{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958\n", "\n", "On s'intéresse à la concentration en CO2 au cours du temps depuis 1958; les données sont disponibles sur le site web [scrippsco2.ucsd.edu](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)\n", "\n", "Commençons par importer les bibliothèques nécessaires à l'analyse" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "from urllib.request import urlretrieve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement des données\n", "\n", "Chargeons à présent les données. On vérifie si le fichier existe avant; si il n'existe pas on le charge avec `urlretrieve`\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n", "data_path = \"./monthly_in_situ_co2_mlo.csv\"\n", "\n", "if not os.path.exists(data_path):\n", " urlretrieve(data_url, data_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notons qu'un entête est présent qu'il faut supprimer (jusqu'à la ligne 60), ainsi que deux lignes 62 et 63 qui précisent le contenu de la colonne et son unité mais ne sont pas utiles ici" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def skiprows(x):\n", " return x < 61 or x in [62,63]\n", "\n", "data = pd.read_csv(data_path,skiprows=skiprows,skipinitialspace=True,na_values=\"-99.99\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDate.1CO2seasonallyfitseasonally.1CO2.1seasonally.2Sta
019581212001958.0411NaNNaNNaNNaNNaNNaNMLO
119582212311958.1260NaNNaNNaNNaNNaNNaNMLO
219583212591958.2027315.71314.43316.20314.91315.71314.43MLO
319584212901958.2877317.45315.16317.30314.99317.45315.16MLO
419585213201958.3699317.51314.69317.89315.07317.51314.69MLO
519586213511958.4548NaNNaN317.27315.15317.27315.15MLO
619587213811958.5370315.87315.20315.86315.22315.87315.20MLO
719588214121958.6219314.93316.22313.96315.29314.93316.22MLO
819589214431958.7068313.21316.12312.43315.35313.21316.12MLO
9195810214731958.7890NaNNaN312.42315.41312.42315.41MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
1219591215651959.0411315.58315.52315.64315.57315.58315.52MLO
1319592215961959.1260316.49315.84316.30315.64316.49315.84MLO
1419593216241959.2027316.65315.37316.99315.70316.65315.37MLO
1519594216551959.2877317.72315.41318.09315.77317.72315.41MLO
1619595216851959.3699318.29315.46318.68315.85318.29315.46MLO
1719596217161959.4548318.15316.00318.07315.94318.15316.00MLO
1819597217461959.5370316.54315.87316.67316.03316.54315.87MLO
1919598217771959.6219314.80316.09314.80316.13314.80316.09MLO
2019599218081959.7068313.84316.75313.29316.22313.84316.75MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
2419601219301960.0410316.43316.37316.62316.56316.43316.37MLO
2519602219611960.1257316.98316.33317.30316.64316.98316.33MLO
2619603219901960.2049317.58316.27318.04316.71317.58316.27MLO
2719604220211960.2896319.03316.70319.14316.79319.03316.70MLO
2819605220511960.3716320.03317.20319.70316.86320.03317.20MLO
2919606220821960.4563319.59317.45319.04316.93319.59317.45MLO
....................................
77420227447572022.5370418.71417.91418.94418.18418.71417.91MLO
77520228447882022.6219416.75418.30416.77418.36416.75418.30MLO
77620229448192022.7068415.42418.91415.04418.55415.42418.91MLO
777202210448492022.7890415.31418.92415.15418.74415.31418.92MLO
778202211448802022.8740417.03419.29416.71418.95417.03419.29MLO
779202212449102022.9562418.46419.38418.25419.15418.46419.38MKO
78020231449412023.0411419.13419.06419.45419.37419.13419.06MKO
78120232449722023.1260420.33419.55420.40419.61420.33419.55MKO
78220233450002023.2027420.51418.97421.39419.83420.51418.97MLO
78320234450312023.2877422.73419.96422.89420.10422.73419.96MLO
78420235450612023.3699423.78420.38423.77420.37423.78420.38MLO
78520236450922023.4548423.39420.81423.23420.66423.39420.81MLO
78620237451222023.5370421.62420.82421.73420.96421.62420.82MLO
78720238451532023.6219419.56421.12419.67421.27419.56421.12MLO
78820239451842023.7068418.06421.56418.06421.58418.06421.56MLO
789202310452142023.7890418.41422.02418.28421.88418.41422.02MLO
790202311452452023.8740420.11422.38419.95422.19420.11422.38MLO
791202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
79220241453062024.0410422.62422.55422.85422.77422.62422.55MLO
79320242453372024.1257424.34423.56423.85423.06424.34423.56MLO
79420243453662024.2049425.22423.65424.91423.31425.22423.65MLO
79520244453972024.2896426.30423.50426.41423.58426.30423.50MLO
79620245454272024.3716426.70423.29427.25423.84426.70423.29MLO
79720246454582024.4563426.63424.06426.65424.11426.63424.06MLO
79820247454882024.5383425.40424.62425.10424.36425.40424.62MLO
79920248455192024.6230422.71424.30423.00424.63422.71424.30MLO
80020249455502024.7077421.60425.12NaNNaN421.60425.12MLO
801202410455802024.7896NaNNaNNaNNaNNaNNaNMLO
802202411456112024.8743NaNNaNNaNNaNNaNNaNMLO
803202412456412024.9563NaNNaNNaNNaNNaNNaNMLO
\n", "

804 rows × 11 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date.1 CO2 seasonally fit seasonally.1 \\\n", "0 1958 1 21200 1958.0411 NaN NaN NaN NaN \n", "1 1958 2 21231 1958.1260 NaN NaN NaN NaN \n", "2 1958 3 21259 1958.2027 315.71 314.43 316.20 314.91 \n", "3 1958 4 21290 1958.2877 317.45 315.16 317.30 314.99 \n", "4 1958 5 21320 1958.3699 317.51 314.69 317.89 315.07 \n", "5 1958 6 21351 1958.4548 NaN NaN 317.27 315.15 \n", "6 1958 7 21381 1958.5370 315.87 315.20 315.86 315.22 \n", "7 1958 8 21412 1958.6219 314.93 316.22 313.96 315.29 \n", "8 1958 9 21443 1958.7068 313.21 316.12 312.43 315.35 \n", "9 1958 10 21473 1958.7890 NaN NaN 312.42 315.41 \n", "10 1958 11 21504 1958.8740 313.33 315.21 313.60 315.46 \n", "11 1958 12 21534 1958.9562 314.67 315.43 314.77 315.52 \n", "12 1959 1 21565 1959.0411 315.58 315.52 315.64 315.57 \n", "13 1959 2 21596 1959.1260 316.49 315.84 316.30 315.64 \n", "14 1959 3 21624 1959.2027 316.65 315.37 316.99 315.70 \n", "15 1959 4 21655 1959.2877 317.72 315.41 318.09 315.77 \n", "16 1959 5 21685 1959.3699 318.29 315.46 318.68 315.85 \n", "17 1959 6 21716 1959.4548 318.15 316.00 318.07 315.94 \n", "18 1959 7 21746 1959.5370 316.54 315.87 316.67 316.03 \n", "19 1959 8 21777 1959.6219 314.80 316.09 314.80 316.13 \n", "20 1959 9 21808 1959.7068 313.84 316.75 313.29 316.22 \n", "21 1959 10 21838 1959.7890 313.33 316.35 313.31 316.31 \n", "22 1959 11 21869 1959.8740 314.81 316.69 314.53 316.40 \n", "23 1959 12 21899 1959.9562 315.58 316.35 315.72 316.48 \n", "24 1960 1 21930 1960.0410 316.43 316.37 316.62 316.56 \n", "25 1960 2 21961 1960.1257 316.98 316.33 317.30 316.64 \n", "26 1960 3 21990 1960.2049 317.58 316.27 318.04 316.71 \n", "27 1960 4 22021 1960.2896 319.03 316.70 319.14 316.79 \n", "28 1960 5 22051 1960.3716 320.03 317.20 319.70 316.86 \n", "29 1960 6 22082 1960.4563 319.59 317.45 319.04 316.93 \n", ".. ... .. ... ... ... ... ... ... \n", "774 2022 7 44757 2022.5370 418.71 417.91 418.94 418.18 \n", "775 2022 8 44788 2022.6219 416.75 418.30 416.77 418.36 \n", "776 2022 9 44819 2022.7068 415.42 418.91 415.04 418.55 \n", "777 2022 10 44849 2022.7890 415.31 418.92 415.15 418.74 \n", "778 2022 11 44880 2022.8740 417.03 419.29 416.71 418.95 \n", "779 2022 12 44910 2022.9562 418.46 419.38 418.25 419.15 \n", "780 2023 1 44941 2023.0411 419.13 419.06 419.45 419.37 \n", "781 2023 2 44972 2023.1260 420.33 419.55 420.40 419.61 \n", "782 2023 3 45000 2023.2027 420.51 418.97 421.39 419.83 \n", "783 2023 4 45031 2023.2877 422.73 419.96 422.89 420.10 \n", "784 2023 5 45061 2023.3699 423.78 420.38 423.77 420.37 \n", "785 2023 6 45092 2023.4548 423.39 420.81 423.23 420.66 \n", "786 2023 7 45122 2023.5370 421.62 420.82 421.73 420.96 \n", "787 2023 8 45153 2023.6219 419.56 421.12 419.67 421.27 \n", "788 2023 9 45184 2023.7068 418.06 421.56 418.06 421.58 \n", "789 2023 10 45214 2023.7890 418.41 422.02 418.28 421.88 \n", "790 2023 11 45245 2023.8740 420.11 422.38 419.95 422.19 \n", "791 2023 12 45275 2023.9562 421.65 422.57 421.58 422.48 \n", "792 2024 1 45306 2024.0410 422.62 422.55 422.85 422.77 \n", "793 2024 2 45337 2024.1257 424.34 423.56 423.85 423.06 \n", "794 2024 3 45366 2024.2049 425.22 423.65 424.91 423.31 \n", "795 2024 4 45397 2024.2896 426.30 423.50 426.41 423.58 \n", "796 2024 5 45427 2024.3716 426.70 423.29 427.25 423.84 \n", "797 2024 6 45458 2024.4563 426.63 424.06 426.65 424.11 \n", "798 2024 7 45488 2024.5383 425.40 424.62 425.10 424.36 \n", "799 2024 8 45519 2024.6230 422.71 424.30 423.00 424.63 \n", "800 2024 9 45550 2024.7077 421.60 425.12 NaN NaN \n", "801 2024 10 45580 2024.7896 NaN NaN NaN NaN \n", "802 2024 11 45611 2024.8743 NaN NaN NaN NaN \n", "803 2024 12 45641 2024.9563 NaN NaN NaN NaN \n", "\n", " CO2.1 seasonally.2 Sta \n", "0 NaN NaN MLO \n", "1 NaN NaN MLO \n", "2 315.71 314.43 MLO \n", "3 317.45 315.16 MLO \n", "4 317.51 314.69 MLO \n", "5 317.27 315.15 MLO \n", "6 315.87 315.20 MLO \n", "7 314.93 316.22 MLO \n", "8 313.21 316.12 MLO \n", "9 312.42 315.41 MLO \n", "10 313.33 315.21 MLO \n", "11 314.67 315.43 MLO \n", "12 315.58 315.52 MLO \n", "13 316.49 315.84 MLO \n", "14 316.65 315.37 MLO \n", "15 317.72 315.41 MLO \n", "16 318.29 315.46 MLO \n", "17 318.15 316.00 MLO \n", "18 316.54 315.87 MLO \n", "19 314.80 316.09 MLO \n", "20 313.84 316.75 MLO \n", "21 313.33 316.35 MLO \n", "22 314.81 316.69 MLO \n", "23 315.58 316.35 MLO \n", "24 316.43 316.37 MLO \n", "25 316.98 316.33 MLO \n", "26 317.58 316.27 MLO \n", "27 319.03 316.70 MLO \n", "28 320.03 317.20 MLO \n", "29 319.59 317.45 MLO \n", ".. ... ... ... \n", "774 418.71 417.91 MLO \n", "775 416.75 418.30 MLO \n", "776 415.42 418.91 MLO \n", "777 415.31 418.92 MLO \n", "778 417.03 419.29 MLO \n", "779 418.46 419.38 MKO \n", "780 419.13 419.06 MKO \n", "781 420.33 419.55 MKO \n", "782 420.51 418.97 MLO \n", "783 422.73 419.96 MLO \n", "784 423.78 420.38 MLO \n", "785 423.39 420.81 MLO \n", "786 421.62 420.82 MLO \n", "787 419.56 421.12 MLO \n", "788 418.06 421.56 MLO \n", "789 418.41 422.02 MLO \n", "790 420.11 422.38 MLO \n", "791 421.65 422.57 MLO \n", "792 422.62 422.55 MLO \n", "793 424.34 423.56 MLO \n", "794 425.22 423.65 MLO \n", "795 426.30 423.50 MLO \n", "796 426.70 423.29 MLO \n", "797 426.63 424.06 MLO \n", "798 425.40 424.62 MLO \n", "799 422.71 424.30 MLO \n", "800 421.60 425.12 MLO \n", "801 NaN NaN MLO \n", "802 NaN NaN MLO \n", "803 NaN NaN MLO \n", "\n", "[804 rows x 11 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier indique qu'à cause d'une éruption en 2022, la station MLO n'a pas pu faire de relevés et sont alors relevés par MKO. Nous nous intéresserons ici qu'à MLO et allons donc supprimer les entrées correspondantes à MKO. De plus, des NAN sont présents dans le jeux de données, on va donc supprimer les lignes correspondantes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_MLO = data[data[\"Sta\"]==\"MLO\"].dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Un premier affichage des données\n", "nous allons à présent pouvoir plotter les différentes courbes:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/gjGGx3a3saosg4vKMnAPjVHX7aHanoAOzTOUZmpG9FulAUIpNWec6LKGlNr6R+gYGKW2a4ibVhSRm+qkqddLW/8I1XZgWFQwsVJJvTU6xKSUmrVa+4YxxvqQ7/WO0dY/Qm6qE9eQL1xTaVlRGkOj44yOW7WXQj2ID28oJzE+lhuWF8xY++c67UEopWaty77xAhv//QWCQcOhNmuznxvtrT9frLU2DKvOS6EoIgkuFCAS4mL40PoFZKc4p7nV5w8NEEqpWampxxs+PtHtocFesXTFImujsC3HXWQkxZGdHM/SiMnnBVqm+5zRAKGUmhV8/gBPH+wI7xu9t7k/fK2px8tJt5cUZywXlmYA0Osdozo3BRGhJj+FxLgYlhSmzcutQaNF/0sqpWaFx/e08amH9/CTrY0AHLb3jwZri9DariGq8lJIT4wjxS7NHRpOcsbGsP3eq/nVJ9dPe7vPZxoglFKzQmhTn91N1uTzSfcwC/NTSHXG0tjj5WDrACtL0hEREuOt6quhAAGQlhBHWkLc9Df8PKarmJRSs0KofMbgiJ9g0HCsc5ClhWnExTjY3tCLdywQTnQbtctr1ORrkb1o0h6EUmra+fwB7nn8IM8c6gifq+u2JqE7BkbY3dxHa98I1y8voCwriRP2tQX2NqD33LCEa5bkc2lV9vQ3fh7RAKGUmnYvHXfzyx3N3PXQHowx7G7qZcdJa2ipY2CUw/aS1kurc04plVFur1D6wLoy/ufONcTG6EdYNOl/XaVU1AWCJjzHAFYCXEivd4yHtzcD8N6LihkeC7CrqY/0xDjyUp2nZEJrFdbppQFCKRV1//i7Q1z01c0Mj/kBaOub2NSnwe1lx8lebriggKsW5wHw4nEXi/JTEZHwsBKgVVin2ZQChIhkisgyEakUEQ0qSqk35Rd2D2FXYx8ATb3DxNof9rWdQ7T1j7AoPy28R/TQqJ+Fdi2lFcVW3sOqsozpbva8d8ZVTCKSDtwNvB+IB1xAApAvItuA7xtjXpiWViql5qyAnfgGcKRjkI01Oexu6uPapfk8faiTnY29GANVeclU5EwsW11or1BKT4rj5S9eGV7aqqbP2Za5/gb4GbDRGNMfeUFEVgMfEpFKY8yPo9lApdTc1jU4Gj5u7Rum3uWh1zvGFYtyeaG2Ozw5XZ2XQlZyfPi+CyOWsGpF1plxxuEiY8y1xpifnx4c7Gu7jTGf1+CglDqdPxDkoW1NnHRbtZTqI3Z9a+0bYcdJa5hpXUU22clOOgZGEZlYoXTfjUtIT4zTzX1mgSklyonICqA88v7GmMej1Cal1Bz2692t3PfbQywuSOWZz1/O04c6SYyLYU15ph0geshLdbIgO4mclHja+kcozUwiIc4aQvr4xko+dlkFIjohPdPeMECIyIPACuAwELRPG+CsAUJEEoCXAKf9Or8xxvyziFwI/ABrPsMP/B9jzA77MfcAHwMCwGeNMc++lTellJo5oU19Glxe/IEgfzzYwbVL88lLdbKzsZdg0LCqLAMRoSInmf2tA1TlnlqBVYPD7DCVHsR6Y8zSt/DcPuAqY4xHROKAV0TkaeArwJeNMU+LyA3AvwNXiMhS4HZgGVAE/ElEFhpjAm/htZVS0+R3+9rwBwy3ri4BrExogLFAkN1NffQPj7O2Igt/IMjoeJAGt5fr7E181pRn8dt97ZTnaInu2WgqAeI1EVlqjDnyZp7YGGOA0OBjnP1j7J/Q4GI60G4f3wI8YozxASdFpA5YC7z2Zl5XKTV9vD4/n3tkHwDvWVWMwyG09o0QFyOMBwyv1rkBqMhJZnR84rteuZ3b8BerS0hNiGVjTe70N169oakEiJ9iBYlOrF6BYH3+r3ijB4pIDLAbqAa+Z4zZLiKfB54Vkf/AmiS/xL57MbAt4uGt9rnTn/MTwCcAysrKptB8pVS0dEasUGpwe0hNiONQ+wA3XlDIUwc62NZgrVAqz0nGM+oP3ze0QikhLoZbLnzdP3M1S0wlQDwIfAg4yMQcxJTYw0MXikgG8ISILMf6cP8bY8xjIvKXwI+Ba7ACz+ueYpLn/BHwI4A1a9a87rpSavp0DUwEiHqXF2PAGPjA2jKeOtDBjsZenLEOCtMSGEmc6EHoCqW5YSoBotkY8+TbeRFjTL+IbAGuB+4EPmdf+jXwP/ZxK1Aa8bASJoaflFKzgMfn5zt/PsEd6xdQmpV0Sg+irW+EoVE/InBhWQapzliGfH7Ks5NxOIRkZywXl2dyUVlmeMWSmt2mEiCOicgvgN9jDTEBb7zMVURygXE7OCRi9RK+gfWhvwnYAlwFnLAf8iTwCxH5f1iT1DXAjjf1bpRSUfW7fW388KUGDrcP8tDH13G8y0NcjBA00N4/Qr3LQ2VOMknxseSnJzDU7aE8ZyLJ7dd3XXKWZ1ezzVQCRCJWYHhHxLk3XOYKFAI/techHMCjxpinRKQf+E8RiQVGsecTjDGHReRR4AjW8te7dQWTUrPLdntOoceuzLq3uY9FBamMjAVo7RthV2Mf77qwCIDcFCd13R5doTSHvWGAMMb81Vt5YmPMAWDVJOdfAVaf4TH3A/e/lddTSkVfY4+VHd3U46VrcJQdjb185qoa9rX0c6C1nyGfn0X2BHTfsBVEKjVAzFlvWJnVruD6exFxiUi3iPxORCqmo3FKqdkjGDScdFkBYngswIvHXRgDmxbmUJyRSLs9YR3a4Ce0OumaJfkz02D1tk2ldPcvgEexhoyKsCaWH4lmo5RSs8N3nz/BA8/VYozhtYYehnx+3mknub103AVYS1aLMyY28imzcxzu2lTJsa9eT3aKc/obrs6JqQQIsYv2+e2fh5hk+alS6vzi9fn5j+eO853n62jtG+GlEy7iYoQPrV8AWJv6FGckkpoQR3FmYvhxJfaxiOhqpTluKgHiBRH5koiUi8gCEfki8AcRyRKRrGg3UCk1PYwxp2Q772+ZKORc5/KwvaGXlSUZ4R7C0KifRQXWfENxxsRKJWesBoXzxVQCxPuATwIvYC1N/RTwUawM6V1Ra5lSalo9tqeNxf/4DO39Vi2l411D4WtH2gc51DbAusqsU/aFDgWI0FJW3fXt/DKVVUw6Ia3UPPDY7lYAnj/WzR3rF7CnuZ/MpDg8Pj/bGnrwBw0XFKcTGzPxvXKxHSDyUhP48xc2UZyROOlzq7lpKquYEkTkb0XkcRF5TEQ+b5fyVkqdR0b91vBSXbeHgeFxnj3cyY0rCslOdrLPHm4qybR6CjdcYE1UryyZ6DFU5abonMN5ZipDTD/DKsH9HeC7wFLg59FslFIq+uq6hxgaHQfA7fGFg0Br3zAvnnDh8wf5i9Wl5KTGM2QX2iu1A8S33nchO+69WpPgznNTyaReZIxZGXH7BRHZH60GKaWib2djL7f94DUuKE7n95+5jD8f7cIYKM5IpLVvhLpuDw6BJYWpZCdby1RTnbGkJVofGc7YGPJStbdwvptKD2KviKwP3RCRdcCr0WuSUupcu+E/X+ZDP94evh3KYTjYNsDoeIBdjX1kJ8dz7dJ8WvtGaHB5KMlMwhkbQ1VuCgA5qU7d6W2emUqAWAdsFZFGEWnE2sBnk4gcFJEDUW2dUupt6x8e40jHIC+fcFPbaa1MOtQ2EL7e2jfC0c5BlhalUZKZiMfnZ29zP5X2NqBXLc4D4KTbO/2NVzNqKkNM10e9FUqpqGnsGQ4fb613s6gglUPtgyzITqKpZ5gjHYMc7Rjirk2V4SS3tv4R3rHMKpFxSVU2l1XnhLcJVfPHGXsQIpICYIxpOtMP0DNtLVVKTcng6Dgf+vF2DrZavYSjHYPhawfbBtjb3IdryMcNFxQC1vLWQNBweU1ueJUSQKU9tORwCA99fF04g1rNH2cbYvqdiDwgIpeLSHipgl2872Mi8izau1Bq1nnucBcvn3DzmV/uAeCVOjcFaQmsq8iitXeE3+1rJyHOwV2XV+GMdfCiPR+xxB5iCqnSFUrz3hmHmIwxV4vIDVhZ1JeKSCbWPg21wB+AO40xndPTTKXUVO1usvZs8PispamH2gZYVZZBbIyDg639BNqshLf0pDhKMhOpd3nJS3WSlhCHMRNl1kI9CDV/nXUOwhjzR+CP09QWpdQ5cMyeiHZ7xmjuGaapZ5jbVpfQ6x2ne8iH2zPGey+ySnGXZSVR7/KGVyqJCLetLmEsECQ/TauwzndTmaRWSs1ih9oGCBrDipIMjDEc7xyiOCORtv4Rnj1sdfKXFaVT2zXE8JiVLR0KCKX23g1VeRPDSd+8bSVKwdSWuSqlZqnxQJCbvvMKN3/XSk1q6x/BOxbgysW5ADx3JBQg0shLnegRhJawVudZgeKisszpbLaaI7QHodQcY4wJJ6w1RuQmjI4HwiuXrl6Sz0PbmtnZ2EdOipO8tARyTwkQVmC4/eIyVi/IZGlh2jS+AzVXaA9CqTlkd1MvS/7pmXCiW2vfSPja8a4hXq13k+KM5bLqHFKc1ve/ZUXWh39e6kSNzUK7ZHd8rINlRemaIa0mdbY8iAtEZJuItIjIj+xVTKFrO6aneUqpSD97rYnR8SD/u7URsArrhRxuH6S2c4ilRWnExTjISYkHIgPERA/C4dCAoN7Y2XoQ/w38C3ABcBx4RUSq7GtxUW6XUgor6W3MHwQgGDS8Vm/lpjb1WENLB1oHSE2IJTEuhhNdHk66h6nItuYXXEM+wJqgBshIiuOD68p47FMbpvttqDnqbAEixRjzjDGm3xjzH8CngWfswn26J7VSUTYwPM7qr27m7x+zSp419njpHvIR4xBOdHsIBg0v1Lq4YlEehRkJ1Lk8uD2+cAnuuzZVkZEUF56wFhHuf88FrF6gOwWrqTlbgBARSQ/dMMa8ANyKtReE5twrFWWvNfQwHjA8sbeNMX+QQ+1WyYwbLiikf3icnY29uD0+rlyUS35qAtsbrN5Fhb3952eurmHPfdeSFK9rUdRbc7YA8Q1gSeQJY8wB4Grg8Wg2Sil16vxCc6+Xw20DxMc42FidA8AjO1sQgU0LcylIT8BnD0VV5ExkQOtcg3o7zhggjDG/MMZsA6twX6gekzGm2Rjz19PVQKXmC9eQj+cOT1SvaeufWKFU1+3hUPsAiwtTw/WSNh/poiYvhewUJ/lpEyuUFmRPFNxT6u046zJXEfmUiDQDTUCLiDSJyP+ZyhPbe1nvEJH9InJYRL4cce0zIlJrn//3iPP3iEidfe26t/qmlJqL3vej1/jEz3fT0mv1HOq6PRSmWx/87f2jHGobZFlROvn2OY/PzwJ7QjpUFiMhzqH7Qqtz5mzLXO8D3gVcYYzJNsZkAVcC77SvvREfcJW9XemFwPUisl5ErgRuAVYYY5YB/2G/3lLgdqz9r68Hvi8i+peu5oVg0NDgslYmvXTCxXggyK7GPq5Zkk+sQ9jX0s/AyDjLitIoiOwt2KUyQucSNTioc+hsPYgPAe81xjSETtjHfwl8+I2e2Fg89s04+8cAnwK+bozx2ffrtu9zC/CIMcZnjDkJ1AFr3+T7UWpOGBkLcN23XuKZQ9aQUmTC24kuDwdaBxgZD7ChKpucFCcv1Fr/TJYXp5PsnJh0XpifCkBWspXzcKk9P6HUuXDWISZjzOgk50aA4FSeXERiRGQf0A1sNsZsBxYCG0Vku4i8KCIX23cvBloiHt5qnzv9OT8hIrtEZJfL5ZpKM5SaVsYYdjf1nVI6+2evNfLZX+4Nn9vR2Ett1xCfeng3AEc7Jzb1qXd52H7SWpG0tiKLnNR4hkb9xDiExQWpp7zWUjsJ7uLyLL56yzK+ceuKaL41Nc+cLUC0isjVp58UkauAjqk8uTEmYIy5ECgB1orIcqz6T5nAeuDvgEfFyvOfbLnF6/ItjDE/MsasMcasyc3NnUozlJpWTx3o4Nb/3soTe9vC5/7pd4d5cn87e5r7ANjTZP1OsZegHu0YRASuXpxHS+8w2xt6WZifQk6Kk8J0a1K6NDMxPL/wpXcu5o71ZeEaSg6H8KEN5af0LpR6u8721/RZrF3lXgF2Y31YXwxcijUcNGXGmH4R2YI1t9AKPG6sr1I7RCQI5NjnSyMeVgK0v5nXUWo2eM3ORwht9Tk0Oh6+1tQzzOoFWRyxr3nG/IwHgmw+0sXSwjSyP7p1AAAgAElEQVSq81J4+YSbkfEAl1ZZw0VVuSlspiscKMBKglMq2s62zPUwsBx4CSgHKu3j5fa1sxKRXBHJsI8TgWuAY8Bvgavs8wuBeMANPAncLiJOEakAagCt+aTmnNAqpMYe6/fe5v7wtc5Ba9T2iJ30Zoy1Wulw+yA3XFBIcWYiY4EgXYM+KuyM6NOHlZSaLmfsQYhINZBvjHnwtPMbRaTdGFP/Bs9dCPzUXonkAB41xjwlIvHAgyJyCBjD2rrUAIdF5FHgCNbWpncbYwJv/a0pNT1eOeEmJSGWC0szAMKrkToHrGCwq7GXUL5a18AoAyPjtPWPsL4yi20Nvbxa5wagJi+FmIjEtlDJjOuXF3DzsaLwLnBKTZezDTF9G/iHSc6P2NfedbYntrOuV01yfgy44wyPuR+4/2zPq9RsYozhjh9vB6Dx6zcyOh4IJ7h12AFiZ2MfS4vS8I0H6Rwc5Y8HrSm8d60sYltDL1vtAnyVucn4gxPTbqEeREJcDP/1/tf9U1Iq6s42SV1uf8ifwhizC2vISal572TEhj3DY34a7Sqr1XkpuD0+hsf87G3pY82CLArSE+gc9PHKCTclmYncvLIIgFfr3DgEyrKSKc6YmGcI9SCUmilnCxAJZ7mWeJZrSp23/nCgg1u+9yq93jEAtp/sDV9rcHmp77YCxCVV2QA8f6yb0fEga8ozyU9LoGtglNquIZYUppGaEEeqMxafP0hpVhLxsQ5SE+IozkgkP80Z3vBHqZlytgCxU0ReV3NJRD6GtapJqXnn3t8eZH9LP7+1l7Bus1csgZW/sL+13yqoV2MtwX5qvzWctGZBFgVpCXQOjlLX7WGRneBWmGF9D6uM6C08/fmNvPzFq6bl/Sh1Nmf7ivJ54AkR+SATAWEN1qqj90S7YUrNBsc6BynKSCQtwdojK2jPEexv7ccYw/aGXt6xNJ/njnTR6B5mS203K0rSwwXznq/tJi/VSUF6QriGEsBCe2VSfloCx7s84T2igfBrKTXTzrbMtcsYcwnwZaDR/vmyMWaDMabzTI9T6nwxMDLO9d9+mb/8wWvh24OjfgBqO4do7h2mc3CUjQtzyUlxsrXezfEuD7esKqbADgZj/iA1+daHf2QNpdDS1dBucZW5Ot+gZp83HOS0Nwp6YRraotSs8soJa/npsc4h/IEgB1qtfIaKnGRaeofDWdEXl2dSnJEQno9YVpRGqjOWpPgYhscCVOe+PkCEzv3lmlJa+0a4YXnhtL0vpabqrLWYlJrPaiPqI7X2jfCDF+tJS4jlPauK8Y4F2NnYR3yMg+rcFIoiVh9VZCcjIjjEymmoyrOCQWmWdZ/3rCoOb+Rz6+oSXv3SVWTaxfaUmk10mYRStkNtA+xp7uPDG8oBqO0aCl+r7Rpie0MvH7usIrz8dMuxbmryU4iNcYQDRHpiXPjDPjslHo/PH+4tZCTFs/Pea8hJ0WCg5gYNEErZ7nxwBz3eMTbW5JKb6mRLrYtrl+az+UgXW+vc+IOGRQWpE5v4DIyywa6XFDqXFdET+Mcbl7KtoYfV5Znhc7mpzml8R0q9PRoglLL12LkN2xp6WFSQis8f5LbVJbxY62LLcau0fFVuyilBYEmhNdkc2YMIuWZpPtcszZ+u5it1zukchJqXRsYCfOHR/TS4rD2tBkZOrbha12WdX1SQSnFmIk09w4hYG/RE7v+8xC63va4ii5tXFvGt9104je9CqejSHoSal5470slje1rpHBzh4Y+vp6lnomRGc68XfyCIM9ZBSWYSxRmJnHR7Kc9OJjH+1C09F9nLVbNTnFovSZ13NECoeWFXYy8+fzC8Jee+FmvJao/HGlY6YfcYSjITaXQP4/UFqMq1qquWZFrDR5Flt++7cQkxDiEnRecU1PlLA4Q67wWDhr+wk93q/+0GYhwS3rOhpXcYYwx7mvtIccZy5aI8ntjbRv/wGGsrsgDCSW81eRPZzh/fWDnN70Kp6acBQp2XxvxB4mOtKbZjnRPLVY91DrKsKJ1mO0B4x6zy3L/f387lC3OoyEnG4/Pj8fmpseslvXdVCf3D4/zVpRXT/0aUmkE6Sa3OO08f7GDhfU9zzE5023FyoqBeS+8wg6Pj1Lu8XFCcDsAzhzoZHPXzrhVF4RpKYJXsBijLTuJfbl6myWxq3tEAoc47D756EoDH91gVV493e8LXWnpH2FrnJhA0fHjDAgCeOmBVXF1enH5KgIgcUlJqPtIAoeY8YwzWrrWWwRGroF5oaKmpx8vKknRSnbG09g3z4nEXqQmxvGtlETEOYV9LPynOWEoyEynJnAgQZVlJKDWf6RyEmvPe/f2tLClI5eu3rsAYQ0ufNb/Q4PJgjOF4l4eN1TmMBQwtfSMc6xjksuocEuJiKLFzHKryUhAREuJi+LvrFtHo9hIbo9+f1PymAULNaa19w+xv6Wd/Sz//cvMye5vPABlJcbT1j9DcO4xryMeFZRl4fH62n+xlYGScT1xurVBakJ1MU8/wKcNJd19ZPVNvR6lZRb8iqTnFHwjSavcQAA61TVRcbeoZDpfofveFxRgDT9g7v62tyKIkMymcMb3QXqGUb9dGitwLWill0QCh5pR7nzjEZd94gT67blJb/0j42km3h5dPuClIS+AyOyHud/vayU9zsig/NVxuGyZKcF+3rACYqKmklJqgAULNaj5/4JQJ6F/tagHg5Tqrp9DWNxEgTnR5eKXOzcaanHBy20m3l0UFaYgIpRET0Hl2z+Gapfk8/4VN4UChlJqgAULNWp0Doyy67xke3t4MEM5+BjjcNgBAncvD4oJU8lKdPHO4k4GRcS6tziEvbaIERqW9f0N1xDyD2Jv5AFTmppxyWyll0QChZq3f7rPmD/7zzycAwlt8Apzo9jAeCLK7sZeLy7Moz0nmcLs1H1Gdl0J28kSAqLL3ey7PSeYrtyzjB3dcNF1vQak5TVcxqVljb3MfDS4vt64uAQjvAe0bDwCwv2WAhDgHl9fkUtft4VDbAN6xAOsrs+mPKNddlJFIjGOiR1CVO9FzCO0Wp5R6Y1HrQYhIgojsEJH9InJYRL582vX/KyJGRHIizt0jInUiUisi10WrbWp2+uuf7eILv97PIXv46ECr9Xtw1M/Q6Dj7W/tZXpROaVYSnYOjbGvoBWBdZVZ4G8+EOAeZSdamPaEgsahAJ6CVeiui2YPwAVcZYzwiEge8IiJPG2O2iUgpcC3QHLqziCwFbgeWAUXAn0RkoTEmEMU2qlnCNeTDbZfePtoxSHFGIq19IywvTuNQ2yBNPcMcahvgjvULKExPYHgswJ+OdlGdl0JOijNcdrskMyk8n/Dwx9cxHgiSrSW5lXpLotaDMJZQEZw4+ye0HOVbwBcjbgPcAjxijPEZY04CdcDaaLVPzbxAcOJ//8snXOHj1r4RDtq9iOvt1UVbarvx+YOsLM0Ir1Da3dTHypIMgHAPIrI8xvrKbDbW5Eb3TSh1HovqJLWIxIjIPqAb2GyM2S4iNwNtxpj9p929GGiJuN1qnzv9OT8hIrtEZJfL5Tr9spojjnYMsvyfn+WnWxsBaLS39MxJiT8lQISWnz57uAuAC4rTKUyf2PJzYb41v3Dl4jw+sK6ML9+8bBrfhVLnt6gGCGNMwBhzIVACrBWRFcC9wD9NcvfJ1hma150w5kfGmDXGmDW5ufrtcK76ze5WRsYD/HKHNcrY1jdCfmoCFTnJtPYNc6C1n/LsJKpyU4iLEQ62DRAf66AsK4mC9ImEtxo7QOSlJvBv77mAUi2wp9Q5My3LXI0x/cAWrGGkCmC/iDRiBY49IlKA1WMojXhYCdA+He1T0ffc4U6eOdQZvv1avbVHQ9/wGP5AkD3NfZRlJVGSmWT1IFoHuKAkA4dDwkNKlTnJxDgknOQGUJOnE9BKRUs0VzHlikiGfZwIXAPsNcbkGWPKjTHlWEHhImNMJ/AkcLuIOEWkAqgBdkSrfWr6tPQO84mf7+auh3bTMTDC6HiAY52DOAS6Bn3sa+nnpNvLX6wuoSQzkbb+EdoHRllhb+gTqpMUSnSLi3GwIDuJnJR4raGkVBRFcxVTIfBTEYnBCkSPGmOeOtOdjTGHReRR4AjgB+7WFUxz05g/yP7Wfi4utyqm7rfzGQBqO4fISXESNPDO5QU8faiT5491A7CqLAMTMap4QYkVIFITrGWrkZnQz/3N5YyOBXE4NANaqWiJ5iqmA8aYVcaYFcaY5caYr0xyn3JjjDvi9v3GmCpjzCJjzNPRapuKru88f4LbfvAar9r1ko51TOwJXe/yhoeXrl9uTUA/f6ybGIewIDv5lA17lhWlAbDWDjSXVodTZnDGxpBu5zsopaJDM6nVObfVDgCbj3RxaXUOxzoHqclLoWNg1J6AHmBFSTqX20tQj3UOUZmbTHysg5LMiSGjUM/hI5eWc8uqIvJSE17/YkqpqNEAod42fyBIjEPCCWqNbi8A9S6rXtL+1gE2VGYjYuU41HYO8d6LislIisMZ68DnD4bLYZRlJfF/37GQDVXZ4eePi3FocFBqBmixPvW2uIZ8XPL15/nu83UA9HnH6LH3amhweXmx1oVryMdNKwopykhkx8lePD4/i+0S3KEM6ND8gojw6atqWL0ga2bekFIqTAOEelMa3V6eOjCx+viZQx10D/l4YPNxjDHUuazk+aWFaXQNjnKo3Up4u6wmh6KMxPCObovtDXpG7UJ81REF9ZRSs4MGCPWmfPaRvXz6F3vZa5fe3tsysULJNeSjrtsKEJdWZ+MPGrbW91CWlURSfOwpS1IX2Vt+blpozUOssFcsKaVmDw0Q6ow6B0bZ9M0X2FpvrUYKBk24wuqeZisw7GvpJzk+BoDjXR42H+kiJyWei8oyAdhxsjdcTTUyQCQ7remvb962kh3/cDU1+ZrwptRsowFChX3nzye45/GD4du/2NFMU88wD75yEjh1/+eW3mEGRsZpcHm5ZZVVMqup18vWejc3rSiiOPP1vYVQoFizIDN8LcYh5KXpBLRSs5GuYlJhD2w+DsBfXVrOwvzU8LaeTT3WVp+hfRrAChAH7d7EO5bm86udLWxv6GV0PMiSwlQWRvQIQoFhSWEav/rEeoo0+1mpOUEDhAKs+YOQY51D1OSlcMAOCPUuD16fn+ePdZOeGMfqBZk09w6HM6RXlWVSkJbAllorI7omP5WEuJjw80Vu2LOucmL5qlJqdtMhJgWcut9zXbeHBrcX15CPTQtzCRo46fayq6mPdRVZVOYk09I3zN7mfipzkklPjKMoI4HBUT8wsWT1wY+s4bLqHCpykmfkPSml3h4NEPNU58Ao9zx+kKFRa9np3uZ+4mKE/DQn9S5PeDjpxhWFABxuH+Ck28uqskxKs5IYHQ/y/LEuVpZaG/aEJqAL0hJIszOgr1qcz0MfX0dcjP6ZKTUX6RDTPPWFX+/j1boe1ldmccuFxexp7mNpYRrZKU7quz3U2aW1N9ZY9Y+eOtABWAX1Rsas3IWggZX28tTQvEJVnvYWlDpf6Fe7eeKHL9bzzKGO8G2vz/qQP9g6gD8Q5EBrP6vKMqnKTeak28vupj6qc1MoSEsgIc7ByyfcxDiEFSXplGZNTDKvsHsQoVIZSfH6nUOp84UGiPNQU483nLAG1t7PX3v6GHc9tIcxfxAAt8ealN7X0s+xziFGx4OsKsugNCsJnz/I1voerl6Sh4iEeweLC1JJio89peLq0kKr4uq7VhZx+8Wl3H1l9XS9TaVUlOnXvfPQX/1kJw1uL3/628upzkulwTURLOpdHorSE2nts3IaDrYNsKuxF4CLyjI50T1Rmvv2i8sAKEpPpMHlZVWZ1VtIiIvhq7csw+UZC69Wio918PVbV0zL+1NKTQ/tQcxxI2MB1vzrZn6zuxWwiuU12NVUnz5obfH5x4MTW30e6xzkp681AvCeVcX4/EGePWxlP5dkJlKePTGHUJZt9RSykuMBWFU6keD2oQ3l/O21C6P2vpRSM08DxBy3td6N2zPG3z92ACCcuwDW0tRA0PCrnc2srbCqo7b2jvDScRcrS9K5bXUJAK819LCsKB0RoTI3hU9dUcU/v2tp+HnuvXEJX3338vCKJqXU/KBDTHPc7iYrfyEQNAyP+TloJ68tL07jZI+Xl064aB8Y5Z/etZS6bg/Huz3sbennU5uqWBCRn7DU3r0N4O+vX3zKa+SnJfCh9Qum4d0opWYT7UHMMY/uauHuh/eEl5oebh8MXzvQOsD+1gEqc5JZUZLBSbeXbfU9xMc4uHJxHgVpCTx7uJNA0LCuMouCiBpISwrTXvdaSqn5TQPELNY9OMptP9jKSXtOwRjDF39zgD8c7OD5Y1ZZiyMdg+Fkta7BUQ609rOiJJ3KnGT6h8fZUutiSVEaztgYijISwquYlhamEeOQ8GstL9IAoZQ6lQaIWaTPOxbObAZ48NVGdjb28bPXGgHCK4/Ammxu6x/BNeTjykXWngoHWwfoGvSxoiQjPNlc2zXEKjuAFKZby1VzU51k2zu5feaqaj6wroxK3bBHKXUanYOYJXz+AJd/8wViHMLOe68hLsYRXp7a0mtVUw0VxwOrXtIjO5qJcQjvvrCYH7xYz+ajXQCsLE0nPTE+fN+VpVa2c0G6NaRUnj2Rx/CFdyyK7htTSs1ZGiBmiQOtAwzZxe5OdHlYWpQWTnYLDTE9d7iLrOR4Fhek0t4/wuDoOEsL0yjPSaYow8pViHEISwvTcUT0DS+0l6cWZVgBYnGBDicppd6YBohp4g8EiXEIIjLp9eNdEwlq9S4PSfExnOyxAkNL3wjDY37+dLSLd68qxhjD5iPd+INB3rncWnq6KD+VBpeXhfmpJNo7vG2sycHtGQv3GN6xtICvvzfIu+0NfpRS6mx0DiIKvvPnE9Tc+0f8AWtCOBA0XPnAFv7hiUPh+zzwXC2bvvlCeBjpUNsg8XbV0waXl689fZQYEe7aVMWYP8gvd7QwPBbgpgsKKUpPxO3x0T88TlWuNdcQKnlRnDGxMunnH1vH05/bGA5Kyc5Ybl9bdspeDUopdSZRCxAikiAiO0Rkv4gcFpEv2+e/KSLHROSAiDwhIhkRj7lHROpEpFZErotW26Ltgc3HGQ+YcI7CjpO9tPSO8MsdzQSCBoBf72qlqWeY771QD8D2kz1cVpNDXqqTtn5rt7YbVxRyabW1wc6vdjaT6oxlXWX2KTuylWVZvYNrluYDsF435FFKnSPR7EH4gKuMMSuBC4HrRWQ9sBlYboxZARwH7gEQkaXA7cAy4Hrg+yIy677qjo4HGB7zh2+7PT6u+9ZLvGDvpha0AwBYS1CBU6qodgxYcwedg6MAvFLnYmDY2tt59YJMijMTqe0con1glEUFqeHVSMe7PCwsSCXGIacEiEq7B7GkMI2Xv3glH720IkrvXCk130QtQBhLqEpcnP1jjDHPGWNCn7DbgBL7+BbgEWOMzxhzEqgD1karfVPVMTByyu3bfvAaF311c/j2o7taqO0a4t7HDwLQNTQavtbaN0IwaHjmcCdpCdZ0T3PPMH+091a4tDqb7iEf++zVSStK0inKSGS/vddzdW4KhekJxNr5CqF9nosihpEqciaWp5ZmJeFwTD7HoZRSb1ZU5yBEJEZE9gHdwGZjzPbT7vJR4Gn7uBhoibjWap+bMa/Wudnwtef57d42wEpUO9g2wOh4kG47EOxutIaRXB4fo+MBGlze8ONb+4bZ29JH16CPT26qAqCpd5hX6twUpidw/bICjIEX7KS3FcUZlJzSO0ghNsaBM9b637Qo3woGxRmJXLU4j49cUn5KsptSSp1LUQ0QxpiAMeZCrF7CWhFZHromIvcCfuDh0KnJnuL0EyLyCRHZJSK7XC5XNJodtq/F+mb/2B6rUmpjz3D42sHWAYwx7G/tJzEuhvGAocHlZWdjLyLWzmutfSO8fMKNQ+CD68qIixEae7zsauzjogWZ5KZaPYE/HOygPDuJ9KQ4ijNfP79QGNqPwZ6Ijo1x8OBHLuZfbl4W1fevlJrfpmUVkzGmH9iCNbeAiNwJ3AR80BgTCgKtQGnEw0qA9kme60fGmDXGmDW5ubnntJ1Do+OMjgfCt0N5CG7PGAB77ElngGOdQ7T1j+D2jHHdMmuCuLVvmJ+/1sTF5VksL0qntW+Eox2DlOckk5EUT2lmEs8e6qRzcJRNC3PJT7OymV1DPq63l6sWpU8EiHi75/DVW5Zz7w1LWGdXZFVKqekQzVVMuaEVSiKSCFwDHBOR64G/B242xgxHPORJ4HYRcYpIBVAD7IhW+04XCBpu/u6rXP3Ai+Hlqcc6rdyEk24Pxhj2NPeR6owlP81Jg8vLn49aQ0PvWlkEwJbjLnq8Y9y5oZySzEQGRsbZ2djHEjsxrTgzMdwLuaQqm0UFqeHXv9Auh7HCznqOtKEqm7++vPKMORRKKRUN0UyUKwR+aq9EcgCPGmOeEpE6wAlstj/wthlj7jLGHBaRR4EjWENPdxtjAmd68rfrhy/Ws6upj+9/8CLiYhzsbe4LZyx3DIySm+qkrnsIZ6yD0fEgvd4xdjX2cWFZBr7xIK19w7g8PqrzUrisJgewMp2B8M5rAL3esXAp7UK71EVOipPijMRTPvDLc6zhpLzUBL723gtYEFEOQymlZkLUAoQx5gCwapLzZ9y02BhzP3B/tNoU6WtPHwNgf0s/a8qz2BUxfNTSO8zh9gHGA4Y71pfy0LZmdjf1Uds1xK2riznWMcTW+h48Pj/vXlWEMzaGgrQEOgdHyU6OpzA9AVfEXEIoia3AHj5asyAzHBz+6aalfPPZ2lN2cnv/2rKov3+llHoj8zKT2uef6Jhsre8hEDT85NWT4dVCLX3DPHeki4ykON6zylqF+4sdzQBctTifksxEOgdH8fj84cS0UK9haVEaIkJJZICwexCrF2QS4xA+sG4iAHz0sgqOfOU6zW5WSs068zJAtPdP5Cq8Vt/D0Y5BugZ9fPnmZcQ6hAa3lxeOdXPVojxKs6wP+i21LorSE6jKTT5lpdEGO0AsL7Yrptqb8IT2cQbIS7UmozctzKXu/ndy+cJTJ9d1bkEpNRvNy2J9w2N+lhSmkRjnYHdzHy8et5bLXrEoj6KMRH63t52+4XGuWZpPTrKTuBhhPGBYXmzt21ycMTE/ENpXYWWJPclcYgUKEWH3fdfQ6x07JQBoMFBKzRXzsgexrCidpz+3kU9dUc2YP8hPXm1kQXYSBekJlGUl0Tk4SlyMsLEmB4dDSE+MAya25Vxkl7z4yi0TeQiX1eTw5Kcv5Y6IvZuzU5zU5KeilFJz0bzsQYRU5FgTw26Pjw1V1lLVC0rS7UznRFITrMAwaO/TEAoQualOjv/rO1+XxbyiJAOllDpfzMseREjkRHK1veVmaNI5sgbTfTcuAeCiiOWrWuJCKXW+m9c9iIS4GKpyk6l3edlQZQWGy6pzuLA0g9svnkjq/vCGct6/toy4mHkdT5VS84xMVLqYe9asWWN27dr1tp7D4/PjEEiKn9exUik1j4jIbmPMmje637z/VExxzvv/BEopNSkdM1FKKTUpDRBKKaUmpQFCKaXUpDRAKKWUmpQGCKWUUpPSAKGUUmpSGiCUUkpNak4nyomIC2iK4kvkAO4oPn+0zMV2z8U2g7Z7umm7z40FxpjcN7rTnA4Q0SYiu6aSbTjbzMV2z8U2g7Z7umm7p5cOMSmllJqUBgillFKT0gBxdj+a6Qa8RXOx3XOxzaDtnm7a7mmkcxBKKaUmpT0IpZRSk9IAoZRSalLzKkCIyIMi0i0ihyLOrRSR10TkoIj8XkTSIq6tsK8dtq8n2OdX27frROS/RCSq+4++mXaLyAdFZF/ET1BELpwD7Y4TkZ/a54+KyD0Rj5nN7Y4XkZ/Y5/eLyBUz0W4RKRWRF+z/dodF5HP2+SwR2SwiJ+zfmRGPucduW62IXDcX2i0i2fb9PSLy3dOeaza3+1oR2W23b7eIXDUT7X7TjDHz5ge4HLgIOBRxbiewyT7+KPBV+zgWOACstG9nAzH28Q5gAyDA08A7Z0u7T3vcBUBDxO1Z227gA8Aj9nES0AiUz4F23w38xD7OA3YDjuluN1AIXGQfpwLHgaXAvwNfss9/CfiGfbwU2A84gQqgfib+vt9Cu5OBy4C7gO+e9lyzud2rgCL7eDnQNhPtftPvc6YbMO1vGMpP+4c/yMRkfSlwxD6+AXjoDH8YxyJuvx/44Wxp92mP+Tfg/rnQbrs9v8cKzNn2P7isOdDu7wF3RNzvz8DamWp3xOv9DrgWqAUKI/4Gau3je4B7Iu7/rP0hNavbHXG/jxARIOZKu+3zAvRgBecZbfcb/cyrIaYzOATcbB/fhvWPH2AhYETkWRHZIyJftM8XA60Rj2+1z023M7U70vuAX9rHs73dvwG8QAfQDPyHMaaX2d/u/cAtIhIrIhXAavvajLVbRMqxvrFuB/KNMR0A9u88+27FQMsk7Zvt7T6TudTuW4G9xhgfs+fve1IaIKzhgrtFZDdWV3HMPh+L1ZX9oP37PSJyNVb0P91MrBU+U7sBEJF1wLAxJjSOPtvbvRYIAEVYQx5fEJFKZn+7H8T6R70L+DawFfAzQ+0WkRTgMeDzxpjBs911knPmLOej6k20+4xPMcm5WdduEVkGfAP4ZOjUJHebNbkHsTPdgJlmjDkGvANARBYCN9qXWoEXjTFu+9ofscalHwJKIp6iBGiftgbbztLukNuZ6D2A9X5mc7s/ADxjjBkHukXkVWAN8DKzuN3GGD/wN6H7ichW4ATQxzS3W0TisD6sHjbGPG6f7hKRQmNMh4gUAt32+VZO7XWG2jftfydvst1nMuvbLSIlwBPAh40x9TPV7jdj3vcgRCTP/u0A7gN+YF96FlghIkkiEgtswhp37gCGRGS9vdrgw1jjj7Ol3aFztwGPhM7NgXY3A1eJJRlYjzU2O6vbbf99JNvH1wJ+Y6wgaAkAAAR1SURBVMy0/53Yr/Fj4Kgx5v9FXHoSuNM+vjOiDU8Ct4uI0x4aqwF2zIF2T2q2t1tEMuD/b+9+Q6uu4jiOvz89CIvJrEyzQkb0SAYTMYVJECSCoyihpEXQg/m4DCqC/mARtVoEhfWsnhT9McJFWcy2B4KmVOLYtGFQCBEFQRksZKR9e3DOzePt59rG2t3uPi+47P5+9/c7v+/Zv3PPOb/7PewjzfscalTc09boSZC5fJDeUf8E/ElquXuAB0kTot8CveSJyHz8fcAJ0vjzi8X+9Xnfd8Du8px5EvctwJGKcuZt3EAL8EH+fn8DPLJA4m4jTUyOAYOkNMpzHjdpGDRId94N50cXacJ/iNSrGQKuLM55PMd2kuLOmQUQ9yngV2A8/3zWzPe4SW8q/iiOHQZWNOL3ezoPp9owM7NKi36IyczMqrmBMDOzSm4gzMyskhsIMzOr5AbCzMwquYEwW8Ak3Z4/nWs269xA2IKXU0DX0pv/LOnHYvvSBsa1W1Jnsb1S0llJPbNU/m1AZ0ScmMKxQ5JaZ+O6tnj4cxDWVCTtAsYj4qUGx3E10B8Rm4p9D5A+4T4REZvnOJ4eYHlEvDCX17WFzT0Ia2qS7pf0Ze5NvC7pkpx59bSkvpypd0DSRkkHJH0vqSufu0PS3vz6SUlP5P1LJX2mtEDQcUl3VVz6blJu/1I3sBO4QdI1uaxaLL25vMNFWo+3Jb0i6Ysc17aiXo/leo1Iemqy+uaXPiLluzKbMjcQ1rQktQPbSMMwa0nJKe/JL7cC+yNiHSkz6y7gVtI/9meKYjbkc9YB9yqtztcFnIqIjohoBz6vuPwm0uJBtVjagCsi4igptfn24thWUmLIDuAwKXNszYpc1p3A87msLmA1sBFYC3RK6pysvpGSTi7NOYHMpmTRZ3O1prYZuAn4OuVB4zLOr4FwJiJq/9hHgd8j4qykUVJ+pZqBiPgNQFI/KQfPENArqRf4OIrka4VVwC/Fdjfwfn7+HmmhoVeLWGq9jaPAzcV5/ZHGgUck1dYJ2AJsBY7l7RbS+iXLJqkvOZ5VwOmKeM3+xQ2ENTMBb0bEkxfsTNl5y/Uz/gImiufl30X9JF1ExJik9aSeRJ+kTyLiubrjzgBLiu1u4CpJtUyf1+Ysqj/UxXKu7voTxXMVX5+NiDfq6vVQVX0LS3JcZlPiISZrZoPAdknL4Z+7nVZPs4wtkpZJuhy4AziU38mPR8RbwMuk4ad6Y8CN+bprSOs9XxcRbRHRBvRxfrhrugaAniLN+PW5jhetb56LWM6FPQqzSbmBsKYVEaPA08CgpBFgP7BymsUcBN4hDee8GxHDQAfwlaRh4FHS2t/19pHSrkOaHN5b9/qHzHDSOCI+Jc1jHMlDYnuAlv+o7wbgYEScm8k1bXHyba5mFyFpB9AeETtncK5IjcvWmNkSmrNK0mvAnog40OhYbOFwD8Lsf5Anlh8m3W00Hxxz42DT5R6EmZlVcg/CzMwquYEwM7NKbiDMzKySGwgzM6vkBsLMzCr9DWRU0MVHFQPLAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"])\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier propose aussi directement les données de la quantité de CO2 dans l'atmosphère en enlevant la composant saisonnière ; nous allons donc dans un premier temps afficher ces données déjà traitées" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En soustrayant les deux nous pouvons alors en déduire les variations saisonnières" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zoomons un peu autour de 2020 - 2024 pour mieux se rendre compte des variations" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2020, 2024)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")\n", "plt.xlim([2020,2024])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse des données\n", "\n", "Bien que le fichier d'origine nous fournisse un jeu de données pré-traitées; nous allons désormais tenter de retrouver ces résultats en:\n", "- identifiant la composante lente en un polynôme de degré 2 en fonction du temps\n", "- identifiant par la suite la composante périodique en effectuant une analyse spectrale une fois la composante lente enlevée." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Composante lente\n", "\n", "on considère un polynôme de la forme $C(t) = a + b t + c t^2 + d t^3$. Nous allons appliquer une régression linéaire (grâce à [`np.linalg.lstsq`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html))\n", "\n", "Commençons par récupérer les tableaux numpy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "temps = np.array(data_MLO[\"Date.1\"])\n", "CO2 = np.array(data_MLO[\"CO2\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour que les temps soient de taille plus raisonnable, nous allons soustraire le temps initial à tout les temps afin de commencer à zéro:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "temps = temps - temps[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construisons ensuite la matrice qui nous permettra d'effectuer la régression linéaire:\n", "\n", "$$ A_{i,j} = t_i^j $$\n", "\n", "Cad,\n", "$$ A = \\begin{bmatrix}\n", " ... & ... & ... \\\\\n", " 1 & 1^2 & 1^2 \\\\\n", " 1 & 1,1^2 & 1,1^2 \\\\\n", " 1 & 1,2^2 & 1,2^2 \\\\\n", " ... & ... & ...\n", " \\end{bmatrix}$$" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [1.00000000e+00 8.50000000e-02 7.22500000e-03 6.14125000e-04]\n", " [1.00000000e+00 1.67200000e-01 2.79558400e-02 4.67421645e-03]\n", " ...\n", " [1.00000000e+00 6.62536000e+01 4.38953951e+03 2.90822795e+05]\n", " [1.00000000e+00 6.63356000e+01 4.40041183e+03 2.91903959e+05]\n", " [1.00000000e+00 6.64203000e+01 4.41165625e+03 2.93023532e+05]]\n" ] } ], "source": [ "A = np.column_stack([temps**0,temps, temps**2, temps**3])\n", "print(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons à présent résoudre le système linéaire: $$ Ax = b$$" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimation des coefficients de la régression: a = 314.165 ppm, b = 0.853 ppm/annee, c = 0.009 ppm/annee^2, d = 0.000 ppm/annee^3 \n" ] } ], "source": [ "param = np.linalg.lstsq(A,CO2,rcond=None)\n", "a,b,c, d = param[0] \n", "print(f\"Estimation des coefficients de la régression: a = {a:.3f} ppm, b = {b:.3f} ppm/annee, c = {c:.3f} ppm/annee^2, d = {d:.3f} ppm/annee^3 \" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons désormais afficher le CO2 au cours du temps et y superposer notre estimation. Commençons par définir une fonction qui renvoie la composante lente:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def CO2_comp_lente(t):\n", " return a + b*t + c*t**2 + d*t**3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ajoutons une nouvelle **colonne** à notre jeu de données avec notre estimation:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "CO2_estimation_lente = CO2_comp_lente(temps)\n", "data_MLO[\"CO2_comp_lente\"] = CO2_estimation_lente" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, nous pouvons afficher notre estimation:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2_comp_lente\"],\"--\",label=\"estimation\")\n", "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"],label=\"données\")\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Composante périodique\n", "\n", "Afin d'identifier la composante périodique, nous allons soustraire la composante lente à notre jeu de données et ensuite appliquer une transformée de Fourier qui permettra d'identifier la fréquence de l'oscillation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 10)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CO2_periode = CO2 - data_MLO[\"seasonally\"]\n", "plt.plot(data_MLO[\"Date.1\"],CO2_periode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de parvenir à ceci, nous devons tout d'abord trouver la fréquence d'échantillonnage de notre signal. Pour se faire, nous allons prendre la différence entre deux pas de temps:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.08489999999983411" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "delta_T = temps[5] - temps[4]\n", "delta_T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant nous pouvons calculer la fft" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'Fréquence (Année$^{-1}$)')" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iUgRgfw87FNcY84AxpswYU1ZQUDAYxYk7rZ6eTU/W8fjup/DXJHR4rFKxoS81ipOA64AldDc9GfvnfhORdMBljGmyb58P/Ah4Hrge+Kn9/bmBnMfJWo6qUaQmWt/b4jwo/PGgw2OVig19CYrLgQlDsL7TSOAZe0npBOCvxpiXReQjYJmI3Ajsw5q3ocJo83hxCSTb/RH+5cbjvUZhdHisUjGlL0GxDmvf7EGdjW2M2Q2cEuZ4LXDOYJ7LqVo91oKA/v0bUpOswIj3IbI+HfWkVEzpS1CMBLban/Q77GPGGHPp0BVL9UVbZ1eg2QkgNdE/6qkrWkUaFEZHPSkVU/oSFHcF3RZgEdBjD201/Fo6vIEObHBSZ7b1XfsolIoNxxz1ZO9L0QBcCDyC1Sz0u6EtluqLVo83MHcCnBQU/lFPUS6IUgqIvBXqFOAqrNpDLdb6S2KMOXuYyqaOoa2zi/TgpqckZ4x6QmsUSsWUSE1PW4G3gYuD9qL4xrCUSvVJS4eXzJTgGoUzRj3pzGylYkukpqdPA4eAN0TkDyJyDt373qsY0OYJ7aNwu4SkBBetnXHemW1/1wqFUrGh16AwxjxjjPkc1l7ZbwLfAEaKyP0icv4wlU9F0NrZFZiV7ZeW5KZdaxRKqUHUl87sFmPMY8aYi4AxwFqspb9VlLV2eEOGxwKkJbod0PTk/65BoVQsOK4lRo0xdcaY3xtjBrR8hxocrUc1PYHVod0a5xPutDNbqdgSv2tRn+B8PkNbpzfQge2XmuSO+1FPOjxWqdiiQRGn2rtC96LwS0100x7nNQrdM1up2KJBEaeO3rTILyUx/rdD7R71pEGhVCzQoIhT/ualo5ueUhLdtHfG944/uhWqUrFFgyJO+edKOK3pKXizIm16Uio2aFBEQUtHF3c+s4F/rD84gNcI3bTILyXRFddBEZwN8V4zUsopNCiGSKRtPH+/YhePfbiPW/+6ho0HGvr1+r01PaUOQh+Fz2f49fId/O/yHXQO88bVwc1NrXG+XLpSTqFBMQR+/MJmJt35Ev/98tYe93V0eXn43T2cMXEECS7h7+sr+3UO/0W0R2f2IAyPXb3vCPe8tp1fvLadFzeEr/XsrG7m6TUVg34xD87Xljgf5quUU2hQDLKV5XU89E45Xp/h/jd3sXpvXcj97+2spbmji5vOmsDCSfm8vPFQv87T6ull1FOCm44uH74BtO+/vPEQCS4hNy2RZav297jf6zN85bHVfHPZOr7z1IZ+nyeckBpFh9YolIoFGhRHaenoGtBF9o/vlpOXnsTH/3Ee2amJ/Pn9vSH3v7r5EBnJCSyYOIJFk/LZW9vK4aaOXl6td629NT3ZwdHR1f8moze2VbNocj7XnV7Ce7tqaWjrDLn/lU2H2F7VTGqim1c2Hupxf3+8t7OG+9/cFahRJLldtHZ6B/RvoZQaHBoUQR58ezcn/eAVzvvlCqqb2sM+puJIK3/5YC/761p73FfX4uH1LVVcMaeY3PQkLjy5iFc2VdFifzL2+gyvba5i8dQCkhPczBmXA8Da/fXHXdZA01Py0TWKge2b3erporymhTljc1k4KR9jrFpSsJc3HmJEehKPffk0PF4fr2+u6te5/No7vVzz4If898tbOVDfBkBGSgLGdE8sVEpFzwkdFJ1eH2v319Pc0cXmykbufnELEwsyqKxv544n1/d4fH2rh0t+8y7ff3YjF9z7FjuqmkLu/+fWajq9hktnFwNwxZxi2jq9gealj/cdoabZw/kzRwEwqzibBJewdv+R4y57oDM7sedaT0C/Rz7tqGrGGJg6KoPZ43JITnDx/q7awP1dXh9vbqvm7GmFzBmbQ3ZqIh/tqYvwisf2alDQvGT3iaTbAegf3aWUip4TOijW7D3CZb99lw921XLPa9vITE7gyVvO4KtLJvHmtsNsqgwdkfSr5Tuob/Vw/7VzSXS7uOv5TSH3v7G1msLMZGYVZwEwrySXcXlpPP1xBQB/X1dJktvF4qkFgDU5buqoTNZXHP/IpxaPlyS3iwR36D9hih0c/a1RbLPDb+qoLJIT3MwryeX93d1Bsamykcb2LhZPLUBEmDMuh4/3HX+NKNimygYS3cL4EWm8vaMGgIzkROt9aIe2UlF3QgfFKWNzSHQLf3h7N69vqebmT0wkOzWRz59eQkZyAr9bsTvw2CMtHp5YuZ8r5o7hkycVcduSSby3q5Z1drNRe6eXt7YfDlxAAUSEy+YU896uWvbUtPDs2kqWzhpFVkpi4HWnF2Wx7VBozQRg66FGlt77Fg++vbvHfQBtnq4ezU4QFBT9vMBuO9RESqKLcXlpACyYMIItBxs50uIBYH2F9X7njMsFYPbYHLZXN9HU3v9+im2HmphYkMH4/HQONVpNfpnJVt9Liw6RVSrqTuigSEl0M6s4mw/L68hLT+JfzhgPQHZqIteeNo5/rK9kX63VF/HYh3tp6/Ty5TMnAPC5U8eSnuTmj++WA1bzSVNHV6DZye+KOcUYA4t//iYNbZ1cc9q4kPunjcqkuqmD2ubuDm1jDN9ato6th5p4+J3ysGVv9Xh7NDuBNY8CrGG4/VFe08L4Eem4XVbYLZg4AoAPy61axbqKBkakJzE6OwWwgsIY2HigsV/nAysophdlUZiZTJUdFP6mJ51LoVT0ndBBATB+RDoA3z5/KunJ3SOIvriolASXiwfe3kV7p5dH3tvLJ6YUMHVUJgCZKYlce3oJz62rZOuhRv7ywV6Kc1JZMGFE6Ovnp3OGfbG9+awJnH7U/dNGWc1UwbWKNfvq2VTZyIT8dCob2gMdvMFaPT03LYLgGkX/Rj3tq2ulZERa4OeTx+SQkZzA8i3VdHl9rNl7hJPHZAdqTTNGW+XfcrB/QdHQ1snBhnamjsqkIDM5MForw651aR+FinXDtRJCVWM7y7dU9XuS7kAkHPsh0SEiS4FfAW7gQWPMT4fiPN86fwozirK46tSxIcdHZqXw6XnF/O2j/azZW09Ncwe3LJ4Y8phbPjGRZav2s/TetwH44SUzcbl6biv+6Bfn09LRRU5aUo/7phVZwbPlUBNnTMoHrCG0iW7h5589hSvue48PdtXy6XljQp7X6unqMTQWumsU/fnP6/MZ9te1smRaYeBYUoKLC2aO4uVNhxCB3TUtfHXJpMD9hZkp5Gcks7mfQbGnpgXACsWgQMywQ/tErFEYY1hZXkd5TQsLJ+UzNi+tx2Pe31XLm9uryU5N5IYzSsN+aBgsH+2p4/m1lXzzvCnkpvf8PzycOr0+3tlZw+HGDsrG5zKhIGPIzmWMYX1FAzurm5lZnBX4UOe/79XNVfzyte1sPdTEyKxkrpw3llsWTwz5wOnX0tFFaqI77PWhL+W4781d/OLVbYElbopzUrn93MlcMae4Rz/lUIjJoBARN/Bb4DygAvhIRJ43xmwe7HONyU3jy2dNCHvfd5ZO4+N99Ww+2MitZ0/sURvITU/iwS+UceczGykZkcbV88eFfZ1EtytsSADkZySTn5HM1qAL7br99cwoymLW6GxcAntqW3o8L9zudgCpSf0fHnu4uYOOLl+PC9Plc4p5ak0Fy1ZV8KVFpVwxNzS0ZozO6neNYv8Rq2lv3Ig0Or3dcyYyYmjUU0eXl5++tJW3th9melEW580YySWnjA7Uqo7Heztr+J9Xt9HU3sUdF0wNjIDz83T5+Pb/reP5ddaM/US3cPflJ/HZMuuDjDGGXy/fyS9f306CS+jyGV7acIi/fvk0MoP6vsAayl1e08Ks0dm9XuA9XdbIP0+Xj3kluSGB4/MZ7n5xCw/ZzZ8bDjRw+7mTmVeSG+hna/N4uX/FLpZvqaKuxcPccbn8+4XTKc5JPe7fDUBjeyfbDzVhgDljc0IughsqGvjmsrXsqG4GQAS+cHoJ/37hdJITrHJXN7Xzj/UH2V7VRGFmCteePo7CzJQe56lv9bDhQAPJCW5OKs7uEbSHGtr5xt/WhgzkOHd6ITcsLKWmuYO/friPD8vrmFiQzu3nTGbzwUZ+88ZOnlxdwY8uncmSaYVsrGzkrx/u5Z0dNVQ2tJOdmsi3zp/CtaeVBJp2AfbVtvK//9zBh+V1uF3CGRNHcOOiUiYUZFDV2M7/e24jr2yq4uJTRnP9ghK2HGzkmY8PcMeT6/n18h384OKZnDtjZL9+330Vk0EBzAd2GmN2A4jIE8ClwKAHRSQ5aUn842tn0tzeRXZaYtjHlI3P45VvnDWg80wvymSr3fTk9Rk2VDTwmXljSEpwMTonlb21PedstHq85Gf0/OP3/8H0Jyj85xl3VFAsnDSCH186k/d21fKt86eGLf8f36nF0+UjKeH4Pt3ss+ejjM1No6m9u/bgH/U0HDWKleV1/PfLWxHg0/PG8LmysYFPfm0eLzf/ZTVvbT/MjKIsVu89wgvrD7LlYBPf/eS0wGvsOtzM9kNNzBidRYndnHm0rYcaueGRjyjKTsHtEm7+y2r+5zOn8Bm7tmiM4fvPbuD5dZV849wpfOqkUfzohc3c8eR6dlY3s3hqAX96by8vbzrEFXOK+a8rTuKt7Ye55bE1nHvPCn5zzVxOHZ+Hz2d45L09/PTlrXi6fCS6hS8uLOXbF0wlMejC+86OGr71f2uparT6xwozk7n78pM4d3oh1U0d/PiFzbyw/iCfP30c80py+e5TG7jhjx+RmZLA58rGkplizdw/UN/GGRNHMKkwg+VbqvmwvJa/fvl0pozMDJxr44EGnvhoH20eH6dPyOPS2cUh/1eMMfzlg738+IUteOz1xUrz0/n2+VMZk5vKq5sP8fsVuxmRkcR9185l2qhM/vT+Xh55bw+vb6nmolOK6Oj08fjKfXR0+RiRnsSRVg+Pvr+Hn15xMktndQeyp8vHhb9+J9Ckm5WSwBcXlfKlMyeQnuTmtc1VfPfpDbR3evnBxTNYNDmfVzZV8bsVu3h9SzUAI9KT+PFls7j61LGBMFu99wjfeWo9N/15dWDNtbQkN2dPK+Taoize3VnD/3tukx0ms5hQkM6Db5fzuxW7cAmcM20knV4fT66u4PGV+yjOTaWyvh2XwJ2fms6XzixFRCgbn8fV88fx+pYq7nltO69uPnTCBkUxELx2RAVwWjQK4nZJryExWKaOzOTPH+yly+tjR3UzLR4vs+3JeCUj0tgbZnKf1fTUs0kiMDO7H0Hhv2gfHRQiwnULxnPdgvFhnzejKAuP18euw81ML+qunvt8hvLaFiZGaB7YX9dGXnoS6ckJFGQkB44H5lEMcHhsU3snr2yqoig7hYV2016wj/bU8fmHPmREehI5aUl87+kNvLzxEPd89hQSE1zc+MhHrNp7hJ99+mQ+e+pYfD7Dnc9u5HcrdlHd1M6iSfm8sukQr2zqngvy7fOn8NUlk0PO09LRxa2PrbEurv+6gKyURL706Cq++9R6irJTWDBhBPev2MWyVRV8bckkbj/Xev5D15/Knc9s4IG3dvPAW7txu4TvXzidGxdZF43zZ45i2c0L+Lf/W8fVD3zAJ6YUsLumhfKaFs6ZVsjnF5Tw0oaD/P6t3WysbOC+a+aRnZbI8+sq+daytUzIz+CHl8wiOcHFz17Zxpf/tIpRWSnUtnQgCN88bwq3LZlkXaBK8thU2cAzHx/g4XfL8RmYMy6HX3z2lEBte2d1M1f/4QMu/c27fHXJJAozk/lgdx1Pf1xBWqKbtOQEnlpTwYNvl/Ofl8+irCSX2hYP//HsRl7aeIjFUwu47vQSWjxefvX6dm7965rA7/Cy2aP54SWzAn+PP7hkJounFvDwu3v4g/27ufCkIm47ZzITCzLYWd3MN/62ln/9y2oumz2ai04eTX1bJ0+s3MeB+ja+ed4UZo7O4m8f7efe13fw+Mp9pCUlUF7TwrRRmfzmmrlMKrT+704qzOQLC0pYtfcIhZnJTB2Z2aPJZ15JLi/ctog3t1WzYnsNM0Zncdns0YGa3lcWT+T5dZX8+IUtXPbbdwPPu+SU0dx54XRGZlk1n8NNHfzp/T3sq2vl8tlpfHremB4fPhLcLpbOKuKc6SMHtApDX8VqUISr04es5SAiNwE3AYwbF77JJ15MK8qio8vH7pqWwOS2+aXWH964vHRe3thzYb623pqeBjCPYl8nHIx3AAAYLUlEQVRdKyIcd7PBzKAObX9QGGO489kNPL5yP9cvKOGHl84K+9yKI62Bpq78zO6gSEtKQGRg6z01tnfyyXvfDnxyvHFRKd+/cHqgyaihrZOvPf4xxTmpPHXLGeSmJfLYh/v40QubOeeeFSS6XRxp8fDrq+Zw8SmjAXC5hP+8bBYFGUnc9+Yunl5zgOQEF984dwpnTyvgoXfK+fmr20lLsj6lfrC7lq0HG1m7v57ymhb+8qXTAk0hv712Lp+5/z2uf3glI7NSOFDfxtKZo/j6uVMC7yEpwcX/XHkKty2ZzP4jrUwsyGBUdmhTyrySXJ665QzueW07H5bXUpyTyjfPm8JFJxchIpw9tZBTx+fx789s4Jx7VjA6J4X1FQ2UleTy0L+cSnaqdSE7Y9IIln20n4/31VOQlcy180sYFzSwYWxeGmPz0lg6qwhPlw+vz/RosplUmMFzty7kP57dyP+8ss3+t3Rz/YLxfOO8KWSlJPDa5iruen4TV/7ufaaMzKCqsYM2j5fvLJ3GzWdNCNTmPjlrFB/srqWj08f4/PTARTvY4qmFLJ5aSHunF2NCl92fVJjBU7ecwb2vb+fhd8t5dm33ApwusS7cCW4X50wfyeq9dfz2jV14unzcevYkLjq5KDAwxC8zJZGzpxYSSUqim6Wzilg6q6jHfSLCpbOLWTy1kBfWV1Lf2sn80jxOHZ8X8riCzOSwNfdwEt2ukFriUInVoKgAgnuXxwAhy6waYx4AHgAoKyuL6wWB5tv/Ud7eUcN7O2sozU8PXKxLRqRxpLWTxvbOkPkXrZ3hg6Ivo54O1LdRfriFRZNDP2Hvr2tldHbqcTcfjR+RTnKCi82VjVwx1zr28f56Hl9pVQoffX8vt549icKsnm3F++paOak4G+gOOQC3y+rQbmzvPSj21rbw9o4azpleSFF2z3D7zT93UtnQxoNfKGPF9sM89E45GckJfP3cyXh9hv/6xxYONbbz7FcWkme34X/+9BLmjsvlJy9tIcElfHXJJOaVhP4hu13CN8+fyjWnldDq6WJ0Tmrg9/7Lz86mpcPLf7+8lfNmjOQHz28KNCt+67wpnDGx+3eenZrIk7ecwW/+uYPdh1u4Y+lULj55dNgOz3Ej0kIu2kfLtZtCenNl2VimjcriV8t30NjWyfcvnM4XFowP+bdOTnDbNcdeXyYg0v+R0TmpPHh9GbsON5PkdjMyOznQJApw/sxRnDEpn2c+PsAL6yqZMjKT28+ZzOSgpiqwLoJnTi44dmGgx0U9uJx3LJ3GV86exPaqpsDGXi6RkBrBvJI8Hv6XvLCvMdis4fclw3KuwRKrQfERMFlESoEDwFXANdEt0tAZNyKNiQXpPL+ukp1VTVw2p3suxthc6+JQWd9G1qigoOjwkhpm1JPbJSS5XRHXSLry/veobGhn+bc+EdIstK+ulbF5x98JmeB2MW1UJhuChu09vaaClEQXj33pND59//u8urmKz58e+sfh9RkOHGnjwpOKAmX3E4S89CTq7Il+RztQ38Zlv32XI62d3Pt6Mi/ctijkk/aemhb++G45V84bw7kzRrJkWiGtHi+/Wr6DdRX17KxupuJIG58rG8spY3NCXnvG6Cz+fOOxWzqP/mQPVo3jx5fNZMnPV/CTl7YEFjn86RUn8bmjRtaBddG488IZxzzXYDhpTDYPXl82LOcSESYVZvZ6f0ZyAtedXsJ1pw/PBTMjOYG59iRRdfxich6FMaYL+CrwCrAFWGaM2RT5WfHtgpmjWLe/nhaPl6tO7W5KG5llNcf4OxzBGoXj8frITAmf88mJrl5nZq/dX09lgzWp7b43doXct6+ulZK88B2xxzK/NI+P99fT3umlo8vL39cd5IKZo5g7zlrG5I2t1T2ec7ChjS6f6dEnAtaIljy7QzKcJ1buo6Gtk19fPYdWTxc//kfoOIf/enELSW4X37ar8C6X8PMrT+br507mnR01GAM/ueIkfnjpzH6930iKslO5ZfFEXtxwiG1VTVy/oISr5o/r1ygppWJBrNYoMMa8CLwY7XIMl9uWTGZ7VTMFmUmcNCY7cNzfweWfsQzdQ0YzwozXhsj7Zq/eay1AuGhSPm/vOIwxBhGhzePlcFNHxOaNSBZMHMEf3i5nzd4jNLZ30tDWyeVzihER5pfm8c+t1YFz+QVGPIUJCpcIeWlJHGzouYqvMYan1xxg4aR8LjllNFsPNnL/il1sPdTItFFZvLerhlc3V/FvF0wNae4SEb5+7hRuPXsSLpGQGsxgu/6M8dzz2nbAGoKtVDyLyRrFiSg1yc2D15fxkytODjleYHfwVgcFRbPdbt9rUCT1HhR7alrITEnggpkjqW7qYH+d1dG7t86aqxHuot0Xp47PI8ElvLTxEI99uI+CzGQW2aOM5o7Lpa7F02OY7/5eRllBd40iXNPTjupmDtS3cdHJVpPVjYtKGZGexI2PrGL34WZ+9PfNFOekcuOi0rBlTXS7hjQkgEAHMUBxbv/mFCgVKzQoYlxKopuctMSQpqemDmsBvoxemp5SEnrfN3tPbQul+emcWmp13K20lwjfVd09Q7o/MlMS+fTcMfz5g728vaOGm8+aEOgs9O+7sWZf6HLq++pacbuEonBt/SLkZSRR1+rpsf/4B/YkKH/H8IiMZB65YT4NbZ0s+cUKtlU1cdfFM3rt4BwuWfa/T38nnykVKzQo4sDIzJSQpid/jSKzlxpFSpKb9s7wo578i/5NKcwkIzkhsBrszupmRIg45+FYvnbuZOaMy+HS2aO5bkF3J+WUkZmkJrpDOrvBmkNRnJMaMvokweVfeRfy0pLwdPl6zKX4YLc1BHRM0Cf1WcXZPHHT6dx01gT+/MXTesx4jobb7LkUJf1szlMqVsRsH4XqVpiVTFXQdqnN9tyC3msUrrA1ivZOLwfq27hi7hhcLmF6USabKq2lN3YebqY4J3VAawYV56TyzFcW9jjudgnTijLZXBm6zMe+utYezU6JbhddPi8iEhiyWtfsCWlmW7O3ntMm5PXoHJ5VnM2s4mxixZfOLOW6BSVRr9koNVBao4gDI7NSQvoomvrZR3GooR1jYKz9SXzm6Gy2HGzE6zPsrG4OO6FpsMwcncXmg40hzUj7wwzH9Y/Pd9l9FAB1QSOfqpvaOdTYHph7EctERENCOYIGRRwYmZVMdVMHPnvpyKZj1Ch6G/VU22LVSvwd5DNGZ9Hq8bLlYCO7qpsDS6gPhRlF2TS1dwU6z5s7uqht8fToPPfPMvXPowBC9urwL7EcD0GhlFNoUMSBkVkpeH2GWnsEUHcfRfg1qFISw3dm1zRbz8+311TyLx3woxc24/H6+EQfZ8H2h3+ZD//2sr2NeEpyW81JLume0BY8RHZDRSMiMFODQqlho0ERB/xrA/k7tJs7OnG7hJTE8P98KYnusEt41B4VFKX56cwem8PK8joykhMoGz90SxhMHZWJ2yWBPpHegiLRbnoSEQozU0h0S8jGTRsONFCan95rs5tSavBpUMQB/+zs6iY7KNq7yEhO6HWmb0qiK+zqsTV2E05e0N4EN501gdL8dO5YOvW413g6HimJbiYVZARqFOX2hkXhOrPBGvXkdgmjc1KpONIdFBsPNHCy1iaUGlb6sSwOdM/Oti70TR1dET9Rp/bS9FTb3EFWSkJIIHzqpCI+dVLPlS6HwszRWbyzswaAjZWNFOek9tjQKcnt78y2QrA4J5UD9uZG/o7sWBrZpNSJQGsUccDf+Rxoemrv6nWdJ7CCostn6PSGNj/VtHhClvIebrPH5VDd1MHe2hY2VNSH7ZBODBr1BDAmNzXQ9KQd2UpFhwZFHEh0u8jPSOquUbRHrlGk9LJvdm1zB/np0QsK/5Ie/9hwkD21rSFrWvn5O7P9rWrFOWlUNXbQ0eVlZfkREt3CyWNyejxPKTV0NCjiRGFm91yKuhZPSD/D0VKSwm9eVNPsIT+z9+cNNf8+Gz972drQpqyk57LP3X0UVlL453ZsOdjEyvJaTh6TM6BJgUqp46dBESdGZiVTZXdm1zR3RGxCSrGbbzqOWsbjSIuH3LToBYWIcMPC8QCMH5HG/NKeo6wCu3XZ8/L8j1m+pYr1FQ1hn6OUGlramR0nRmalsLGykS6vj7pWT2CIazipYWoUxhjq2zqjGhRgrfSakZzAaRNGhB215e9o99j9KwWZyUwqzOB//7kTsPbtUEoNL61RxInCrBRqmjs43NyBMVCQ0fsFP7BvdtBies0dXXh9hpy08JP0houIcNX8cZT2skqtf9RTcEf85faOf7PH5jB7rPZPKDXctEYRJ0ZmJWMMbD1o7b8csUZhB0VrUFDUt1pLkwfvkxCLEu3ObE9Xd1DcevYk5pfm6XLdSkWJBkWcGGnPzvZPWIvUR5Fmj4hq6+wKHPMHxdHzFmJNYpgaBXQvN6KUGn7a9BQn/JPu/EtgRKpRpNt9FP4tUwHq26zlO6Ld9HQsiYE+CnOMRyqlhosGRZzwL+Ph3/wnP0IfRbpdo2j1hKlRxHjTU6CPoiv8xktKqeGnQREnCjKTyc9IouJIG9mpiREn3KUnWfeF1ijio+nJPz9kKNedUkodH+2jiBMiwmmlI/jHhoMsnlrQ64KA0D08tqUjqEZhL1Ee653ZN39iAmlJbq46dWy0i6KUsunHtjgyzd5Y6LTSEREfl5TgIsntCtlrur6tk/Qkd8x/Uk9OcPOlMyeE7KOtlIourVHEkS+dOYGMlAQ+M2/MMR+bluzu0UcR681OSqnYpEERR1KT3NywsLRPj01PSgjpo2ho88R8s5NSKjZFpX4vIj8QkQMistb++lTQfd8TkZ0isk1ELohG+ZwgLSm0RnGktZPcdA0KpdTxi2aN4pfGmJ8HHxCRGcBVwExgNPC6iEwxxvTchUdFlJ6cENpH0eph2qisKJZIKRWvYq3H8FLgCWNMhzGmHNgJzI9ymeJSerKb1qBRTw1tnWTH+GQ7pVRsimZQfFVE1ovIwyLi35igGNgf9JgK+5g6TmlJ3TUKY4zVma19FEqpfhiyoBCR10VkY5ivS4H7gYnAbOAg8Av/08K8VNi1HETkJhFZJSKrDh8+PCTvIZ6lB/VRNHd00RUDK8cqpeLTkPVRGGPO7cvjROQPwAv2jxVA8EyrMUBlL6//APAAQFlZmS4MdJS05O5RT/GyIKBSKjZFa9RTUdCPlwMb7dvPA1eJSLKIlAKTgZXDXT4nCK5RNLTFxzpPSqnYFK1RTz8TkdlYzUp7gJsBjDGbRGQZsBnoAm7VEU/9k56cQKvHS5fXpzUKpdSARCUojDHXRbjvbuDuYSyOI/lrDw1tnRxpjY8lxpVSsSnWhseqQZJrr8Ja39bZvXKsNj0ppfpBg8Kh/M1M9a0eapo6EOkOD6WUOh4aFA6VazczHWnp5HBzB3lpSYFtRpVS6njolcOhclKt2sORVg/VjR0URNhjWymlItGgcKgcewHA+larRqFBoZTqLw0Kh8pMTiDBJRyx+yg0KJRS/aVB4VAiQk5aIkdaPRzWoFBKDYAGhYPlpCWxt7YVj9dHQYYGhVKqfzQoHCw3LZHtVU0AWqNQSvWbBoWDTSzIoKbZmpU9ZWRmlEujlIpXGhQOdur4PAAyUxKYqkGhlOonDQoHm19qBUVZSS4uV7itPpRS6tiiuWe2GmJjclO5+JTRfGrWqGgXRSkVxzQoHExE+N+r50S7GEqpOKdNT0oppSLSoFBKKRWRBoVSSqmINCiUUkpFpEGhlFIqIg0KpZRSEWlQKKWUikiDQimlVERijIl2GQZMRA4DewfwEvlAzSAVJ9qc9F5A308sc9J7gRPz/ZQYYwqO9UKOCIqBEpFVxpiyaJdjMDjpvYC+n1jmpPcC+n4i0aYnpZRSEWlQKKWUikiDwvJAtAswiJz0XkDfTyxz0nsBfT+90j4KpZRSEWmNQimlVEQndFCIyFIR2SYiO0Xku9Euz0CIyMMiUi0iG6NdloESkbEi8oaIbBGRTSJye7TLNBAikiIiK0Vknf1+fhjtMg0GEXGLyMci8kK0yzJQIrJHRDaIyFoRWRXt8gyEiOSIyJMistX+G1ow4Nc8UZueRMQNbAfOAyqAj4CrjTGbo1qwfhKRs4Bm4E/GmFnRLs9AiEgRUGSMWSMimcBq4LI4/rcRIN0Y0ywiicA7wO3GmA+iXLQBEZFvAmVAljHmomiXZyBEZA9QZoyJ+3kUIvIo8LYx5kERSQLSjDH1A3nNE7lGMR/YaYzZbYzxAE8Al0a5TP1mjHkLqIt2OQaDMeagMWaNfbsJ2AIUR7dU/WcszfaPifZXXH9CE5ExwIXAg9Eui+omIlnAWcBDAMYYz0BDAk7soCgG9gf9XEEcX4ycSkTGA3OAD6NbkoGxm2nWAtXAa8aYuH4/wL3AHYAv2gUZJAZ4VURWi8hN0S7MAEwADgN/tJsFHxSR9IG+6IkcFBLmWFx/ynMaEckAngK+boxpjHZ5BsIY4zXGzAbGAPNFJG6bB0XkIqDaGLM62mUZRAuNMXOBTwK32k258SgBmAvcb4yZA7QAA+5/PZGDogIYG/TzGKAySmVRR7Hb8p8CHjPGPB3t8gwWuxngTWBplIsyEAuBS+x2/SeAJSLyl+gWaWCMMZX292rgGaym6XhUAVQE1VifxAqOATmRg+IjYLKIlNodPlcBz0e5TIpA5+9DwBZjzD3RLs9AiUiBiOTYt1OBc4Gt0S1V/xljvmeMGWOMGY/1d/NPY8zno1ysfhORdHvQBHYzzflAXI4eNMYcAvaLyFT70DnAgAeBJAz0BeKVMaZLRL4KvAK4gYeNMZuiXKx+E5HHgcVAvohUAHcZYx6Kbqn6bSFwHbDBbtcH+HdjzItRLNNAFAGP2iPtXMAyY0zcDyl1kJHAM9bnExKAvxpjXo5ukQbkNuAx+wPwbuCGgb7gCTs8VimlVN+cyE1PSiml+kCDQimlVEQaFEoppSLSoFBKKRWRBoVSSqmINCiUUkpFpEGhlFIqIg0KFddEZKGInBntcsSagfxeRGSCiDwkIk8OdrlUfNKgUDFHRLz2BjL+r/G9PG4O1qzTuNrXQURSRWSFPVMbEblcRIyITBuk1x/Q78Veev/Go14zSUTeEpETdjWHE5nOzFYxR0SajTEZvdwnWP9v43Z5axG5FUgwxvzK/nkZ1jIfy40xPxjGcpwE/OSow1+0F8ZDRJ40xnwm6PF3Ye3h8thwlVHFBq1RqJgnIuPtLR3vA9YAY0Xk8/b2omtF5Pf+T+f24++0t7h9XUQeF5Fv26+xMegx3xaRH9i3e7xW0Dn/YG9f+qq9oJ//+V8QkfX29qZ/7u11enlL1wLP2c/JwFrb6kasBfaOfs89zn+M+8KWIdxxY8wGY8xFR31VR/ineNYuuzrBaFCoWJQa1Oz0jH1sKtY2r3OANOBzWHsIzAa82BcwEZmHdcGdA1wBnBrpRCIyvbfXAiYDvzXGzATqgU/bz5kJ3AksMcacAtx+jNcJPl8SMMEYs8c+dBnwsjFmO1AnIsFLQoc9f2/39VaGvpYtqIwjROR3wBwR+V7QXRs5xu9TOZO2N6pY1GZf0IDALnd7g/aYPgeYB3xkr/iZirVzHMCZwDPGmFb7ucdaOr6313oLKDfG+FevXQ2Mt28vAZ70769sjKkTkWsilClYPtaF3e9qrN3iwNrb4WqsWhMRzt/bfTm9lCGrj2XDfj+1wL+GOe4VEY+IZNpb1KoThAaFihctQbcFeNQY871eHhuu462L0Bp0SqTXssOpI+iQF+sC63/O0ec4Vpn82vznFpERWKEzS0QM1nL3RkTusB/b2/l7u6+393JbH8vWF8lA+yC8jooj2vSk4tFy4DMiUgggInkiUmLf9xZwuT2yKBO42D5eBRTazSrJwEV9eK1I5/+sfaFHRPL6+jrGmCOAW0RSgM9gNaeVGGPGG2PGAuXAouP/lUR8L/15jz3Y7/ewMaazn+VTcUqDQsUdY8xm4PvAqyKyHngNa9QQxpg1wN+AtVhbqb5tH+8EfgR8CLyAvcNcpNeKcP5NwN3AChFZB9xznK/zKlYYXI217Wawp4Br+vSL6FmusGXoz3vsxdlAvG4epQZAh8cqR7NHNjUbY34e7bL4iTXP4ZvGmOuiXZbjISJPA98zxmyLdlnU8NIahVLDzBjzMfBGhOGzMccerfWshsSJSWsUSimlItIahVJKqYg0KJRSSkWkQaGUUioiDQqllFIRaVAopZSKSINCKaVURBoUSimlItKgUEopFZEGhVJKqYj+P9Hj6xPMBXarAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CO2_periode_fourier = np.real(np.fft.rfft(CO2_periode))\n", "fourier_freq = np.fft.rfftfreq(CO2_periode.size,delta_T)\n", "\n", "plt.plot(fourier_freq,CO2_periode_fourier)\n", "plt.ylabel(\"Amplitude\")\n", "plt.xlabel(\"Fréquence (Année$^{-1}$)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous observons alors 2 pics distinctifs autour de 1 et de 2 Année$^{-1}$ qui correspondent à des oscillations de longueur caractéristique une année et 1/2 année respectivement.\n", "Nous allons ainsi nous intéresser aux pics autour de ces deux fréquences\n", "\n", "Commençons par définir les bornes notre étude, la première à 0., la seconde à 1.5 et la troisième est à 2.5 " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "bornes = [0, 1.5, 2.5] \n", "indices_bornes = []\n", "for borne in bornes:\n", " indice = np.nonzero(fourier_freq > borne )[0][0]\n", " indices_bornes.append(indice)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ensuite, pour chaque composante nous allons en estimer la fréquence et afficher sur un graphe la fréquence trouvée." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La fréquence d'oscillation est située à: 0.984 Année^-1\n" ] }, { "data": { "image/png": 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YY07g6R7rYd1jjSUKY8wJavR6ssbsqGeJwhhzgpLKE3s9JcZbooh2liiMMScoqaVEYeMoopslCmNMNYdTqahy1myjsEQR1SxRGGOqeRYtapVgicJ8xxKFMaaaZy2KZK/usYlxsZRbr6eoZonCGFOtenW7WqqeVDVcYZkws0RhjKlWW9VToq2bHfUsURhjqpV4rZftYetmG0sUxphqtVU9JcZbooh2liiMMdU8ieLkacYBG0sRxSxRGGOqldTRPRasRBHNLFEYY6qVurvHJsXXkiisMTtqWaIwxlQrqa56OnEcBViJIppZojDGVNtyoIg2SXGkJp24HgVYG0U0C3qiEJHnRSRfRNZ7besgIp+KyDb3z/Zer90tIttFZIuInB3s+IwxLqrKoq0FTOqbRlzsdx8N3zVm2+JF0areRCEirUTkHhF51v28v4ic34BrvAicc9K2XwOfq2p/4HP3c0RkMHAFMMR9zJMiEosxJui25x9n/9EyJg9IP2G7NWYbf0oULwDlwKnu5znA//p7AVVdDBw6afNM4CX37y8BF3ltf11Vy1V1J7AdGOfvtYwxjbdoawEAkweknbA90RJF1PMnUfRV1YeBSgBVLQWkidftrKr73efbD3Ryb+8O7PXaL8e9rQYRuUVEVojIioKCgiaGY4xZvO0gfdNT6NG+1QnbbQoP40+iqBCRZEABRKQvrhJGMNSWgGqdiUxVn1HVLFXNSk9Pr20XY4yfyiodLMsurFHtBF6N2ZWWKKJVXP27cC/wMdBTRF4FJgHXN/G6eSLSVVX3i0hXIN+9PQfo6bVfDyC3idcyxtTjm52HKK9y+kwUVqKIXvWWKFT1U+ASXMnhNSBLVRc28brzgevcv18HvOe1/QoRSRSRPkB/4JsmXssYU49FWwtIiIthQp+ONV6zcRSmzhKFiIw+adN+989eItJLVVf5cwEReQ2YCqSJSA6uEsqDwBsichOwB5gFoKobROQNYCNQBdymqtYnz5ggW7y1gPF9Opwwa6yH9XoyvqqeHnH/TAKygLW42hCGA8uA0/y5gKpeWcdL0+vY/37gfn/ObYxputwjpWzLP87lWT1rfd3GUZg6q55UdZqqTgN2A6PdDcdjgFG4uq0aY1qAL7d5usXW3ikkPtbVx8RKFNHLn15Pp6jqOs8TVV0PjAxeSMaYUPpqeyGd2iQyoHPrWl8XERLjYmzd7CjmT6+nTSLyHPAPXF1VrwY2BTUqY0xIqCpLswuZ2LcjInUPj/Ksm22ikz+J4gbgR8Cd7ueLgaeCFpExJmR2FBRTUFTOqZk1ezt5S4yLsUkBo1i9iUJVy4BH3Q9jTAvydXYhABPqSRQJsVaiiGb1JgoR2Ukto6NVNTMoERljQmbpjkK6tk2id8dWPvdLjI+1RBHF/Kl6yvL6PQnXmIcOwQnHGBMqnvaJKQPSfbZPgJUoop0/I7MLvR77VPUvwBkhiM0YE0Tb8o9TWFxRb7UTuBqzbRxF9PKn6sl7hHYMrhJGm6BFZIwJia93uNonTu3rX6KwuZ6ilz9VT494/V4F7AQuD044xphQ+XpHId3bJdOzg+/2CXD1erKqp+jlT6K4SVWzvTe4J+wzxjRTTqeybGch0wd19mv/hLgYisurghyViVT+jMx+y89txphmYkteEYdLKv1qnwBXY7aNo4hevmaPPQXX2tVtReQSr5dScfV+MsY0Uw1pnwAbmR3tfFU9DQTOB9oBF3htLwJ+EMygjDHBtXLPYbq3S6Z7u2S/9k+Mi7USRRSrM1Go6nvAeyJyqqp+HcKYjDFBtin3GEO7p/q9v/V6im6+qp5+qaoPA98XkRprSqjqT4IamTEmKIrLq9hZWMxFo7r7fUxiXAzllTaOIlr5qnryzBC7IhSBGGNCY/OBY6jC4K5WojD+8VX19L7750uhC8cYE2wbco8BMKQBVU82jiK6+ap6ep9aJgP0UNULgxKRMSaoNuYeo32reLqk+t95MSE2BqdClcNJXKw/vepNS+Kr6ulPIYvCGBMyG3KPMaRb23onAvSWEOdZN9sSRTTyVfW0yPO7iCQAp+AqYWxR1YoQxGaMCbBKh5MteUVcPzGjQcd5EkVFlZOUxCAEZiKaP5MCngfMBXYAAvQRkR+q6kfBDs4YE1g7Co5TUeVkSDf/2yfANY4CsAbtKOXvpIDTVHU7gIj0BT4ALFEY08xsdDdkN6THE5xYojDRx5/KxnxPknDLBvKDFI8xJog25B4jKT6GzPTWDTruuzYKG0sRjfwpUWwQkQ+BN3C1UcwClnvmf1LVd4IYnzEmgDbmHmNgl1RiY/xvyAZXryfApvGIUv6UKJKAPGAKMBUowLUU6gW45oJqNBHZJSLrRGSNiKxwb+sgIp+KyDb3z/ZNuYYxxkVV2ZB7tMHtEwBprRMAOHC0LNBhmWag3hKFqt4Q5BimqepBr+e/Bj5X1QdF5Nfu578KcgzGtHj7jpRyrKyqwe0TAP07uxa13Jp33O81LEzL4U+vpz7AHUCG9/5BHHA3E1fJBeAlYCGWKIxpsuoR2Y0oUbRNdg3Q25ZXFOiwTDPgTxvFu8DfgfeBQFdQKvCJiCjwtKo+A3RW1f0AqrpfRDoF+JrGRKWNuceIETilS8MTBUD/zq3ZYokiKvmTKMpU9fEgXX+Squa6k8GnIrLZ3wNF5BbgFoBevXoFKTxjWo5N+4/RJy2F5ITYRh0/sHMbXlm6G4dTG9wYbpo3fxqzHxORe0XkVBEZ7XkE4uKqmuv+mQ/MA8YBeSLSFcD9s9auuKr6jKpmqWpWenp6IMIxpkXbln+cgV3aNPr4AZ3bUF7lZO+hkgBGZZoDfxLFMFwr2j2Ia/DdIwRgHigRSRGRNp7fgbOA9cB84Dr3btcB7zX1WsZEu7JKB7sLi+nXqQmJwp1krPop+vhT9XQxkBmE+Z06A/PcE5PFAf9U1Y9FZDnwhojcBOzBNW7DGNME2QXFOBUGdG7YQDtv/Tu5jt2WV8TZQ7oEKjTTDPiTKNbiWjc7oKOxVTUbGFHL9kJgeiCvZUy025bvKgX0b0KJIiUxju7tktmadzxQYZlmwp9E0RnY7P6mX+7epqo6M3hhGWMCaVvecWJjhIy0Vk06z8AubdhqVU9Rx59Eca/X7wKcBtRYQ9sYE7m25hWR0bFV9SywjdW/c2u+2nbQFjCKMvX+T7vXpTgKnAe8iKtaaG5wwzLGBNL2/ONNqnbyGNCpDRUOJ7sKredTNKkzUYjIABH5vYhsAv4K7AVEVaep6hMhi9AY0yRllQ52FRY3qSHbw9O91qqfoouvEsVmXKWHC1T1NHdysDmGjWlmdh509Xjq17npJYq+6a0RsUQRbXwlikuBA8ACEXlWRKbjaqMwxjQjng/1QJQokhNi6d2hFdus51NUqTNRqOo8VZ2Na63shcDPgM4i8pSInBWi+IwxTbQ939XjqU9aSkDO179zGxt0F2X8acwuVtVXVfV8oAewBtfU38aYZmBrXhG9A9DjyWNA59bsOlhsy6JGkQb1b1PVQ6r6tKqeEayAjDGBtS3/ePWo6kAY0LkNVU5l58HigJ3TRDbrCG1MC1Ze5WB3YQkDAtCQ7eHpZusZ7W1aPksUxrRgOw8W43Aq/QJYovCM7t5lJYqoYYnCmBbMMy9TIAbbebRKiKNLahI7D9qgu2hhicKYFmx7XhExApnpgenx5NEnLYWdB62LbLSwRGFMC7Y17zi9O6aQFB+YHk8eGWkp1pgdRfyZFNCYZqGiysn8tbks3JJPx5QEurRNJqNjK84a0iVql+7cmlcU0B5PHplpKRwuqeRISQXtWiUE/PwmsliiMAHjdCoi4F6MKmRKKqr4x9LdPP/VLg4cK6NzaiKlFQ6OlVUBML5PB/5yxUi6tk0OaVzh5pnj6fzhXQN+bs/gvZ0HixnVyxKFx8rdh3h3dS6xMUJcjNApNZGrJ/SmVULz/qht3tGbsHM6la+zC/nnN3v4dEMeFQ4nsTFCQmwMUwakM3tcTyb3Tw/aN/pN+49x+z9XsaOgmIl9O/LQZcOZ3D8NEaG4vIoP1+3n3vkbOPexL/nTrBFMH9Q5KHFEou35x3EqDOySGvBzZ5yQKNoH/PzNjary4pJd3P/BJuJjY4iPFaqcSkmFg1eW7uaBi4dzWv+0cIfZaJYoTKP9Z8MBHvhwE7sKS2jXKp4rxvWkfasEHE7lSGkFH607wMcbDtCtbRI/P2sgl47uHrDShqry6rI9/OHfG2mbHM8/bhpf40ZMSYxjVlZPxvRuz+3/XM1NL63goUuHMXtsr4DEEOm2HHCNc/DM+BpIvTq0IkasiyxAaYWD38xbx7zV+5gxqDN/nj2C1KR4AJZlF3L3O+u4+u/LmDWmB/ddNDTg7UWhYImigUoqqiitcJAQF+N6xMaEvKol3PKLypgzfwMfrjvAKV3a8NgVIzl7SJcaN8Dvzx/C55vyePbLbO56cy0frtvPA5cMo3NqUpOuX17l4O531vHOqn2c3j+NP18+kvQ2iXXun5nemnd+PJEfvLyC38xbT9e2yUwekN6kGJqDLXlFJMTFkNGxaava1SYhLoaeHVqRHeWJQlX5yeur+WxTHv/vzAHcNq0fMV6l5/GZHfnwztN5/PNtPLVoB/uOlPLcdVnNriqqeUUbJrlHSvlkwwE+35zP0uxCKh1a/Vr3dslMHpDGlAHpnNY/ndaJwX9LVZVN+4vIOVxCYXEFh0sqSGudSL9OrenXqXX1t5lgWLA5n5/+aw2llQ5+cfZAbpmcSXwdK50lxMXwvWFdOXtIF176ehcPfbyZM/+8iDkXDuHiUY0rXRwpqeCHr6xk2c5D/HRGf35yRv8Tbsy6JMXH8uRVo5k192t+/Ooq3rz1VAZ1DXyVTCTZfKCIfumtg7YSXUZH6/n0xoq9fLoxj9+eO4gfTM6sdZ+k+Fh+ec4p9OvUmrveXMt1z3/D89ePpU0Q79NAs0Thw4bco8xdlM0H3+biVFdf9Bsm9aF7u2QqqpyUVzlYt+8o/167n9e+2Uu7VvH8eGpfrj01IyjFy7JKB/PX5PLikl1s3H+szv1G9WrH1eN7c97wrgGN4+WvdzFn/gYGd0vlsStG0Tfdv940MTHCDZP6MHVgJ+56cy0/f2MtH647wP9dPJRODShdZBcc5+aXVpBzuJTHrhjJzJHdGxR/m6R4XrhhLBf97b/c8MJy5t8+qUHXb262HihiYt+OQTt/n7QUVuw6hKpGXakaYE9hCX94fyOnZnbkptP61Lv/JaN7kBgXy52vr+bqv3/DyzeOo21y05LFvNU5ZKa1ZkTPdk06T31EVevfK8JlZWXpihUrAna+VXsO89hn21i0tYDWiXFcNb4Xs8f2JLOOD8ZKh5MVuw4zd9EOFm0toEtqEj87sz+XjekZkEbciion/1i6m78u2M6h4goGdm7DtRN7M6JHOzqkJNCuVTz5x8rZnn+czQeO8c7qfWQXFNOuVTw3TurDLZMzm5QwHE7l/z7cxN+/2smMQZ15/MqRjS46O5zKC//dyR//s4Wk+Fjuu2goF47o5vOYKoeTv3+1k0c/20pSfCzPXpvF2IwOjbo+uL4AXPrUEk7N7Mjz149tkR9yR0sqGfGHT/j1907h1il9g3KNl5bs4t75G/jmN9NbdMKtjcOpzH76a7bkFfHxTyfTvZ3/Peo+3ZjHj19dyeCuqbxy8/hG1wAsyy7kqueWMX1QJ56+JqtR5xCRlapa78GWKLx4J4gOKQncOCmDa07NaFDWX5pdyEMfb2b1niMM6ZbK788fzPjMxn2rU1U+Wn+Ahz7ezO7CEk7vn8Zt0/oxvk8Hnx9uqsrXOwp5YckuPt2YR4/2yfz+/MGcObhzgz8Udx4s5pdvrWX5rsPcMCmD3503OCDJb0fBce56cy2r9xxh1pge/M/MITWST5XDydLsQzzw0SY25B7jzMGduW/mULq0bfqH0gv/3cn/vL+Rhy8bzuVZPZt8vpPtKSzh3+tyWb/vKOv3HUMEnrhyFMN7BPebn8ey7EJmP7OUF24Yy7SBnYJyjcVbC7j2+W94/ZYJTGjk33hzNXfRDh78aDN/mT2Si0Y1rGQL3yWLod3b8vKN4xpcDbX3UAkz//Zf2rWK593bJjU62Vii8FNZpYP31+a9TTbpAAAa5ElEQVTyj2V7WLv3CB1SErhlcibXTOhNSiPbG1SVf3+7nwc+3ETu0TLOGdKFn505oEG9T1buPsz9H2xk1Z4jDOzcht+cN4gpjWiAXbLjIHPmb2Br3nEmZHbgtmn9OK1fWr0Jw+F0dff74382Ex8bw5wLhnDpmB4Nvr4vVQ4nj32+jb8u2E6fjilcNzEDEVeX2/W5x/hsUx5HSipJa53IfTOHcM7QLgH79u90Klc+u5SNucf4z88m060B3wh9Ka1w8NTC7cxdlE2Fw0mvDq0Y2j2VtXuPcqi4gievGs20U4Lzwe3tla93cc97G/j67jOCNn5k76ESTn94AQ9eMowrxkVHTzKAgqJypv5xARP7pfHMNWMa/Tf58foD3P7PVQzv0Zbnrx/r98DF4vIqLn1qCfuOlPLebZPqrOnwhyUKP6zcfYgbX1zB0dJK+nVqzdXjezErq2ejE8TJSiscPLM4m2e/zKa4oorzh3fjx1P7ckqXNrX+cTmcyopdh3h56W4++HY/6W0S+fmZA7g8q2lVWJUOJ68u3c1Ti3aQd6ycYd3bcu2pvZk6sFON3kKVDifvrt7H3EU72FFQzPRTOnH/xcMC8i2+Ll/vKORn/1rDgWNl1dvaJMUxY1Bnzh7SmSkDOpGcEPg2nz2FJZzz2GLG9G7PyzeOa3ISWppdyF1vriXncCkXjezGr783qPp9yy8q48YXl7NpfxH/e9FQrgzyB+tv561j/tpcvr33rKBVrTmcyqB7PuaGSRncfe6goFwjEv123jr+tXwvn/xscpM+pAE+WrefO15bTXqbRP4ye6TP2od9R0pZsv0gb67IYcXuQ7xww7hGfXn01uwThYicAzwGxALPqeqDde3b2ERxvLyKe95dz+VZPZmQ6bs6pykOF1fw7JfZvLhkFyUVDrq3S+a0fmmM6NkOh9NJaaWDnMOlfLz+APlF5STHx/KDyZn8cHJmwJIWuLqVzlu1j6cXZ1f3VhnSLZVBXVNxOJVKh5NVuw+Te7SMQV1TuX1aP84dFrhv8b5UVDk5WlrpGtkNpCbH19mbKpBeWbqbe95dz/9cOITrJmY0+jwbc48xa+4SOqUm8X8XD+PUWhqRi8uruO2fq1i4pYB3fjyR0UEcqHbZU0sQgTdvnRi0awCc+edFZKSl8Oy1jasjb2625x/n7L8s5qrxvfjDzKEBOefavUe48/XV7DlUwo+n9uPqCb3pnJqIiHDweDlvrczhzRV72VHgumc7piRw19kDA/Jlo1knChGJBbYCZwI5wHLgSlXdWNv+gW7MDpZDxRV8uG4/X207yJIdB6unmABIjIth6sB0zhvejemndApogjiZ06ls3H+MRVsLWLS1gD2FJcTHCfExMXRrl8xNp/Vh6sD0FtnIezKnU7n55RV8ua2At26d2KjeI3nHyrjob/9FFd69bZLP0tfx8iqm/nEhmWkp/OuHE4LyHqsqw//nE2aO7Mb/XjQs4Of3dsvLK9h5sJhPfz4lqNeJFD94eQVf7yhk4S+mkta67rE7DVVcXsWc+Rt4c2UOAB1SEsjo2Ip1+45S6VDGZrTne0O7MrFfRwZ0auNXl3B/+JsoIrV77Dhgu6pmA4jI68BMoNZE0Vx0SEng6gm9uXpCb6ocTvKKykmMiyE5Ppak+NiQTVwXEyMM7d6Wod3bctu0fiG5ZqSKiREemTWC85/4ih+/uooPf3I6bVv53zBYUlHFzS+5qi/fvPXUeqvoWifG8dMZ/fndu+v5bFM+Zw4O/JQi+4+WUVRWxcAArmpXlz5pKSzcUoDDqS1+4sVvdh7i04153HXWgIAmCXDNIvDHWSO4ekJv1uw9wsbcY2zLL+LaUzO4clxP+gVwPZHGiNRE0R3Y6/U8BxgfpliCIi42pkFd6kzwtE9J4G9XjWbW3CX8vzfX8Mw1WX59YyurdPDjV1exIfcoz16bxZBubf263uyxPXn+vzt58KNNTBuYHvABcd9N3RH8AYV90lKocDjJPVJKzw6BHwEeKVSVBz/aROfURG46rfaBdYEwome7oI+JaIxIXY+itrv0hDoyEblFRFaIyIqCgoIQhWVaqpE92/Hbcwfx2aZ87vtgIw6n7yrZ4+VVXP/CNyzaWsD/XTysQZMNxsfG8KtzTmFHQTFvrMhpaug1bMlzJ4oQlCg8kwO29Kk8FmzJZ9WeI9w5fUBQOlZEukhNFDmAd+f2HkCu9w6q+oyqZqlqVnp6y5+3xwTfdRMzuGFSBi/8dxc/+sdKSiqqat3vSEkFVz23jOW7DvOX2SMb1TX0rMGdyerdnkc/21rndRpry4EiuqQmNagKrbEyPbPIFoR3tbu8Y2W8t2YfR0srA35up1P503+20qtDK2ZlBbaLeHMRqYliOdBfRPqISAJwBTA/zDGZFk5EuPeCIdx7wWA+25TH7KeXsiH3KE536aK8yjVl9Pce+5JN+48x9+oxDZ5GxPtad509kIKicj74dn8g/xls2n8sKDPG1ia9TSJtEuPCVqI4eLyc+/69kckPL+DO19cw6cEveOjjzRQUlQfsGh9vOMDG/cf46Yz+IemJF4kiso1CVatE5HbgP7i6xz6vqhvCHJaJEjdM6kOvDq2447XVnPf4V3RMSWBcnw6s3nOEA8fKGN2rHX/9/ijG9G78NCLgWlApMz2FN1bsZVaARoeXVFSxNa+Is4LQSF4bESEzPYXsgtAniq+2HeSWV1ZQVungktE9mDmyG/9avpe5i3bwwn938tTVY5o8Kt3hVP786Vb6dWrd6C8FLUFEJgoAVf0Q+DDccZjoNH1QZxbeNZVFWwtYsqOQZdmF9O7YikcuH8HEvh0D0q1VRLg8qycPfrSZHQXH/Z5k0Zdvc47iVEK6mFBmemuWZReG7HrgKt399t11dElN4tnrsqrfu9P7p/PzguPc8dpqfvSPlbx843jG9Wl8Qn9vzT625x/nyatGt/heXb5EZznKGD90Sk1iVlZPHp09kiV3T+dfPzyVSX5Mf9IQl4zuTmyM8MbyvfXv7IfVe44Arsb5UMlMSyH3aFnA21p8eeG/u9hdWMK9Fw6pkWAz01vz8o3j6N4umRtfXM66nKONukZxeRWPfLKVId1SOWdIl0CE3WxZojAmjDq1SeKMUzrx9qocKh3OJp9v9Z7D9ElLoX1K6Nax9kxjEarqp/xjZTzx+TZmDOpU5xQWHVsn8o+bx9M2OZ5rn19GdiMa2x/5ZCv7jpTyPxcOCdgAt+bKEoUxYXbF2J4cPF7BF5vzm3QeVWX13iOMCnE//L6dQttF9uH/bKHC4eS35w32uV/Xtsn88weu4Vc/fnUVpRUOv6+xZu8RXlyyk2sm9CarCVPatxSWKIwJsykD0unUJrHJ1U/7jpRSUFTOqF6hTRQZHVMQoVHf2htq7d4jvLUyhxtP60Mfd9dcX3p3TOEvV4xiS14Rv39vvV/XqHQ4+fXb39KpTRK/PGdgU0NuESxRGBNmcbExXDamBwu25HPgaFn9B9TB0z4RyoZscC312b1dckiqnl5csovUpDhub8DUM1MGpHPHtH68uTKHN1bUn4yfWZzN5gNF/GHmkGa1XGkwWaIwJgJcntUTp8K81fsafY7Ve46QFB8TsjEU3jLTW5N9MLglitIKB59sOMC5w7o2+AP8zhkDmNSvI/e8u54l2w/Wuo+q8vSiHfzpky2cN6wrZ0V5A7Y3SxTGRICMtBSyerfnrZV7aeyMzqv3HmZ493ZhGRTW1z2WIpizUX+xOZ/iCke9S+fWJjZGeOyKUXRvl8z3n1vGnPkbTmizqHQ4+c28dTzw0WbOHdaVRy4fEcjQm72IHUdhTLS5bEwPfv3OOtbsPdLg6qPyKgcb9h3jhtMyghNcPTLTW1NS4eDAsbKgraj33pp9dGqT2OilhdNaJ/Lvn5zGwx9v4cUlu1i0tYBRvdpxvKyK3YUlbMkr4rZpffl/Zw6M+l5OJ7MShTER4tzhXUmKj+HtVQ2fKHBj7jEqHE5G9Qxt+4RHX8/kgEFqpzhaWsnCLQWcP7xbkwa+tUqIY86FQ/jnzeNJjIthWfYh9hwqITU5jkdnj+AXZ59iSaIWVqIwJkKkJsVz9pAuzF+Ty+/OG0xSvP+zlH7XkB2eKaq/G0txnEn90gJ+/v9sOECFw8mFIxte7VSbif3S+PinkwNyrmhgJQpjIshlY3pwrKyKzzblNei41XuP0L1dMp1Tg7e2uS+dUxNJSYitXq4z0N5fm0vvjq0Y0cO/NT9MYFmiMCaCTOybRte2Sby10v/qJ6dTWbHrUEin7TiZa3LA1kEZdJdfVMZ/tx/kwhHdomJ53khkicKYCBIbI1wyujuLtxaQd8y/MRUr9xxm/9GyoCyr2hCZ6SnsyA98F9kPv92PU2lUbycTGJYojIkwl41xjal4deluv/aft3ofyfGx4U8Uaa3JPVpKWaX/U2X44/PN+fTr1Jr+IVixz9TOEoUxEaZPWgpnDu7MS1/vprjc94ysFVVOPvh2P2cP6UxKYnj7pmSmp6AKOwNY/VRSUcWy7ENMrWPyPxMaliiMiUC3TunL0dJKXq9n/qeFW/I5WlrJzFHhX1QnMz3wXWS/3lFIhcPJ1CYuQGSaxhKFMRFoTO/2jMvowN+/zPY5/fh7a3LpmJLA6UHoktpQfdNbEyOwJa8oYOdctLWA5PhYxvYJz/gQ42KJwpgIdevUTHKPljF/TW6trx8rq+TTTXlcMKIbcRGwlnNSfCyZ6a3ZmHssIOdTVRZuKWBi344kxvk/psQEXvj/uowxtZo2sBMDO7fh6cU7cDprzqH08foDVFQ5uSgCqp08BndNZdP+wCSKnQeL2XOohKkDrX0i3CxRGBOhRIRbp2ayNe94jbYKVeXtlTn0SUuJqEFoQ7qlsu9IKUdKKpp8rkVbCwCYMsDaJ8LNEoUxEez84d04rV8av5m3jn+4u8uWVji48/U1LNt5iCvH9YyoQWiDu6UCBKT6aeGWAjLTUujVsVWTz2WaxuZ6MiaCxcfG8Nx1Wdz26ip+9+568ovKWbA5n/W5R/nlOQP5wemZ4Q7xBIO6uhPF/mNMbEIDe1mlg6XZhXx/fK9AhWaawBKFMREuKT6WudeM4af/WsPjn2+jdWIcz16TxYwwD7CrTVrrRDqnJja5RLE0u5DyKidTbPxERLBEYUwzEB8bw+NXjGJCnw6c2rcj/TpF7ijlId3asrGJDdoLtxSQGBfDhEauPWECyxKFMc1EbIxwzakZ4Q6jXoO7prJoawFllY4GTZXubdHWAiZkdmz08SawwtKYLSJzRGSfiKxxP871eu1uEdkuIltE5OxwxGeMabzB3VJxOJVteY2bIHB3YTE7DxZbt9gIEs4SxaOq+ifvDSIyGLgCGAJ0Az4TkQGqGthZxowxQTO4ukH7KMMa0XXX0y3Wpu2IHJHWPXYm8LqqlqvqTmA7MC7MMRljGqBXh1a0ToxjQyMbtBdtKaBXh1ZkWLfYiBHORHG7iHwrIs+LiGcil+6A98iiHPc2Y0wzERMjDOraplE9n8oqHSzZUcjUgekRNT4k2gUtUYjIZyKyvpbHTOApoC8wEtgPPOI5rJZT1Zy7wHX+W0RkhYisKCgoCMq/wRjTOJ6pPGqbesSX5bsOUVrpsPaJCBO0NgpVneHPfiLyLPBv99McoKfXyz2AWmdEU9VngGcAsrKyGvbXaIwJqsHdUin+2sHuQyX0SUvx+7hFWwpIiLVusZEmXL2euno9vRhY7/59PnCFiCSKSB+gP/BNqOMzxjTN4K6uRuyGVj8t3FrA+MwOtEqwnvuRJFxtFA+LyDoR+RaYBvwMQFU3AG8AG4GPgdusx5Mxzc+ALq1JiI1h9Z7Dfh+Tc7iE7fnHbTR2BApLolDVa1R1mKoOV9ULVXW/12v3q2pfVR2oqh+FIz5jTNMkxsUyPrMDC7bk+32MdYuNXJHWPdYY00JMG9iJHQXF7C70b2nUzzfl071dMn3T/W/TMKFhicIYExTTB7lKBl9srr9UUVBUzqKtBZw/oqt1i41AliiMMUHRu2MKmekpfiWK99bsw+FUZo3pEYLITENZojDGBM30UzqxLPsQxeVVde6jqry5IoeRPdtF9Ky40cwShTEmaKad0okKh5Ovth+sc5/1+46xJa+Iy6w0EbEsURhjgmZsRgfaJMbxxaa6q5/eWrmXhLgYLhjeLYSRmYawRGGMCZr42BgmD0hnwZb8WqfzKK9y8N7aXM4e0oW2reLDEKHxhyUKY0xQnXFKJ/KLymudTfbzTfkcKam0aqcIZ4nCGBNUrplg4c2Ve0/YXl7l4Lkvs+mSmsRp/dLCFJ3xhyUKY0xQdWydyJXjevHy17t57stsAKocTn76+hpW7TnCL88ZSGyMjZ2IZDbzljEm6O6bOZSjJZX87webSE6IZc2eI3y0/gC/O28Ql4y2aqdIZ4nCGBN0sTHCo7NHUlrp4LfzXJNF/2R6f24+PTPMkRl/WKIwxoREQlwMT141ml+89S19OrbiZzP6hzsk4ydLFMaYkEmKj+WJK0eFOwzTQNaYbYwxxidLFMYYY3yyRGGMMcYnSxTGGGN8skRhjDHGJ0sUxhhjfLJEYYwxxidLFMYYY3wS1ZpzxDc3IlIA7G7AIWlA3UtuRY7mEKfFGBjNIUZoHnFajP7rrarp9e3UIhJFQ4nIClXNCncc9WkOcVqMgdEcYoTmEafFGHhW9WSMMcYnSxTGGGN8itZE8Uy4A/BTc4jTYgyM5hAjNI84LcYAi8o2CmOMMf6L1hKFMcYYP7XoRCEi54jIFhHZLiK/ruV1EZHH3a9/KyKjIzDGq9yxfSsiS0RkRKhj9CdOr/3GiohDRC4LZXzua9cbo4hMFZE1IrJBRBZFWowi0lZE3heRte4YbwhDjM+LSL6IrK/j9Ui4b+qLMez3TX0xeu0XtnvGb6raIh9ALLADyAQSgLXA4JP2ORf4CBBgArAsAmOcCLR3//69UMfob5xe+30BfAhcFmkxAu2AjUAv9/NOERjjb4CH3L+nA4eAhBDHORkYDayv4/Ww3jd+xhgJ943PGL3+JsJyzzTk0ZJLFOOA7aqaraoVwOvAzJP2mQm8rC5LgXYi0jWSYlTVJap62P10KRCOlej9eS8B7gDeBvJDGZybPzF+H3hHVfcAqGqo4/QnRgXaiIgArXEliqpQBqmqi93XrUu475t6Y4yE+8aP9xHCe8/4rSUniu7AXq/nOe5tDd0nmBp6/ZtwfZMLtXrjFJHuwMXA3BDG5c2f93IA0F5EForIShG5NmTRufgT41+BQUAusA64U1WdoQnPb+G+bxoqXPeNTxFwz/itJa+ZLbVsO7mLlz/7BJPf1xeRabj+4E8LakS18yfOvwC/UlWH68twyPkTYxwwBpgOJANfi8hSVd0a7ODc/InxbGANcAbQF/hURL5U1WPBDq4Bwn3f+C3M9019wn3P+K0lJ4ocoKfX8x64vqU1dJ9g8uv6IjIceA74nqoWhig2b/7EmQW87v6DTwPOFZEqVX03NCH6/f99UFWLgWIRWQyMAEKVKPyJ8QbgQXVVYG8XkZ3AKcA3oQnRL+G+b/wSAfdNfcJ9z/gv3I0kwXrgSoLZQB++azgcctI+53Fio9w3ERhjL2A7MDGS38uT9n+R0Ddm+/NeDgI+d+/bClgPDI2wGJ8C5rh/7wzsA9LC8H+eQd0NxWG9b/yMMez3TX0xnrRfyO+ZhjxabIlCVatE5HbgP7h6FjyvqhtE5Fb363Nx9TQ4F9cfVAmub3ORFuPvgY7Ak+5vHlUa4snE/IwzrPyJUVU3icjHwLeAE3hOVX12XQx1jMB9wIsisg7XB/GvVDWks4yKyGvAVCBNRHKAe4F4rxjDet/4GWPY7xs/Ymw2bGS2McYYn1pyrydjjDEBYInCGGOMT5YojDHG+GSJwhhjjE+WKIwxxvhkicIYY4xPliiMMcb4ZInCNGsiMklETg93HJGmKe+LiGSKyN9F5K1Ax2WaJ0sUJuK4F3FZ4/XIqGO/UbhGBS8NZXxNJSLJIrJIRGLdzy8WERWRUwJ0/ia9L+qaBv2mk86ZICKLRaTFzuZg6mYjs03EEZHjqtq6jtcE199tpE297TcRuQ2IU9XH3M/fALoCn6vqnBDGMQx44KTNN6p7nQ4ReUtVL/Pa/15c62m8GqoYTWSwEoWJeCKSISKbRORJYBXQU0SuFpFv3CWOpz3fzt37/9a93OhnIvKaiNzlPsd6r33uEpE57t9rnMvrms+6lyT9RESSvY6/1r3M5loReaWu89TxT7oKeM99TGtgEq6psK+o5d9c4/r1vFZrDLVtV9V1qnr+SQ9fC+i8647dRBlLFCYSJXtVO81zbxuIa1W1Ubhmfp0NTFLVkYAD9weYiIzB9YE7CrgEGOvrQiIyqK5zAf2Bv6nqEOAIcKn7mCHAb4EzVHUEcGc95/G+XgKQqaq73JsuAj5W15oYh+TE9adrvX5dr9UVg7+xecXYUUTmAqNE5G6vl9ZTz/tpWiarbzSRqNT9gQa4vkEDu9W17Ca4Fh4aAyx3zwyazHdLSZ4OzFPVEvex8+u5Vl3nWgzsVNU17v1W4poyGlyLCr3lmdVVVQ+JyPd9xOQtDdcHu8eVuBawAdfSqFfiKjXh4/p1vdaujhhS/YwN97+nELi1lu0OEakQkTaqWlTX8ablsURhmotir98FeElV765j39oa3qo4sQSd5Otc7uRU7rXJgesD1nNMbasl+orJo9RzbRHpiCvpDBURxTX1uIrIL9371nX9ul6r699yh5+x+SMRKAvAeUwzYlVPpjn6HLhMRDoBiEgHEentfm0xcLG7Z1Eb4AL39jygk7taJRE4349z+br+5e4PekSkg7/nUdXDQKyIJAGX4apO662qGaraE9hJ45ftrCuGxvwba3D/ewtUtbKR8ZlmyhKFaXZUdSPwO+ATEfkW+BRXryFUdRXwL1zrTr8NfOneXgn8AVgG/BvYXN+5fFx/A3A/sEhE1gJ/buB5PsGVDK4E5p302tvA9/16I2rGVWsMjfk31mEarkWLTJSx7rGmRXP3bDquqn8Kdywe4hrn8HNVvSbcsTSEiLwD3K2qW8IdiwktK1EYE2KquhpY4KP7bMRx99Z615JEdLIShTHGGJ+sRGGMMcYnSxTGGGN8skRhjDHGJ0sUxhhjfLJEYYwxxidLFMYYY3yyRGGMMcYnSxTGGGN8skRhjDHGp/8P9n+xidgX6+IAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "La fréquence d'oscillation est située à: 1.968 Année^-1\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def trouver_freq(borne_inf,borne_sup):\n", " indice_freq = np.argmax(CO2_periode_fourier[borne_inf:borne_sup]) + borne_inf\n", " freq_max = fourier_freq[indice_freq]\n", " amplitude_max = CO2_periode_fourier[indice_freq]\n", "\n", " print(f\"La fréquence d'oscillation est située à: {freq_max:.3f} Année^-1\")\n", " plt.plot(fourier_freq[borne_inf:borne_sup],CO2_periode_fourier[borne_inf:borne_sup])\n", " plt.plot([freq_max], [amplitude_max], \"xr\", label=\"frequence d'oscillation\")\n", " plt.ylabel(\"Amplitude\")\n", " plt.xlabel(\"Fréquence (Année$^{-1}$)\")\n", " plt.legend()\n", " plt.show()\n", " return freq_max\n", "\n", "frequences = []\n", "for i in range(len(indices_bornes) -1 ):\n", " freq = trouver_freq(indices_bornes[i], indices_bornes[i+1])\n", " frequences.append(freq)\n", "frequences = np.array(frequences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin nous allons pouvoir identifier la partie périodique du signal en: $$ f(t) = \\sum_{i=1}^{3} (c_i \\cos(f_i t) + s_i \\sin(f_i t)) $$\n", "\n", "Comme précédemment, nous allons définir le problème $$ Ax = y$$ avec $x$ les temps, $A$ les échantillons des fonctions de base à chaque temps et $y$ les mesures de CO2 (une fois la composante lente enlevée)." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0. 1. 0. ]\n", " [ 0.86505184 0.50168248 0.49662938 0.86796271]\n", " [ 0.51158037 0.85923543 -0.47657104 0.87913596]\n", " ...\n", " [ 0.33487846 0.94226133 -0.77571283 0.63108605]\n", " [-0.1647631 0.98633317 -0.94570624 -0.32502262]\n", " [-0.6359243 0.77175144 -0.19120057 -0.98155099]]\n" ] } ], "source": [ "def assembler_A():\n", " tableau_colonnes = []\n", " for freq in frequences:\n", " colonne_c = np.cos(2.*np.pi * freq*temps) \n", " colonne_s = np.sin(2.*np.pi * freq*temps) \n", " tableau_colonnes.append(colonne_c)\n", " tableau_colonnes.append(colonne_s)\n", " return np.column_stack(tableau_colonnes)\n", "A = assembler_A()\n", "print(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, nous pouvons résoudre le système linéaire" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Les paramètres estimés sont [ 0.09456166 0.1400933 -0.03932497 0.03291844]\n" ] } ], "source": [ "w = np.linalg.lstsq(A,CO2_periode, rcond=None)[0]\n", "print(f\"Les paramètres estimés sont {w}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Définissons enfin la fonction périodique $f(t)$ grâce aux $w_i$ obtenus précédemments:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.1400933 0.03291844] [ 0.09456166 -0.03932497]\n" ] } ], "source": [ "w_c = np.array([w[i] for i in range(len(w)) if i%2])\n", "w_s = np.array([w[i] for i in range(len(w)) if (i-1)%2])\n", "print(w_c,w_s)\n", "def CO2_comp_periode(t):\n", " return np.dot(w_c, np.sin( 2.*np.pi * np.outer(frequences,t))) + np.dot(w_c,np.cos(2.*np.pi * np.outer(frequences,t)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, nous pouvons afficher la composante périodique sur un graphe" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(temps,CO2_comp_periode(temps), label=\"estimation\")\n", "plt.plot(temps,CO2_periode, label=\"mesure\")\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 (ppm)\")\n", "plt.xlim(0,10)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin essayons d'afficher le taux de CO2 au cours du temps en extrapolant jusqu'à 2027." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "temps_zero = np.array(data_MLO[\"Date.1\"])[0]\n", "plt.plot(temps_zero + temps,CO2, label=\"mesures\")\n", "\n", "\n", "temps_extrapol = np.hstack([temps, np.arange(temps[-1] + 0.8, temps[-1] + 3, 0.8 )])\n", "plt.plot(temps_zero + temps_extrapol, CO2_comp_lente(temps_extrapol), label=\"modèle\")\n", "plt.xlabel(\"Temps (Année)\")\n", "plt.ylabel(\"CO2 ppm)\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }