{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Présentation du sujet\n", "\n", "En 1958, Charles David Keeling a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé [\"Keeling Curve\"](https://fr.wikipedia.org/wiki/Courbe_de_Keeling). L'exploitation des données sera réalisée avec le langage Python 3." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement des données" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# On importe les lib nécessaires au chargement, au traitement et à la présentation des données.\n", "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données que nous utilisons pour étudier l'évolution du taux de CO2 dans l'atmosphère sont disponibles sur le [site web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)" ] }, { "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\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
0adjustedadjusted fitfilledadjusted filled
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
4195803212591958.2027315.71314.43316.20314.91315.71314.43
5195804212901958.2877317.45315.16317.30314.99317.45315.16
6195805213201958.3699317.51314.71317.87315.07317.51314.71
7195806213511958.4548-99.99-99.99317.25315.15317.25315.15
8195807213811958.5370315.86315.20315.86315.22315.86315.20
9195808214121958.6219314.93316.20313.99315.29314.93316.20
10195809214431958.7068313.21316.09312.46315.35313.21316.09
11195810214731958.7890-99.99-99.99312.44315.41312.44315.41
12195811215041958.8740313.33315.20313.61315.46313.33315.20
13195812215341958.9562314.67315.43314.77315.52314.67315.43
14195901215651959.0411315.58315.54315.63315.57315.58315.54
15195902215961959.1260316.49315.85316.28315.64316.49315.85
16195903216241959.2027316.65315.37316.99315.70316.65315.37
17195904216551959.2877317.72315.42318.09315.77317.72315.42
18195905216851959.3699318.29315.48318.66315.85318.29315.48
19195906217161959.4548318.15316.02318.05315.94318.15316.02
20195907217461959.5370316.54315.87316.67316.03316.54315.87
21195908217771959.6219314.80316.07314.82316.13314.80316.07
22195909218081959.7068313.84316.73313.32316.22313.84316.73
23195910218381959.7890313.33316.33313.33316.31313.33316.33
24195911218691959.8740314.81316.69314.54316.40314.81316.69
25195912218991959.9562315.58316.35315.72316.48315.58316.35
26196001219301960.0410316.43316.39316.61316.56316.43316.39
27196002219611960.1257316.98316.34317.28316.64316.98316.34
28196003219901960.2049317.58316.27318.04316.72317.58316.27
29196004220211960.2896319.03316.70319.14316.80319.03316.70
.................................
740201907436612019.5370411.78410.97412.29411.51411.78410.97
741201908436922019.6219410.01411.56410.15411.73410.01411.56
742201909437232019.7068408.48411.98408.44411.96408.48411.98
743201910437532019.7890408.40412.02408.57412.17408.40412.02
744201911437842019.8740410.16412.44410.15412.40410.16412.44
745201912438142019.9562411.81412.74411.70412.61411.81412.74
746202001438452020.0410413.30413.25412.90412.83413.30413.25
747202002438762020.1257414.05413.28413.82413.04414.05413.28
748202003439052020.2049414.45412.87414.83413.23414.45412.87
749202004439362020.2896416.11413.29416.28413.44416.11413.29
750202005439662020.3716417.15413.74417.05413.64417.15413.74
751202006439972020.4563416.29413.73416.38413.84416.29413.73
752202007440272020.5383414.42413.64414.79414.04414.42413.64
753202008440582020.6230412.52414.10412.63414.25412.52414.10
754202009440892020.7077411.18414.70410.91414.45411.18414.70
755202010441192020.7896411.12414.75411.02414.63411.12414.75
756202011441502020.8743412.88415.16412.57414.82412.88415.16
757202012441802020.9563413.89414.82414.08414.99413.89414.82
758202101442112021.0411415.15415.10415.22415.16415.15415.10
759202102442422021.1260416.47415.70416.10415.31416.47415.70
760202103442702021.2027417.16415.61417.02415.45417.16415.61
761202104443012021.2877418.24415.44418.41415.59418.24415.44
762202105443312021.3699418.95415.53419.14415.72418.95415.53
763202106443622021.4548418.70416.11418.42415.86418.70416.11
764202107443922021.5370416.65415.84416.76415.98416.65415.84
765202108444232021.6219414.34415.90414.53416.12414.34415.90
766202109444542021.7068412.90416.42-99.99-99.99412.90416.42
767202110444842021.7890-99.99-99.99-99.99-99.99-99.99-99.99
768202111445152021.8740-99.99-99.99-99.99-99.99-99.99-99.99
769202112445452021.9562-99.99-99.99-99.99-99.99-99.99-99.99
\n", "

770 rows × 10 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "0 adjusted \n", "1 Excel [ppm] [ppm] [ppm] \n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.71 314.43 316.20 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", "6 1958 05 21320 1958.3699 317.51 314.71 317.87 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.25 \n", "8 1958 07 21381 1958.5370 315.86 315.20 315.86 \n", "9 1958 08 21412 1958.6219 314.93 316.20 313.99 \n", "10 1958 09 21443 1958.7068 313.21 316.09 312.46 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.44 \n", "12 1958 11 21504 1958.8740 313.33 315.20 313.61 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.77 \n", "14 1959 01 21565 1959.0411 315.58 315.54 315.63 \n", "15 1959 02 21596 1959.1260 316.49 315.85 316.28 \n", "16 1959 03 21624 1959.2027 316.65 315.37 316.99 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.09 \n", "18 1959 05 21685 1959.3699 318.29 315.48 318.66 \n", "19 1959 06 21716 1959.4548 318.15 316.02 318.05 \n", "20 1959 07 21746 1959.5370 316.54 315.87 316.67 \n", "21 1959 08 21777 1959.6219 314.80 316.07 314.82 \n", "22 1959 09 21808 1959.7068 313.84 316.73 313.32 \n", "23 1959 10 21838 1959.7890 313.33 316.33 313.33 \n", "24 1959 11 21869 1959.8740 314.81 316.69 314.54 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.39 316.61 \n", "27 1960 02 21961 1960.1257 316.98 316.34 317.28 \n", "28 1960 03 21990 1960.2049 317.58 316.27 318.04 \n", "29 1960 04 22021 1960.2896 319.03 316.70 319.14 \n", ".. ... ... ... ... ... ... ... \n", "740 2019 07 43661 2019.5370 411.78 410.97 412.29 \n", "741 2019 08 43692 2019.6219 410.01 411.56 410.15 \n", "742 2019 09 43723 2019.7068 408.48 411.98 408.44 \n", "743 2019 10 43753 2019.7890 408.40 412.02 408.57 \n", "744 2019 11 43784 2019.8740 410.16 412.44 410.15 \n", "745 2019 12 43814 2019.9562 411.81 412.74 411.70 \n", "746 2020 01 43845 2020.0410 413.30 413.25 412.90 \n", "747 2020 02 43876 2020.1257 414.05 413.28 413.82 \n", "748 2020 03 43905 2020.2049 414.45 412.87 414.83 \n", "749 2020 04 43936 2020.2896 416.11 413.29 416.28 \n", "750 2020 05 43966 2020.3716 417.15 413.74 417.05 \n", "751 2020 06 43997 2020.4563 416.29 413.73 416.38 \n", "752 2020 07 44027 2020.5383 414.42 413.64 414.79 \n", "753 2020 08 44058 2020.6230 412.52 414.10 412.63 \n", "754 2020 09 44089 2020.7077 411.18 414.70 410.91 \n", "755 2020 10 44119 2020.7896 411.12 414.75 411.02 \n", "756 2020 11 44150 2020.8743 412.88 415.16 412.57 \n", "757 2020 12 44180 2020.9563 413.89 414.82 414.08 \n", "758 2021 01 44211 2021.0411 415.15 415.10 415.22 \n", "759 2021 02 44242 2021.1260 416.47 415.70 416.10 \n", "760 2021 03 44270 2021.2027 417.16 415.61 417.02 \n", "761 2021 04 44301 2021.2877 418.24 415.44 418.41 \n", "762 2021 05 44331 2021.3699 418.95 415.53 419.14 \n", "763 2021 06 44362 2021.4548 418.70 416.11 418.42 \n", "764 2021 07 44392 2021.5370 416.65 415.84 416.76 \n", "765 2021 08 44423 2021.6219 414.34 415.90 414.53 \n", "766 2021 09 44454 2021.7068 412.90 416.42 -99.99 \n", "767 2021 10 44484 2021.7890 -99.99 -99.99 -99.99 \n", "768 2021 11 44515 2021.8740 -99.99 -99.99 -99.99 \n", "769 2021 12 44545 2021.9562 -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", "4 314.91 315.71 314.43 \n", "5 314.99 317.45 315.16 \n", "6 315.07 317.51 314.71 \n", "7 315.15 317.25 315.15 \n", "8 315.22 315.86 315.20 \n", "9 315.29 314.93 316.20 \n", "10 315.35 313.21 316.09 \n", "11 315.41 312.44 315.41 \n", "12 315.46 313.33 315.20 \n", "13 315.52 314.67 315.43 \n", "14 315.57 315.58 315.54 \n", "15 315.64 316.49 315.85 \n", "16 315.70 316.65 315.37 \n", "17 315.77 317.72 315.42 \n", "18 315.85 318.29 315.48 \n", "19 315.94 318.15 316.02 \n", "20 316.03 316.54 315.87 \n", "21 316.13 314.80 316.07 \n", "22 316.22 313.84 316.73 \n", "23 316.31 313.33 316.33 \n", "24 316.40 314.81 316.69 \n", "25 316.48 315.58 316.35 \n", "26 316.56 316.43 316.39 \n", "27 316.64 316.98 316.34 \n", "28 316.72 317.58 316.27 \n", "29 316.80 319.03 316.70 \n", ".. ... ... ... \n", "740 411.51 411.78 410.97 \n", "741 411.73 410.01 411.56 \n", "742 411.96 408.48 411.98 \n", "743 412.17 408.40 412.02 \n", "744 412.40 410.16 412.44 \n", "745 412.61 411.81 412.74 \n", "746 412.83 413.30 413.25 \n", "747 413.04 414.05 413.28 \n", "748 413.23 414.45 412.87 \n", "749 413.44 416.11 413.29 \n", "750 413.64 417.15 413.74 \n", "751 413.84 416.29 413.73 \n", "752 414.04 414.42 413.64 \n", "753 414.25 412.52 414.10 \n", "754 414.45 411.18 414.70 \n", "755 414.63 411.12 414.75 \n", "756 414.82 412.88 415.16 \n", "757 414.99 413.89 414.82 \n", "758 415.16 415.15 415.10 \n", "759 415.31 416.47 415.70 \n", "760 415.45 417.16 415.61 \n", "761 415.59 418.24 415.44 \n", "762 415.72 418.95 415.53 \n", "763 415.86 418.70 416.11 \n", "764 415.98 416.65 415.84 \n", "765 416.12 414.34 415.90 \n", "766 -99.99 412.90 416.42 \n", "767 -99.99 -99.99 -99.99 \n", "768 -99.99 -99.99 -99.99 \n", "769 -99.99 -99.99 -99.99 \n", "\n", "[770 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# On charge les données disponibles dans le fichier \".csv\" (ov enlève l'entête qui fait 54 lignes)\n", "raw_data = pd.read_csv(data_url, skiprows=54, sep = ',')\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utilisation et mise en forme des données\n", "\n", "- Nous l'exploitation des données numériques, nous décidons d'enlever les lignes 0 et 1 car elles contiennent seulement des informations textuelles, ou des unités de mesure. On retiendra le point important qui est l'unité utilisée pour exprimer les quantités de CO2 : le ppm ou partie par million.\n", "\n", "- Pour ce qui est des dates, on garde uniquement les colonnes associées au mois (Mn) et à l'année (Yr) de la mesure. \n", "\n", "- De plus, on garde seulement les premières colonnes appelées \"CO2\" et \"seasonally\" puisqu'elles correspondent aux données non lissées. Les données \"CO2\" sont les données brutes et les données \"seasonally\" corresondent aux données brutes auqelles on a soustrait les variations saisonières. \n", "\n", "- Finalement, il faut enlever les espaces inutiles dans le nom des colonnes qui gènent le dépouillement. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnCO2seasonally
2195801-99.99-99.99
3195802-99.99-99.99
4195803315.71314.43
5195804317.45315.16
6195805317.51314.71
7195806-99.99-99.99
8195807315.86315.20
9195808314.93316.20
10195809313.21316.09
11195810-99.99-99.99
12195811313.33315.20
13195812314.67315.43
14195901315.58315.54
15195902316.49315.85
16195903316.65315.37
17195904317.72315.42
18195905318.29315.48
19195906318.15316.02
20195907316.54315.87
21195908314.80316.07
22195909313.84316.73
23195910313.33316.33
24195911314.81316.69
25195912315.58316.35
26196001316.43316.39
27196002316.98316.34
28196003317.58316.27
29196004319.03316.70
30196005320.03317.22
31196006319.59317.47
...............
740201907411.78410.97
741201908410.01411.56
742201909408.48411.98
743201910408.40412.02
744201911410.16412.44
745201912411.81412.74
746202001413.30413.25
747202002414.05413.28
748202003414.45412.87
749202004416.11413.29
750202005417.15413.74
751202006416.29413.73
752202007414.42413.64
753202008412.52414.10
754202009411.18414.70
755202010411.12414.75
756202011412.88415.16
757202012413.89414.82
758202101415.15415.10
759202102416.47415.70
760202103417.16415.61
761202104418.24415.44
762202105418.95415.53
763202106418.70416.11
764202107416.65415.84
765202108414.34415.90
766202109412.90416.42
767202110-99.99-99.99
768202111-99.99-99.99
769202112-99.99-99.99
\n", "

768 rows × 4 columns

\n", "
" ], "text/plain": [ " Yr Mn CO2 seasonally\n", "2 1958 01 -99.99 -99.99\n", "3 1958 02 -99.99 -99.99\n", "4 1958 03 315.71 314.43\n", "5 1958 04 317.45 315.16\n", "6 1958 05 317.51 314.71\n", "7 1958 06 -99.99 -99.99\n", "8 1958 07 315.86 315.20\n", "9 1958 08 314.93 316.20\n", "10 1958 09 313.21 316.09\n", "11 1958 10 -99.99 -99.99\n", "12 1958 11 313.33 315.20\n", "13 1958 12 314.67 315.43\n", "14 1959 01 315.58 315.54\n", "15 1959 02 316.49 315.85\n", "16 1959 03 316.65 315.37\n", "17 1959 04 317.72 315.42\n", "18 1959 05 318.29 315.48\n", "19 1959 06 318.15 316.02\n", "20 1959 07 316.54 315.87\n", "21 1959 08 314.80 316.07\n", "22 1959 09 313.84 316.73\n", "23 1959 10 313.33 316.33\n", "24 1959 11 314.81 316.69\n", "25 1959 12 315.58 316.35\n", "26 1960 01 316.43 316.39\n", "27 1960 02 316.98 316.34\n", "28 1960 03 317.58 316.27\n", "29 1960 04 319.03 316.70\n", "30 1960 05 320.03 317.22\n", "31 1960 06 319.59 317.47\n", ".. ... ... ... ...\n", "740 2019 07 411.78 410.97\n", "741 2019 08 410.01 411.56\n", "742 2019 09 408.48 411.98\n", "743 2019 10 408.40 412.02\n", "744 2019 11 410.16 412.44\n", "745 2019 12 411.81 412.74\n", "746 2020 01 413.30 413.25\n", "747 2020 02 414.05 413.28\n", "748 2020 03 414.45 412.87\n", "749 2020 04 416.11 413.29\n", "750 2020 05 417.15 413.74\n", "751 2020 06 416.29 413.73\n", "752 2020 07 414.42 413.64\n", "753 2020 08 412.52 414.10\n", "754 2020 09 411.18 414.70\n", "755 2020 10 411.12 414.75\n", "756 2020 11 412.88 415.16\n", "757 2020 12 413.89 414.82\n", "758 2021 01 415.15 415.10\n", "759 2021 02 416.47 415.70\n", "760 2021 03 417.16 415.61\n", "761 2021 04 418.24 415.44\n", "762 2021 05 418.95 415.53\n", "763 2021 06 418.70 416.11\n", "764 2021 07 416.65 415.84\n", "765 2021 08 414.34 415.90\n", "766 2021 09 412.90 416.42\n", "767 2021 10 -99.99 -99.99\n", "768 2021 11 -99.99 -99.99\n", "769 2021 12 -99.99 -99.99\n", "\n", "[768 rows x 4 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# On enlève les deux premières lignes\n", "num_data = raw_data[2:]\n", "# On supprime les deux colonnes de données \"Date\"\n", "num_data = num_data.drop(num_data.columns[[2,3,6,7,8,9]], axis=1).copy()\n", "# On enlève les espaces pour le nom des colonnes\n", "new_columns = [y.replace(\" \",\"\") for y in num_data.columns]\n", "num_data.columns = new_columns\n", "num_data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A partir des colonnes Yr (année) et Mn (mois), on construit la colonne période au format approprié pour la représentation graphique des mesures." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnCO2seasonallyperiod
2195801-99.99-99.991958-01
3195802-99.99-99.991958-02
4195803315.71314.431958-03
5195804317.45315.161958-04
6195805317.51314.711958-05
7195806-99.99-99.991958-06
8195807315.86315.201958-07
9195808314.93316.201958-08
10195809313.21316.091958-09
11195810-99.99-99.991958-10
12195811313.33315.201958-11
13195812314.67315.431958-12
14195901315.58315.541959-01
15195902316.49315.851959-02
16195903316.65315.371959-03
17195904317.72315.421959-04
18195905318.29315.481959-05
19195906318.15316.021959-06
20195907316.54315.871959-07
21195908314.80316.071959-08
22195909313.84316.731959-09
23195910313.33316.331959-10
24195911314.81316.691959-11
25195912315.58316.351959-12
26196001316.43316.391960-01
27196002316.98316.341960-02
28196003317.58316.271960-03
29196004319.03316.701960-04
30196005320.03317.221960-05
31196006319.59317.471960-06
..................
740201907411.78410.972019-07
741201908410.01411.562019-08
742201909408.48411.982019-09
743201910408.40412.022019-10
744201911410.16412.442019-11
745201912411.81412.742019-12
746202001413.30413.252020-01
747202002414.05413.282020-02
748202003414.45412.872020-03
749202004416.11413.292020-04
750202005417.15413.742020-05
751202006416.29413.732020-06
752202007414.42413.642020-07
753202008412.52414.102020-08
754202009411.18414.702020-09
755202010411.12414.752020-10
756202011412.88415.162020-11
757202012413.89414.822020-12
758202101415.15415.102021-01
759202102416.47415.702021-02
760202103417.16415.612021-03
761202104418.24415.442021-04
762202105418.95415.532021-05
763202106418.70416.112021-06
764202107416.65415.842021-07
765202108414.34415.902021-08
766202109412.90416.422021-09
767202110-99.99-99.992021-10
768202111-99.99-99.992021-11
769202112-99.99-99.992021-12
\n", "

768 rows × 5 columns

\n", "
" ], "text/plain": [ " Yr Mn CO2 seasonally period\n", "2 1958 01 -99.99 -99.99 1958-01\n", "3 1958 02 -99.99 -99.99 1958-02\n", "4 1958 03 315.71 314.43 1958-03\n", "5 1958 04 317.45 315.16 1958-04\n", "6 1958 05 317.51 314.71 1958-05\n", "7 1958 06 -99.99 -99.99 1958-06\n", "8 1958 07 315.86 315.20 1958-07\n", "9 1958 08 314.93 316.20 1958-08\n", "10 1958 09 313.21 316.09 1958-09\n", "11 1958 10 -99.99 -99.99 1958-10\n", "12 1958 11 313.33 315.20 1958-11\n", "13 1958 12 314.67 315.43 1958-12\n", "14 1959 01 315.58 315.54 1959-01\n", "15 1959 02 316.49 315.85 1959-02\n", "16 1959 03 316.65 315.37 1959-03\n", "17 1959 04 317.72 315.42 1959-04\n", "18 1959 05 318.29 315.48 1959-05\n", "19 1959 06 318.15 316.02 1959-06\n", "20 1959 07 316.54 315.87 1959-07\n", "21 1959 08 314.80 316.07 1959-08\n", "22 1959 09 313.84 316.73 1959-09\n", "23 1959 10 313.33 316.33 1959-10\n", "24 1959 11 314.81 316.69 1959-11\n", "25 1959 12 315.58 316.35 1959-12\n", "26 1960 01 316.43 316.39 1960-01\n", "27 1960 02 316.98 316.34 1960-02\n", "28 1960 03 317.58 316.27 1960-03\n", "29 1960 04 319.03 316.70 1960-04\n", "30 1960 05 320.03 317.22 1960-05\n", "31 1960 06 319.59 317.47 1960-06\n", ".. ... ... ... ... ...\n", "740 2019 07 411.78 410.97 2019-07\n", "741 2019 08 410.01 411.56 2019-08\n", "742 2019 09 408.48 411.98 2019-09\n", "743 2019 10 408.40 412.02 2019-10\n", "744 2019 11 410.16 412.44 2019-11\n", "745 2019 12 411.81 412.74 2019-12\n", "746 2020 01 413.30 413.25 2020-01\n", "747 2020 02 414.05 413.28 2020-02\n", "748 2020 03 414.45 412.87 2020-03\n", "749 2020 04 416.11 413.29 2020-04\n", "750 2020 05 417.15 413.74 2020-05\n", "751 2020 06 416.29 413.73 2020-06\n", "752 2020 07 414.42 413.64 2020-07\n", "753 2020 08 412.52 414.10 2020-08\n", "754 2020 09 411.18 414.70 2020-09\n", "755 2020 10 411.12 414.75 2020-10\n", "756 2020 11 412.88 415.16 2020-11\n", "757 2020 12 413.89 414.82 2020-12\n", "758 2021 01 415.15 415.10 2021-01\n", "759 2021 02 416.47 415.70 2021-02\n", "760 2021 03 417.16 415.61 2021-03\n", "761 2021 04 418.24 415.44 2021-04\n", "762 2021 05 418.95 415.53 2021-05\n", "763 2021 06 418.70 416.11 2021-06\n", "764 2021 07 416.65 415.84 2021-07\n", "765 2021 08 414.34 415.90 2021-08\n", "766 2021 09 412.90 416.42 2021-09\n", "767 2021 10 -99.99 -99.99 2021-10\n", "768 2021 11 -99.99 -99.99 2021-11\n", "769 2021 12 -99.99 -99.99 2021-12\n", "\n", "[768 rows x 5 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Avec isoweek, on définit une période pour chaque date de mesure.\n", "\n", "final_data = num_data.copy()\n", "\n", "ind_min = min(final_data.index)\n", "ind_max = max(final_data.index)\n", "\n", "\n", "final_data['period'] = [pd.Period(freq ='M', year=int(final_data['Yr'][k]), month=int(final_data['Mn'][k])) for k in range(ind_min, ind_max+1)]\n", "final_data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnCO2seasonally
period
1958-01195801-99.99-99.99
1958-02195802-99.99-99.99
1958-03195803315.71314.43
1958-04195804317.45315.16
1958-05195805317.51314.71
1958-06195806-99.99-99.99
1958-07195807315.86315.20
1958-08195808314.93316.20
1958-09195809313.21316.09
1958-10195810-99.99-99.99
1958-11195811313.33315.20
1958-12195812314.67315.43
1959-01195901315.58315.54
1959-02195902316.49315.85
1959-03195903316.65315.37
1959-04195904317.72315.42
1959-05195905318.29315.48
1959-06195906318.15316.02
1959-07195907316.54315.87
1959-08195908314.80316.07
1959-09195909313.84316.73
1959-10195910313.33316.33
1959-11195911314.81316.69
1959-12195912315.58316.35
1960-01196001316.43316.39
1960-02196002316.98316.34
1960-03196003317.58316.27
1960-04196004319.03316.70
1960-05196005320.03317.22
1960-06196006319.59317.47
...............
2019-07201907411.78410.97
2019-08201908410.01411.56
2019-09201909408.48411.98
2019-10201910408.40412.02
2019-11201911410.16412.44
2019-12201912411.81412.74
2020-01202001413.30413.25
2020-02202002414.05413.28
2020-03202003414.45412.87
2020-04202004416.11413.29
2020-05202005417.15413.74
2020-06202006416.29413.73
2020-07202007414.42413.64
2020-08202008412.52414.10
2020-09202009411.18414.70
2020-10202010411.12414.75
2020-11202011412.88415.16
2020-12202012413.89414.82
2021-01202101415.15415.10
2021-02202102416.47415.70
2021-03202103417.16415.61
2021-04202104418.24415.44
2021-05202105418.95415.53
2021-06202106418.70416.11
2021-07202107416.65415.84
2021-08202108414.34415.90
2021-09202109412.90416.42
2021-10202110-99.99-99.99
2021-11202111-99.99-99.99
2021-12202112-99.99-99.99
\n", "

768 rows × 4 columns

\n", "
" ], "text/plain": [ " Yr Mn CO2 seasonally\n", "period \n", "1958-01 1958 01 -99.99 -99.99\n", "1958-02 1958 02 -99.99 -99.99\n", "1958-03 1958 03 315.71 314.43\n", "1958-04 1958 04 317.45 315.16\n", "1958-05 1958 05 317.51 314.71\n", "1958-06 1958 06 -99.99 -99.99\n", "1958-07 1958 07 315.86 315.20\n", "1958-08 1958 08 314.93 316.20\n", "1958-09 1958 09 313.21 316.09\n", "1958-10 1958 10 -99.99 -99.99\n", "1958-11 1958 11 313.33 315.20\n", "1958-12 1958 12 314.67 315.43\n", "1959-01 1959 01 315.58 315.54\n", "1959-02 1959 02 316.49 315.85\n", "1959-03 1959 03 316.65 315.37\n", "1959-04 1959 04 317.72 315.42\n", "1959-05 1959 05 318.29 315.48\n", "1959-06 1959 06 318.15 316.02\n", "1959-07 1959 07 316.54 315.87\n", "1959-08 1959 08 314.80 316.07\n", "1959-09 1959 09 313.84 316.73\n", "1959-10 1959 10 313.33 316.33\n", "1959-11 1959 11 314.81 316.69\n", "1959-12 1959 12 315.58 316.35\n", "1960-01 1960 01 316.43 316.39\n", "1960-02 1960 02 316.98 316.34\n", "1960-03 1960 03 317.58 316.27\n", "1960-04 1960 04 319.03 316.70\n", "1960-05 1960 05 320.03 317.22\n", "1960-06 1960 06 319.59 317.47\n", "... ... ... ... ...\n", "2019-07 2019 07 411.78 410.97\n", "2019-08 2019 08 410.01 411.56\n", "2019-09 2019 09 408.48 411.98\n", "2019-10 2019 10 408.40 412.02\n", "2019-11 2019 11 410.16 412.44\n", "2019-12 2019 12 411.81 412.74\n", "2020-01 2020 01 413.30 413.25\n", "2020-02 2020 02 414.05 413.28\n", "2020-03 2020 03 414.45 412.87\n", "2020-04 2020 04 416.11 413.29\n", "2020-05 2020 05 417.15 413.74\n", "2020-06 2020 06 416.29 413.73\n", "2020-07 2020 07 414.42 413.64\n", "2020-08 2020 08 412.52 414.10\n", "2020-09 2020 09 411.18 414.70\n", "2020-10 2020 10 411.12 414.75\n", "2020-11 2020 11 412.88 415.16\n", "2020-12 2020 12 413.89 414.82\n", "2021-01 2021 01 415.15 415.10\n", "2021-02 2021 02 416.47 415.70\n", "2021-03 2021 03 417.16 415.61\n", "2021-04 2021 04 418.24 415.44\n", "2021-05 2021 05 418.95 415.53\n", "2021-06 2021 06 418.70 416.11\n", "2021-07 2021 07 416.65 415.84\n", "2021-08 2021 08 414.34 415.90\n", "2021-09 2021 09 412.90 416.42\n", "2021-10 2021 10 -99.99 -99.99\n", "2021-11 2021 11 -99.99 -99.99\n", "2021-12 2021 12 -99.99 -99.99\n", "\n", "[768 rows x 4 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_data = final_data.set_index('period').sort_index()\n", "final_data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "for k in [2,3]:\n", " final_data[final_data.columns[k]] = final_data[final_data.columns[k]].astype(float) \n", "final_data.drop( final_data[ final_data['CO2'] <0 ].index, inplace=True)\n", "final_data.drop( final_data[ final_data['seasonally'] <0 ].index, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Présentation graphique des données\n", "\n", "Cette section présente les variations brutes de CO2 mesurées, ainsi que les mêmes données corrigées des variations saisonières.\n", "\n", "- On observe une tendance globale à la hausse de la quantité de CO2 dans l'atmosphère\n", "- On observe des variations saisonières autour de cette tendance globale" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "final_data['CO2'].plot(label = 'Données brutes')\n", "final_data['seasonally'].plot(label = 'Données corrigées des variations saisonières')\n", "plt.xlabel('Période')\n", "plt.ylabel('Quantité de CO2 en ppm')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Caractérisation de la période des oscillations\n", "\n", "On cherche à caractériser la période de ces oscillations. Pour ce faire, on divise les données CO2 brutes par les données corrigées des variations saisonières. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "oscillations = final_data['CO2']/final_data['seasonally']\n", "oscillations.plot(label = 'Données brutes')\n", "plt.xlabel('Période')\n", "plt.ylabel('Oscillations saisonières : variations relatives')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut estimer la période caractéristiques des variations saisonières" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEKCAYAAAAvlUMdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvXmYXGWZ8P27q5fqfUl3dbbOnkAIEJIAYU1AUEBHB8EZhfncUHH4BMVlcNRZ1Hlfv9dx3mFGRWVEGWRGEEeGAZVxEgUSVs0eSNJblk6600v1XtVLdVfX/f1xTnUqnV5Od9epqnOqftd1ru4661Onn37u+7m3R1SVDBkyZMiQId54kt2ADBkyZMjgTjICJkOGDBky2EJGwGTIkCFDBlvICJgMGTJkyGALGQGTIUOGDBlsISNgMmTIkCGDLWQETIYMGTJksIWMgMmQIUOGDLaQETAZMmTIkMEWsqc7QUS+BfxvYBD4DXAJ8FlV/Xeb22Y7lZWVunz58mQ3I0OGDBkcxZ49ezpU1TfdedMKGOAmVf2iiNwGNAF/CrwIOF7ALF++nN27dye7GRkyZMjgKESk0cp5VkxkOebPdwFPqmrXrFuVIUOGDBnSBiszmF+KSA2GiexTIuIDhuxtVoYMGTJkcDrTzmBU9UvAVcBlqjoCDAC32t2wDBkyZMjgbKYVMCJSANwL/MDctQi4zM5GZciQIUMG52PFB/OvwDBwtfm5CSOqLEOGDBkyZJgUKwJmlap+CxgBUNVBQGxtVYYMGTJkcDxWBMywiOQDCiAiq4CQra3KkCFDhgyOx4qA+RpGguUSEfkp8Dvgi3Y2ysnsrPPjD2Tk73S0B4Z4dn9zspvhCE52DtA3NJLsZqQ8kYhyvKM/2c1wBJGIJuQ5VqLItgG3Ax8FnsSIJnvJ3mY5k6buAT786B/47gv1yW5KyvPgtjru/9l+TnYOJLspKc3g8Cjv/u7L/J/njyS7KSnPT3/fyI3/+BKNnRkhMx13PbaL//Wrw7Y/x0oU2XPATcBLqvorVe2wvVUO5b/2GRr5Kw2ZVzQVQyOj/PpgCwCvH8u8q6nYfqSNvqEwL9X6UU2M1ulUfrGniYjCjjp/spuS0jT3DLKjzk9pfs70J88RKyayfwS2AIdF5D9E5E9EJM/mdjkOVeU/9zUjAsf8/bT0Dia7SSnLtsNtBEJhsjzCa0c7k92clOaZvU0AtPQOcdSf0cwn46g/yIGmXgBers8oLVMRVYTfu2Gx7c+yYiLboaqfAlYCPwTeD7Tb3TCncaCpl2P+fj5y1XIAXm3IDJyT8fSeJhaX5fOuixfy2tHOjGY+CR3BEDvrO/ij9QsBeKU+o5lPxrP7mvEI3Li2ijeOdhIejSS7SSmJqvLMvmYuW1bO0ooC259nqVy/GUX2PuAe4HLgJ3Y2yok8s7eJ3GwPn3v7ecwrzOW1jJlsQtr6hni53s9tGxdzzaoK/IEQR/3BZDcrJfnVgdOMRpT7b1zD0nkFGdPrJKgqz+xv5prVldy2aTGBUHhsNpPhbN5q7qOhPchtm+yfvYA1H8xTwBHgBuB7GHkxn7a7YU5iZDTCLw+28I4L5lNakMPVqyp4paEjo5lPwH/tayaicPumxVy9qhIgYyabhGf2n2bdwhLOm1/MtWsqeeNYFyMZzfwc9jR2c6pr0FRaKhGBVzJmsgl5Zl8zuVke3n3xooQ8z2om/ypVvUdVX1DVTA8fx45aP139w9xuagXXrK6kPaOZn4Oq8vTeJjYuLWOlr4gl8/JZXJbPaxlz4jkc9Qc5cKqH2zYafWrL6kqCoTD7T/UkuWWpxzP7msnPyeLmCxdQXpjLRYtKeaUhY04cT3g0wnMHTnPD2ipKC+x38MMUAkZEbjB/LQBuFZHbY7eEtM4h/Oe+JioKc9l6nrH+zjWmZp7xw5zNodN91LUFed+magBEhKtXVfDG8c6ExeU7hahP4Y83GJrm1asq8UjGgT2e4XCEXx1s4aYL51PoNYrDX7umkn0newiGwkluXWrxckMHHcFQwsxjMPUM5jrz53sm2N5tc7scQ+/gCL890s57LllETpbxOpdWFLBkXn7GZj6OX+wx/FTvWX9men716gp6BkY40tqXxJalFrE+hfklRsBmaUEOF1eXZRz943iptp3ewRHeu/HMoLlldSXhiPJGxvR6Fs/sbaasIIe3nV+VsGdOKmBU9avmr3+nqnfFbsD/SkzzUp/n32xhOBwZM2VEuWZVJW8cy0SzRBkOG9PzqJ8qylUrjdne65nBYIy9Jw2fwvgw0i2rKznQ1JvJ6o/hv/Y3U1GYy5bVlWP7Ll1eTl6OJ6PgxRAMhdl2uJV3r19Ibral2K64YOVJT0+w7xfxbohTeWZvM6t8hayvLj1r/9WrKwkMhXnrdEYzB0PT7Oof5n2Xnj1oLijNY2VlYcbRH8N/7m0mL8fDzRctOGv/tWsqGc1o5mPEWg+ys84MZd7sLDavqODlzGxvjP9+s4WhkQi3baxO6HOn8sGsFZH3AaXj/C8fBTKJlsCprgH+cKKL2zdVI3J2gemrV1UA8GpGiwLg6b1NVBblsmWN75xjV62q4PfHOjMRUsT4FNYtoMh79oKzm5aWU5CbldHMTX7zlmE9uH0Cn8KW1ZUczSQ8j/HMvmaWVRSwaWlZQp871QzmfAxfSxln+182AXdPd2MReVRE2kXkrUmOi4h8R0QaROSgiGyKOXaLiNSax74Us/8pEdlvbidEZL+5f7mIDMYce9jKl58rz5gZsbduODfkr7LIy9oFxRkBA3T3D/NCTTu3blg85qeK5epVlfQPj/JmcyZ3IepTmMgRm5vt4YoV8zIhuCbP7Gtmpa+QixeXnnPs2jWGySwTFAEtvYO8fqyT925YfI4ibDfZkx1Q1WeBZ0XkKlV9fRb3fgx4CHh8kuPvBNaY2xUYK2ZeISJZGPk278BY3GyXiDynqodV9QPRi0XkH4HYEemoqm6YRTtnRTQj9ooV86gunzgj9prVlfzbG40MjYySl5OVqKalHL88eJqRUR2LHhvPlSvnAYYfZtPS8kQ2LeWYyKcQy7VrfLxYe5jmnkEWl+UnuHWpQ3PPIG8c6+IL7zhvwkFz7YJiKou8vFLfwfsvW5KEFqYOz+4/jSrn+IkTgRUfzD4RuVdEvm/OSh4VkUenu0hVdwJdU5xyK/C4GrwBlInIQmAz0KCqx1R1GPiZee4YYvSo92NUd04K+0/1cLyjf9JBE+Da1ZUMhyPsPtGdwJalHk/vaeKChSWsW1Qy4fEKc7aX7o7+yXwKsWwxNfN0jyZ7bv9pAG6dpJ6WiHDt6gpebehI6xB4VeWZvc1sWlrG8srChD/fioD5N2ABcDOwA6gGAnF49mLgVMznJnPfZPtj2QK0qWpsXfwVIrJPRHaIyJY4tG9K/nNvM95sD++8eMGk52xeMY9sj/Dq0fSdpje0BzjQ1Mv7pom9v2pVBbtOdBEKjyaoZanHf08SkRjLmqoiqoq9aW36MawHTdPW07p2jY/O/uG0DoE/3NJHbVuA26ZQhO3EioBZrap/A/Sr6k+APwIujsOzJzIG6hT7Y7mTs2cvLcBSVd0IfB54QkQmVJdF5JMisltEdvv9s9MCh8MRfnnwNO9YN5/ivMkzYgu92WxYUpbWdcme3ttMlkcm1TSjXL2qklA4wr6T6Zup/sy+ZlZWnhuRGIuhmVfy2tH0TU490hKgri14Vu7LRFy7OjrbS9//v2f2NpOTJbz74oVJeb4VARMNuu8RkYuAUmB5HJ7dBMQaR6uB01PsB0BEsjEWQHsquk9VQ6raaf6+BzgKnDfRQ1X1h6p6mape5vOdG9Fkhd2NXfQMjEwYvTKeq1dXcrC5l96B9Mxd+MPxLjYtLcNX7J3yvM0r5uGR9K1L1h4Y4vfHu3jvxukdsdeuqaSrf5jDLempmf/y4GlysoQ/mmbQXFCax5qqorSNuhuNKM8eOM3bzq+ivDA3KW2wImB+KCLlwN8AzwGHgW/F4dnPAR82o8muBHpVtQXYBawRkRUikgvcYZ4b5e1Ajao2RXeIiM8MDkBEVmIEDhyLQxsn5OpVlbzwhesmDLkdz7WrK1GF14+l38CpqtS2Brhg4cS+l1hK83O4eHFp2uZ4HGo2hMWVKyumPTeqmaermezAqR7WLSq1NGheu6aSPxzvYmgk/UyvJzr78QdCvH3d/KS1wcp6MD9S1W5zXZiVqlqlqtOGAYvIk8DrwPki0iQiHxeRe0TkHvOU5zGEQAPwCPAp83lh4D7gfzCqOP9cVQ/F3PoOznXubwUOisgBjCTQe1R1qgCDObPSVzRhyO14NiwpIz8ni9fS0A/T1D1IMBTm/AXFls6/alUl+051MzCcfjWkaloNt6aVd1VVksf584vTsqCjqlLTGmDtfGt9assaw/S6pzH9Am1qWow+tc6CgmcXk4Ypi8jnp7pQVR+c5vid0xxX4N5Jjj2PIYAmOvbRCfY9zcQVB5JObraHzSvmpeU0vdYcNNcusNbBr1pVwcM7jrL7RPdY4dB0oaa1j8Vl+ZaXsb12TXqGwPuDIbr6h1m70JqAuWJFBTlZwsv1HVwzSei3W6lp7SPLI6yuKkpaG6ZSwYun2TJY5NrVlRzz99PaO5TspiSU2jbrWjnA5cvLyU7TZZRrWgKW3xMYAmY4HGHXCVsn6ilH7QxmemAE2mxcWp6Ws70jLQFWVhYmVQGZKtHy64lsiJu5evWZsjHvuzQ54YLJ4EhLH9Xl+eeUPJmMgtxsNi4t4/U0MycOhyMc9Qe58QLrVW6vWDGP3CwPr9R3WPIFuoWo2cfqrBiMsjEP/raOrv5h5iXJ2Z0Malr72JjkxGUrK1qeJyK/i5Z8EZH1IvLX9jfNPVywoIR5hblplw9T2xqY0UAAhh/mzeb0qhh81B8kHFHWzsBWXpCbzYalZfz+eHrNYGpaA1QVe2ckKK42A23+kEbvqm9ohKbuQdbOYFZsB1aiyB4BvowZrqyqBzEc7Rks4vEIFy4qoaE9fVa4DIVHOdbRP+MOfsWKeUTUiBRKF2rMRMCZvqvVVUWc6hqwo0kpS01r34xMicCYDyKd3lWdaUq8wKKvyi6sCJgCVf3DuH3pF+YzR5bMK6CpO30quza0BxmNqGVnbJRoOYtTXenzrmpaA+RmeVgxw1Ie1eX5dPYPp03UXXg0Qn170FLYeyyl+TmU5GXT1J0+AubIDANs7MKKgOkQkVWY2fQi8icYmfMZZkB1eT5d/cP0p8kyrmds5TMTMPOLvWR7hOae9BkMaloCrK6yFvYeS7TIanOaKC4nOgcYDkc432KIciyLy9NLwatp6aMkL5uFpcldWcVKj74X+BdgrYg0A58F7pn6kgzjGRsMetKjk9e2BcjN9rC8YmZaeXaWh4Vleek1GLT2zcpWHq2mnC7vasyUOAuzT3V5ftq8JzBmxWsXliS8PP94phQwIuIBLlPVtwM+YK2qXquqjQlpnYuoLo8OBumhmde0BlhTVTRpVeCpqC5LH22zu3+Ytr7QrAbNJWnWp2pbA7PO6zAEzABG+p27iUTMChpJdvDDNAJGVSMYWfWoar+qxqOKclpyRsCkx8BZ0zJzZ2yU6GCQDtTMwVZeWeQlN9uTNn3qSEuAFZWFeLNnntdRXV5A//AoPWlQE7C5x6igMZOoRLuwol5uF5G/EJElIjIvutneMpfhK/LizfakRSRLV/8w7YEQF8zSwVhdXkBbXygtSvfPNoIMjOjE6rL0Mf3Uts3OlAjppeAdaZl9n4o3VgTMxzD8MDuBPea2285GuRERYXGa2IGjg+ZsZzCLzcHgdI/7Kx/UtgaYV5g7bbXpyVicJrO9YCjMqa7Z53VEBUw6BI/UtAYQgfNmEQwRb6ZNsVbVFYloSDpQnSaRLGdqkM1V2xyYceiu0zjSGmDtguJZO2OrywvYdro1zq1KPWZa12481WVGkE06/P/VtPaxbF4BhRYraNjJzD2wGWZNuvgW5qqVp4s5IxJR6mZR7SCWdMmFmWkNsvGU5GdT7M12fZ8CI+w92fkvUTICJoFUl+fTPTBC0OW5MEdaA5w/f/Za+YKSPLI84nphfLJrgMGR0TnZyqvHzInuHjhrWvso8maPfd+ZcsZE7e4+NTg8yvHO/llFJdpBRsAkkHRIjItq5bPVNMHMhSl1fy7MXPI6okT71CnXvyujT80lryMdTNR1bQFUk5/BH8VKsctrRKTQ/P2DIvKgiCyzv2nuIx3yFqJa+VxrIKVDYtyRlgAegTVVc5/BuPldqeqcwt6jRPuUm3Nh5ur/jDdWZjA/AAZE5BLgi0Aj8LitrXIpUW3TzYPBmZUZ56ZBVZcXuHqmB8ZgsLyikPzc2a/X4SvykpvlcbXS0to3RN9QeM6Jg9Xl+QRDYXoH3ZsLc6S1j/ycLJbOK0h2UwBrAiZsrj55K/BtVf02mQXHZkVlUS7ebHcPBjWtfWaI5NxW0asuz6ctMOTqXJia1r4528o9HveHv8dPaXH/bC+6cJ3Hk9wSMVGsCJiAiHwZ+CDwaxHJAqZd11VEHhWR9ug6MhMcFxH5jog0iMhBEdkUc+wWEak1j30pZv/XRKRZRPab27tijn3ZPL9WRG628L0Sjoi43vRT2xpg2bwCCnLnFiJZXV6AKrS4NBdmYDhMY9dAXGzlbu9T0cKpczeRuduCoKrUtPYlvUR/LFYEzAeAEPBxVW0FFgP/YOG6x4Bbpjj+TmCNuX0SwxSHKcC+Zx5fB9wpIutirvsnVd1gbs+b16zDWKPmQvOZ3zfvk3K43dFYO0cHfxS3a5t1bUFU5z5ogvGuml08K65t7WNRaR6l+dPqtVPi9nqA7YEQ3QMjKePgBwsCRlVbVfVBVX3Z/HxSVaf1wajqTmCqJeRuBR5XgzeAMhFZCGwGGlT1mKoOAz8zz52KW4GfqWpIVY8DDeZ9Ug4358KMhUjGSSsH9w4GNWY5j9mW04mluryAjuAwg8PuNCfWxElpKc3PocjFuTCpVCImipUosttFpF5EekWkT0QCItIXh2cvBk7FfG4y9022P8p9pkntURGJLjg93TUpQ3V5gWtzYerboyGSc+/gZ3Jh3DkY1LQGKMzNmnVeRyxuLoMyMhrhqD8Yl8KNbjdRz6Vwql1YMZF9C/hjVS1V1RJVLVbVeHyDibxQOsV+MMxoq4ANGIue/eM09zr3oSKfFJHdIrLb7/fPrMVxYGwwcGEnH1tkLA6DQXaWhwUlee6dwbT2cV6cnLHRPuXGXJhj/n5GRjVuWrmbLQg1LaYpsWBupsR4YkXAtKnqERue3QQsiflcDZyeYj+q2qaqo+YyAo9wxgw26TXjUdUfquplqnqZz+eLyxeZCW42/dS0BsjL8cQtRNKt2qbhjI1fOQ83O6/PVJuO37tyo3IHZxYZSyWsCJjdIvKUiNxpmstuF5Hb4/Ds54APm9FkVwK9qtoC7ALWiMgKEcnFcN4/B2D6aKLcBrwVc687RMQrIiswAgf+EIc2xh03Dwa1bX2cN7+YrDiFSLo1IKKtL0TPwEjcon2iuTBuHDhrWgPkZAkrffEperq4LJ+AC3NhhsMRGtqDKeV/AQvVlIESYAC4KWafAv851UUi8iRwPVApIk3AVzHDm1X1YeB54F0YDvkB4C7zWFhE7gP+B8gCHlXVQ+ZtvyUiG8znnwD+3LzmkIj8HDgMhIF7VTUlPZ5uzoWpaQlw4wVVcbtfNBdmOBwhN9s9VY3GljOIUzl1j0dYVOZOc2JNSx+rfEXkzGJl1ImItSCU5pfG5Z6pwFF/kHBEU24GY6Vc/12zubGq3jnNccVYZ2aiY89jCKDx+z80xf2+AXxjhs1MOFFH46kud2mb/kCIzv7hOSfDxVJdnm/kwvQOsqzCPWX77XDGunW2V9saYPOK+K1vGGtBuHCRewRMVGlJhWWSY7ESRVYtIs+YSZNtIvK0iFQnonFuZcm8AppcFvFjRwd3qznRDmesG/1VvQMjnO4dirvSAm7sUwFyszwpt36SlXnnv2L4OBZhhP7+0tyXYZa4cTCY63odE+HWgIh45XXEUl2eT0cwxNBISlqGZ0VtWzQqMX7vqqwgh8LcLNf1qSOtAdbMLyI7TqbEeGGlNT5V/VdVDZvbY0Diw69cRHV5AT0DIwSG3ONoPNISoLLIS0XR7BYZm4iFpe7LhRkOxy+vIxY3zvZqW+OfOGiYqN1nTqxp6Uup/JcoVgRMh1mmP8vcPgh02t0wN3MmMc49nby2Lf41kM7kwrjnPR3rCMY1ryOKG2d7R1oDlObnsKAkL673dVtx0M5giPZAKKVqkEWxImA+BrwfaMVIbvwTc1+GWTKmbbrE0d83NMKRlgDrq+PvNHVbYtybTb0AXJCZwUzLEXMNmLksMjYRbqvdduh0fHOF4omVWmQnVfWPVdWnqlWq+l5VbUxE49yK27TN1xo6GY0oW9bE33LqNnPGy/Ud+Iq9rKma23IG46kq9pKT5R5zYu/gCAebetm8PH4RZFGqy/PpG3JPLszL9X5yszxsWlaW7Kacw6RhyiLyRVX9loh8lwnKrqjqZ2xtmYupKMwlL8fjmsFgZ72fwtwsNi0tn/7kGbK4PJ/WPnfkwoxGlJ31fm5cOz/uWrmRC+Oe2d4r9R2MRpTrz7dHaQGjXNNcKzSnAi/V+tm8Yt6cl8iwg6laFC0PszsRDUkn3ORoVFV21vm5alWlLQLATbkwB5t66BkY4TobBk0wTT8u8eu9WNtOaX4OG5bEXyuPtSCsW5R6ZqWZ0NwzSH17kA9cvmT6k5PApAJGVX9p/jqgqv8Re0xE/tTWVqUB1eX5rsiFOdE5QFP3IH++daUt94/NW3C6gNlR50cEtqyutOX+1WUFvFDbbsu9E0kkorxU62fLmkpbwm7d5K/aUWsU7L3uvNQM7LXy1/uyxX0ZZoBbcmF21hkd3A7/C8CSscHA+cJ4R52fS6rLKC/MteX+1eX5+APOz4U53NJHRzDE286PX9mhWMoLcsjPyXLF/99Lte0sLstndZx9evFiKh/MOzFqhS0Wke/EHCrBqPeVYQ7E5sIU5znXDryzzs/SeQUstymDeEFpHh5x/vIG3f3DHDjVw6dvWGPbM6rnnQl/X+VLzQHHCi/WGLMwu0yJ0XJNTl8/Zzgc4dWGDm7duDjuPr14MdUM5jSG/2UI2BOzPQek5Jr3TsINuTDD4QivH+tk63n2mHwAcrI8LCx1/mzvlYYOImrfoAnuMf28VOdnfXUplXFM2h2PGywIuxu76B8e5foUNY/B1D6YA8ABEXlCVd0Rz5dCLInJhUnF+HUr7GnsZmB4lK02mceiuCExbkedn9L8HC6pti+U1A3h7939w+w72c19Ns70wBDGe0/22PoMu9lR6ycnS7jaJp9ePLDig1kuIr8QkcMiciy62d4yl+OGwWBnvZ9sj3DVqgpbn+P0ZEtVZUed4bSO11o5E1FVnOf4XJid9X4iii3hybFUl+fTOzhCn4PLNe2o83PZsnkUeVMvPDmK1WKXP8Dwu7wNeBz4NzsblQ7MK8x1vKNxZ52fTUvLbfchVZcXjOXCOJHDLX34AyGut8lpHSVrLBfGuX1qR62f8gJ7Z3pwdi6ME2npHaSmNWC7IJ4rVgRMvqr+DhBVbVTVrwE32Nss9xN1NDp1MPAHQhw63Wer/yVKdXk+EYXW3iHbn2UHO8xIu61rEvOunDrbi0SMmd515/lsnemB88v2R8OT7VZa5ooVATMkIh6gXkTuE5HbgNT+Vg6hujyfUw4dDF5t6ABgawIcjE43J+6o9bNuYQlVcS7aOBGLHTyDebO5l87+4YQMmosd3qdeqvWzsDSP8+andrSgFQHzWaAA+AxwKfAh4CN2NipdcHI2/846w5SRiFUBq8ucGx0VGBphT2O3rdFjsVSXFzg2F+bF2nZEEqO0RMs1OdFENjJqhCdff74vZcOTo1gpdrlLVYOq2qSqd6nq7ar6xnTXicij5iqYb01yXETkOyLSICIHRWRTzLFbRKTWPPalmP3/ICI15vnPiEiZuX+5iAyKyH5ze9ja108uTnU0RiLKzvoOrl1jvykDzuTCOFHbfO1oJ+GIJizTOjrbO+3A8PcXa/1sWFLGPJsSUWNxcrmmvY3dBEJhrjsv9Q1JkwoYEfln8+cvReS58ZuFez8G3DLF8XcCa8ztkxiBBIhIFvA98/g64E4RWWdesx24SFXXA3WcXVHgqKpuMLd7LLQv6TjV0VjTGqAjGEqITwEgN9u568LsqPNT5M22pRDoRDg1F6YzGOJgUw/XJ3DQdGq5ppfqjOjNa1bbG70ZD6aKb4tGiv3f2dxYVXeKyPIpTrkVeFxVFXhDRMpEZCGwHGhQ1WMAIvIz89zDqrot5vo3MNamcSyxjsZ4rw9iJzvrTad1AhO8qssLaHKYVq6q7Kj1c/WqioRVgnaq83pnvR9VeNvaRPapfPafcl4uzEu1fi5dZn/0ZjyYtNer6h5zNnG3qu4Yv8Xh2YuBUzGfm8x9k+0fz8eA/475vEJE9onIDhHZMtlDReSTIrJbRHb7/f7Ztz4OONV5vbPOz/nzi5mfAKd1FGORKGcNmkf9/TT3DCbM/wIwvySPbI84rk+9VOunsiiXixLg04vixKXL2/qGONLSl/LRY1GmVKtUdRTwiYgdRtGJjPc6xf4zF4r8FUZezk/NXS3AUlXdCHweeEJEJpwSqOoPVfUyVb3M50tuDLkTc2EGhsPsPtGdkPDkWKrL82npHWRk1Dm5MNHw5ERWunViLsyoGZ689TwfngT49KIsLnNeuaYz4cmpnf8SxUoK6AngVdPv0h/dqaoPzvHZTUDsIgbVGPXPcifZD4CIfAR4N3CjaV5DVUNAyPx9j4gcBc4jxdeyERGWzHNW3sLvj3UxPBpJqHkMDG0zmguzZF5BQp89W16qbWd1VdGYXyRROC0XZv8pY50cu6onT8aYBcFB5Zp21PmZX+Jl7YLiZDfFElYMw6eBX5nnFsdsc+U54MNmNNmVQK+qtgC7gDUissKcOd1hnouI3AL8JfDHqjr2HyQiPtOch4isxAgccEQ5G6dFsuyo8+PN9nC5DUvZTkV0MHBK3tDg8Ci/P96VlHU6nJbAu6O2HY/AlgQFjUQZC7JYJP2AAAAgAElEQVRxyAwmPBrh5XojETXVw5OjTDuDUdWvz+bGIvIkcD1QKSJNwFeBHPOeDwPPYywH0AAMAHeZx8Iich/wP0AW8KiqHjJv+xDgBbabL/gNM2JsK/B3IhIGRoF7VLVrNu1ONIvL8tl7sjvZzbDMzno/V6ysIC8nK6HPdVp01BvHOxkOR5IiYBaV5dMeCDlmmekXa42SQ2UF9ocnx1JZlIs32+OY2d6+Uz30DYUd438BCwJGRHzAF4ELgTGvrqpOWS5GVe+c5rgC905y7HkMATR+/+pJzn8aeHqq56UqVcVeegZGCIVH8WYndtCeKae6Bjjm7+fPNi9N+LMXlBpdzyn5HS/VtJOX42HzisTO9MAoegnQ2R9iYWl+wp8/E9r7hnizuZe/uOm8hD9bRKgq8eIPhBL+7NnwQk07WR7hmhSunjweK+rNT4EaYAXwdQyfzC4b25RWVJUYa150BIeT3JLp2Xa4DYC3XzA/4c/OzfZQXpBDRzD1BwNVZfvhNras8SV8pgfgKzb6lBMGzu1HjD71jnULkvJ8X5EXvwP6FMD2w21csWIepfmpH54cxYqAqVDVHwMjZojyx4ArbW5X2uCkwWDboVbOn19s2+qV0+Erdoa2eeh0H6d7h7hpXeIFMTirT20/3MbSeQVJq6nllD51zB+koT2YtD41W6wImGiQeIuI/JGIbMSI7MoQB3xFhjkj1Tt5d/8wu050cdOFyevgThkMth1qxSNwYxJmeuAcARMMhXmtoZN3rJufNKe1U/rUdtN68I4LkzPTmy1WwpT/t4iUAl8AvguUAJ+ztVVphFMGg9/VtBNReEcSNShfkZc9DgiI2Ha4jcuWz0tITa2JqCwynpvqfWpnnZ/h0UiS+1Qe3QMjKR8Qsf1wGxcuKhnL3XEKVt7o71W1V1XfUtW3qeqlqmqlFlkGC1Q4ZDDYdqiVBSV5XLw4cZnW44lqm2b6U0rS2NlPTWsgqaYMb3YWZQU5tKd4n9p+uI3yghwuW5aYOm0TccYHmrrvyh8IsedkNzclyU81F6wImNdEZJuIfFxEktcTXEpOlod5hbn4g6m7mNbg8Cg76/3cdGHyTBlgCJihkQjBUDhpbZiOqCkj2YOBryi1TT8joxF+d6SNG9bOJzsreTMHX1HqWxBeqGlDk2w9mC1WyvWvAf4aI0x5j4j8SkQ+aHvL0ohUHwxeaehgaCSS/EHTAebEbYfaWLugmKUVya024CtO7eioXce76BsKJ33QdEqfqi7P54KFzsjej8WS6qCqf1DVzwObgS7gJ7a2Ks1IdUfjtkOtFOdlc8XKxOd0xJLqAREdwRC7G7u4KQUcsSnfpw634c32JLym3XjGBEyKCuP+UJiXGzqSGggxF6YVMCJSIiIfEZH/Bl7DKCy52faWpRGprG2GRyP89kgbN6ytIieJpgw4Yy9P1Xf1whEjECIVQkmjs+JU9FdF84SuXV1JQa6VOCP7qExxE9nL9X6Gw8m3HswWK3/dA8B/AX+nqq/b3J60JNZ5nWpayp7GbroHRlKig6e6vXzb4VYWl+Vz4aLkF070FXsZHBmlf3iUIm9yB/HxHGkJ0NwzyGdunLAwR0KJJvC2B1LTB7rtcBtlBTlcvtyZ7m8rPW+lpqIa5CJ8RYbzOhAKU5JiiwhtP9xGbpYnoWuaTEZpfg45WZKSAqY/FGZnfQd/tnlpSigJsb6FVBMw2w63IgI3rE3+TA9S15wYHo3wQk07N6ytSmogxFyw4uTPCBebSVVHo6qy7XAb16yuSIlByuMRKou8KRl+O2bKSGIiaiyp2qfAUFo2LS0fa2OySVUBs+tENz0DIylhcp0tzhSLLqMqRQeD2rYAJ7sGklYnaiJSdTDYdqiN0vwcNid4GYPJSFUB09wzyKHTfUmPHoslVeuRbTvcagZCJN96MFsyAiYFSNXBYNuhNkTg7etSpzx4KoZ0j4xG+F1NOzdekDqmjDP+qtTyLfw2WvIklQRMCibwplIgxFyw9N8gIu+e6nOGuZGqAmb74TY2LikbK/+eCqRixN2u4130DqZGIESU8oJcsjyScu9q++E2VvoKWeVLTnHLiYgm8AZSKIH3SEuApu7BlDG5zharovFyjFUtJ/vsGkZGRmhqamJoKLGa349uXUiRN8CRI0cS+tzJGI0on7m0gNL87JRpE8BtK+DtC+dx+PARpvKl5+XlUV1dTU6O/UETqZLTEYvhr8pNKaWld3CEN4518oktK5PdlLOIKlD+QChlgmy2H25LqUCI2WJJwKjqV6f67CaampooLi5m+fLliY0GaumjyJudMuvNdwRDRHoGOX9+Md4krGkyGR3BEKd7BlmzsGTSvBxVpbOzk6amJlasWGFre1SVbYda2bLGl3KmjFTzV71U2044oillHoOzLQipMrPadriVS1MoEGK22GYwFpFHRaRdRN6a5LiIyHdEpEFEDorIpphjt4hIrXnsSzH754nIdhGpN3+Wxxz7snl+rYjcPNt2Dw0NUVFRkfBQ05wsIRxJHRtw3+AI3uyslBIuADke4+8SHp38XYkIFRUVCZmFjq39koKmjFRzXm873EZlkZeNS8qS3ZSzSDUTdSoGQswWOz2SjwG3THH8ncAac/sk8AMAEckCvmceXwfcKSLrzGu+BPzOrI/2O/Mz5vE7MOql3QJ837zPrEhGHkO2x0N4NJLw5wKEw2EeeughQiHjHyw8GqE/NEpJfmpp5MCYEz0cmfpdJepvOLb2y9rUCYSIkkozmFB4lB21ft5+QRUeT/LzhGJJtQTe7YdaAVKi5NBcsU3AqOpOjLplk3Er8LgavAGUichCjDI0Dap6TFWHgZ+Z50avidZB+wnw3pj9P1PVkKoeBxpwWDmb7CxhxJzBZGVlsWHDBi688EIuueQSHnzwQSLTDKizRVX57Gc/y/r16/F6jX+0YCiMorOyRxcVzc3EcOLECZ544olJj2dnGYPTyBQzmETyYq2fS5eVU1GUeqYMX7GXjuAwkRSYGe9p7CYYCidlue3pKCswE3hTZLb3Yq2fVb5CViRp5dh4YqUW2TUiUmj+/kEReVBElsXh2YuBUzGfm8x9k+0HmK+qLQDmz6jaONU1jiDb42F0NIKqkp+fz/79+zl06BDbt2/n+eef5+tf/7otzxURHnroIbZu3Tq2b2B4FI8I+bn2mMfC4cmjdaYVMB5rM5hEMDAc5nBLH5tXpEbuy3h8RV5GI0r3wHCym8KeE8ZCcZen4LsSEXxFXtr7ki9gIhFl78luNq+oSHZT4oKVGcwPgAERuQT4ItAIPB6HZ080T9Yp9s/mXueeKPJJEdktIrv9fv80t00cOVmCwjl+mKqqKn74wx/y0EMPoaoMDQ1x1113cfHFF7Nx40ZefPFFAB577DFuv/12brnlFtasWcMXv/jFsXsUFRXxV3/1V1xyySVceeWVtLUZuQh+v5/3ve99XH755Vx++eW8+uqrAHR09/H1B+7jis2b2bhxI88++ywAhw4dYvPmzWzYsIH169dTX18/4Xf5whe+wKZNm7jxxhuJvuPrr7+er3zlK1x33XV8+9vf5qMf/Si/+MUvzmojwJe+9CVefvllNmzYwD/90z8xOjrKAw88wOWXX8769ev50SM/xCNCc/Nptm7dyoYNG7jooot4+eWX4/BXmBkHTvUyGlEuTeKCWVPhi0ZHpYBmvudkN+fNL6I0PzWitMaTKuHvDf4ggaFwyvapmWJFwITNcjG3At9W1W8D8ViYoAlYEvO5Gjg9xX6ANtOMhvmzfZp7nYOq/lBVL1PVy3y+1MmQjZp+JvLDrFy5kkgkQnt7O9/73vcAePPNN3nyySf5yEc+MubM3r9/P0899RRvvvkmTz31FKdOGZO6/v5+rrzySg4cOMDWrVt55JFHALj//vv53Oc+x65du3j66af5xCc+QSSifOfBb3Hd9W9j165dvPjiizzwwAP09/fz8MMPc//997N//352795NdXX1OW3t7+9n06ZN7N27l+uuu+6smVdPTw87duzgC1/4wqTv4Zvf/CZbtmxh//79fO5zn+PHP/4xpaWl7Nq1i127dvHII4/Q1tzI0z9/iptvvpn9+/dz4MABNmzYMMs3P3v2mss3b1ySmoNBqjivIxFlb2N3Sg+aqeKv2tto9KlNS1MrEGK2WPHiBkTky8CHgC2m8zweashzwH0i8jPgCqBXVVtExA+sEZEVQDOG8/7PYq75CPBN8+ezMfufEJEHgUUYgQN/mGsDv/7LQxw+3TfX25zFukUlfPU9F56z/4zpZ+LJWjTL+JVXXuHTn/40AGvXrmXZsmXU1dUBcOONN1JaaixpvG7dOhobG1myZAm5ubm8+91Gbuyll17K9u3bAfjtb3/L4cOHx57R19dHe1cPr+94gdde+A0/+v53ACOy7uTJk1x11VV84xvfoKmpidtvv501a9ac006Px8MHPvABAD74wQ9y++23jx2L7p8J27Zt4+DBg2Oznd7eXk6dOM6FGzbyN5+/j5GREd773vcmR8A0drPSV0h5YW7Cn22FVClBdNQfpG8ozKalqS1g9p/qTXYz2NPYTXlBjiv8L2BNwHwAY4D/mKq2ishS4B+mu0hEngSuBypFpAn4KqZgUtWHgeeBd2E45AeAu8xjYRG5D/gfIAt4VFUPmbf9JvBzEfk4cBL4U/OaQyLyc+AwEAbuVdVRC98tZciZwnl97NgxsrKyqKqqmrKcRdRJD0agQNTXkZOTMxZVFbs/Eonw+uuvk5+fP3adPzCEovz8P37BResuOOv+F1xwAVdccQW//vWvufnmm/nRj37EDTfcMOX3io3mKiw880+TnZ09FrigqgwPT+wnUFW++93vcvPNZyLPGzv7GRqJsHPnTn7961/zoQ99iAceeIAPf/jDU7YlnqgatvJUdFpHic5gkl0cdI+plaf0DKbIS1d/iNGIkpXEKLe9J7vZtLQ8JSpyx4NpBYwpVJ7GmBUAdADPWLjuzmmOK3DvJMeexxBA4/d3AjdOcs03gG9M166ZMNFMwy4mc177/X7uuece7rvvPkSErVu38tOf/pQbbriBuro6Tp48yfnnn8/evXtn/MybbrqJhx56iAceeAAwTGxl1WvYcv2NPPz97/Hd734XEWHfvn1s3LiRY8eOsXLlSj7zmc9w7NgxDh48eI6AiUQi/OIXv+COO+7giSee4Nprr53w2cuXL2fPnj28//3v59lnn2VkZASA4uJiAoHA2Hk333wzP/jBD7jhhhvIycmhrq4OKZzHyeYW3rbpAu6++276+/vZu3dvQgXM8Y5+ugdGUnrQLPRmU5CblfQZjBO0cl+xl4hCZzBEVUlySiP1DAxz1N/P7ZvONT07lWkFjIjcjZGnMg9YhRGd9TCTDPQZZofHI2SJEB5VBgcH2bBhAyMjI2RnZ/OhD32Iz3/+8wB86lOf4p577uHiiy8mOzubxx577KyZy0z4zne+w7333sv69esJh8Ns2bKF+7/6D/zFX36F//t3X2H9+vWoKsuXL+dXv/oVTz31FP/+7/9OTk4OCxYs4G//9m/PuWdhYSGHDh3i0ksvpbS0lKeeemrCZ999993ceuutbN68mRtvvHFsdrN+/Xqys7O55JJL+OhHP8r999/PiRMn2LRpE6qKz+fjkcd/xu9ffYXPfexOcnJyKCoq4vHH4xF3Yp2oVr4phQUMpIZvYc9Jw/+Sylp5NCCiPZA8AbPvZA9ASpsSZ4yqTrkB+4FcYF/Mvjenu84J26WXXqrjOXz48Dn7EkVNS582dgST9vzQSFgPnOrWjsBQ0tpghc7gkB441a2hkfCU59n5t/zS0wf1oq/+RkdHI7Y9Ix687/uv6h3/8nrSnt8ZDOmyv/yVfu/F+qS1wQq7T3Tpsr/8lb5Q05a0NvzDb2p05Zd/rf2hkaS1wSrAbrUwxlqJIgupkfAIgIhkM33YcIZZEJtsmQz6hw23VarV1BrPmDkxicmWexsNW3mqZaWPJ9nht/vMSLtLU1wrT4WAiL0nu7lgYXHK///NBCsCZoeIfAXIF5F3AP8B/NLeZqUn2R5J6qAZTbDMy0mNNU0mYyybP0nCuG9ohLr2gCNMGck2ke1p7CbbI6yvTu2w28okl4sJj0bYf6on5QXxTLEyknwJ8ANvAn+O4Xz/azsbla7kZHmSmqE+EApTkJuV0rZygJyxGUxy3tX+kz2opnZUVBRfkZfewRFC4eQEVe5p7ObCRSW2VYWIF/m5WRR7s5MmYGrbAgwMj6a8T2+mTDkXM3NefqKqHwQeSUyTko+qJqngpTAaUSIRTbjpZTSiDI1EHFEePCualDrFDEZtXJ1wT2M3InDJklLbnhEvon/PjuAwi8vypzk7voyMRjjQ1MOdm5cm9LmzxVeSvNnemQRLdwmYKWcwauSS+EQkNTPJbCAvL4/Ozs6kLJ9qtVKwHQwOGwUuC7yprWkCeESmrD6t5noweXn2RAPtPdnN+fOLKU6RxammIpnZ/Eda+hgaiThipgfJXY5778kefMVeqssTqwTYjRVv0gngVRF5DuiP7lTVB+1qVDKprq6mqamJZNQpGxoZpSM4jHZ7yc1OrB8kMDRC72CYrN68lHdcA7T3DdHlEfomqWIcXdEy3oxGlP0ne/jjDYvifm87SKaAcUKCZSy+Yi+H4ly5wyp7Gru51EUJllGsCJjT5uYhPjXIUpqcnBzbV0GcjLeae7n7iVd4+IOXcssFiV0L4mOP7eJk1wC//fym6U9OAf6/H/+eYCjMM59KbImY+vYAgVBqlz2JJdkCZlFpHgtLnaGVJysgwh8IcbJrgA9e6QxT4kywksn/dQARKVDVAfublL6MhUomOKxUVdl3sttRK+j5irwc7+if/sQ4s7fRSIZzilZeUZg8AbO3sdtRTmtfsZdgKMzAcDihocLRoqlO6VMzwcp6MFeJSA2wz/y8SUS+b3vL0pCKIi8eSfxg4ISyJ+OJapuJ9pXtaeymojCXZRUFCX3ubMnN9lBekIM/aP/y0bGc7hnkdO+Qo/pUVXR5gwT//+092U1OlnDhotQPGpkpkwoYEYkat/8ZuBmz/L2q7gW2TnZdhtmT5RHmFSZ+mr7HgREsvmIvoXCEQGjyxcvsYO/JbjY6zFaeDNOPE7XyZJkT9zZ2c9HiUvJyUj/AZqZMKGBEpJQzYcmiqo3jTnFUpWInkZzBoIeSvGxW+ea23HEiScZg0NU/zPGOfkcNmpCcPrWnsZu8HA8XLCxJ6HPngi8JyZbD4QgHm3odpdzNhMlmMH8CfM38/aSIXAOoiHhF5AHgSCIal44ko7TH3kZDK3dC9FiU6GCQyGVu9zosKiqKryg5feqS6jJyslK7KkQsviT4QA+39BEKOyeUe6ZM+NdX1R+r6jHz4z3Ap4ALMda9v4RJyuxnmDu+Ii8dCdSgnFT2JJZkDAZ7T0bLnjjLVp5of9Xg8CiHTvc5btCcV5ibcB+oWxMso1iJIusA/p8EtCUDZw8GibDzO6nsSSzJMJFFy544zVbuK/YyNBIhGAonJDn0YFMP4Yg6rk9leYSKBCdb7jnZzeKyfBaUJmeJALuZVMCIyBdV9Vsi8l0mqJ6sqp+xtWVpiq/Yy/BohN7BEcoK7C+gsPekc8qexFKan0NOliRsMIiWPbnjcuflKsRGRyVCwOwxHfwbHaiVVxV7E7oC6D6HhXLPlKlmMFE/y+5ENCSDQaxmnggBs6fROWVPYhGRhJb2qGkJOKrsSSyxfWplAgI59jZ2s9JXyLxC51WYSmRAREuvEcp9twP7lFUmFTCq+kvz509me3MRuQX4NpAF/EhVvznueDnwKMZKmUPAx1T1LfPY/cDdgACPqOo/m/ufAs43b1EG9KjqBhFZjiEUa81jb6jqPbNte7KIjWRZM9/ewgkRs+zJexxS9mQ8iQyI2NPYBTjPlAhnBEwiNHNVZe/JHm5cW2X7s+zAV+SlpiUw/YlxIJq061b/C1hbMvk84C+A5bHnq+oNk11jXpcFfA94B9AE7BKR51T1cMxpXwH2q+ptIrLWPP9GEbkIQ7hsBoaB34jIr1W1XlU/EPOMfwR6Y+53VFUTWzskziTSeV3fHiQQCjt2DQpfsZfmnsQkEO492cOCkjwWJbgicTxIZPjtic4BuvqHHSmIwehTHcFQQiqaR0O51y1yTij3TLFSD+E/gIeBHzGz/JfNQEM0Gk1EfgbcCsQKmHXA/wFQ1RoRWS4i84ELMGYgA+a1O4DbgG9FLxTDA/5+YEpB5zQS6byOJsM51QbsK/ay/1Tv9CfGgT2N3Y4dNMf8VQlQWpxW4HI8vmIv4YjSMzhiu4lv78lu1i92Vij3TLHyzcKq+gNV/YOq7oluFq5bjBHWHKXJ3BfLAeB2ABHZDCwDqoG3gK0iUiEiBcC7gCXjrt0CtKlqfcy+FSKyT0R2iMgWC21MOUryssnN9iREwBw63UtxXjbLHVL2ZDy+Ii9d/SFGbV7ZsndghOaeQS52WHhyFI9HqEyQv6qmpQ9vtichvh47iAZEtAfsnRmPRpQjLX2OC3mfKVYEzC9F5FMislBE5kU3C9dNNL8cPxJ8EygXkf3ApzHqnYVV9Qjw98B24DcYgmh8TZA7gSdjPrcAS1V1I/B54AkROWfuKSKfFJHdIrI7GSX5p0NEqEqQo7GuLch584sdVfYkFl+xl4hCZ7+976q+3bDJnzffmYMmJM55Xd8eZHVVEVkOStqNJVEWhFNdA4TCEc5b4O4C9VYEzEeAB4DXgD3mZiWyrImzZx3VmPXMoqhqn6reZfpNPgz4gOPmsR+r6iZV3Qp0AWMzFRHJxpj5PBVzr5Cqdpq/7wGOAueNb5Sq/lBVL1PVy3w+n4WvkXgS5bxuaA86ftAE+weD+vYgAGuqnDsYJCrirqE9yJqqTJ+ajro2Q2lx8ruywrQCRlVXTLCttHDvXcAaEVlhroh5B/Bc7AkiUhazWuYngJ2q2mceqzJ/LsUQJrGzlbcDNaraFHMvnxlYgIisBNYAx3AgiRgMOoMhuvqHWe3kQTNRAqYtSH5OVsKXHI4niVBagqEwzT2Dtkc/2knClRYHvysrWIkiywH+X85UUH4J+BdVHZnqOlUNi8h9wP9ghCk/qqqHROQe8/jDGM78x0VkFMP5//GYWzwtIhXACHCvqnbHHLuDswUOZvv+TkTCGMEI96hq13TfLxXxFXvHnKV2cUYrd64G5StKTHn1+vYAq6uKHFWrbTy+Yi+dQcNfZZf56qjZp5xUNHU8hblZ5OdkJUBpCbCoNI8ib+LWnUkGVr7dD4AcILoGzIfMfZ+Y7kJVfR54fty+h2N+fx1jpjHRtZM66VX1oxPsexp4ero2OQFfsZfO/mFGRiO2RZic0aCcOxhUFhuTX7s18/q2IFevrrD1GXYT9Vd19Q+Paenxxg19SkQSMturbw+6fvYC1gTM5ap6ScznF0TkgF0NynBmmt4ZHLatRlF9W4BibzYLSpxbA6kgN5sib7at2mbf0AitfUOO9r/A2bkwdgmYhvYgOVnCsnnOjEqMUlXstbVK92hEaWgPctVKZystVrCiHo+KyKroB9O/kVkPxkYSkRhX3xZk9fwix0aQRbE7Oqq+zfmmREhMAm9De4CVlUVkOzyvw+4ZTFO3EUHm5JmeVaz0hAeAF0XkJTPh8QXgC/Y2K705MxjYF4tf7/Bonyh2B0Q0mCHKTh8MEuG8rm83lBankzClJWMiA1X9nYiswaj/JRjRW4ldvSjNsHsw6O4fpiMYcrzZB4x3daS1z7b717cFycvxUF3ubLNPpc2z4qGRUU52DXDbxvG51M7DV+Sld3CEUHgUb3b8l2aoM5WW1S5Q8KZj2hmMiPwpkKuqB4H3AE+KyCbbW5bG2D0YNPgNDSqjbU5PXXuQVT7nJg5GKfRmU5hrX3TUUX8QVXcMmlEFryM4bMv9G9qCLCzNo8RhFcxngxUT2d+oakBErgVuBn6CEUWWwSbycrIoybPPee2mJC9fsZfAUJihEXvcgg1tAVe8J7DXt9DggmTUKFUl0eW47TFR15lh7+mAJSe/+fOPgB+o6rOA8xZ6cBh2Dgb1bUEKc52dOBjFzoCIwNAIp3uHXGMrN2Z79gyaDe1BsjzC8kpnmxLB3vyqiBlBdp5L+tR0WBEwzSLyLxiVi58XEa/F6zLMgariPPtMZGa9KKdHkAH4SuyLjjrq7wfcMdMDe82J9W1BllUU2OKzSDR2Rtw1dQ8yNBJxTZ+aDiuC4v0Y2fi3qGoPMA8jsiyDjdg6GLQHHF0iJhY7ZzBjpkSXaJt2Ki317e4xJVYUmQm8NryrepdEJVplUgETU4k4D6M8TKdZRTlEZhll27FLwPQOjtDWF3JNB6+yMeKuoT1IbraHpQ5PHIziK/bSZ4O/ajgc4UTngCv8LwA5WR7mFebaJGDMABuXvKvpmCpM+Qng3RjVk5Wzy+8rYKXgZYZZ4iv20j88Sn8oTGEc6xWN5XW4RNucV5iLiE3aZlvAFRFkUaKzvY5gKK5h1yc6+xmNqGuUFrAvv6quLcD8Ei+l+e6PIIMpBIyqvtv8uSJxzckQJToYtAdCrIijgIkmebnFyZid5aGiMNcWe3ldW9CxKzNORNS30NYXXwET7VNOLnI5nqoSL202RJGlk4MfLDrrRaRcRDaLyNboZnfD0p2FZg2ylt7BuN63vt1IHHRDBFmUyiJv3ENK+6Ol510y0wPG6tq19sb3XdW3BxBxl4BZWJrH6Ti/p0hEjRJNLupT02GlXP8ngPsxFgzbD1wJvA7cYG/T0puFpgBo6Yn3YBB0fOn58Swqy+d0nN/TUb/7ynksKjX61Ome+CotDe1BlpQXkJ/r/AiyKAtL8+kIhhgOR8jNjk/QbHPPIIMjo67xVVnBypu7H7gcaFTVtwEbgdRba9hlRGcw8R4M6tsCruvghrYZ3/dU1+b80vPjKck3svnj/a6cvorlRCwqyzIMvZwAAB7HSURBVEOVuJrJ3LD09kyxImCGVHUIQES8qlqDUZcsg43k5WQxrzA3rtP0wNAILb1DrpuiLyrLp2dghMHh+EVH1bcHyM3yOL70fCwiwsKy/LjOisOjEY75+11RdiiWhTbM9s5U5naXgjcVVgRMk4iUAf8FbBeRZ4HT9jYrAxiaeTx9MNFyHm5zMi4qM2d78XxXbUFW+godX3p+PPHuUye7Bhgejbhu0FwUNVHHUcGrbw9SVeyltCA9IsjAWjXl28xfvyYiLwKlwG9sbVUGwNCiTnUNxO1+blgmeSJitc14OZrr24Osry6Ny71SiUWl+dS0BuJ2vzN5He7qU3YoLfVtAVeZXK1gpZryKrM8DBi5MMsBS3YDEblFRGpFpEFEvjTB8XIReUZEDorIH0Tkophj94vIWyJySEQ+G7P/ayLSLCL7ze1dMce+bD6rVkRuttLGVGZRWXx9C9HEwSUuMvsAYxFx8TL9DA6PcqrbPYmDsSwsM7L5Q+H4mBMbXCpgCnKzKc3PiVufUlVzDSb39ampsDL/fxpjVcvVwI+BFRhJmFMiIlnA94B3AuuAO0Vk3bjTvgLsV9X1wIeBb5vXXgTcDWwGLgHeba5JE+WfVHWDuT1vXrMOuAO4ELgF+L7ZBseyqCyfwFCYYCgcl/vVuSxxMMr8kjxE4qdtRkvPu9EZG40ka+uNT95QQ3uQRaV5FMUxVytViKc5sblnkIHh0cwMZgIiqhoGbgP+WVU/Byy0cN1moEFVj6nqMPAz4NZx56wDfgdgBg8sF5H5wAXAG6o6YD57h/n8qbgV+JmqhlT1ONBgtsGxjOXCxMnRWN/mvmgfgNxsD5VF3rhpm26uFxX1LcRLGNe3B1jtMp9elHiGv9e7aDmDmWBFwIyIyJ3AR4BfmfuseKkWA6diPjeZ+2I5ANwOICKbgWUY+TZvAVtFpEJECoB3AUtirrvPNKs9KiLRVGsrz0NEPikiu0Vkt9+f2tHWZwaDuXfyaOKgG7VyMAeDOA2adW1Bsj3CsorCuNwvlVhYFr8E3mjpeTcqLRDfGUy9i9ZgmglWBMxdwFXAN1T1uIisAP7dwnUT2WF03OdvAuUish/4NLAPCKvqEeDvge0YAQUHgKid6AfAKmAD0AL84wyeh6r+UFUvU9XLfD6fha+RPOI5g4kmDrq1yN6i0ry4hZTWtwVZUVlIjssiyCA22XLuSktzj7tLzy8qy6c7TuHv9W1BKou8lBem11Ja0/4HqephVf2Mqj5pfj6uqt+0cO8mzp51VDMuvFlV+1T1LlXdgOGD8QHHzWM/VtVNqroV6ALqzf1tqjqqqhHgEc6YwaZ9ntM441uY+2BQ78LEwVgWlubT0juE6jk6xYxpaA+4LpQ7Sn5uFmUFOXERxm42JUJMsnMcZjH17UHXWg+mwk4VbRewRkRWiEguhgP+udgTRKTMPAbwCWCnqvaZx6rMn0sxzGhPmp9j/T+3YZjTMO99h4h4zVnWGuAPtnyzBJGT5aGq2BuXGUxde4CcLHFV4mAsi8ryGBgepXdwZE73GRoZpbFrwHVRUbFEhfFciSotq33uFMbR8Pe5+vZU3W1KnArbQj9UNSwi92EsVpYFPKqqh0TkHvP4wxjO/MdFZBQ4DHw85hZPi0gFMALcq6rd5v5vicgGDPPXCeDPzfsdEpGfm/cJm9fYs1B7AonXYNDQFmRlZZHrEgejjPmreoYoK5i9GSIaQeZWrRxgcVkeTd3x0cp9Lk4cjFcuTEvvEMFQ2FV17awyIwEjIh6gKDrLmA4zhPj5cfsejvn9dYyZxkTXbplk/4emeN43gG9YaZtTWFSWR03L3BPj3Jo4GCW2+vS6RSXTnD05bq12EMvC0nx2neie/sRpcLtWvmDMBzo3Ba8uTR38YC3R8gkRKRGRQozZQa2IZJZMThALS43oqLn4FtycOBjlzAxmbtpmfVuQLI+w3IURZFEWluXROzhC/xzyq9LB7OPNzqKyKHfOkWRRpSUdZzBW7CXrzBnLezFmI0uBSWcRGeLLwtI8hkYi9AzM3reQDmYfX5GXnCyZc0BEXVuA5RUFcSvRnopEI8nmMnC29hlmH7fmwEQxFLy596nKolzmpVkEGVgTMDkikoMhYJ5V1REmCP/NYA/xSIyrd9kyyRPh8QjzS/LmHBCRDisOnlkKYvYD55nKwO7tU2Dmwsx1VtyeXouMxWJFwPwLhjO9ENgpIssASz6YDHNnYRzswPUuThyMZVHp3DKvQ+FRTnT2u37QPFMpeC5KiztrkI1nUdncgmxUlYY29ystk2ElD+Y7qrpYVd+lBo3A2xLQtgzEbzBYXlnoarMPzL046DF/PxGFVS4fNBeUmvlVcxDGDe0BygtyqHC52WdRWR7BUJi+odmZqFv7hgiEwq4XxJNhZclkL/A+jCrKsef/nU1tyhBDPHwLDe1BLljofg1qYVk+bW+2MBrRWRX0TIcIMjDyq3xF3jkFRBh17YoRcVfh1PHE5sKULJh5OHY6LjIWixWV9lmMQpJhoD9my5AA5upbGBoZpbGz37UlYmJZVJrHyKjSEZxdpeD69iAegRWV7jYlgiGMZ2v6GSs97+KgkShzzYUZK3KZBu9qIqzkwVSr6i22tyTDpCyaQyTL8Q7D7JMOU/TYUOX5JXkzvv5oe5Cl8wrIy3H0Kg+WWFSaR23b7PKr/MEQvYMjrvdVwdyXTk4XU+JkWJnBvCYiF9vekgyTsrBs9lVd3bqK5USMmTNmKYzr2wNpIYjBdF73zK52W0Nb+uR1VBV78cjsg2zSxZQ4GVYEzLXAHnOVyIMi8qaIHLS7YRnOsLA0n9beISKRWQwGaWT2GTNnzELbDI9GON6RHqZEMKITB0dmV7stnTLTs7M8zC+ZXfBI1JS4Ok3NY2DNRPZO21uRYUoWlZm+hf4QVcUzM/00tAdYVlGYFmaf0vwcCnKzZhUd1dg1wMiopsWgCWfMic09gzOu3VbfHqQkLxtfsXf6k12AkQsz8z6VTqbEybASptwIlAHvMbcyc1+GBLFwDmt41LelT5KXiMx6kaixysBp8q7mkl9VbyajpovZxwiImHmfakjzCDKwVovsfuCnQJW5/buIfNruhmU4w2wXHhsxzT7ppEEZy9zOfDCILsjm9hyYKHPJr2pIkwiyKItK82a11lC6R5CBNRPZx4ErVLUfQET+Hngd+K6dDctwhtkundzY2U84omnVwReV5lPTOvPoqPq2AItK8yjy2raCRUox2/yqzmCIrv7htPFVgWFBCIUjdPUPU1Fk3SxY3x6gOC+bqjQxJU6EFSe/ALHrqowy8fLEGWyivCAHb7ZnxjOYdEzyWliWR0cwxHA4MqPrGvxB1xdujGW2+VV1aVKDLJZo8MhMoxONCLKitDElToQVAfOvwO9F5Gsi8jXgDeDHtrYqw1mIyKxqItW3BxGBVb40GgxK81H9/9s79yA7qjqPf77zysyEZCYvwkxISAKJEoOEGJKlKAO+0cUHrAgpKTGICKKilFuia5UKu7WAysoCbmSBNaiIWpEFthSBAAEFDAmEkBDITBKQ8JiZvMg8MpN5/PaPPnfSud5H3+HeuXemz6eq694+p0/fX5/p6V+f3/md3w9a9kfvq4GBIPT8cTHqJxha7LbmUZ4mORVDXQsTpDOIz0tLKqJM8l8PLAf2AHuB5Wb2k0IL5jmchrrcXSWbWjuYVl9DTdXo9yBLEPaOispr+w7Q3TsQq4cmBKO9odxT48ZUcNQQFrKOVA7NV0VXxrs7etjdeTB291QyaRWMpPHucyJBNOVfAr8AXnFlWZF0hls/0yzpyhT1EyTd7dbXrJU0P1R3uaRNkjZL+nqo/IeSXnRt7pZU78pnSjogaYPbViT/3kimoa4mZ4+fppb2WJkyIHhoQm6T180xiQycTENdDS37c1tf1dQSrOuIk9ln0tgqqsrLclLGcb2nksk0grnTfa4H1oW2xH5GJJUDNxOso5kHLJM0L+mw7wAbzOzdwOeAG1zb+cAXgcXAicCZkhKplR8E5rs2W4Fvh863zcwWuO2SbDKOJBrrq2lt76avP9rcQv+AsX1XZyxWW4dpHIJL9+DDIGYmsmn1ucdua2qN30tLWZk4Kse1ME0xCZyajbQKxszOdJ+zzGx2aJtlZrMjnHsx0Gxm283sIHAXQdDMMPOA1e53XgRmSpoKHA88ZWZdZtYHrAHOcsc94MogmA86OvLVjmAa62sYMGhpj/YweHVPFwf7BmL3BlVTVc6E2sqc7OVNrUHGwQkxixc1OLcQ0fSzp/MguzoOxnJeoaGuOqd7qrm1g7FV5YNLDOJKlHUwq6OUpWAa8Gpof6crC/MccLY752LgGAKFsQlYKmmSpFrgY8D0FL9xIfDH0P4sSc9KWiPpvRFkHDHkuhYmTjHIkmmoy80hojmmGQcbcgytMzjSi+G8Qq5ONk2t7RwXo8Wo6cg0B1Pt5lomu7mSiW6bCTRGOHeqnk029l4DTJC0Afgq8CzQZ2ZbgGsJzGH3EyiivnBDSf/iyn7lit4AZpjZScAVwJ2JeaSkdhdLWidpXVtbW4TLKA1yXQuTSJMcxwdnY330t83B0PMxfCtvzNE7KnFPxdHs01BXzZv7u+mPOF+VcFGOO5lGMF8imG95p/tMbPcQzK1kYyeHjzqOBl4PH2Bm+81suZktIJiDmQLscHW3mdlCM1tK4MHWlGgn6QLgTOCz5pbXmlmPme1239cD24C5yUKZ2S1mtsjMFk2ZMiXCZZQGuY5gmls6aKirZlx17kmSRjq5rOZva++hvTueGQfrayupriyL/Gbe1BKYfRpjaPZpqK+hf8Boi2Cifqurl9b2Hq9gyDwHc4OZzQK+GZp7mWVmJ5rZTRHO/TQwR9IsSVXAecC94QMk1bs6gIuAx8xsv6s70n3OIDCj/drtnwF8C/iEmXWFzjXFORYgaTYwB9geQc4RwbjqSsaNqcjhbTOeZh8ITGT7u/vo7OnLemycTYmH1ldFH8EcF9OFgwmlGsWTrLktfmuF0pE1LoaZ3ei8uuYB1aHyO7K065P0FeBPQDlwu5ltlnSJq19BMJl/h6R+4AWCsDQJVkmaBPQCl5nZXld+EzAGeNDd6E85j7GlwFWS+giiDVxiZnuy9sAIIli3kP1tM7FwcNniGcMgVenRGHJVzhbSpKklvqZEyG2xZVNLB++dM3JG/fkknDqZLP9WcYygkY6sCkbS94DTCRTMHwjcjv8MZFQwAGb2B9cmXLYi9P1JgpFGqrYpJ+nN7Lg05auAVdlkGskEk9fZ36Be23eAA739sX1oJh4Gr+3rzqpgmtviFXo+mYa6atZszT4XOWj2ielbeWMO66uaWjuorixjmps3jTNRQsV8GvgA8KaZLSdYlxLP/8Yi01gfzRe/OeZRXAcfBhHMiYl0BnE0+0Awt9AWIXZbwuwzN6b3VF1NJTWV0XINJczTZWXxvKfCRFEwB8xsAOhzXlmtQJR1MJ4801BXw+7Og3T39mc8Lq4LBxNMHV+NFM3jbltbPD3IEjTWVUeK3RZ3s4+kyKnLm1vaY9tPyURRMOtcOJb/JvAiewZYW1CpPClJeJK9meXBGSwcHBO7hYMJKsvLOHLcmKwOEXvdwsG4mhIhepytrS3e7NNYV5P1paW9u5fX3+qO9T0VJsok/5fd1xWS7gfGm9nGworlScWhtTAHmDl5bNrjgnUd8b7Bo3hHNbfFd+FggqhzCwkPsjibfRrqqtmaZb5qW1snEE+vxFSkVTCSFmaqM7NnCiOSJx1R0tyaGc0tHZy1MDloQrxorKthyxv7Mx4zmCY5pqZECDtEZFHGrR38w+xJwyFSydIYmq+qqkht/El4JcYtBmA6Mo1gfpyhzoD351kWTxYGXSUzvG227O+hvacv9m9QDXXVPLSlBTNLO4Hf3NpBTWV5rM0+Y8dUML66IuNLS3t3L2+81R1bp5EEjfWH5qumT6xNeUxzawdVFWVMnxDfeypMWgVjZu8bTkE82ampKmfi2KqMduBDIWLi/QbVWB+kud3b1cvENHNR3uwTkM2cOOiVGPN7Kpx4LJ2CaWrtYPbksVSUR5neHv1kMpGdnamhmf0+/+J4stFQlznN7aC3j3/bBIKHQToFs621gyUxN/tAIlJwhpeWGKZJTkWU1MlNre0smD5huEQqeTKZyD6eoc4Ar2CKQENdDTv3dqWtb2rtoL62kkkx9SBLEH7bnD+t7u/qO3r6vLePo7G+hg2v7ktb39TaHph90ry1x4VD6Q1Sv+B1Hexj594DnPOeVIHf40kmE9ny4RTEE43G+mr+umN32vptzoMsrgsHE2Rzv93mMw4O0lhfw96uXg4c7E+ZXruptYNjpxxBecxNidnmq7a3dWLmR3phMpnIzjezX0q6IlW9mV1fOLE86Wioq6G9u4+Onj6OGHP4n8/M2NrazkfnNxRJutIhW5rbOAe5TKYhFMjx2BQedU0tHSya6c0+kHm+KjH/GXfzdJhMM1GJhRbj0myeIpApDMruzoPs6+r1D00OpblNN7fQ1NpOVXkZM2Ju9oGkQI5JdPb08dq+A/6ecmSar2pq6aCiTBwzKf0atbiRyUT2M/f5g+ETx5ONxMNga0vH3/na+wn+wwlit6V+29zW2sEs7+0DHHppeWhLC0tmT6Qy1CeDYYdi7kGWoKG+hrU79vDM3/aycMbho7omd09V+ntqkCgpk1e6UDGJ/QmSbi+sWJ50vGPqOCaNreJrdz3L9+/dzL6ug4N1zYkhun8YAHDMxLFsfO0trn9wK+3dvYfVxTlfTjLTJ9RyxruO4udPvMwZP3nssOjKTTEPnJrMZxZNp3ZMBWf/9Akuv+vZw8IRNbd2+H5KIoqqfbeZDbqYuLwsJxVOJE8m6moreeAbSzn35Onc8eTLnP6jR7njyZfp6x+gqbWDcWMqmDreB7sGuOLDc/nQ8VP5z9VNLL3uEW59fDvdvf109/bz6p4ur2AcZWXiv85fyG0XLKJ/wLjg9rVctPJpduzqHDQlHuNNiQAsmF7PI988ncvedyx/3PQm7/vRo1z/wEvs7TzIK7s7/UgvCbmMw+kPkJ4DTk8k/JI0EVhjZicMg3wFZdGiRbZu3bpiizFktryxn6vue4Ent+9m7tQj6Os36morufvLpxZbtJLi+Z1vcd2fXuTxpl001lVz1sJp3PzINm5cdhIfP7Gx2OKVFD19/fz8Ly9z48PN9PT1U19bxaSxVdz/9aXFFq3k2Lm3i2vvf4n7nnud8dUV7O/ui809JWm9mS3KdlyUEcyPgSckXS3pauAJ4Lq3K6Dn7XN8w3ju/OISVpy/kAO9/Wzf1eknY1NwwtF1/OILS7jzoiVMGV/NzY9sA7zZJxVjKsr50mnH8vA3T+NTC6bR1t7DvMbxxRarJDl6Qi03LjuJVZeewiznxn1CijVXcSbrCAZA0jyC2GMCVpvZC4UWbDgY6SOYMN29/dyz4TWWzJqUMdJy3DEzHnihhY0793HFh94R+7Ud2Xh5Vyf1tZXU18Z74W42BgaMXZ09HDmuOvvBo4C8jWAkHQtsM7ObgOeBD4Yn/bO0PUPSS5KaJV2Zon6CpLslbZS0VtL8UN3lkjZJ2izp66HyiZIelNTkPieE6r7tfuslSR+JIuNoobqynHNPnuGVSxYk8ZF3HcU/f+SdXrlEYObksV65RKCsTLFRLrkQxUS2CuiXdBxwKzALuDNbI0nlwM3AR4F5wDI3EgrzHWCDmb0b+Bxwg2s7H/gisJggRfOZkua4NlcSjKLmAKvdfmKUdR7wLuAM4KdOBo/H4/EUgSgKZsDM+oCzgRvM7BtAlKXii4FmM9tuZgeBu4BPJh0zj0BJYGYvAjMlTQWOB54ysy7322uAs1ybTwIr3feVwKdC5XeZWY+Z7QCanQwej8fjKQJRFEyvpGUEI4z/c2WVEdpNA14N7e90ZWGeI1BcSFoMHAMcDWwClkqaJKkW+BiQiCA31czeAHCfR+bwex6Px+MZJqIomOXAKcC/mdkOSbOAX0Zol8rAnexRcA0wQdIG4KvAs0CfmW0BrgUeBO4nUER9efg9JF0saZ2kdW1tmdOfejwej2foRFEw24FbgI2Sqs1sh5ldE6HdTg6NOiAYmbwePsDM9pvZcjNbQDBCmgLscHW3mdlCM1sK7AGaXLMWSQ0A7rM16u+5895iZovMbNGUKVMiXIbH4/F4hkJaBSOpQtJ1BA/ulQSjllclXScpionsaWCOpFmSqggm4O9N+o16VwdwEfCYme13dUe6zxkEZrRfu+PuBS5w3y8A7gmVnydpjBtlzQHWRpDT4/F4PAUgU8KxHxJETZ5lZu0AksYDP3Lb5ZlObGZ9kr4C/AkoB243s82SLnH1Kwgm8++Q1A+8AHwhdIpVkiYBvcBliUgCBGa130r6AvA34Bx3vs2SfuvO0+fa9EfsB4/H4/HkmbQLLSU1AXMt6QDn+vuicxMe0YymhZYej8czXERdaJlpBGPJysUV9kvKvvx/BLB+/fpdkl55G6eoA97Kgyj5Ok8+zzWDYISYD0rx+vIpUyn2VSn2OeSvr0rx+kqxn6Aw13dMpBZmlnID/hf4XIry84F707WL0wbcUkrnybNMbaUmUwn3ecn1VSn2eT77qhSvrxT7qdjXl2kEcxnwe0kXAusJXH5PBmo4tOgx7txXYufJ57n2ZT8kMqV4ffmUqRT7qhT7HPLXV6V4faXYT1DE64sSrv/9BOFXBGw2s9VDk80zkpC0ziLYWD2+r3LB91U0Rks/ZRrBAGBmDwMPD4MsntLilmILMILwfRUd31fRGBX9FClcv8fj8Xg8uRJlJb/H4/F4PDnjFUyMkHS7pFZJm0JlJ0p6UtLzku5zi2mR9FlJG0LbgKQFrq5K0i2Stkp6UdI/FeuaCkE++knSuKTyXZJ+UryrKgx5vKeWueM3Srpf0uRiXVMhyGM/nev6aLOLtFLa5MsVzm+lvwFLgYXAplDZ08Bp7vuFwNUp2p0AbA/t/wD4V/e9DJhc7GsrxX5KqlsPLC32tZViXxHMBbcm7iOClOzfL/a1lWA/TSJYGzPF7a8EPlDsa8u0+RFMjDCzxwgCh4Z5B/CY+/4gkGo0soxDseAg+Gf4d3fOATPblWdRi0oe+wkAlyzvSODxPIpZEuSpr+S2sZIEjCdFoNqRTJ76aTaw1cwSYeAfStOmZPAKxrMJ+IT7fg6HR6ROcC7uJtehdNlXS3pG0u9ckrjRTk79lMQy4DfmXjtjQE59ZWa9wKUEKdlfJ0hEeFvhxSw6ud5TzcA7Jc2UVEGQbDFVm5LBKxjPhcBlktYTBDc9GK6UtAToMrOE7biCIBXCX8xsIfAkQfDT0U6u/RTmPFIrntFKTn3lorNfCpwENAIbgW8Pq8TFIad+siDg76XAbwhGwy+TPU9WUcm6DsYzurEgVfWHASTNBf4x6ZDkh+NuoAu42+3/jsOjYI9KhtBPuGNPBCrMbH3BhSwRhtBXC1y7ba7Nb4ErCy9pcRnKPWVm9+FW1Eu6GCjpiPF+BBNzQnl3yoDvAitCdWUEQ/e7EmXOzHMfcLor+gBBioRRTa79FCLlvMxoZgh99RowT1IiA+CHgC3DI23xGMo9FWozAfgycOtwyTsU/AgmRkj6NYFimCxpJ/A94AhJl7lDfg/8T6jJUmCnmW1POtW3gF84t9s2grTao4Y89hPAZ4CPFVDcopKPvjKz1yX9AHhMUi/wCvD5YRB/2MjjPXWDGxUDXGVmWwso9tvGr+T3eDweT0HwJjKPx+PxFASvYDwej8dTELyC8Xg8Hk9B8ArG4/F4PAXBKxiPZ5iRdKqk9xZbDo+n0HgF4/HkGUn9LgruJhdKpzZUdxKBW/dTGdrfKmleDr/3eUk3vT2pPZ784xWMx5N/DpjZAjObTxD+45JEhZk9a2YXufhbf4ekclc/6hevekY/XsF4PIXlceA4AEnnS1rrRjc/k1TuyjskXSXpr8Apkh6VtMjVJfKkbJJ0beKkkpYryMezBjg1VD5F0ipJT7vtVDyeIuEVjMdTIFzE248Cz0s6niAy7qlmtoAghtRn3aFjCfKELDGzP4faNwLXAu8niNd1sqRPSWogyMlzKkFYlbA57QbgP8zsZIJQ7iUdSsQzuvGhYjye/FMjaYP7/jhB6PmLgfcATwcpT6ghSLIFgbJZleI8JwOPJvJ/SPoVQQgRksp/A8x15R8kiOuVOMd4SePMrD1P1+bxRMYrGI8n/xxwo5RBXCKtlWaWKgx9t5mlioqrFGUJ0sV4KgNOMbMD0UT1eAqHN5F5PMPDauDToWi4EyUdk6XNX4HTJE128zXLgDWu/HRJk1wulXNCbR4AvpLYSeRy93iKgVcwHs8w4LzCvgs8IGkjQYrchixt3iBIvPUI8BzwjJnd48q/T5Ds7SHgmVCzrwGLJG2U9AIhDzaPZ7jx0ZQ9Ho/HUxD8CMbj8Xg8BcErGI/H4/EUBK9gPB6Px1MQvILxeDweT0HwCsbj8Xg8BcErGI/H4/EUBK9gPB6Px1MQvILxeDweT0H4f24kJPNoCVlPAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "oscillations = final_data['CO2']/final_data['seasonally']\n", "oscillations[200:250].plot(label = 'Données brutes')\n", "plt.xlabel('Période')\n", "plt.ylabel('Oscillations saisonières : variations relatives')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe que la période des oscillations est de 1 an, ce qui est cohérent avec le côté saisonier de ces variations. Une analyse par transformée de Fourier Rapide (FFT) permettrait d'identifier plus précisément cette période. Nous considérons qu'un analyse visuelle suffit en premier lieu." ] }, { "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 }