diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 8ccf8315a00e417f6a56f0ab0311ce1831710e09..4b59af207f9d3a7d5da117ad68db0663710ec46a 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -9,13 +9,981 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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28196003219901960.2049317.58316.28318.03316.71317.58316.28
29196004220211960.2896319.03316.70319.15316.79319.03316.70
.................................
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407.49 \n", + "733 2018 12 43449 2018.9562 409.23 410.15 409.08 \n", + "734 2019 01 43480 2019.0411 410.92 410.87 410.31 \n", + "735 2019 02 43511 2019.1260 411.66 410.90 411.26 \n", + "736 2019 03 43539 2019.2027 412.00 410.46 412.25 \n", + "737 2019 04 43570 2019.2877 413.52 410.73 413.73 \n", + "738 2019 05 43600 2019.3699 414.83 411.43 414.54 \n", + "739 2019 06 43631 2019.4548 413.96 411.39 413.91 \n", + "740 2019 07 43661 2019.5370 411.85 411.04 412.34 \n", + "741 2019 08 43692 2019.6219 410.08 411.62 410.21 \n", + "742 2019 09 43723 2019.7068 408.55 412.05 408.49 \n", + "743 2019 10 43753 2019.7890 408.43 412.06 408.61 \n", + "744 2019 11 43784 2019.8740 410.29 412.55 410.20 \n", + "745 2019 12 43814 2019.9562 411.85 412.78 411.74 \n", + "746 2020 01 43845 2020.0410 413.37 413.32 412.92 \n", + "747 2020 02 43876 2020.1257 414.09 413.33 413.84 \n", + "748 2020 03 43905 2020.2049 414.51 412.94 414.85 \n", + "749 2020 04 43936 2020.2896 416.18 413.35 416.31 \n", + "750 2020 05 43966 2020.3716 417.16 413.75 417.08 \n", + "751 2020 06 43997 2020.4563 416.30 413.75 416.42 \n", + "752 2020 07 44027 2020.5383 414.49 413.71 414.85 \n", + "753 2020 08 44058 2020.6230 412.59 414.16 412.72 \n", + "754 2020 09 44089 2020.7077 411.25 414.77 411.03 \n", + "755 2020 10 44119 2020.7896 411.22 414.85 411.18 \n", + "756 2020 11 44150 2020.8743 412.95 415.21 -99.99 \n", + "757 2020 12 44180 2020.9563 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally CO2 seasonally \n", + "0 adjusted fit filled adjusted filled \n", + "1 [ppm] [ppm] [ppm] \n", + "2 -99.99 -99.99 -99.99 \n", + "3 -99.99 -99.99 -99.99 \n", + "4 314.90 315.70 314.43 \n", + "5 314.98 317.45 315.16 \n", + "6 315.06 317.51 314.71 \n", + "7 315.14 317.24 315.14 \n", + "8 315.22 315.86 315.19 \n", + "9 315.29 314.93 316.19 \n", + "10 315.35 313.21 316.09 \n", + "11 315.41 312.43 315.41 \n", + "12 315.46 313.33 315.20 \n", + "13 315.51 314.67 315.43 \n", + "14 315.57 315.58 315.54 \n", + "15 315.63 316.49 315.86 \n", + "16 315.69 316.65 315.38 \n", + "17 315.77 317.72 315.41 \n", + "18 315.85 318.29 315.48 \n", + "19 315.94 318.15 316.03 \n", + "20 316.03 316.54 315.87 \n", + "21 316.12 314.80 316.07 \n", + "22 316.22 313.84 316.73 \n", + "23 316.31 313.33 316.33 \n", + "24 316.39 314.81 316.68 \n", + "25 316.47 315.58 316.35 \n", + "26 316.56 316.43 316.39 \n", + "27 316.64 316.98 316.35 \n", + "28 316.71 317.58 316.28 \n", + "29 316.79 319.03 316.70 \n", + ".. ... ... ... \n", + "728 408.65 408.90 408.09 \n", + "729 408.91 407.10 408.63 \n", + "730 409.18 405.59 409.08 \n", + "731 409.45 405.99 409.61 \n", + "732 409.72 408.12 410.38 \n", + "733 409.98 409.23 410.15 \n", + "734 410.24 410.92 410.87 \n", + "735 410.48 411.66 410.90 \n", + "736 410.70 412.00 410.46 \n", + "737 410.92 413.52 410.73 \n", + "738 411.14 414.83 411.43 \n", + "739 411.36 413.96 411.39 \n", + "740 411.57 411.85 411.04 \n", + "741 411.79 410.08 411.62 \n", + "742 412.01 408.55 412.05 \n", + "743 412.22 408.43 412.06 \n", + "744 412.44 410.29 412.55 \n", + "745 412.65 411.85 412.78 \n", + "746 412.86 413.37 413.32 \n", + "747 413.07 414.09 413.33 \n", + "748 413.26 414.51 412.94 \n", + "749 413.47 416.18 413.35 \n", + "750 413.67 417.16 413.75 \n", + "751 413.89 416.30 413.75 \n", + "752 414.10 414.49 413.71 \n", + "753 414.33 412.59 414.16 \n", + "754 414.57 411.25 414.77 \n", + "755 414.80 411.22 414.85 \n", + "756 -99.99 412.95 415.21 \n", + "757 -99.99 -99.99 -99.99 \n", + "\n", + "[758 rows x 10 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", + "import numpy as np \n", "#import isomonth\n", "import requests\n", "import os\n", @@ -23,7 +991,9 @@ "data_file = \"weekly_in_situ_co2_mlo.csv\"\n", "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"\n", "if not os.path.exists(data_file):\n", - " urllib.request.urlretrieve(data_url, data_file)" + " urllib.request.urlretrieve(data_url, data_file)\n", + "raw_data_prov = pd.read_csv('./monthly_in_situ_co2_mlo.csv',skiprows=54)\n", + "raw_data_prov" ] }, { @@ -37,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -941,19 +1911,19 @@ "[756 rows x 8 columns]" ] }, - "execution_count": 65, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "raw_data_prov = pd.read_csv('./monthly_in_situ_co2_mlo.csv',skiprows=54)\n", + "\n", "raw_data = pd.DataFrame({\"Year\":raw_data_prov[' Yr'][2:].astype(int), \"Month\":raw_data_prov[' Mn'][2:].astype(int), \n", " \"CO2 seasonally\":raw_data_prov[' CO2'][2:].astype(float), \"CO2 seasonally adjust\":raw_data_prov['seasonally'][2:].astype(float),\n", " \"seasonally fit\":raw_data_prov[' fit'][2:].astype(float),\"seasonally adjusted fit\":raw_data_prov[' seasonally'][2:].astype(float), \n", " \"CO2 seasonally filled\":raw_data_prov[' CO2'][2:].astype(float), \"CO2 seasonally adjust filled\":raw_data_prov[' seasonally'][2:].astype(float)})\n", "#raw_data = raw_data.drop([0,1])\n", - "raw_data\n" + "raw_data" ] }, { @@ -974,17 +1944,25 @@ "\n", "Moreover, it is said that the missing data are completed with the value -99.99\n", "\n", - "The month and year can be then converted to panda Period " + "The month and year can be then converted to panda Period.\n", + "\n", + "Let us then check that eventhought some data are missing, every month are present in the data. Then we store the number of month since the beginning of the measurements. " ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "raw_data['period'] = [pd.Period(str(y)+'-'+str(m)+'-15') for y,m in zip(raw_data['Year'],raw_data['Month'])]\n", - "sorted_data = raw_data.set_index('period').sort_index()" + "sorted_data = raw_data.set_index('period').sort_index()\n", + "for y1,m1,y2,m2 in zip(sorted_data['Year'][:-1],sorted_data['Month'][:-1],sorted_data['Year'][1:],sorted_data['Month'][1:]):\n", + " if np.abs(m1-m2)>1: \n", + " # if the consecutive month are bigger than 1, either we change of year, either some data are missing\n", + " if np.abs(y1-y2)>1:\n", + " print(\"problem with the dates \",str(y1)+'-'+str(m1)+'-15',\" and \",str(y2)+'-'+str(m2)+'-15')\n", + "sorted_data[\"nb of month from the beginning\"] = np.arange(0,np.size(sorted_data['Month']))" ] }, { @@ -996,17 +1974,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "nb_year = 10\n", + "nb_year = 60\n", "sorted_data=sorted_data[~(sorted_data[\"CO2 seasonally\"] < 0)]\n", "sorted_data[\"CO2 seasonally\"][-12*nb_year:].plot(label='raw data')\n", "sorted_data[\"CO2 seasonally adjust\"][-12*nb_year:].plot(label='interannual variation')\n", "sorted_data=sorted_data[~(sorted_data[\"seasonally fit\"] < 0)]\n", "sorted_data[\"seasonally fit\"][-12*nb_year:].plot(label='smoothed data')\n", - "plt.plot([sorted_data[\"period\"][-12*nb_year],sorted_data[\"period\"][-1]],[sorted_data[\"CO2 seasonally adjust\"][-12*nb_year],sorted_data[\"CO2 seasonally adjust\"][-1]])\n", "plt.legend()\n", "#sorted_data = data.set_index('period').sort_index()" ] @@ -1020,9 +2020,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cj/KwQ9kJKPcl17MxufkrjQ6lfGbYCq+wWMzR7UujZWp79enrvWRYuXuF6jYOvKpYOSpbvWuT++woDQY21nu6QVvIZ8kIs95z1UO2jag4CuZeuU/dgCWznqSU0uHcpwwSWiqaNSx7U+rrHbnOn81GDd4KFjD63J60TMFSpLSYDE2wXMyRzx4+xjYooaRoMC/vWQhhwXHqsljIUsgdXm8b7zoK5lu5+3lznZ6xGuSDdo/e4CgPC+7mNxi2KmUzmWSzsQErPqG6I9uMZ9Pu9al3+pxeTICW8eC9izknv2I6UlrzeGYw7bl702Cm6SA4GpmOZJt95mkGzQYFFwXzrdw9vYscUkKjY2YTTmvwUFgq5Q3zgh48rJv02TfoPe/Wj+YZYJwSMiN7WsmrwnLJDgfsdehNe7GTxQJKLphb71bXSdp7PrNBx2laZ6yCacfJK4lso1InCuZeuXvxgmDuZfh5F6aTuV48bCbjDLYyXSo2/cC7lJChg+fvzWWteJLjQ6xGss0qd29qJLn9bdxxavg4TkWzZZh+SWRIPXdrmDY0TMG0Z+PFw4IdDni5dJSHBfOVDN6hulnP3SvP4Mg2n1CdHGKlYDq5OK2fQckFg8rdZ71tOE6FbIZyIXvk70zn0jw994KiPNOEqhV4zd0A85bWi4dVso0mVD28C3AOnilaZjCQVJvJJFS9KigAlgo5Or2BsctC/NbbeFnglGYeMF8W6Lvehs+W88z5Q53fCjYcp2nvOpfNUMpnjPdyhMXcKnevrDpY8Nzr0zsHlWyTnOReo+t53ZdJmqDW6jKQ0w+8eZpgeu2zLdnT9piSbcqgeTXzAJTyGaNlgcHW2xQt4++8mDrT/aHz4vGuDVfqREFs5S6EuFsI8XtCiM8KIT4thPgOHV8sLobNJX60jKGDt9fokBGwUppehjmQTmRhRPaUsQdD2QYrGfaGrfDTnxlsREoJeJIevLcjO2s8iTxNwapmuSTW2zQF50WNOLLNOS/7rS7Sw3kxLTsqdHjuPeD/kFJ+IfA64B8LIV6m4XNjwffAl8y2Z3s1T4EdSmhaBYWSbfrAe9WagzlvrtLosJDPHmmegjFP0uC79vPmTD4zTN/fYLZSx69ixQbn7kuDGXZekjAsURFbuUspN6WUf+7+9z7wWeCuuJ8bF360zKLhbs29urd3sVQ0m3zZqyfjSXq14YMzwGshb65MbbfuHS4PDblRDthjvQ0m+PyqRsC0Ie9SLmQp5pIwpt2p0Qo4+7vTN5Nf8XMWley5o2XGIYS4DDwE/KnOz42CpGkZT++5YE62XzMP2FI23p6NqWTuLGoEzBhyP97bkW0uv7Ln4z2D+SjN75nBTGTqNYFTwWR+xS9aUbLntlpGCLEE/ArwnVLK2pS/f5cQ4qoQ4ur29rYusZ7wqz82PYdi1897Nsg/+zXUgNkD71dBAWbr+6cNaVMwe+C9eW8l21R+xc95AVg2Ssv4895gZr39Or/HZZvY436NiUr23NEyAEKIPI5i/3kp5X+b9jtSyoellFeklFc2NjZ0iPWFX/2x6YRTpdH19p4Nbn6v0QND2YUc7d6AroHbmPYaHbIZwcrEBM6hbINRg1fzFJiNlGYZNKPKZmakZK55a6/R8dxj5by5SGmW82Jy1PFMWmZOq2UE8DPAZ6WUPxr/K+nBtKvmxmGqbEpK6TaXzAgdDWzA3frs0BFMGRaH955WfwxmS8X8aDCTNMEsBTukhAwZlkIuw8KUJDKYjtK8janJTuhZCtZ0lDZtfPi47Hn03L8EeDvwlUKIR91/vkbD58bC3pSr5sZhqppANU95yTapbLxu5hnKNkoJeVNRYM6Yzqw/thApeSobg1VCyqB5GVOTF6T4VQiBOUUX2JgaWu/VhfzUCjglu2749quwmG6GQkBK+YfA9CdOEJVGd3i13DSY8mx2697dqUoumPHmdn0qVsDs3Oldn/p6MHfga03v5imAQi5DIZvhwICim5VkMx01eMkFc+utjGkShjwoDWbEc/fovp6U3ej2h+89acxth2pStExlRolaOZ9FGErmBsnog5nbmPxCdSXblEcF3klNR7YpmmB2kg3MvetZ+9vEBSm1ptPMk4jnPstxKpiOTP2fGY7XZMi5Ve5BNr/RUN2DGskYvLR5t+5cJjCt/hjMzhyZFaovG0qo7s1IsoFK5pp5Zq8hVkoumMuv+Bo0Q8OsZlFRYC6Zq9716pQKODBcrOBxN8Sk7OOUVJ1L5d7pDah3+jO9iySoEUe2GU+yEiDPAPo3v5rA6fvMhRzNbp++Zk5yVqemkm3Em/MZYgXm8yuzIiXQT//N6tQEd0Cdof294lEBB2Ypz1k5pdRzt4QhPeGr6Mx0lFVmJH3ANSyGyrVmGRXQv/lnJZEPyda85sOZNrMoOAMHfvZ6mzOmFZ8kMoyqOnSvdyBjajCh6rfHTOZX/AbEgZ17a8NiLpX7rs/IXYWlUo56p6+9e3C33kEI79ARzFFCuzM2vylPclYVw7hs3c89MuQzOGAjCVX/pOaw5luzYam1evQHMphh0fzcs/IMSrYpytPPe3Zk64+KW90+zW5/ZrQCx2um+1wq9+HI3RkbsD+QtLp6G3qc0DHvGTqCOU+yEqBEDfQr2L26fxWDUdmNDrmMYNmnQsFU8nyW565qvnXnOIJ6z2DOc/czpssGixX89jeYya/Map6ClJaxhlklU8BQGejehLsetz+Nw2QZpt+Bz2czFHMZA577bGVjqsZ+dwbvDWarZfySmo5s/V6s3zx1BVORUhBjulh0OqF7mjuhZxlTMJNfCbS/04SqHewGKo8z58X6hW9gpsGk2x+w3/IeYjUuO1laRr8XOztU1+/NDQZyZkUWuOutnRpJMlIKYkxNvWt/GgzMUJ7b+20ANpaLnr+Teu6WMKvWHMyFrVv7Lc76bAJHtv5SMUWNnPZp3AIzM152D5zN7xexjGqQ9dbY36l7zzlRWDYwnfFOvUNvIDm/WvL9PRPrvVVrAfjus6WCmbLXnf02Zxb99/ewU1SjUau3exy0e5xd9l9vE5HSLXe9z694y1b5lVS5G8ZWrcVyKTf18gYFU7TMZrXFhdUF398xQctsVp0NeMFnAwJGauw3ay0K2YyvkjVVY3+r2uLCDAW7WMwhJTQ6+mTfqs4+8GDmXs9bVceY+im6xaIZZXOr1ppp0Ex4sUrBznrXJiJT9a7PrngbNVP5lTiYS+W+GfDAg95KhoN2j/1Wb+bmXy7qv7R56F0E2vx6vefbVefA+4XqJi7NkFImpmw2q02AgIZcs0GrtVhfKlDIeR9fdWmzCUU306AZcJxuuwr23EzZ+qPiW7UWpxcLvs6iI/t4DQ+bY+U++9CB3lKxwN6cCc+mGky5m9j8m4EOvP464L1Gl05vMFO2ihp0Ntbcdo3pudXZFIVuY3qr2pyp5BzZer3Ybn/A9kE7kAMBuo1pMM/dCC1TbQVfb0PD2qJgjpX7DO/ZQPXG7YDeswnPZrPqUiOzEnylfCKhejGXJZ8Vmp9Zec9JeO4tchnB+gz+2UQy91atPdOgjWTre+at/TZSBtjfBXO0TBDDoju/EoT6g9RzN45Ob8BOAO/CBC0T1LswcanArWqTc6tFz5GkI9lZrR6slDKQMQX9m38UrcyK0vRHDcqbm73e+jng27UW54Kst2a+P2h0aCK/cqvaYq2cD0SN6L796nYtmOduquQ2KuZOud8OmHgxkd2+5XqSs3lBM57khRV/JQf6D/yQGgmobHRHKxDCmGpUNmEMWkfj7VftXp/deieQ567bsASnHQ2crdps6s+RrTcqbvf63Kl3Ar3rJUOTT6Ni7pT7KKvur+gyGaH9ZWxWgyVehhywxqghCDUCTmKzofFSgaDUCOifDHmr2iKbEawvzeK99RvTwN6zZtlbNadSJpii05tfCVqxYoJ2vFUNuL+LeqdhhlvvlJYxiucrwZXNouZkV9DwTbcnGYYa0U0JBaVGQD//vFltcW65SDYANQL6lM1wvQO8a90lt7eGidwkaLAmxVzGd24SQDGXIZcRyXjumvn+zYBUFKTK3TiC8oKgutmSCNX1hq2KGgnGC+pVNkGpESVbJ99/q9YMfOhA33rXmj2a3X5I2Xr2WZj11k7L1NpcmFHyCqML6HWtd7cfLI8G+g150ESukp2OHzCIzWqLpWKO5ZK/dwFmOMlENmBIBQv6FN3tWjBqBNx7JhNY73LBuf1Km0GrBatxh/Fkrp4IMWi9N+hvxQ9agqlk66I8h1U6IZwXbfs7jOdeMDNTJyq0KHchxM8KIbaEEJ/S8XlxEPTAg95O0VbXTbwksAFvucomyHOPaAJ9nuTZANQI6E3mKmrkfIAkshBCazJ3FB0GMWh61/tWrcVCPstKafY9nc6oY335lVu1YJGpI1ufIVeFCmEiJZ2RabmQ9R2UNpJt5varqNDluf8H4I2aPisWNkNsQJ2ejUq8BOFCdU9nHIXqwXhv0FcCGsaYLpX0Kdj9do9Gp5+QsgmXZwCdhnx2N7CCzvyKlJLb1Xag/Q16Z+ircQthomJdCva2y/WHWe/j0sikRblLKT8B7Or4rLjYrDRDKXddFStBKwnGZev0JLMZ4Tu1TkF3zfdmNdx619t6GkzC5FZAbzJ3s9pCCP/BXQq6Kbjb1RbnfGacjEMn379b79DpDwJFpqB3fw8rsoKU+mrOZ21Wg+V1HNnHazLkXHHuo/bo2ZsA9HoXYUoCh7I1eu5BqZHlopOL0CE7DDUCehtMwiQWQb8x3Vgqkve5kEXBiOceUMHqNORhqkZAb1R8u9ailM+wshCAGiloNqYBu4Hh+M10t6bchRDvEkJcFUJc3d7eNiJDJV4CH/iSCU8yuKLTxsOGyjO4no0GoxaeGtG3+cPwsKBX2WwG7CkAvZ7kYCAD19eD3vr+0WiNEI6TxkjpwupCIGpETWfUud6p5z4DUsqHpZRXpJRXNjY2jMi4FdJ7Xirm6PYlbQ3TGVWVzlKAxAu4M8YToEbUBtRBR4WlRnQ2mIyoEfvJ89sBBqUpFHNZ59JmHdRIo0O3L0N47vqUzdBzD+HF7rc0VQjVglNRoO9d79TbgWb2j+Qer5nuc0XLhEksAiwW9L2MMBYeVAOVPpogaImazgaT8KG6PkroVrXFmcWi79jbw7L1csBBjSnoa5YLU/IKemkCVfIaJK8DbgJb0wX0Qaa8jkPXu75dDd6dquSC/jsLokJXKeQvAH8MvEQIcVMI8fd1fG5YbFZCKpuSUjZ6PMlwB16P577f6lIPQY0IIbTdDnQ7pDenPBstUUOIqiglW8cz19s9aq1eYHrCka2HohiOGQ6pbHRQcJtuniFIXgdGF9DHjYoHA8lWrR34mZVsPc5LOOrvuNEywTiEGZBSvlXH58SFqkkNUgMMI5pgX5NX9eKz64F/X5d3EZbrByfppKNTVHnuoZWNpue++3Q58O/rUrCjjsXgNIG2dx2iWxL0lr2GjUzHo4ZZs5b8sNtwq3RCG3J9xjRMXgdegAlVG1Dt6EESLzBOE8TbCL3+gK39sLSMLu8iXKgO+pKLt2pN1peCUyM6L0gJGyktFXJ0+vFvvxpFK2E9dz2RUkbARoBuYEeuSp7riUyDRmgAZbdqpRHzbN0K6UCA2yynaY8FmdmvUMxlyGqeqRMHc6XcN6stLobwYBdczr0RcyPsHHQYBLjEYBxLmroHg45hHcdCIUuzqyeJHEbBlt31bsZUNo1Oj2qzG2q91buOW4YZxZjqNOTrS0VyAUowAUo5PesNo6sUg6Kseb3Dvmsd5ba33BLjWTP7FYQQlAtZrXf1xsFcKffn9sIluhbyejb/c5UGQCjDojZ/qxdP9s29BhkRzrNZyGdpadiAN/eaXFyLsN4xD95zew4XetdaeEPeiin75l4TIcIpm3Jej7J5rtLkYohnzmQEpXwm9jNXG132271w663pXd/cc87W3afCyda1vy+FoP6GsjVeFBIHc6Pcm50+W/tt7gnxMnR5F9d33Q0YQvaCJi/22d0GF9cWAlMj4Dx3oxvPkxwMJNd3G9x7ZjHw/zOKlPSsd5h3rcuQX99tcH6lFIpH1uVJOusdXtkksb9LGte7XMhyetH/+shx6Fzvu0+FXG9NsnVgbpT7DddoN6IJAAAgAElEQVTC3xNi8+sK1Z+900AIuBTCu1CbX4eiC3vgS4Vs7EO3td+m0xuEUrCFbIaMiO89R1HuZU2G5cZuI5SSA+ddNzvxaLBuf8DzlWaoZwZXuSdhTDVFSjd2m9x9qhw4jwZ6DFq71+f2fou7Twc/00PZKS2jF9fvRN+ASXhzZU2b//qdRqQD34rJuT97pw6EW28hhDZlE9abK2miCSJ5cxpC9ecrTQYynPcMriHX5rknQ8tEMaat7iBWPuu5vSZShtvfkHruRqA2YCiaQFfoGFHBQrzNf9DucafeCb35dYbqSWz+G7vOeof15iCeMW11+9yqtUI/c9l95jgNPVHXu1yIb1iu7zY4vVgIdEfCuFyId7akdKi/0N6zKztOjX0UKgpSz90Iru82WCrmOFUOvgHzWadbU4eii6LkIB5NcEMZtNPBDRq4nHvMCqEbu04i964QVBS4yj02NdIMf+g0KJvn3Csc7zkT/pn7A0knxiUON3Yd2VGUTVwqKqr3DNCIcbZ26x0anX4ijtMNN2kfiQZLPXe9uB7BmwPn4MXZ/CqRGyXRBfE24LMRqCjQE7aqRG6QyYjjiLv5lTcXxYOFeMomqvesFF0rBu9+fbdBPitClbwq2Uk6L3GqVobec4SkJsRU7rsNCrlM4J4CBR00mC7MjXJ/9k499AaE+HyoSuRG9STjbH7luYdJIo/Ljhu2hjVoEF+57xx0aHbDe3MjBRt/vaN4zxBf2Vw6VQ7c/j8uO0600usPeG6vyT0hqZGSW72lw3uOvN4x3/WlUwuBa9zHZesow9SBuVDug4Hkxl4ztJKD+J678p7DcP0A5bzbwRdH9m6d1YX8zNvoJ6E2fxxqJkqeARwlG+eZI3P9GhTs9TsNSvnw3txCIb6iux6hSseRHc952ay26A1k6PXOZTMUspnYBg3CJXJBj3KPEq0o2annrhG391uhy/IU4r6MyKG6lgPfjOY9xwxbVSL3npBcP8RP8EX2njWE6pGpv6Ehj2FMdxuhvWdHdrz9HXW9AUr5TGzveX2pMBxlEBS6aJmwdJCSnSp3jYhSBqkQ17O5fqceOpEL+kLHSN5czMqRGxENGsRPqCpjGqanAEat+HGjhqjPDNHXu9roUm12I0dKOtY7kbMVcX/HLXutNrrUWr3I6x03n6UL86Hch2WQ4V9G3FkQ0b25eBuwP5Dc3GtwbwzlHrWxZkRFRVQ2MQ982J4CiN+KL6WMbUyjrvewQS+CbCdSipdbyWVEqHnqI9m5mJx7RO85puM0yqNFeeb4+SxdmBvlns2IUHM3FOImnKJ6c3E5yc1qk24/PBcK8QemxQnVk1rvuLJ36x3qEcrylFyIbsij1lwr2Z3+gF7EMszruw3uOrUQOpEL8fIrvf6A5yvhewogfqQUd71Bzz3BcTE3yv3iWil0WR6oMCrai1CJ3CgeLMSjKOJSURB9A0ZN5MKooScqonrPEI9/jktPQELKPabsG7GMafRIabPaoj+QkbznuAo2rvMC8SfN6sBcKPdnI1ZuQDxaRiVyYymbqMo9YhmkkgtxPJsYBi0fvVszaofoUHYMw6JFuUc88Nd3G5wq51kJ0SGqEJd/jsp7QzznZahgE6JlVhcirremsSI6MBfKPZ53Ef3Ax+GeIZ6yedZtaonChY68i6hRQz3ygS8VskgZjZN8ruLO+wjZIaqgQ9lcSkLZxNzfEK2BqtbqsteIlshVsk9itHJ9N/yAtqHsmPkVnTjxyj1OWR7Em5AYx5uDeK3h1yM2tUC8UcdOIrcZKZELzmxziKbodKx3nHd9drk4VBxh5QKRL0iJ6z07ssM/d5yqKIiXPL+x5+TRwtzPoJDPCrIZEdl7vhlhno3C3HHuQog3CiGeEEI8KYT4Lh2fGRRxuGdwmomiJpyu34meyIV4pWJxuOdSIbqC3aw2IzW1KOhQNpGfO6YnGV3JRe9p6A+k2yF68pR7uRC9W/P6bpO71hYC3zo1jjjTRweu8xLdmMbvX9GF2MpdCJEFfhJ4E/Ay4K1CiJfF/dygiFMGCaOX0YpAE8RJ5ILy3KPxsM/eiVYGqeRCNF5waEwjrnccDvjGbrQOUYU4DVQ3YoTqI2UT/l3HNqYxEnxRh5WNy446y+dGDO8Z3EqdCLJv77fo9AeRuH4lF/RcbRgXOjz31wBPSimvSSk7wC8Cb9bwuYEwuoYrqnKP3j0YtQ53JDvaXab7LaepJWwjj4KahhmFErrpTkaMvN4xNn+UixsmZUd55m5/wGY1/JVrh2RHzK/oULAQzZDf2GuwUspFqoqCeJTnzdhnKxMpalDrHfVs6RgtrQs6lPtdwI2xP990f2YFt6otFvJZVhbCtSgrxEk4hb00eJrsKJtge78NwNmVaB4sRFc2SvbGclTv2XlPUWTfqsVc74jPfEddgB5yIuMh2RFvY9rady6IDnNH7iG5hegJvttx1zufpd0L363Z7Q+4U+9EfmYlO8q7Vusd9bnj7G/d0KHcp7lRR96mEOJdQoirQoir29vbGsQ62Npvc3alGMubg/AvQ0rJ9kE7spKD6HPVt5RyX7ZvWLZqLVZKudAdokO5ipOM4FVt77cjUzLg9jREkKsOfJx3HTW/slWLZ0zjJPi29uPt76HjFPIS+DsHHaSMud4Rlft2zLOl6wIgHdCh3G8Cd4/9+RLw/OQvSSkfllJekVJe2djY0CDWwdZ+i7MxFSyEp2UqjS7dvoylYKPO/YjrPUP0aZhxDVpUzl1K6Sj3ONFKzAOfiLI5aFPMZVgpRYtM4+Q4tvfb8RyIiIn7kYKNt8+inq1cRrAWmYqao4Qq8EngxUKI+4QQBeBbgF/X8LmBsBVzA0bd/FsaNuBCxLkfWmRH3PxbtZgHPqJnU2126fQHsWSXC1l6A0knZPJc13pHitJqLTaWY0SmEe8NkFLG9tyjn63kIqXt/TbrS8XQc9wV1CXwc+G5Syl7wD8BPgx8FvhlKeWn435uUGzXYoaOCXoX5YhzP7b32xSymciJLojBucemoqJxkjoUbFRlo971ehxKKGLyfPugHduoQPhnrrV6dHoDLVFxWCU7yiklwbm3Y+WyhpVRx8BzjxbrTUBK+VvAb+n4rDBodvrst3uxXkbUhh5d3oWSvRyinHJrP543B9E4dyml67lrUDYRjamO9W51+6EM49Z+i1PlPIVcdF+onM9yu9oK/f9t77e5bz1agx5ANiMo5DKhKThdVBSE74QeGdNCdNkxCgaiNE7pkK0bJ7pDVSnYJGiCLQ3eRdSa2O39NusxDh1EKwusd/o0u/14oXpETnL0rpMxLHGeGaIf+Li0I0Qz5Fqclxhna62cp5iLlrRXsqNUCMWNTCF64l43Trhy18N7QwRlU2tTLmRZKkYPfqJGDU6iy76y2aq5CjZGpFTIZshmRPgDX4tvTEfJ8/DKJq6CjTL+tt3rU2l04xuWCPkVLUnNGPs7TlUUqGcOl+PoDyR3NCj340LLnGzlXtNQ7x3Vm9O0CSDi5tcgO6x3MQzVl6Iruqic5NZ+m4V8lsUIs10U4nDuWtY7pNydgw4QT8FCNEM+omXiR8VRooY4ZxpGzxxm+uhu3elnSGK9TeBkK3cNtEzkjH4tXgkmjF+aEVy2avDQITtse/aIiooftkaKVmL0M0A0ZaOqRuKudzmCstHBe0P09S7EKMGEGM7LQXzPvZTPMpDQCVGsoIOKgvgX0ujCCVfubfJZEfr+0nFkM4JiLvxFvnFrgGG8Oza47DuuN5dkqB47ZC6EX++4/QyO3PBRmqoa0cG59weSbj+4ch/SYDH3WZSZOsqgxTGmQ9oxBPc9TNrHoN8gWue5LmMa9+5YXTjZyr3mWPg4GxAi8s+akmwQzrPREa0o2WFbw5UxXYthTMGZxBklyaZDyQGhIpZtTd5clAhx+0CTsoloyGM/c4T9vd/u0e4NNDgQEdZbA+0I8UZ568TJVu77LTZiWngI/zIanR4H7V7szR8lwafNu4jQGq4SXXGNaSkKBxyznwFGCjZMpLSleb3DKNmtWhsh4EyMkkCIlszVEikNnzl4YlNHHu2Q7KSMaeq5x4OOrDqE99x1VBJANG9OR4UQROP79RnTcLSM6mdIIoEdd9bIUHaEEtDtgzany4XII6VHssPTBDo8dzV9NJr3rOlshTSmy8VcpAtZDslOaZn4iNtNphC2ckRHjbuSC+E2oNr8Ory5KLK1GNOQno0uYxorVI9tWNzO3JDKJq5cR3Ym1DN3egP2Gt3YBs2RHa7efEg7xjzXUcqMdVTAQZpQjY1Ob8CuhqoRCH9Jti5lE6UVf2u/FbvBw5EdvnJEhzfnyM5FyzPENKalXJRoJd7gLoVIhkWnsgkhd0cTPQHhKThtvHfE/R23ORBG6x3lEnidOLHKXW1AHd5F2FIxVcUQd/MXc+HH3+poYILwFEW3P2C3oceYhp3Yt6UpVM9kBKV8JpJBi520jxIp1Vp69nfISzN0UX8Qvr5/WIIZ8X6GcbkQzpDv6DpbhfBlmCZwYpV7khtwyx0LerocjxrJZMI39Oio0oHwm1/HjO2h7EI4mkBHZ+xQdmjDEj+xqORCcGOq474AhXI+F6oyShcVBW59fwTqL3bSPmJ+RUu0EuMCIJ04ucpd44EPS8tsxRwLOo6FCJtflzcHwTe/LioKwivY7YM2WQ3GdCg7gQMflpYZ3Regx5iGka2r3BbC32WqLY8WctSxrqQ9xLsgRSdOrnLXVMUA0apldGwCCKdsdMzYViiH3Py6uvfAubc2DCe5VWuzvlTQZ0zDKhtNewyCr7eusjwIr2y29/WUYCrZYYoVdCbtIbzzokN21JlRunGilbsQ8caCKkThgHV4VBDOc9cxY3soNyQto2PG9qTsoBeV6FKwEG69dQ3ugvDKZljvrZEmCPrcW/t6SjAhijGNP1cGIij3Az1Je4g+7VU3Tqxy395vcWaxQE7DBgw792Nb0waEcJ67rm5JJRfChOrxZ2yPZIelCTQa0xCGXNfgLiUXQhjTA52RUrjKkaQiU1WCGbdSBsIXK+j03EcUXPibt3TixCp3pwZYkzeXDz73o+cO7tIpO+j1a7q6JSHagddRggnhS0C3NfGwEK4ySmdisRTSoJmIlMLI1qXcw0TFd+p6ulMhfGWU1rM19NzThGok6KVGgjeY3KnrqxpxZAe/fk1nUjN8qN7S4tXAWDI3gGzHmOrhYSHcEC1dJa8wGnUcXHb8EccKYcswtXruhRAKtqbPe4Zw/RTb+20yAk4v6skzQMq5R4auEjUI9zJ0cqFKduAkm4YZ2wr5bIZ8VgSuZNDpPYdRNkNjqsGDVbKDUyP6kvYQju/fPog/4nhcLgTb31JKbRVZEF7Bgh7PHcJRcOpi7KyWpH2028Z0I5ZyF0J8kxDi00KIgRDiiq4vNQv9gWTnoKNvE4R4GTquextHuZClEZCb29IwY3scYULmLU1VDJCwMQ2R4NM1uGsoO4RhURNPtcgNESlVm106/fgjjhUUDRYkn6WTGnFkZ0I5LzqfGcINqDOBuJ77p4BvAD6h4bsERqXRoT+QsW6jH4ea+xGE+9bZmg2qezAYLbOjqcFDIQxFsaOpoQbCeZLa1ztMpHTQ5pSmqhEIN8BLVwMThDOmOvMMSraU0O7N3uOji7H17bMw7zqJ9TaJWLtWSvlZKeUTur5MUFSaXQBOaWhqgXDJxb2GZtkheNi9RodTi/FmqU/KDuJJtrp9Wt0BaxqfGYKNgt1rOBUrOtc76KHbq3e0cLBJyw6jbHbrznqf0SY7eNXKXqPDSimnz5iGWO87Bx3OLNp3XkziRHLuVVe5ry7oUXRhytSqzS65jBg2KsSF0x3bCxS2Vptdbc8MwStHtK93iM2vW3a5kKU3kHQCeJK61zsoByyl1Co7TAI76Xe9GvMimHGEqYzSut65E1LnLoT4mBDiU1P+eXMYQUKIdwkhrgohrm5vb0f/xkDV9Z51bYRyiM1faXRZK+e1USNh7nqsNLusLWj0JAPSBOrAx72BaSg3RKmYkq0zzwDBlc2aTmMakO+vd/r0BlL7eod519oUXcj1TsKYdvsDDto9bbKjDKgzgZknRkr51ToESSkfBh4GuHLlSqxZmENlk8AGrBnYgOAYllk15DXNnk3QoU6VRnLeXKXRZbmY09KsNi671e3PfJ5Ko8tLzi1rkQtQzme5XW3N/D3dClZVRoWJlFa0RUrBy4y1K/eAzktNs/MCx+OqvRNJy1RcHlZnqA4BPfdmR+sGDDqHQkpJpaHfsASlokA/DRaEc681u9oUzWHZAQ25zgMf0HMfRqYao7SgV+3Vml2EgOWinkgpDN9vxHNPIFoJI9sk4pZC/m0hxE3g9cCHhBAf1vO1/FFtOkpBu7IJGqprSu5B8DK1hgrVNXPuQTwbZUx1UUL5rCAb8Pq1SrOr1aMKem9trz9gX2OoDsE54EpTr/MCwRP31WaXlVJey5A2GCszTshzTyLPANHuCdaNWOZZSvmrwK9q+i6BUWl2WDIQqgelCV58Vl+oHjSZW0nQu9C9+VW3ZlDOXbeChdnvutbS60BA8Ia1mol3HZSC02xMg663lJJqo6s1WnEMWrBcFujL4Q1lp7RMeOg+8MVcBiECehe6qZGAZZgqVNftxYYK1TUlNUFRFLNpmapmZRM0uag7iQyqYW12Q0/FwLsOY8hN5JRmrXerO6DTH2iX3ekP6M0oVjBiTE86LZMUdCvYkSdpP1QPSgmpUF0n/xw0dKxoDtUheCWD9jxDQBpMd15HyQ4yoM4ITZAPNsPIBDUCs9fbCO8dMCI3JTtV7hGgO3SEYJZWhepaPaqAHPAwo685bO30BvRnXL+m+8Ar2UFCdd0J1SHnHvjA601qQjBlk8/q66WA4DRB1VQCOwEFG3i9NVeDKdnHvs79OEJ3qA7BOElTWXWYHbZWNNf2w9htTAFkG1nvGZ5ks9un0x9oNWhB534k+q5dY6qrlwJGdxbMgvZS3yS954B3mVaaXRYLWW2dsRButIcpnEjlrjtUh2Ce5LBqxIDnHtSw6KyWCZrMNea5zyiFNHngkwnVg1WO6PaewaHgZs1O0t0ZC1DIZsgEyGclTcskoU9M48QpdxWq6wyXIVhy0cQGLA+Hls325nSOPYAxL3YWHWVi8wfwJE0kNQMfeAOh+kLAd11t6O2MdWTPrhxpdPp0+zKRfJaRHEcIQ67dmKZNTOGhQnXdyiZIDbIRHjbguGHdYw8guKKrGPPcA1JROrnQXPDS03IhSyGn74gcd0/SRHQI4Qy57tkyECBqMEQ7prRMSJjw5iDYyzAhu5DNOA09ARKqug98kIYeE6E6JJfjCDr3Q/dcGQjDuXe0NspBcusNwRwn3Z2xEKLM2JAx7fYl3QAzo0zhxCl3E94cBKNlTMgehq0BDryJQwf+ns1Bu0df4xArhUCepKF3HTRq0B2qBx19oLvUF4JdmmFKuQfpjtXdGQvBR3uYUu4Q/I5iEzhxyt1Y6JjP0Wj7J5wqDf1ZdQjGz+keewDjnKT3cxs78GFyHAYMS5DSUxPPDP5lmP2BZL/d025YRpVR3p6k7qFh47KTStpDsGSudmN6DGa6nzjlbqIkEGCxmA1U+6x7EyjZsypHTFQILbkhcL3t/dwm8gwAi4Uc7Z5/92C12SWbEVpDdYByMTezO9ZUuS34D0zbb3WRUr/zMqLgjq8hNxWZ+j1zu9en2e0bc5xmlWGaxIlT7lUDQ5UAFos56jM892qzw6rmTQDOWNR6Apu/XJx9vaApamSxONuLrTSdm3l0JpEBFgtZDnwMmpKt/ZkD5DhMKVg1etdXtinHqZCbWYZpImk/3GMB1ttUpFQPMPnUFE6gcldJTd2epJMA8buhx1Gwer1IgKVi1tew9AeS/ZbesQcwUjZBPHfdXuxQ2fjK7ml/z+AY8lkUnAkaTD2z37s2MVcGxt71DM89I2CpoD9S8nvPSrYJWkYIfB0nE3NlIPj0UZM4ccq90nBC9UWN9d4w7tn4Hzyd3ZLjsoNsQFMK1k/ZmPIklVc1S9no9qhg9nqrO2N1P3Mhl6GQzfjKNua5B6TgVhf0JjXBMSyzPFjds/PBKVZYLPhH5KYKNEaUZ+q5B4YqUdMdqquXcTBD0Zni3H03oKEDH0TZmJK9GMSwNPRTI+DmV3yUTc1QqA5QLmZ9owZz6x2Mczex3uWCv+duqtzWke3/rk0VaARxFk3jxCl3E9wcOIcO/MMoEwPLwOUkA3jPJmSXZyg6E0OslFyY7UnqPnQwO79i6sCD864Dee7aCwaCRWnGnBefS+CbXf2dsSPZuYAFA4Yi0xl0lEmcOOVuInyDkSfp5bm3un06vYERb26xmPONGEy0Zg9lF2bJ1j/ESsmFGTSYKWVTyPoeOlPeM8yO0kxxwKNIyV/RmaLBBhLaHvksUwrWke3vvJjrm0k999AwURIII8/GK3w0lehyZDulYl6ejalyxKFsnwNvot5byQXvZNdg4MwQMhKtFHI0u33PUccmLkYZl+1LgzU6LOSzMy9LDy23mBwts+TK9nIiTClYcCOlAJ67bqM2a3/bwIlT7qZC9VmlSybutRzJztEbSDoeNd9mPZvczKSmSWPq5cUedHoMpJlnXppRAmrac59FwZlScuCvbExy7uDtOBnd3wFox+VSjqzmJLKq1JlVlWUScS/I/iEhxONCiMeEEL8qhFjT9cW8UGnon7sBs5XN0Jsz4T3PKEk0VWsOo6jBCybqvWF2pY56ZlNJTfDOr4w4dxPvejYNZiJiKOWd0bteysZkUnNWZZRJ5T4rx2EqMlWVOrP6KUwiruf+UeBBKeUXAZ8D3h3/K3nDVGs2zA6jKgaTmrMMi7pMQOeEwqHsGaViJuq9YXYdsOmkJvgYlqb+O2OHsou5mU01Jvb3sCzQQ3a90zcyQwhm88+mOfdZlWgm5CrZJ5Zzl1J+REqpvv2fAJfifyVvmGrNhrEEn8+BB1PeswqZvWWb2oCLRX9axlSOI5/NUMhlvGkwo9GKf3Kx2uiwXMxpr/eGYKV5xpSNTzLX7P72j0yHSWRDjtNs58Xk2Tq5nvs4vg34bY2fdwQmD/ywm20GTWBqA4L35q80ukbGHoCrbDzkmuqMVVjyOXimOpFhdremqWgFZldGmcopgT9FYZL6C+K5m+iMhWDFCkY99wQ595mrKYT4GHB+yl99t5Ty19zf+W6gB/y8z+e8C3gXwD333BPpy5qs985kBOV81nvzm9yAQ87da/N3jIw9AEfBeimb/Za5Aw/+hsVskm12QtVYpFTI0eo6l5JPS+KZ9ty9lI0qGDBCec4ow1Tjlc1ESqNihWkVSKYiU1DG9BgrdynlV/v9vRDincDfAr5K+gyLllI+DDwMcOXKFe+h0j4wWcUA7gwMn2oZE63ZMFaG6ePZ3L++pF0uOJtfTWfMTYwyNhkpgf/mN1mdNDOBbTRUH5UkLpcOy+j0BjQ6fWOy/SqjTNXXw+wyTKO0o8rttPtHlLu6stOEQQPnXe8cdIx8dhDErZZ5I/DPgK+TUjb0fCVvmPTcQXmxXgfeHD0xaqDyoWUMbkCYPp3R9HqXfSp1qs0uhVyGUt5AEnlWZZTBA1/28WJNRivgKLpZCWyTZZjeZ8tktOKdz2p1B3T6AyNVUUr2SZ4K+RPAMvBRIcSjQogPaPhOnqgOOzVN8s9eCT4z434hmGdjMukD0xWd6UjJr1KnaqgzVskF78ooEzchDWX7lAVWDVIjjmxvCs6kch+WYSbiuXuPOrZiTBMshYxF5EopX6TriwSB+Zfhv/lNJdn8Ls1odfu0DY09gLHmLR9P0iRFsb3fnvp3JhOLQ2M65V2rem+TSU1Htt96G0rm+gzwUhejqL2oE6PpjN7VMpdOLWiXC+MzjKY5L+aoP5jdIGgaJ6pDtdLQfyP9OPwaekwe+GLO8WymbUDjCtanksFUa/a47CTKP1UZ5sEU2fVOn97AzBArGFM2U2SbznGUi96jd9V6m4iUlOwkPfepzovBMRMwu1LHNE6Ucj+zVOTV954y9vl+HJlJ3lsI4VlvbuPAw/S5H1WDA8uUbC9janK9wTtkNh0d+s35thGZeikbkzklJXsaDWayMxZm0WCmq8Fy9AfSc2CaaZwo5f6PvvwBfu7vv9bY5y95cMD9gaTW6nLKkIUHb/7ZZCs8jM1Z8VB0JoZYKfhy7oamfyp4hcymvbmyD99vsisXHGPqpWwqjY6xCE3JnkaDmY6U/CJT0zmlpC/sOFHK3TTKHhMSVWesqYSqkj3twKtxv+aVzfSowZRccBJ8XpdkOzSYufX2aqAa8bCmmpi8+f6KwXk6MD4w7eg+qxmkHcHHmFqoyILptIzJzlhI/qq9VLmPQXHAk2HrcNyvwc3vrWwMh+o+Q7RMNvPA+CTOw7K7/QEH7Z5Rw+JFCZns1IRRddJUGszQhEIFv2Ftpi6iUfCaoW98vWfklEw1JsLssSKmkSr3MSwWnUsFWt3DnqRp7wK8uzXNJ318OGALnjscPXimuVAIQIOZ8uby3sbUZMkr+I9dcO4HNmlMPXJKhiOlhby3526yMxb8K9FsIFXuY/BKvpicCKngNQag0uwYK1GDkbKZuvmbHaPUiNfmt2FMvSqjTL/rXDZD0WNgWqVheL09Sm4Hbk7JpDFd8ijDNNkZC+5YEY9hbSYr4GB257lppMp9DF5ha8Vw85SSPTXp0zBzIbiCUjaesg0bNJi23nY8dy9qJJ8VQ4/PiGwPCs5k1Qh4X5K93+pZyil55xnMRsXTK3XMr3eaUD02WPJIvtjxJKdvwIrhqhHwixrMV6zA0UhJdWqaauYBb87dKcEsGDOm4H21obX1npCtqBGjXqxHGaYVCs5j1HHF4JgJJRdSWuZYwGs0qR1PcvoGNHVTzDimKTp1IbhJmmBUOTLdmCbDuZubwHlI9hQv1vS79oqUbKy3Vxlmpdkl51InpuDVHVsz2NyUPowAABPsSURBVHUOyV+SnSr3MXhVMlQaXZaKOfJZc8tVdm/oGQyOVuqY9KhguqKzYdC8PHcb1UleZZgmx0wolKdUjkgpLSQ1p9MyNqgRrxkvKolsPFLy4NxNGvKkL8lOlfsYvMoCTd0jOg5FCU1OZ6w0zdwZO45p3bHDUN0K5z7xzIbrvcG7DNOKMZ2y3qqZx4aCPfLMlqrBYErU0DBLjTiyj1KepjtjAUq5ZC/JTpX7GLwSIKZLAmH8hvijXpVxWmaKJ2nDe/byJKvNLisG673BvwzT9HpPG+Blgxop5TNTlY3paavgXfNtumLFkX20O/ag3XPujDX4zLMuADKNVLmPwavm23SDB4y82HFKqNcfsN8y28wDKtnlQcuY9OY8yjBNjx4A73ddbZiXPa1yxEZFltcl2VY4d5+yV/POy9FLyW08M8y+w9UkUuU+Bu9Q3Wz98bjs8U1YazmbwrRn41ycPLn5zVeseJVh2ljvabcx9foD9ttmB2jB9G5ke8rmKP9setoqjI8+OEr/GacdC9OMqXnqD5K9JDtV7mMo5jJkM2LqwUvCkxzNlTG7+Zemce4WaBkl+0gC20KkNC2Za82YTvOeLSQ1QdX3H+XcbTwzTPHcbdCOU4xpzUKeAZK9JDtV7mNwwtbDZYE2qhhgOic59OYsKLppHHA+a7ZEDaaXYZq85k5h2jRMW8Z0sZCl0xvQHavUsdFLAdOnM1YaXaMNTHD47lgFZ9qq+UhpsZCl25d0ekfX20Z+JZ0tc0wwyZHZqGKA6TSB6aFh47I7/cGhza+GhpksUXNkT6EoLBjTaZdm2ORhYcKwWJI9bTpjzXBJoJILhynP/Za9Z4bDhsXaevvcWWAaqXKfwGSZ2tCbM80BT6FlqpaokWmVIzbCZXDD1slIyQItM+2GnoqlSGnaDKNKo0shmzE69gCmX5JteoYQTB91bKO+HsZKbsee21ak5HdvrWmkyn0Ck6NJbVSNOHKPbkBrNMGUZgsbiS5H9uHNb6NEzZF7lCYwPcRKYZonqago05HSNP7Z9AwhGNV8T1OwNrxnOGxYbMwQgmQvyY6l3IUQ3yeEeEwI8agQ4iNCiIu6vlhSmBzgZfp2nKHcKRf5Kk9ypWQnZJ70qkw/Mxwtw7R24AtHS09tJZGVYRlPbFabHeMKFtzpjFNyHKbXe1jzPWV/W4vSOoedNhu0Y5KXZMf13H9ISvlFUspXAr8J/AsN3ylROJ7kUc/dtBerLm2eDNWXSzlyBscewPQa+4qFem84WoZpK1LKZgSlfOaQorMxcgHGWvEnPEkbxrRczB56z61un3ZvYOldJ2XIj3ruNmY2QbKXZMfSGlLK2tgfF4FkrvnWiMk6YBtt+EPZE8PDTF/eoDCtxt70NXcKXp67rahhcr2XiuaN6eIUDthGJzIcnc44ilZsvOvDhtxGZyxMX28b0Qoke0l27F0shPgBIcQN4Fvx8dyFEO8SQlwVQlzd3t6OK9YYyhMT5Gx5c+BswkY7AQU7kcy1cc3duOx6e1qiy85zH6YJzM8QgulzVmz0UsDR6YxWnZeEPfck3vXiFMfJFmYqdyHEx4QQn5ryz5sBpJTfLaW8G/h54J94fY6U8mEp5RUp5ZWNjQ19T6AZS8Wj3nMpn6FkOPECR2tiKw1LG3Cixt5WYhGOlmHaNKblwuG5H7ZC9aVpPQ0WPXcYKRvTd5hOyp405At5s52x4N1DYsuBgGQu7JiZqZNSfnXAz/ovwIeA74n1jRJGuZCj2e3TH0iyGWGlFV5hcZJ/bna5sLZgXu5Ejb2tRBeMrn5rdvoUcplEPUkbVSMwemYVpamxB7ajtNOLBWv13uBEDXv1zvDP1tZ7yLmPU0K2OPfkLsmOWy3z4rE/fh3weLyvkzwmS+RsbUBHdu6IN2cnyXa4NM8qFTVxaXO10aWYsxQpTVBCNurrYTQwTSU2h2MPLOV14PB625OdS4z3htEzq85Y013QjuzkbmOKW2P3g0KIlwAD4Fng2+N/pWQxaujps1zKDzs1baBcyHK71gLsNfPA0emMNoaGKUw2UNk68OAous1Kc/hnW7InL20eTYS0Z8hH79pejqNcOFoKaeOZsxmnnl1RUaoz1krSPsFLsmMpdynlW3R9keOCyZnu1UaXy+tlO7LHPElbzTwwUjbqmW3Ve8PRmm+bkdL4KFgppRuq26HgxoeH2ZohBEcvya40O2QzYvhzo7InEti1Zpd7Tts6W0f3t93k+TFMqL7QsDjh2dhozR7KHkuo2jzwcFjZ2GoLV3JhVINsc72Xxuaqt7oDOv2BvahhbICXVd57YuyCalYz3czjyM4eKcNMwpDbqtIB73trbSBV7hOY5CRtc+6N9oSCtalsOiNlIwQsl+xVb4y82J49gzbmSdpM5MLh0bu2ZgjBlJySpRJMR3aO3kDScadh2qTgyoVR85atuTKO3ORomVS5T2A8uWizew8OlwXa9C4c2SNFV2t2WS6aveZuKHdC2VQtlX/C4VGwNkN1OGxMbb7ryYYeW+WfMEYJtfu0e32a3b4Vrh8OV0bZjJSSvCQ7Ve4TWBrjgG1278Hh5IutsQcj2dmxUN3O0DBH7uHRBzYujpiU3ej0rHbGwnQazGad+zj/bO2Zx8oC1XrbqFiBw/cE2zSmSV6SnSr3CYxzwNZD9TErb1v2+MA0W1U6jtyRN9fpDWh0+lapEXAMi62ZNkPZYwm+qhspmR57AEcvybY1/RMON1DVLBvT8TEXNYuGJclLslPlPgG1AfcaXeu8d3nMq7JNE4xfd2drzgkcrkEeJZEtVawUR63hNrtywVU2YwrWlgc7eUm2rWYeODz51Pb+Lk9Epra6zh3ZRy+gt4FUuU9gZSHHS88v8+/+4Bqfu70P2PPmTrlK7c+e3rU69gCcZ9ystrhVbVlrzYZRDbIj1169N4xomZ399likZI+OGue9bUUrwLDG3tY1dwrjF6TYTGqC47wk0UuhZE/eW2sDqXKfgBCC97/1IRqdPv/qt5yGW1sH/nX3n+YND5zh//uNz/DHT92xxvUDfNuXXEZK+Ke/+Bfs1jvWohWANz14nl++eoMPf/o2YC9SeujuNU6V8/zQR55gt961Vu8NsFzKsd/qcmO3YTVSUrKf3DqwrmAVBVdpdux77hN9BTbPVlKXZKfKfQpefG6Z7/v6B2l2nc1gS9nkshl+8m2v4vxqib96rmrVm3vR2WW+/+sfHEYNNmV/75tfzr1nFvnhjzwB2FM2a+UC/+JrX8ZfXK/wS5+8bq3eG+Atr7rEUjHHP/xPV7m937K63m9/3b188pk9fvSjdtf7vvVFNpaLvP93nmRrv+3ItqRk7zldptMb8KHHNq0b08VijucqTfoDuxPRU+XugW989SW++crdrC8Vhh6HDZxaLPBv33GFxUKW04v2vAuAt7z6En/nyiXAnkcFTj39T7ztIfJuQtGm7K9/5V38tS/YYM/S5SQKl9cXef/bXsXnbu9zY7dprTMW4J1vuMzffPk5/vOfXAfsVoO99xu/iCdu7/PTn3jK7aUwe8uYwjdducQXXVrlu//7X3Fjt2EtxwHwDQ/dxeO39ofOiy2kyt0HP/iWV/D7/9dXWPPmFF5yfplf/vbX856ve7lVuQDf+3UP8s7X38tXvPSsVbkvv7jKD3z9g9y/vsi5lZI1uUII/uXffpByIWuVigL461+wwbvf9IWAPe8ZnGd+71u+mEunnImjNhXdV7zkLG977T3OLWPFHBkLvRTg3HT2o3/nlTQ7fZ6v2o2UvuU19/C2197DT338KX7t0eesybVjNk8ohBDDpJttvPziaiJyFwpZvvfNDyYi+5uu3M03XbnbutxLp8r823dcsdK0NYl/8GX3sbKQ4/X3r1uVu1rO81Pf+mp++CNP8AXnlqzK/u6v+UL+6Mkd6+v9orNLvPtNL+U9v/EZq9EhwHu+9uU8efuA//u/Psb960u84pL58y2SuNvvypUr8urVq9blpkiR4njguUqTaqPLyy6uWJU7GEh+/Hc/z1e99JwVBTuOnYM2//svPcr3fO3LedHZaAZVCPGIlPJKoN9NlXuKFClSnAyEUe4p554iRYoUc4hUuadIkSLFHCJV7ilSpEgxh0iVe4oUKVLMIVLlniJFihRziFS5p0iRIsUcIlXuKVKkSDGHSJV7ihQpUswhEmliEkLsA+NTdFaBqsev+/0dwDqw4/P3cT47juy4n52U7HS9j5fsOOs96+/Tdx1ObtzP1rHe56WUyz6/M4KU0vo/wNWJPz/s87uefzfts8L8/wE+O7JsDZ+diOx0vY+X7Djrnb7r+VvvWb8z/s9xoWV+I+Lfmf7sOLLjfnZSstP1fuHITt+1Xdkm1/sIkqJlrsqA8xFsflYq+/jKfaHKfiE+c5Kyj/szh/l+SXnuDx/Tz0plH1+5L1TZL8RnTlL2cX/mwN8vEc89RYoUKVKYxXHh3FOkSJEihU4Ezbza+ge4G/g94LPAp4HvcH9+Gvgo8Hn336fcn59xf/8A+ImJz/oB4AZwYFM2UAY+BDzufs4PWn7u/wH8pfs5HwCyNuSOfeavA5+y/MwfxymvfdT956xF2QWccPlz7jt/i4U9tjz2rI/ilNC9z+IzvxX4K+Axd7+tW5T9za7cTwPv1Sz3bwCPuM/2CPCVY5/1avfnTwI/jst8WJIdSpdJKY+lcr8AvGpsA38OeBnwXuC73J9/F/Cv3f9eBL4U+PYpm+B17ucFVe5aZOMo968YO/h/ALzJ4nOvuP8WwK8A32JDrvv33wD8F4Ipd53P/HHgSkL77HuB73f/O4OPotO93mOf+wjw1yzt7xywpZ7T/f/fY0n2GeA6sOH++T8CX6VR7kPARfe/HwSeG/usPwNej3Oufhv9Z9pPdihdJuUxVO5TFujXcCzaE8CFsUV7YuL3/q7X5g+zILplu3//Y8A/TOC58zjlU99sQy6wBPyhu4FnKnfNsj9OCOWuWfYNYDHB/f1i9zv4epK6ZLv7ahu4F0fRfQB4lyXZ/wvwsbE/vx34N7rluj8XwB2g6P7O42N/91bgp00886TsiZ8H1mXHmnMXQlzGsWZ/CpyTUm4CuP8+exJkCyHWgK8FfsembCHEh3G8q33gv1qS+33AjwCNIPI0ywb490KIR4UQ/1wIEfj25Tiy3fcL8H1CiD8XQnxQCHHOtNwJvBX4JemeftOypZRd4B/h0AfP4xjzn7EhG4cSeakQ4rIQIgd8PQ79YULuW4C/kFK2gbuAm2N/d9P9WSDElB0Jx1a5CyGWcCiF75RS1k6ibHfz/QLw41LKazZlSyn/Jo5XUAS+0rRcIcQrgRdJKX81wv+r45m/VUr5CuDL3H/ebkl2DrgE/JGU8lXAHwM/bEHuOL4FZ58FgoZ3ncdR7g8BF3H473fbkC2l3HNl/xIO3fkM0NMtVwjxcuBfA/+r+tG0rxPkO2uQHQnHUrm7m+dXgJ+XUv4398e3hRAX3L+/gOOVHnfZDwOfl1K+LwHZSClbOMnNN1uQ+3rg1UKIZ3ComS8QQnx81nfU9cxSyufcf+/jcP6vsST7Dk6koozaB4FXWZCrPuuLgZyU8pGAv69D9isBpJRPudHCLwNvsCQbKeVvSClfK6V8PQ7F8XmdcoUQl3De5zuklE+5P76JY8QVLuFELb7QJDsSjp1yd8PpnwE+K6X80bG/+nXgne5/vxOHvzq2soUQ348z6Oc7bcoWQiyNbZwc8DU4FRxG5Uopf0pKeVFKeRknEfY5KeWXz/iuup45J4RYd/87D/wt4FM2ZLvK7TeAL3d/9FXAZ0zLHcNbCei1a5T9HPAyIcSG++e/gVMRYkM2Qoiz7r9PAf8b8O90yXVptg8B75ZS/pH6ZZc+2RdCvM79zHfM+q66ZEdGmISAjX9wFIPECfVUmdfX4GTJfwfHSv8OcHrs/3kG2MUpm7oJvMz9+XvdPw/cf7/Hhmwcqy5xNrz6nH9gSfY54JOMSsXej+PZGV/vsb+/TLBqGV3PvIhTLaKe+cfwKf80sM/uBT7hftbvAPfYWm/gGvDSBM7Wt+Ps78dwjNsZi7J/AceAfgafSrAocoH/F6hzuMz0rPt3V3CchqeAn2B2KaRO2aF0mZQy7VBNkSJFinnEsaNlUqRIkSJFfKTKPUWKFCnmEKlyT5EiRYo5RKrcU6RIkWIOkSr3FClSpJhDpMo9RYoJCCG+XQjxjhC/f1kI4VtXnyKFbeSS/gIpUhwnCCFyUsoPJP09UqSIi1S5p5g7uEOa/gfOkKaHcEatvgP4QuBHcaZX7gB/V0q56Y5J+J/AlwC/LoRYxpm+98PuzJwP4Ixxfgr4NinlnhDi1cDP4owe+EN7T5ciRTCktEyKecVLgIellF8E1IB/jNOt+41SSqWYf2Ds99eklH9dSvkjE5/zn4B/5n7OXwHf4/783wP/VDrzTVKkOHZIPfcU84obcjSf4z8D/w/OBQgfdacBZ4HNsd//pckPEEKs4ij933d/9B+BD075+c8Bb9L/CClSREeq3FPMKybnauwDn/bxtOshPltM+fwUKY4VUlomxbziHiGEUuRvBf4E2FA/E0Lk3bnZnpBSVoE9IcSXuT96O/D7UsoKUBVCfKn782/V//VTpIiH1HNPMa/4LPBOIcRP40zfez/wYeDHXVolB7wPZ4qkH94JfEAIUcaZwPj33J//PeBnhRAN93NTpDhWSKdCppg7uNUyvymlfDDhr5IiRWJIaZkUKVKkmEOknnuKFClSzCFSzz1FihQp5hCpck+RIkWKOUSq3FOkSJFiDpEq9xQpUqSYQ6TKPUWKFCnmEKlyT5EiRYo5xP8PxLEexcEBUiAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data[\"CO2 seasonal variations\"] = sorted_data[\"CO2 seasonally\"] - sorted_data[\"CO2 seasonally adjust\"]\n", "sorted_data[\"CO2 seasonal variations\"][-12*nb_year:].plot()" @@ -1032,8 +2055,235 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This seasonal variations are rather close to a cosinus function with a one year period. In the other hand, the interannual variations are nearly linear. A FAIRE : une régression linéaire pour montrer la proximité et la pente " + "This seasonal variations are rather close to a cosinus function with a one year period. In the other hand, the interannual variations are nearly linear. \n", + "\n", + "Hence, a linear regression is going to be performed in order to get the slope and the intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost of the linear approximation of the interannual variations: 5660.432501216549\n", + "normalized cost of the linear approximation of the interannual variations: 7.5775535491520065\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import lsq_linear\n", + "A = np.ones((np.size(sorted_data[\"nb of month from the beginning\"]),2));A[:,0]=sorted_data[\"nb of month from the beginning\"]\n", + "b = sorted_data[\"CO2 seasonally adjust\"]\n", + "res_lin = lsq_linear(A, b)\n", + "slope,intercept = res_lin.x\n", + "print(\"cost of the linear approximation of the interannual variations: \",res_lin.cost)\n", + "print(\"normalized cost of the linear approximation of the interannual variations: \",res_lin.cost/np.size(b))\n", + "sorted_data[\"CO2 interannual linear variations\"] = slope * sorted_data[\"nb of month from the beginning\"] + intercept\n", + "sorted_data[\"CO2 seasonally adjust\"].plot(label='interannual var')\n", + "sorted_data[\"CO2 interannual linear variations\"].plot(label='interannual var lin approx')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It actually seems that the interannual variations are more like a second order polynomial function" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost of the quadratic approximation of the interannual variations: 204.80913681144062\n", + "normalized cost of the quadratic approximation of the interannual variations: 0.2741755512870691\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A = np.ones((np.size(sorted_data[\"nb of month from the beginning\"]),3));A[:,0]=sorted_data[\"nb of month from the beginning\"]**2;A[:,1]=sorted_data[\"nb of month from the beginning\"]\n", + "b = sorted_data[\"CO2 seasonally adjust\"]\n", + "res_quad = lsq_linear(A, b)\n", + "sq_coef,slope,intercept = res_quad.x\n", + "print(\"cost of the quadratic approximation of the interannual variations: \",res_quad.cost)\n", + "print(\"normalized cost of the quadratic approximation of the interannual variations: \",res_quad.cost/np.size(b))\n", + "sorted_data[\"CO2 interannual quadr variations\"] = sq_coef * sorted_data[\"nb of month from the beginning\"]**2 + slope * sorted_data[\"nb of month from the beginning\"] + intercept\n", + "sorted_data[\"CO2 seasonally adjust\"].plot(label='interannual var')\n", + "sorted_data[\"CO2 interannual linear variations\"].plot(label='interannual var lin approx')\n", + "sorted_data[\"CO2 interannual quadr variations\"].plot(label='interannual var quadr approx')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hence, one can try to estimate the concentration of CO2 by extrapolating using this quadratic function. Here we extrapolate until 2025. In a first time, we store a new data frame with the extended time period." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first measurements was the 1958-01-15\n", + "The first and last date considered are 1958-01-15 and 2025-12-15\n" + ] + } + ], + "source": [ + "print(\"the first measurements was the \", raw_data['period'][2])\n", + "year_0 = raw_data['Year'][2]; year_f = 2026; nb_month_tot = (year_f-year_0)*12\n", + "\n", + "y = year_0; m = 1\n", + "year = year_0 * np.ones(nb_month_tot); month = np.ones(nb_month_tot)\n", + "for current_month in np.arange(1,nb_month_tot): \n", + " if month[current_month-1]<12:\n", + " month[current_month] = month[current_month-1] + 1\n", + " year[current_month] = year[current_month-1]\n", + " else : \n", + " year[current_month] = year[current_month-1] + 1\n", + "\n", + "extrapol_data = pd.DataFrame({\"nb of month from the beginning\":np.arange(0,nb_month_tot),\n", + " 'Year':year,\n", + " 'Month':month,\n", + " 'period':[pd.Period(str(int(y))+'-'+str(int(m))+'-15') for y,m in zip(year,month)] }) \n", + "print('The first and last date considered are ',extrapol_data['period'][0],' and ',extrapol_data['period'][nb_month_tot-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know check that the period are consecutive" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_extrapol_data = extrapol_data.set_index('period').sort_index()\n", + "for y1,m1,y2,m2 in zip(sorted_extrapol_data['Year'][:-1],sorted_extrapol_data['Month'][:-1],sorted_extrapol_data['Year'][1:],sorted_extrapol_data['Month'][1:]):\n", + " if np.abs(m1-m2)>1: \n", + " # if the consecutive month are bigger than 1, either we change of year, either some data are missing\n", + " if np.abs(m1-m2)==11 and np.abs(y1-y2)>1:\n", + " print(\"problem with the dates \",str(y1)+'-'+str(m1)+'-15',\" and \",str(y2)+'-'+str(m2)+'-15') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can know extrapolate and plot the estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sq_coef,slope,intercept = res_quad.x\n", + "sorted_extrapol_data[\"CO2 interannual quadr est\"] = sq_coef * sorted_extrapol_data[\"nb of month from the beginning\"]**2 + slope * sorted_extrapol_data[\"nb of month from the beginning\"] + intercept\n", + "sorted_extrapol_data[\"CO2 interannual quadr est\"].plot(label='interannual var quadr var')\n", + "sorted_data[\"CO2 seasonally adjust\"].plot(label='interannual var')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This quadratic model seems to provide a convincing trend for the estimation of the CO0 concentration in the atmosphere in the newt year. " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {