOerIY5fe6zF7R8jg/JzMzkscceY+3ata5Mj9B8ml13KX+llNxyyy316n+6O+fOO+/khRdeaJOM4eHhjfzW50oquHQkn7f/70/885PVRMfE8Kv/fJCC81cyS7pLqetuXNB0iuO6XLp0iQcffJCcnBz69evHs88+6/Y8uJKSt6amhpiYmFblzm/qvjZ1z3bs2MHy5ct57bXX+OCDD/jb3/7WYh/QfDpnT64/fPgwL774Ips3b6Zr167cddddTd4fJ8312TBdcnl5OacvXOLVt95nyzdf8cWKZbz+3y+yeNVGTl8QdI1UYa7f//73GTt2LJ9++ikzZ87kjTfeICgoiJUrV7Jx40YiIyOZMmVKI9mcqaKXLl3Kk08+yYwZM+qVPGxOxqbSObv7zDnuRx55hJ/8pP5G9/z8/EbpnK+//nree++9Rn1v2rSJVatW8a9//Ys//elPrF69ullZrYyeubvh7rvv5umnnyYlJaXe8abS7NY9vmzZMs6fPw+oVMD//ve/OXPmDKB8yA1rsk6fPp0PP/zQdc65c+c4cuRII5maS//rpLJaUlpykYjISDpFR1NYcIb1a1dSVdPYWFy8VEnZ5Wp2HitiaOoY3npXPSgrVqxwyd+zZ0/OnDlDYWEhFRUVropQdXEag9jYWEpKSlqMoAGIjo4mMTGRRYsWAeph3bFjR6Pzmruv7u7Z2bNnqamp4eabb+Y3v/lNm9L5NjXW6Oho4uPjWbJkCQAVFRWUlZW5Tee8fft2ampqOHr0KJs2bQLg4sWLREVF0aVLF06fPs2yZctc1zSl00mTJrFkyRLKysooLS3lo48+YuLEiU3KfqmyilMnjjPmmok8+tSvKb54gbLSUi5XX/myca5s7rjnJ1w7fRafrfuGPYdP0im6C5GRkeTm5vL11183atuVKvr223nsscfYunUrycnJ5Ofnc+DAAQDeeecdJk+e3Op73RQzZ87k73//u8t/fuzYMZeO6zJu3Dg2bNjg6r+srIx9+/a5UlTfcMMNvPzyy+0qvGMlzDlzN5j4+Hh+9rOfNTreVJrdZ555httuu420tDQmT57sSmk8ZMgQnn/+eWbMmEFNTQ0hISG89tprrtqpzZ0zYMCAen3fd999zJ49m969e7NmzRq3cofYBIOHpJA8dDg3TcKnXmEAAAakSURBVB9PfP8BjEwfS3BQ/dnv+bLLFFyscM0Q7330Fzz58D189O9FTJs6hd69e9O5c2dCQkJcL20TExNJTk5u1GdMTAz33nsvKSkpJCQkMHr06Fbd43/84x888MADPP/881RWVnLrrbcyYsSIeue09b5GRETwox/9yDWDbstqqLmxvvPOO9x///08/fTThISEsGjRokbpmB999FESExNJSUlh2LBhpKWlATBixAhSU1MZOnQoAwcOZMKECa52m9JpWload911lyv//T333ENqamqTxdptSH75s/souXgRKSW33/MA0V26EGq7Mnd7//33eeudd0DY6B7Xk3t/Op+IyEjee/tvDB2WwhB7ssvtUZd6qaJDQvjzn/9MeHg4f//737nllluoqqpi9OjRjWbb7WHGjBk4HA7Gjx8PqBet7777bqMValxcHG+++Sa33Xab60Xu888/T+fOnbnxxhu5dOkSUspGL9Q7Gl5L+dsWdMpf33C+7DLHz5dTU0enQULQt2uEa3kOkHvyYr1Z3eWKCoJsNiLDQjmfv4cHHnigw896Aon26t1JqC2I5N6ev58IBALdzrQl5a+euVsI54N8+sIlLlfXEGoLomeX8HoPONDoAT954hjzH/gRsqaG6KgI/vKXv/hNZo3ntFfvLR3XBDbauFuMrpGhjR7qhoTaguo90AMSB/HBZ+s61AzOarRH73WPa6yH1moHpGeXcIIaRKEECUHPLuEGSaTxB1rvHQtTzdyllG5D3zTepbXLeI216Oh6N+L9opGYxriHh4dTWFhI9+7dtYH3A61ZxmusR0fVu5SSwsJCwsM7zirFNMY9Pj6eY8eOUVBQYLQoGo3GgoSHhxMfH2+0GH7DNMY9JCSExMREo8XQaDQaS6BfqGo0Go0F0cZdo9FoLIg27hqNRmNBDEk/IIQoABpnx2odsYBntdbMh9XGZLXxgPXGZLXxgPXG5G48A6SUca252BDj7glCiJzW5lYIFKw2JquNB6w3JquNB6w3Jk/Ho90yGo1GY0G0cddoNBoLEojG/fWWTwk4rDYmq40HrDcmq40HrDcmj8YTcD53jUaj0bRMIM7cNRqNRtMCpjLuQogkIcT2Oj8XhRCP1vn8MSGEFELE1v6eIIQor3P+/xonvXuaGpMQ4lkhxPE6x2+oc82TQogDQog8IcRMI+VvSFvHE8g6qv3skVo97BFC/KHONQGno9rPGo0nkHUkhHi/zrF8IcT2OtcEnI6aGk+7dCSlNOUPYANOoeI6AfoBy1Hx8bG1xxKA3UbL2p4xAc8Cj7k5ZwiwAwgDEoGDgM1o2T0YTyDraCqwEgir/axHgOuoqfEErI4aHP8v4OlA1lEz42mzjkw1c2/AdOCglNK52ekl4BdAIL8kaDgmd9wI/EtKWSGlPAwcAMb4Rbq205rxBBp1x/QAsEBKWQEgpTxTe06g6qip8QQajf7uhMoT/h/Ae7WHAlVHgNvxtBkzG/dbqR2YECITOC6l3OHmvEQhxDYhxBdCiIl+lbDtuMZUy8NCiJ1CiL8JIbrWHusLHK1zzrHaY2akNeOBwNXRYGCiEOKbWtlH1x4PVB01NR4IXB05mQicllLur/09UHXkpOF4oI06MqVxF0KEApnAIiFEJPAU8LSbU08C/aWUqcDPgX8KIUxZBLTumGoP/RkYBIxEjeO/nKe6udx0q5U2jCeQdRQMdAXGAfOBD2pnVIGqo6bGE8g6cnIb9Q1koOrIScPxtFlHpjTuwGxgq5TyNMpgJAI7hBD5QDywVQjRq3bJVQggpdyC8qsNNkjmlqg7JqSUp6WU1VLKGuAvXFkyHkO9X3ASD5zwq6Sto1XjCWQdoXSxWCo2ATWofB8BqSOaGE+A6wghRDBwE/B+nfMCVUdux9MeHZnVuLu+taSUu6SUPaSUCVLKBJTS0qSUp4QQcUIIG4AQYiBwNXDIKKFboN43sRCid53PvgPsrv1/FnCrECJMCJGIGtMmv0nZelo1nkDWEbAEmAYghBgMhKISOQWkjmhiPAGuI4DrgFwp5bE6xwJVR+BmPO3SkdFvit28OY4ECoEuTXyez5VomZuBPai34luBDKPlb+2YgHeAXcBO1B9i7zqfPYX6Zs4DZhstvyfjCXAdhQLvor6otgLTAlxHbscTyDqqPf4m8BM35wecjpoaT3t0pHeoajQajQUxq1tGo9FoNB6gjbtGo9FYEG3cNRqNxoJo467RaDQWRBt3jUajsSDauGs0Go0F0cZdo9FoLIg27hqNRmNB/n9TTJrNXyIEOgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -3495,11 +3494,11 @@ "fitC = params[2]\n", "fitH = params[3]\n", "\n", - "model_seasonal_fluctuations = signal_triangle(x_data, fitA, fitB, fitC, fitH)\n", + "indexed_data['ModelSF'] = signal_triangle(x_data, fitA, fitB, fitC, fitH)\n", "\n", - "plt.plot(x_data[-30:], seasonal_fluctuations[-30:], 'o')\n", - "plt.plot(x_data[-30:], model_seasonal_fluctuations[-30:], '-')\n", - "plt.legend([\"Fluctuations saisonnières\", \"Modèle triangulaire des fluctuations saisonnières\"])" + "indexed_data['SeasonalOnly'][-30:].plot(style = 'o')\n", + "indexed_data['ModelSF'][-30:].plot()\n", + "plt.legend([\"Fluctuations saisonnières\", \"Modèle triangulaire\"])" ] }, { @@ -3512,29 +3511,58 @@ "\n", "Nous étudions à présent l'augmentation systématique de la concentration en C02 antmosphérique. Nous tentons de modéliser les données `SAFitFilled`, c'est-à-dire les données complétées et ajustées pour omettre les fluctuations saisonnières.\n", "\n", - "Nous essayons un modèle linéaire et un modèle quadratique pour ces données, donnés par les fonctions `linear` et `quadr`.\n", + "Nous essayons des modèles linéaire, quadratique, et exponentiels pour ces données, donnés par les fonctions `linear`, `quadr`, et `expo`." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "def linear(x, a, b):\n", + " return a * x + b\n", + "\n", + "def quadr(x, a, b, h):\n", + " return a * np.square(x - h) + b\n", "\n", + "def expo(x, a, b, c, h):\n", + " return a * np.exp(c * (x - h)) + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Nous utilisons de nouveau la fonction `curve_fit` pour trouver quels paramètres de ces modèles correspondent au mieux à nos données." ] }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 90, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n", + " \n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 320, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3546,29 +3574,24 @@ } ], "source": [ - "def linear(x, a, b):\n", - " return a * x + b\n", - "\n", - "def quadr(x, a, b, h):\n", - " return a * np.square(x - h) + b\n", - "\n", "x_data = np.array(data.index)\n", "y_data = np.array(data['SAFitFilled'])\n", "\n", "params_lin, extras_lin = curve_fit(linear, x_data, y_data)\n", "params_q, extras_q = curve_fit(quadr, x_data, y_data)\n", + "params_e, extras_e = curve_fit(expo, x_data, y_data)\n", "\n", "fitA_lin, fitB_lin = params_lin[0], params_lin[1]\n", "fitA_q, fitB_q, fitH_q = params_q[0], params_q[1], params_q[2]\n", + "fitA_e, fitB_e, fitC_e, fitH_e = params_e[0], params_e[1], params_e[2], params_e[3]\n", "\n", - "model_lin_sys_augm = linear(x_data, fitA_lin, fitB_lin)\n", - "model_q_sys_augm = quadr(x_data, fitA_q, fitB_q, fitH_q)\n", + "indexed_data['ModelSA_lin'] = linear(x_data, fitA_lin, fitB_lin)\n", + "indexed_data['ModelSA_q'] = quadr(x_data, fitA_q, fitB_q, fitH_q)\n", + "indexed_data['ModelSA_e'] = expo(x_data, fitA_e, fitB_e, fitC_e, fitH_e)\n", "\n", - "x_plot = indexed_data.index.strftime('%Y-%m')\n", - "\n", - "plt.plot(x_data, data['SAFitFilled'], '*', color = \"lightgreen\")\n", - "plt.plot(x_data, model_lin_sys_augm, '-', color = \"blue\")\n", - "plt.plot(x_data, model_q_sys_augm, '-', color = \"red\")\n", + "indexed_data['SAFitFilled'].plot()\n", + "indexed_data['ModelSA_lin'].plot()\n", + "indexed_data['ModelSA_q'].plot()\n", "plt.legend([\"Augmentation observée\", \"Modèle linéaire\", \"Modèle quadratique\"])" ] }, @@ -3576,27 +3599,68 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Le modèle quadratique semble correspondre assez précisément aux données, nous allons donc l'utiliser pour extrapoler l'évolution future de la concentration de C02 atmosphérique jusqu'à l'année 2050. Nous calculons le nombre de mois supplémentaires à générer par le modèle affichons le résultat." + "Nous représentons le modèle exponentiel calculé séparément par souci de lisibilité." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "indexed_data['SAFitFilled'].plot()\n", + "indexed_data['ModelSA_e'].plot()\n", + "plt.legend([\"Augmentation observée\", \"Modèle exponentiel\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle quadratique semble correspondre assez précisément aux données, nous allons donc l'utiliser pour extrapoler l'évolution future de la concentration de C02 atmosphérique jusqu'à l'année 2050. Nous calculons le nombre de mois supplémentaires à générer par le modèle, et affichons le résultat." ] }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 328, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3608,12 +3672,13 @@ } ], "source": [ - "months = range((2050 - 1958) * 12 + 9)\n", + "first_year, first_month = data.iat[0,0], data.iat[0,1]\n", + "months = range((2050 - first_year) * 12 + 12 - first_month)\n", "\n", "extrapolation_sys_augm = quadr(months, fitA_q, fitB_q, fitH_q)\n", "plt.plot(x_data, data['SAFitFilled'], '*', color = \"lightgreen\")\n", "plt.plot(months, extrapolation_sys_augm, '-', color = \"red\")\n", - "plt.legend([\"Augmentation observée\", \"Extrapolation par le modèle quadratique\"])" + "plt.legend([\"Augmentation observée\", \"Extrapolation -- modèle quadratique\"])" ] } ],