{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "pd.options.mode.chained_assignment = None # default='warn'\n", "import isoweek\n", "import os\n", "import numpy as np\n", "import scipy.optimize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous téléchargeons les données hebdomadaires de CO2 dans l'atmosphère de l'observatoire du Mauna Loa Oberservatory. Le lien suggeré dans MOOC ne marchait pas (le 11 janvier 2024), donc cet site est utilisé. Dans les deux cas, ce sont les données du même observatoire." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "if os.path.exists(\"co2_weekly_mlo.csv\"):\n", " data_path = \"co2_weekly_mlo.csv\"\n", "else:\n", " data_path = \"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous omitterons les premières lignes car ils sont des commentaires. L'unité de la mésure sont des $\\mu$mol de $CO_2$ par mol de l'air, aussi appelé \"parts per million\" (ppm)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
019745191974.3795333.375-999.99-999.9950.39
119745261974.3986332.956-999.99-999.9950.05
21974621974.4178332.355-999.99-999.9949.59
31974691974.4370332.207-999.99-999.9949.64
419746161974.4562332.377-999.99-999.9950.06
519746231974.4753331.735-999.99-999.9949.72
619746301974.4945331.696-999.99-999.9950.03
71974771974.5137331.466-999.99-999.9950.20
819747141974.5329330.835-999.99-999.9950.01
919747211974.5521330.767-999.99-999.9950.41
1019747281974.5712329.814-999.99-999.9949.97
111974841974.5904329.855-999.99-999.9950.54
1219748111974.6096329.155-999.99-999.9950.37
1319748181974.6288329.066-999.99-999.9950.80
1419748251974.6479328.337-999.99-999.9950.54
151974911974.6671328.065-999.99-999.9950.68
161974981974.6863327.564-999.99-999.9950.53
1719749151974.7055326.726-999.99-999.9949.95
1819749221974.7247326.995-999.99-999.9950.38
1919749291974.7438327.315-999.99-999.9950.76
2019741061974.7630327.076-999.99-999.9950.49
21197410131974.7822327.235-999.99-999.9950.53
22197410201974.8014327.405-999.99-999.9950.51
23197410271974.8205327.647-999.99-999.9950.50
2419741131974.8397327.807-999.99-999.9950.39
25197411101974.8589328.506-999.99-999.9950.79
26197411171974.8781328.616-999.99-999.9950.59
27197411241974.8973328.465-999.99-999.9950.14
2819741211974.9164328.805-999.99-999.9950.20
2919741281974.9356329.397-999.99-999.9950.53
..............................
256020236112023.4425424.136421.00398.78141.49
256120236182023.4616423.497420.84398.39141.28
256220236252023.4808422.197420.32398.78140.46
25632023722023.5000422.624419.91398.34141.41
25642023792023.5192422.355419.18397.93141.66
256520237162023.5384421.345418.36396.93141.19
256620237232023.5575421.284418.03397.30141.67
256720237302023.5767420.836418.10396.80141.75
25682023862023.5959420.026417.36395.65141.46
256920238132023.6151418.984417.25395.24140.92
257020238202023.6342419.312416.64395.22141.71
257120238272023.6534419.275416.42394.45142.09
25722023932023.6726418.644416.27393.92141.81
257320239102023.6918418.522416.15393.52141.96
257420239172023.7110418.335415.65393.79141.94
257520239242023.7301418.297415.34393.46141.97
257620231012023.7493418.316415.30393.52141.96
257720231082023.7685418.535415.39393.58142.05
2578202310152023.7877419.467415.82393.98142.77
2579202310222023.8068418.916416.19394.23141.93
2580202310292023.8260419.057416.41394.47141.75
258120231152023.8452419.285417.00394.80141.64
2582202311122023.8644421.226417.31395.64143.24
2583202311192023.8836421.215418.38395.26142.88
2584202311262023.9027420.312417.81396.21141.66
258520231232023.9219421.022419.23396.43142.07
2586202312102023.9411422.207418.81396.39142.96
2587202312172023.9603422.245419.05397.72142.74
2588202312242023.9795421.865419.41397.53142.12
2589202312312023.9986422.524419.34397.73142.55
\n", "

2590 rows × 9 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 \n", "0 50.39 \n", "1 50.05 \n", "2 49.59 \n", "3 49.64 \n", "4 50.06 \n", "5 49.72 \n", "6 50.03 \n", "7 50.20 \n", "8 50.01 \n", "9 50.41 \n", "10 49.97 \n", "11 50.54 \n", "12 50.37 \n", "13 50.80 \n", "14 50.54 \n", "15 50.68 \n", "16 50.53 \n", "17 49.95 \n", "18 50.38 \n", "19 50.76 \n", "20 50.49 \n", "21 50.53 \n", "22 50.51 \n", "23 50.50 \n", "24 50.39 \n", "25 50.79 \n", "26 50.59 \n", "27 50.14 \n", "28 50.20 \n", "29 50.53 \n", "... ... \n", "2560 141.49 \n", "2561 141.28 \n", "2562 140.46 \n", "2563 141.41 \n", "2564 141.66 \n", "2565 141.19 \n", "2566 141.67 \n", "2567 141.75 \n", "2568 141.46 \n", "2569 140.92 \n", "2570 141.71 \n", "2571 142.09 \n", "2572 141.81 \n", "2573 141.96 \n", "2574 141.94 \n", "2575 141.97 \n", "2576 141.96 \n", "2577 142.05 \n", "2578 142.77 \n", "2579 141.93 \n", "2580 141.75 \n", "2581 141.64 \n", "2582 143.24 \n", "2583 142.88 \n", "2584 141.66 \n", "2585 142.07 \n", "2586 142.96 \n", "2587 142.74 \n", "2588 142.12 \n", "2589 142.55 \n", "\n", "[2590 rows x 9 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data = pd.read_csv(data_path, skiprows=35)\n", "co2_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas des semaines sans données." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [year, month, day, decimal, average, ndays, 1 year ago, 10 years ago, increase since 1800]\n", "Index: []" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data[co2_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous regardons s'il y a des semaines avec des données impossibles, comme des concentrations négatives. Après nous créeons un nouveau DataFrame sans ces semaines." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
7219751051975.7603-999.990326.98-999.99-999.99
8119751271975.9329-999.990329.32-999.99-999.99
82197512141975.9521-999.990329.68-999.99-999.99
83197512211975.9712-999.990329.96-999.99-999.99
84197512281975.9904-999.990330.27-999.99-999.99
11019766271976.4877-999.990333.05-999.99-999.99
40919823211982.2178-999.990342.37-999.99-999.99
41219824111982.2753-999.990342.85-999.99-999.99
41319824181982.2945-999.990342.66-999.99-999.99
4811983871983.5986-999.990340.84-999.99-999.99
5151984411984.2500-999.990344.80-999.99-999.99
5161984481984.2691-999.990345.23-999.99-999.99
51719844151984.2883-999.990345.67-999.99-999.99
51819844221984.3074-999.990345.86-999.99-999.99
1639200510162005.7904-999.990374.67358.21-999.99
178020086292008.4932-999.990385.53368.22-999.99
17812008762008.5123-999.990385.38368.88-999.99
178220087132008.5314-999.990384.45367.73-999.99
\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "72 1975 10 5 1975.7603 -999.99 0 326.98 -999.99 \n", "81 1975 12 7 1975.9329 -999.99 0 329.32 -999.99 \n", "82 1975 12 14 1975.9521 -999.99 0 329.68 -999.99 \n", "83 1975 12 21 1975.9712 -999.99 0 329.96 -999.99 \n", "84 1975 12 28 1975.9904 -999.99 0 330.27 -999.99 \n", "110 1976 6 27 1976.4877 -999.99 0 333.05 -999.99 \n", "409 1982 3 21 1982.2178 -999.99 0 342.37 -999.99 \n", "412 1982 4 11 1982.2753 -999.99 0 342.85 -999.99 \n", "413 1982 4 18 1982.2945 -999.99 0 342.66 -999.99 \n", "481 1983 8 7 1983.5986 -999.99 0 340.84 -999.99 \n", "515 1984 4 1 1984.2500 -999.99 0 344.80 -999.99 \n", "516 1984 4 8 1984.2691 -999.99 0 345.23 -999.99 \n", "517 1984 4 15 1984.2883 -999.99 0 345.67 -999.99 \n", "518 1984 4 22 1984.3074 -999.99 0 345.86 -999.99 \n", "1639 2005 10 16 2005.7904 -999.99 0 374.67 358.21 \n", "1780 2008 6 29 2008.4932 -999.99 0 385.53 368.22 \n", "1781 2008 7 6 2008.5123 -999.99 0 385.38 368.88 \n", "1782 2008 7 13 2008.5314 -999.99 0 384.45 367.73 \n", "\n", " increase since 1800 \n", "72 -999.99 \n", "81 -999.99 \n", "82 -999.99 \n", "83 -999.99 \n", "84 -999.99 \n", "110 -999.99 \n", "409 -999.99 \n", "412 -999.99 \n", "413 -999.99 \n", "481 -999.99 \n", "515 -999.99 \n", "516 -999.99 \n", "517 -999.99 \n", "518 -999.99 \n", "1639 -999.99 \n", "1780 -999.99 \n", "1781 -999.99 \n", "1782 -999.99 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data.loc[co2_data[\"average\"] < 0]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
019745191974.3795333.375-999.99-999.9950.39
119745261974.3986332.956-999.99-999.9950.05
21974621974.4178332.355-999.99-999.9949.59
31974691974.4370332.207-999.99-999.9949.64
419746161974.4562332.377-999.99-999.9950.06
519746231974.4753331.735-999.99-999.9949.72
619746301974.4945331.696-999.99-999.9950.03
71974771974.5137331.466-999.99-999.9950.20
819747141974.5329330.835-999.99-999.9950.01
919747211974.5521330.767-999.99-999.9950.41
1019747281974.5712329.814-999.99-999.9949.97
111974841974.5904329.855-999.99-999.9950.54
1219748111974.6096329.155-999.99-999.9950.37
1319748181974.6288329.066-999.99-999.9950.80
1419748251974.6479328.337-999.99-999.9950.54
151974911974.6671328.065-999.99-999.9950.68
161974981974.6863327.564-999.99-999.9950.53
1719749151974.7055326.726-999.99-999.9949.95
1819749221974.7247326.995-999.99-999.9950.38
1919749291974.7438327.315-999.99-999.9950.76
2019741061974.7630327.076-999.99-999.9950.49
21197410131974.7822327.235-999.99-999.9950.53
22197410201974.8014327.405-999.99-999.9950.51
23197410271974.8205327.647-999.99-999.9950.50
2419741131974.8397327.807-999.99-999.9950.39
25197411101974.8589328.506-999.99-999.9950.79
26197411171974.8781328.616-999.99-999.9950.59
27197411241974.8973328.465-999.99-999.9950.14
2819741211974.9164328.805-999.99-999.9950.20
2919741281974.9356329.397-999.99-999.9950.53
..............................
256020236112023.4425424.136421.00398.78141.49
256120236182023.4616423.497420.84398.39141.28
256220236252023.4808422.197420.32398.78140.46
25632023722023.5000422.624419.91398.34141.41
25642023792023.5192422.355419.18397.93141.66
256520237162023.5384421.345418.36396.93141.19
256620237232023.5575421.284418.03397.30141.67
256720237302023.5767420.836418.10396.80141.75
25682023862023.5959420.026417.36395.65141.46
256920238132023.6151418.984417.25395.24140.92
257020238202023.6342419.312416.64395.22141.71
257120238272023.6534419.275416.42394.45142.09
25722023932023.6726418.644416.27393.92141.81
257320239102023.6918418.522416.15393.52141.96
257420239172023.7110418.335415.65393.79141.94
257520239242023.7301418.297415.34393.46141.97
257620231012023.7493418.316415.30393.52141.96
257720231082023.7685418.535415.39393.58142.05
2578202310152023.7877419.467415.82393.98142.77
2579202310222023.8068418.916416.19394.23141.93
2580202310292023.8260419.057416.41394.47141.75
258120231152023.8452419.285417.00394.80141.64
2582202311122023.8644421.226417.31395.64143.24
2583202311192023.8836421.215418.38395.26142.88
2584202311262023.9027420.312417.81396.21141.66
258520231232023.9219421.022419.23396.43142.07
2586202312102023.9411422.207418.81396.39142.96
2587202312172023.9603422.245419.05397.72142.74
2588202312242023.9795421.865419.41397.53142.12
2589202312312023.9986422.524419.34397.73142.55
\n", "

2572 rows × 9 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 \n", "0 50.39 \n", "1 50.05 \n", "2 49.59 \n", "3 49.64 \n", "4 50.06 \n", "5 49.72 \n", "6 50.03 \n", "7 50.20 \n", "8 50.01 \n", "9 50.41 \n", "10 49.97 \n", "11 50.54 \n", "12 50.37 \n", "13 50.80 \n", "14 50.54 \n", "15 50.68 \n", "16 50.53 \n", "17 49.95 \n", "18 50.38 \n", "19 50.76 \n", "20 50.49 \n", "21 50.53 \n", "22 50.51 \n", "23 50.50 \n", "24 50.39 \n", "25 50.79 \n", "26 50.59 \n", "27 50.14 \n", "28 50.20 \n", "29 50.53 \n", "... ... \n", "2560 141.49 \n", "2561 141.28 \n", "2562 140.46 \n", "2563 141.41 \n", "2564 141.66 \n", "2565 141.19 \n", "2566 141.67 \n", "2567 141.75 \n", "2568 141.46 \n", "2569 140.92 \n", "2570 141.71 \n", "2571 142.09 \n", "2572 141.81 \n", "2573 141.96 \n", "2574 141.94 \n", "2575 141.97 \n", "2576 141.96 \n", "2577 142.05 \n", "2578 142.77 \n", "2579 141.93 \n", "2580 141.75 \n", "2581 141.64 \n", "2582 143.24 \n", "2583 142.88 \n", "2584 141.66 \n", "2585 142.07 \n", "2586 142.96 \n", "2587 142.74 \n", "2588 142.12 \n", "2589 142.55 \n", "\n", "[2572 rows x 9 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_clean = co2_data.loc[co2_data[\"average\"]>0]\n", "co2_clean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous convertirons les premières colonnes en \"datetime\" de pandas, donc un format de temps." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1974-05-19\n", "1 1974-05-26\n", "2 1974-06-02\n", "3 1974-06-09\n", "4 1974-06-16\n", "5 1974-06-23\n", "6 1974-06-30\n", "7 1974-07-07\n", "8 1974-07-14\n", "9 1974-07-21\n", "10 1974-07-28\n", "11 1974-08-04\n", "12 1974-08-11\n", "13 1974-08-18\n", "14 1974-08-25\n", "15 1974-09-01\n", "16 1974-09-08\n", "17 1974-09-15\n", "18 1974-09-22\n", "19 1974-09-29\n", "20 1974-10-06\n", "21 1974-10-13\n", "22 1974-10-20\n", "23 1974-10-27\n", "24 1974-11-03\n", "25 1974-11-10\n", "26 1974-11-17\n", "27 1974-11-24\n", "28 1974-12-01\n", "29 1974-12-08\n", " ... \n", "2560 2023-06-11\n", "2561 2023-06-18\n", "2562 2023-06-25\n", "2563 2023-07-02\n", "2564 2023-07-09\n", "2565 2023-07-16\n", "2566 2023-07-23\n", "2567 2023-07-30\n", "2568 2023-08-06\n", "2569 2023-08-13\n", "2570 2023-08-20\n", "2571 2023-08-27\n", "2572 2023-09-03\n", "2573 2023-09-10\n", "2574 2023-09-17\n", "2575 2023-09-24\n", "2576 2023-10-01\n", "2577 2023-10-08\n", "2578 2023-10-15\n", "2579 2023-10-22\n", "2580 2023-10-29\n", "2581 2023-11-05\n", "2582 2023-11-12\n", "2583 2023-11-19\n", "2584 2023-11-26\n", "2585 2023-12-03\n", "2586 2023-12-10\n", "2587 2023-12-17\n", "2588 2023-12-24\n", "2589 2023-12-31\n", "Length: 2572, dtype: datetime64[ns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weeks = pd.to_datetime(co2_clean[[\"year\", \"month\", \"day\"]])\n", "weeks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec ceci, il est facile de tester s'ils y a des semaines omises. Ce sont les semaines ou il n'y avait pas de moyen sensible mesuré." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1975-09-28 00:00:00 1975-10-12 00:00:00\n", "1975-11-30 00:00:00 1976-01-04 00:00:00\n", "1976-06-20 00:00:00 1976-07-04 00:00:00\n", "1982-03-14 00:00:00 1982-03-28 00:00:00\n", "1982-04-04 00:00:00 1982-04-25 00:00:00\n", "1983-07-31 00:00:00 1983-08-14 00:00:00\n", "1984-03-25 00:00:00 1984-04-29 00:00:00\n", "2005-10-09 00:00:00 2005-10-23 00:00:00\n", "2008-06-22 00:00:00 2008-07-20 00:00:00\n" ] } ], "source": [ "for p1, p2 in zip(weeks[:-1], weeks[1:]):\n", " delta = p2 - p1\n", " if delta > pd.Timedelta('7 days'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant, nous pouvons tracer le développement de la concentration de $CO_2$ pendant le temps." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,\"$\\\\mu$ mol/mol de CO2 dans l'atmosphère\")" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "co2_clean.plot(x=\"decimal\", y=\"average\", title=\"Changement de la concentration de CO2 dans l'air\",\n", " legend=None)\n", "plt.xlabel(\"year\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour séparer les oscillations et la croissance, nous faisons une separation des données en années." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800yeartime
019745191974.3795333.375-999.99-999.9950.390.3795
119745261974.3986332.956-999.99-999.9950.050.3986
21974621974.4178332.355-999.99-999.9949.590.4178
31974691974.4370332.207-999.99-999.9949.640.4370
419746161974.4562332.377-999.99-999.9950.060.4562
519746231974.4753331.735-999.99-999.9949.720.4753
619746301974.4945331.696-999.99-999.9950.030.4945
71974771974.5137331.466-999.99-999.9950.200.5137
819747141974.5329330.835-999.99-999.9950.010.5329
919747211974.5521330.767-999.99-999.9950.410.5521
1019747281974.5712329.814-999.99-999.9949.970.5712
111974841974.5904329.855-999.99-999.9950.540.5904
1219748111974.6096329.155-999.99-999.9950.370.6096
1319748181974.6288329.066-999.99-999.9950.800.6288
1419748251974.6479328.337-999.99-999.9950.540.6479
151974911974.6671328.065-999.99-999.9950.680.6671
161974981974.6863327.564-999.99-999.9950.530.6863
1719749151974.7055326.726-999.99-999.9949.950.7055
1819749221974.7247326.995-999.99-999.9950.380.7247
1919749291974.7438327.315-999.99-999.9950.760.7438
2019741061974.7630327.076-999.99-999.9950.490.7630
21197410131974.7822327.235-999.99-999.9950.530.7822
22197410201974.8014327.405-999.99-999.9950.510.8014
23197410271974.8205327.647-999.99-999.9950.500.8205
2419741131974.8397327.807-999.99-999.9950.390.8397
25197411101974.8589328.506-999.99-999.9950.790.8589
26197411171974.8781328.616-999.99-999.9950.590.8781
27197411241974.8973328.465-999.99-999.9950.140.8973
2819741211974.9164328.805-999.99-999.9950.200.9164
2919741281974.9356329.397-999.99-999.9950.530.9356
.................................
256020236112023.4425424.136421.00398.78141.490.4425
256120236182023.4616423.497420.84398.39141.280.4616
256220236252023.4808422.197420.32398.78140.460.4808
25632023722023.5000422.624419.91398.34141.410.5000
25642023792023.5192422.355419.18397.93141.660.5192
256520237162023.5384421.345418.36396.93141.190.5384
256620237232023.5575421.284418.03397.30141.670.5575
256720237302023.5767420.836418.10396.80141.750.5767
25682023862023.5959420.026417.36395.65141.460.5959
256920238132023.6151418.984417.25395.24140.920.6151
257020238202023.6342419.312416.64395.22141.710.6342
257120238272023.6534419.275416.42394.45142.090.6534
25722023932023.6726418.644416.27393.92141.810.6726
257320239102023.6918418.522416.15393.52141.960.6918
257420239172023.7110418.335415.65393.79141.940.7110
257520239242023.7301418.297415.34393.46141.970.7301
257620231012023.7493418.316415.30393.52141.960.7493
257720231082023.7685418.535415.39393.58142.050.7685
2578202310152023.7877419.467415.82393.98142.770.7877
2579202310222023.8068418.916416.19394.23141.930.8068
2580202310292023.8260419.057416.41394.47141.750.8260
258120231152023.8452419.285417.00394.80141.640.8452
2582202311122023.8644421.226417.31395.64143.240.8644
2583202311192023.8836421.215418.38395.26142.880.8836
2584202311262023.9027420.312417.81396.21141.660.9027
258520231232023.9219421.022419.23396.43142.070.9219
2586202312102023.9411422.207418.81396.39142.960.9411
2587202312172023.9603422.245419.05397.72142.740.9603
2588202312242023.9795421.865419.41397.53142.120.9795
2589202312312023.9986422.524419.34397.73142.550.9986
\n", "

2572 rows × 10 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 yeartime \n", "0 50.39 0.3795 \n", "1 50.05 0.3986 \n", "2 49.59 0.4178 \n", "3 49.64 0.4370 \n", "4 50.06 0.4562 \n", "5 49.72 0.4753 \n", "6 50.03 0.4945 \n", "7 50.20 0.5137 \n", "8 50.01 0.5329 \n", "9 50.41 0.5521 \n", "10 49.97 0.5712 \n", "11 50.54 0.5904 \n", "12 50.37 0.6096 \n", "13 50.80 0.6288 \n", "14 50.54 0.6479 \n", "15 50.68 0.6671 \n", "16 50.53 0.6863 \n", "17 49.95 0.7055 \n", "18 50.38 0.7247 \n", "19 50.76 0.7438 \n", "20 50.49 0.7630 \n", "21 50.53 0.7822 \n", "22 50.51 0.8014 \n", "23 50.50 0.8205 \n", "24 50.39 0.8397 \n", "25 50.79 0.8589 \n", "26 50.59 0.8781 \n", "27 50.14 0.8973 \n", "28 50.20 0.9164 \n", "29 50.53 0.9356 \n", "... ... ... \n", "2560 141.49 0.4425 \n", "2561 141.28 0.4616 \n", "2562 140.46 0.4808 \n", "2563 141.41 0.5000 \n", "2564 141.66 0.5192 \n", "2565 141.19 0.5384 \n", "2566 141.67 0.5575 \n", "2567 141.75 0.5767 \n", "2568 141.46 0.5959 \n", "2569 140.92 0.6151 \n", "2570 141.71 0.6342 \n", "2571 142.09 0.6534 \n", "2572 141.81 0.6726 \n", "2573 141.96 0.6918 \n", "2574 141.94 0.7110 \n", "2575 141.97 0.7301 \n", "2576 141.96 0.7493 \n", "2577 142.05 0.7685 \n", "2578 142.77 0.7877 \n", "2579 141.93 0.8068 \n", "2580 141.75 0.8260 \n", "2581 141.64 0.8452 \n", "2582 143.24 0.8644 \n", "2583 142.88 0.8836 \n", "2584 141.66 0.9027 \n", "2585 142.07 0.9219 \n", "2586 142.96 0.9411 \n", "2587 142.74 0.9603 \n", "2588 142.12 0.9795 \n", "2589 142.55 0.9986 \n", "\n", "[2572 rows x 10 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_min = co2_clean[\"year\"].iloc[0]\n", "year_max = co2_clean[\"year\"].iloc[-1]\n", "co2_clean[\"yeartime\"]=co2_clean[\"decimal\"]-co2_clean[\"year\"]\n", "co2_clean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans cette boucle, nous calculons la moyenne de chaque année séparement." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearyearly average
01974.0329.502121
11975.0331.217447
21976.0332.031765
31977.0333.844423
41978.0335.420755
51979.0336.880192
61980.0338.762308
71981.0340.130577
81982.0341.347551
91983.0343.165882
101984.0344.680000
111985.0346.342885
121986.0347.616154
131987.0349.335000
141988.0351.698269
151989.0353.215849
161990.0354.453846
171991.0355.741346
181992.0356.547115
191993.0357.228077
201994.0358.965192
211995.0360.979245
221996.0362.756346
231997.0363.887500
241998.0366.865385
251999.0368.521538
262000.0369.732830
272001.0371.341154
282002.0373.474808
292003.0375.987500
302004.0377.720385
312005.0380.056078
322006.0382.083585
332007.0384.083462
342008.0385.793469
352009.0387.663077
362010.0390.085192
372011.0391.859808
382012.0394.068868
392013.0396.773269
402014.0398.857500
412015.0401.020769
422016.0404.434423
432017.0406.774906
442018.0408.746346
452019.0411.681346
462020.0414.226154
472021.0416.404808
482022.0418.514615
\n", "
" ], "text/plain": [ " year yearly average\n", "0 1974.0 329.502121\n", "1 1975.0 331.217447\n", "2 1976.0 332.031765\n", "3 1977.0 333.844423\n", "4 1978.0 335.420755\n", "5 1979.0 336.880192\n", "6 1980.0 338.762308\n", "7 1981.0 340.130577\n", "8 1982.0 341.347551\n", "9 1983.0 343.165882\n", "10 1984.0 344.680000\n", "11 1985.0 346.342885\n", "12 1986.0 347.616154\n", "13 1987.0 349.335000\n", "14 1988.0 351.698269\n", "15 1989.0 353.215849\n", "16 1990.0 354.453846\n", "17 1991.0 355.741346\n", "18 1992.0 356.547115\n", "19 1993.0 357.228077\n", "20 1994.0 358.965192\n", "21 1995.0 360.979245\n", "22 1996.0 362.756346\n", "23 1997.0 363.887500\n", "24 1998.0 366.865385\n", "25 1999.0 368.521538\n", "26 2000.0 369.732830\n", "27 2001.0 371.341154\n", "28 2002.0 373.474808\n", "29 2003.0 375.987500\n", "30 2004.0 377.720385\n", "31 2005.0 380.056078\n", "32 2006.0 382.083585\n", "33 2007.0 384.083462\n", "34 2008.0 385.793469\n", "35 2009.0 387.663077\n", "36 2010.0 390.085192\n", "37 2011.0 391.859808\n", "38 2012.0 394.068868\n", "39 2013.0 396.773269\n", "40 2014.0 398.857500\n", "41 2015.0 401.020769\n", "42 2016.0 404.434423\n", "43 2017.0 406.774906\n", "44 2018.0 408.746346\n", "45 2019.0 411.681346\n", "46 2020.0 414.226154\n", "47 2021.0 416.404808\n", "48 2022.0 418.514615" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_data = np.zeros([year_max-year_min, 2])\n", "for i in range(year_min, year_max):\n", " year = co2_clean.loc[co2_clean[\"year\"]==i]\n", " y_avg = year[[\"average\"]].mean()\n", " avg_data[i-year_min, 0] = i\n", " avg_data[i-year_min, 1] = y_avg.values[0]\n", " \n", "yearly_avg = pd.DataFrame(avg_data, columns=[\"year\", \"yearly average\"])\n", "yearly_avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous convertissons le temps dans une année en semaines calendriers car c'est plus intuitive. Après, nous joindrons les données originaux avec la moyenne annuelle." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800yeartimeweekyearly average
019745191974.3795333.375-999.99-999.9950.390.379519.7340329.502121
119745261974.3986332.956-999.99-999.9950.050.398620.7272329.502121
21974621974.4178332.355-999.99-999.9949.590.417821.7256329.502121
31974691974.4370332.207-999.99-999.9949.640.437022.7240329.502121
419746161974.4562332.377-999.99-999.9950.060.456223.7224329.502121
519746231974.4753331.735-999.99-999.9949.720.475324.7156329.502121
619746301974.4945331.696-999.99-999.9950.030.494525.7140329.502121
71974771974.5137331.466-999.99-999.9950.200.513726.7124329.502121
819747141974.5329330.835-999.99-999.9950.010.532927.7108329.502121
919747211974.5521330.767-999.99-999.9950.410.552128.7092329.502121
1019747281974.5712329.814-999.99-999.9949.970.571229.7024329.502121
111974841974.5904329.855-999.99-999.9950.540.590430.7008329.502121
1219748111974.6096329.155-999.99-999.9950.370.609631.6992329.502121
1319748181974.6288329.066-999.99-999.9950.800.628832.6976329.502121
1419748251974.6479328.337-999.99-999.9950.540.647933.6908329.502121
151974911974.6671328.065-999.99-999.9950.680.667134.6892329.502121
161974981974.6863327.564-999.99-999.9950.530.686335.6876329.502121
1719749151974.7055326.726-999.99-999.9949.950.705536.6860329.502121
1819749221974.7247326.995-999.99-999.9950.380.724737.6844329.502121
1919749291974.7438327.315-999.99-999.9950.760.743838.6776329.502121
2019741061974.7630327.076-999.99-999.9950.490.763039.6760329.502121
21197410131974.7822327.235-999.99-999.9950.530.782240.6744329.502121
22197410201974.8014327.405-999.99-999.9950.510.801441.6728329.502121
23197410271974.8205327.647-999.99-999.9950.500.820542.6660329.502121
2419741131974.8397327.807-999.99-999.9950.390.839743.6644329.502121
25197411101974.8589328.506-999.99-999.9950.790.858944.6628329.502121
26197411171974.8781328.616-999.99-999.9950.590.878145.6612329.502121
27197411241974.8973328.465-999.99-999.9950.140.897346.6596329.502121
2819741211974.9164328.805-999.99-999.9950.200.916447.6528329.502121
2919741281974.9356329.397-999.99-999.9950.530.935648.6512329.502121
.......................................
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254320236182023.4616423.497420.84398.39141.280.461624.0032NaN
254420236252023.4808422.197420.32398.78140.460.480825.0016NaN
25452023722023.5000422.624419.91398.34141.410.500026.0000NaN
25462023792023.5192422.355419.18397.93141.660.519226.9984NaN
254720237162023.5384421.345418.36396.93141.190.538427.9968NaN
254820237232023.5575421.284418.03397.30141.670.557528.9900NaN
254920237302023.5767420.836418.10396.80141.750.576729.9884NaN
25502023862023.5959420.026417.36395.65141.460.595930.9868NaN
255120238132023.6151418.984417.25395.24140.920.615131.9852NaN
255220238202023.6342419.312416.64395.22141.710.634232.9784NaN
255320238272023.6534419.275416.42394.45142.090.653433.9768NaN
25542023932023.6726418.644416.27393.92141.810.672634.9752NaN
255520239102023.6918418.522416.15393.52141.960.691835.9736NaN
255620239172023.7110418.335415.65393.79141.940.711036.9720NaN
255720239242023.7301418.297415.34393.46141.970.730137.9652NaN
255820231012023.7493418.316415.30393.52141.960.749338.9636NaN
255920231082023.7685418.535415.39393.58142.050.768539.9620NaN
2560202310152023.7877419.467415.82393.98142.770.787740.9604NaN
2561202310222023.8068418.916416.19394.23141.930.806841.9536NaN
2562202310292023.8260419.057416.41394.47141.750.826042.9520NaN
256320231152023.8452419.285417.00394.80141.640.845243.9504NaN
2564202311122023.8644421.226417.31395.64143.240.864444.9488NaN
2565202311192023.8836421.215418.38395.26142.880.883645.9472NaN
2566202311262023.9027420.312417.81396.21141.660.902746.9404NaN
256720231232023.9219421.022419.23396.43142.070.921947.9388NaN
2568202312102023.9411422.207418.81396.39142.960.941148.9372NaN
2569202312172023.9603422.245419.05397.72142.740.960349.9356NaN
2570202312242023.9795421.865419.41397.53142.120.979550.9340NaN
2571202312312023.9986422.524419.34397.73142.550.998651.9272NaN
\n", "

2572 rows × 12 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2542 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2543 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2544 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2545 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2546 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2547 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2548 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2549 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2550 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2551 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2552 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2553 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2554 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2555 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2556 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2557 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2558 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2559 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2560 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2561 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2562 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2563 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2564 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2565 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2566 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2567 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2568 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2569 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2570 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2571 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 yeartime week yearly average \n", "0 50.39 0.3795 19.7340 329.502121 \n", "1 50.05 0.3986 20.7272 329.502121 \n", "2 49.59 0.4178 21.7256 329.502121 \n", "3 49.64 0.4370 22.7240 329.502121 \n", "4 50.06 0.4562 23.7224 329.502121 \n", "5 49.72 0.4753 24.7156 329.502121 \n", "6 50.03 0.4945 25.7140 329.502121 \n", "7 50.20 0.5137 26.7124 329.502121 \n", "8 50.01 0.5329 27.7108 329.502121 \n", "9 50.41 0.5521 28.7092 329.502121 \n", "10 49.97 0.5712 29.7024 329.502121 \n", "11 50.54 0.5904 30.7008 329.502121 \n", "12 50.37 0.6096 31.6992 329.502121 \n", "13 50.80 0.6288 32.6976 329.502121 \n", "14 50.54 0.6479 33.6908 329.502121 \n", "15 50.68 0.6671 34.6892 329.502121 \n", "16 50.53 0.6863 35.6876 329.502121 \n", "17 49.95 0.7055 36.6860 329.502121 \n", "18 50.38 0.7247 37.6844 329.502121 \n", "19 50.76 0.7438 38.6776 329.502121 \n", "20 50.49 0.7630 39.6760 329.502121 \n", "21 50.53 0.7822 40.6744 329.502121 \n", "22 50.51 0.8014 41.6728 329.502121 \n", "23 50.50 0.8205 42.6660 329.502121 \n", "24 50.39 0.8397 43.6644 329.502121 \n", "25 50.79 0.8589 44.6628 329.502121 \n", "26 50.59 0.8781 45.6612 329.502121 \n", "27 50.14 0.8973 46.6596 329.502121 \n", "28 50.20 0.9164 47.6528 329.502121 \n", "29 50.53 0.9356 48.6512 329.502121 \n", "... ... ... ... ... \n", "2542 141.49 0.4425 23.0100 NaN \n", "2543 141.28 0.4616 24.0032 NaN \n", "2544 140.46 0.4808 25.0016 NaN \n", "2545 141.41 0.5000 26.0000 NaN \n", "2546 141.66 0.5192 26.9984 NaN \n", "2547 141.19 0.5384 27.9968 NaN \n", "2548 141.67 0.5575 28.9900 NaN \n", "2549 141.75 0.5767 29.9884 NaN \n", "2550 141.46 0.5959 30.9868 NaN \n", "2551 140.92 0.6151 31.9852 NaN \n", "2552 141.71 0.6342 32.9784 NaN \n", "2553 142.09 0.6534 33.9768 NaN \n", "2554 141.81 0.6726 34.9752 NaN \n", "2555 141.96 0.6918 35.9736 NaN \n", "2556 141.94 0.7110 36.9720 NaN \n", "2557 141.97 0.7301 37.9652 NaN \n", "2558 141.96 0.7493 38.9636 NaN \n", "2559 142.05 0.7685 39.9620 NaN \n", "2560 142.77 0.7877 40.9604 NaN \n", "2561 141.93 0.8068 41.9536 NaN \n", "2562 141.75 0.8260 42.9520 NaN \n", "2563 141.64 0.8452 43.9504 NaN \n", "2564 143.24 0.8644 44.9488 NaN \n", "2565 142.88 0.8836 45.9472 NaN \n", "2566 141.66 0.9027 46.9404 NaN \n", "2567 142.07 0.9219 47.9388 NaN \n", "2568 142.96 0.9411 48.9372 NaN \n", "2569 142.74 0.9603 49.9356 NaN \n", "2570 142.12 0.9795 50.9340 NaN \n", "2571 142.55 0.9986 51.9272 NaN \n", "\n", "[2572 rows x 12 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_clean[\"week\"]=52*co2_clean[\"yeartime\"]\n", "co2_merge = pd.merge(co2_clean, yearly_avg, how=\"outer\")\n", "co2_merge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec cela, nous pouvons tracer l'oscillation de $CO_2$ en chaque année qui est liée aux différents saisons de l'année." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,\"Oscillation pendant l'année\")" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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a5SWWq+tEZ4jCzFSOd0SPfyjGT6M5W0lWBXYd8IBSas/oDSk5tArsqcc0FVev3cIO25W0YHoh629cyLWPbaOqrsXVWjIEzpuay75D7cwtyefJlYto7QkzPtuPaSrmfOvProsolnOnZLP/SAeZfoPukJkw73pucR47PQZCgM1fvIDPPrnLqq3wp9DVG7EMlsetpGMSmrONoarADrWYbg+W2zcF+JSI/BPL3TRqAn+a04emQNA1EADV9S283tRh1TM4BgLISPWx51A7YLmAPvrwZl453E5FaSFfvXRWQgMB8MqRDsAKUnsxxNrhzJyczdoV5Vz8wMtRxxVw6xM17D7UbtVWBK3Mqu1vNHOsvQefz4hSjdVoNNEM1d00YE8HzdlN7MK7bFoe75iQZaXANrRSUVrI1y8/l0vu75vA01JwDcb2g81ctmZT1DXSfDCAzQBgxsRMXmvqImIq9h0O8Omf7YhraGob2pgzLZdd9us53PCzHRw42kHZtDyeurkvSK7RaPoY0q9CKVWnlKoD3sTKbvoh8APgo/ZjmrOI2KI0p+DNAOYW5fGja+ex4rHt7Gpso6w4nw0rFzIhO3qVbpdKJKTXhHnF+cwvzccQ3AI8L38/1hUVyH71WFfC6+0/Goi6n5Ei7DscIGIqahtaueLBTUQiWjxAo4kl2aXTz4DzgAeANcAs4PGRHpRm7BKvaZBT8LblSxfhTzFYdu/zbH+jmYip2N3YZkllAPPtyuk5RYML7pnKyn4K9kYwVXTdw3AIRfqsyczJ2XSHo2Nxew+38/G1W9wmSLo6W6OxSDa7aYZSao7n/vMismskB6QZmziBXaVU3HRRwxAMQ6ipb8UzH5OZ6uO2J6qobWijvCSfTXdeyKrHdwzpNSNKsftwIOHxLL+PziSNx9vGZZIVZ1cCfSm547L8um5Co7FJ1kjUisgipdRWABFZCGwa5BzNaY632Ky8tIDyknw3pbUwM5WmQJDx2X4KM/vqIBw7EQiGqaq3so121LVyvDPInkOJJ/5kSNZAAPgwqa5vi3ssM9VHbyTC/iNt7DjYjGkLDzYFggNKh2g0ZzLJpsDuB2ZgNR8CKAH2YynBnpIsJ50CO/o0BYIsvvtZwqbCJ/D71csYn53GuCw/Kx7dRnVdC/OK8wibsPtQG7Mm57D3cHvca80rzouqYXAQ+99QogIZqUJ378lxA80vzUdEBpQz12hOR0Y0BdaDLqQ7C3GKzZweDpet2URlaQH3L5/nup684np7D7cz1xb3y0gVuuwJ/fxpuexqjL+KV/SX1ohHZqpBd+/IBpjnFuWxM8G4unojHDgSGFDOXKM5k0k2cL0AaLYznT6BleU0zpP9pDkDcSqtn7njXXSFIkRMRVWd1QvCke2O5UfXlXPetFzXQADsO9RORurw0kzT7dO6es0hGZOhMntqNp9+13QS7Q32HwlQVpSHT6C8JF9XY2vOOpL9xX5VKRUQkWXAB4CfAg+N/LA0YwknaH3OpGwqSgoAiJiK29fXACCGkJ3WtykVQBTsj3E5KfoXw8VSUZwXN7Dck8TmwRDr32CkGbDvSAd3bNgVZXgEOGdiJgJUlBSQYhhWMYgIOtlJc7aRrJFwIoWXAA/ZLU310uoMxpvyuvyRbdx3zVx89gRcU99KdX0rEVPR7QkiK6C1u5fK6YUYYonuVZTkDfpl84nwrY/MHjQgPZABMICnb1/Kli9eyJxpOQNeJ2jSTzDQGf+rx7pQQNhU1NRbleM1trtJozmbSNZIHBKRtcBVwB9t6XBdpnoGE6uQ6jOEyumFbje5itIC+3Y+2WnWDiAnPYUZk3N44oaFzC3O55XD7bz6ZidiSL8J/tzJ2e7ttBTBjEQbiLSU6BPOm5LD/m+8n/OmxjcAJnDZA5u4Y+NOnrp5KfOKrWK8ecV5bP7Cu5P++3c2tFJWlDdg9zyN5kwm2cD1VVjB6+8rpVpFZArwHyM/LM1YIVYhdUJOWpQSbCSieL2pg3dMyOJ4R4iWrhDvnJjNic5eIqbJroZWTPo0k2JRHkdPV6/JZQ9uIctvuG6pYEzR24+vn09aWiqPfXI+i+55LsE1+1JX//395/COiVkUZqbxWlMHs6fmsGeA2ot410oxhE1fvJCJOWk6BVZz1pGUkVBKdYnIP4APiMgHgJeUUn8enaFpRoNk1E4d+e/7r5mLiCWAJyKI4EqBX/vYNjfrqSsUoaKkAKVMqutbyfT7ogrroH8B3P6jnf1etzNkxm0kNHtqNiJCJGLS0jWw22duUR7Lvvc8JlaMISsthY4EhmowqupabSVLON4RdNuoatVYzdlAsp3pPgOsBH5tP/RzEVmnlHpgxEemGXGS6cBmmopr1m1xW4UumF7IxlWLELE6yL3e1EFBZqql9KogYIsxVdf3Kb/GE9vrDEU4f2oOewdYzVeUWO1Gq2KK3hTC4rufdeW+s9N8bp9rB+evae3udWsuFCQ0EJl+g65BgukKWPWzKlJ94laOg1BTryuyNWc+ycYTbgAWKqW+ppT6GrAIy2hoTgPidWAb7LkO1fWW++ZoazdzvvUnLr7vJd7z/Rc4b0o2gtUu1GcIZdPyBh3H3sMBN4Mpy2+d52AAPp+PNSsqouIXhmAJ8tly3xFT0RGM8Mwdy5hb3PeaTr3F603RO5RMv/VVz07z8dNP99UPdYdMMu203Cy/wdwEulI7G9vYUddK2FRUHWyhqq55SO+jRnO6k6yREPoynLBv6yXUaYITXxhKENZ5rkNFST6rN9Sw5J7n3HhBd6/JrkMBMvwGncEIc4ry+OVNi1hgZzV5STOivyiOy6m719pZOMdMrHiCYQhzivLd55sK5pbk97vuuCw/v7p5CeUl+Qh9xiCWrpDJjEnZlBRm8OmfVJNtGymFFQtxnvPQJyp54sYFUYYrFhPLfeUTdDBbc8aTrCzH54B/A35jP/Rh4CdKqf8ehbENCS3LkRzxYhKxrUcdf3thZirHO0PuBL7knucIx8sZtUkxhC1fuohxWX6aAj1c8P0X3Ak4Hj5DKC/O48DRjqjAtiHwytffzyd+soMdrrurwA2Y3/rzKtcVteBthWxcuQjTVFxl98jO9PvoCZukpxhJ6TsJUF6Sl1DbKXbsz6xexozJOVHvo45TaE4XRkWWQyn1AxH5G7AU6zf1KaVU7TDHqDkFxHZgi209Or+0ABGhur6FsqI8nrrJasYTiZjMnpYbV3cpM9WgJ2y6q2oryJ3Okzct4rI1m+OOI8tv8Ozn30NLZ4gP3h/dTc5U8OGHt/Dqm1bcwifC/dfMpbmrlwk5aaxZUcHSe58noqJrF3Y3ttkFexEMgXOn5NAbMdnZGF9HKhYF/QxEug964tiZipJ8CrP6dhDJxHs0mtOJZFNgUUpVA9WjMBbNKSBe61ERsZrx1Ldy5dot/GLVYq59bFtC3aWuXpN5xXn87Pr5/P1ogHdMyGLFo9vZfrDZfY4h0YVrPWGFAIVZfnLSU9zAt8MBT5OgiFK894cv0hWMMKc4D58IEXsHXF5iKdGe6AwxrySf6oMtmFivVdvQxqY7LySiTC76/gv9ekgMhXgGAiDYG2bR3c9SVpTHr25eQnNXb1wJdY3mdCfZ7KZK4D+BUvtc3eP6NEfFJJqWTctDRKhtsAT7djW08uqxAFV1LVGTfGwqa21DG+V3/ZXOUMRy9/RGz66xXqqIqbjoB3+jOxShvCSfb15xPm8fl8nH1m1l76H+K38nU8q7kzGAr182y13BZ/pTEEPI8fvoCoapKC1gYm4axztCDJLAREaqQSiiKJuW5/7tA7HLljvf2dDGxx7axK9uWerWk5SXFKCUQiml3U6a055kA9dPAP+D1cL0Mqze15eN9KA0JwfTVNyxcad7f25RLr++dQlP3bzYDQTPmpLLV3+7x01rdXjnxOx+Xx7HaHSFInHlLnyGWBXQ9v2OYISIveKfkJNOWloqj3yiMio4nZXmwxDrfy8GkJWewuUPbmb7wZaorKeuUIRn7niXnbJr9YGoLC3AZwhzi/J4ZvVSKkv6MqLmFefx3OffzeY7L+Cha+e5leOJiPUi7Wxs53hniA0rF7HpzgsBxZJ7nnM792k0pzPJupualFJPj8pINCedE50hamxXk88Q1n1yPoZhTeEbb1xE+V1/idsXIsvvSyitHQ+fCOUleXzrivM5Z1I2Kx7dTlVdC5meFf/4bD+mqfjMxlrXwMwrzufJlQv554ku3j4+k6se2cbuxjYqSgr45uXncumaTa7xcoxGVyhCZWlBVEDZUbH1BpV/cfNSmjqCoBSrN9Sy7N7n3YLAWIMYS9zDSkV159NuJ82ZQrJG4usi8ijwLBB0HlRK/TrxKUNHRIqx+mhPxso0XKeUum8krq3pT6zkxrgsP2+29SACTR3BqGI4g76GQMl2hFMo9h1p55IHXmb+9EKeuGEhLd29FGam0tzV607cxwI9bm2GAXznw+dx3f/soMYe3y9XLaalu9dNOa30uHfWrJjHuCx/1PW8xAbsDUOYlJvOm+09/QoC43H+1FxeP9ZOoqc4WYKx76lOj9Wc7iSbAvtzYCawj745QymlPj0ig7G0oKYopWpEJAcrQP5hpdQric7RKbCJGSwl05v6avV13uYGmzNSoNszIZZNzSYtNZWa+hZmTs5m35EO99icabnsOdROeUkeYhhu2mo8fIaw9UsX9Vtdm6bimke2sv0N6/Wz03xRbiufwJYvXcTE3PQh/32DEfuaA3W82/fV97Lg3ucSSp1npgq7v34xKSmGO65YI6jRjCWGmgKbbExijlKqUin1b0qpT9n/RsRAACiljiilauzbAazWqNNG6vpnE16J73i+cef40nueY/WGWo53hqiq68tG6o5ZMe853MF9y+dSVpzPKx4DAfD3o+2YwL4j7Xztklnul8oQqxudl7KivLir66ZA0N1F+ET6xTUiCm7fUBv1dzi7g+FOwCc6Q1R7MrAGaol62Y82DdgLo6tXse2NE+6OIt77b5qKpkCQZBZmGs2pJll301YROXeglf1IISLTgXnAttF+rTOReBIc3tV77HGlFLOm5LAvgaaSAm57opZdDa39hPecNNHuXsVlD24my++jOxShsrSQH15dxk2PV7PvcIB5Jfk8dfPifpO6aSpWb6hxYwEVpValdU19K7On5bL7UHtUPwdHXPCtFq6Nz/YzpzifmvrobKbYdF2Af57oHvR61z62nQXTCwCJ0rByFGnv2Fir6yg0px3J7iSWATtF5O8isltE9ojI7pEelIhkA78CPquU6hc5FZFVIlIlIlVNTU0j/fJnBINJcHiPl5fkc8fGnbwyiIT2roZWzp0WX9vIS2cowjmTsglHwiy99wX2Hg4wtzifJ1cu4kRnb7+V9InOkDtR+wTWrChn46rFbPnSRfzqliVUxvwdg+2ShoqI8MubFvfb7aSn+pgzhL8zHlUHW6IMhCPdIcKQdbM0mrFEsjGJ0niPj2R/axFJBf4A/Ekp9YPBnq9jEolJKMERCKKwitlEBFMplt7zXD9Z73hUluQRUcLuQ20opdwVd5pPCA5wAQHmFuex51B7v5W0Uopr1vVVKzupq4n+jqZAkMV3P0vYVK4UyHAyiPrcPyY3/HRHVJxlblEuIga7GlvjZzMlYG5RHv5UHzV1LVb9x+XnuZlWA/2NGs3JZqgxiWSNxL1KqTsHe2y4iPWr+SnQrJT67FDO0UZiaDgT4u3ra6IqrOfbq9ztAwSbvTgNeE50BPmQR07jpS+8m/f/4MUhVTXHm9iTcR8NZlSGgiWFvtUN1M8vzae7N+JKmBt2oFyA256oZkfd4AV2AFvuvAAMobkjyH/+Zi97DrdTaRtF0D0oNINzsjTARkW7CXgfEGsQPhjnseGyFPgEsEdEnCqvLyul/jhC1z8rcdwzVXUt/WoAqutakCR84+Ul+VaHtpjH23si9MQYiHi+fecase6v2BTVRDg/oPU3LnxLmUMnOkNU1/cZxpr6Vl7+wgW8979fpDMYISsthfFZfnw+g/uXl7M4QRc8L+kpwm1PVFHTGO22q/LEUpy/UYsBauIxFjXAhmQkROQW4FbgX2JiEDlAfAW3YaCUehktPT7iOEHqeEViJpCd6qO7d/AiMp8hfP2yc2kKWN1tWESiAAAgAElEQVTZFkwvoLqulYrSfL7+u739AtrxLmfYMYehKKfGHov3A3orQeuK0gI3/bWitACfz6DHqRoPhl1BwYFkw730hFU/AwEwa0oOpmm6Mh1jcSLQjA0GSzg5FQx1J7Ee+F/gbuCLnscDSqnm+KdoxgrOhFh1sJmyony+fcW5XOpRZ+0KhXlweTm3rK9xH/OJUFGaTyhsutXV6SnCJQ9sAiz3TG/EmvhCEZPdcdRhYzGA+dMLo1bTiSbLeMdG8gckImxcuYimQNBtxwpQOb2wXyFcQUZqwl1RvDarsew91M7Cu59jwfQCNq5aPCYnAs3YYCwWYw7JSCil2oA2YLmIFADvBNLB+rEppV4cvSFqBmIobgsR4fFPLeBjazez+1Ab33rmAPNL810/e+X0Qt5/3kRy0lLcvg6zp+Vw/zXzWPa95wFrB+CtE/D66Hc3tjGnOM8V38tJT6ErGKa8JJ+wqdjV2EZlaQFrVpQzISeNSETxelPAbX/qTJZNHUEMW2sp3kQ60j8gwxAm5aVHPRYr32Gaqp+4IfRVoCuiq9EHospOhVUoZhflsbuhdcxMBJqxQTwJmVNNsiqwNwKfAYqAnVjtS7cAF4780DSDMVS3hWkqrn5kK3tsddXquhY233khCG6jIRHhT599F0vutYzCzsZ2mgI9nD81l52NbZgqcYxhTlEeD19XYU2YIq48RmFmKssf2er6D8dnWwZi3nf+QqAnTE56CvNs41Jeks/qDbWuBMf6GxdGGYTCzFSOd7z1WMRgeGMjzvu7/Y1m1xBk+n38ctVCCrPTWHbv80TU0AwEWAq7t6/vC4LPK85n/Y0Lx8REoBk7DDU+d7JINnD9GWA+sFUpdYGIzAS+OfLD0gyFpo4gVQebiSgGdFs0BYJR8tdl03JdF4s3NtDW3Rt13mUPRoeb4hmIjFSDVJ/B0nufjzJUE3LSaAoEqalvJaKswPCJzhAnOoKuRlKgJ8w3rzifCTnpKKXcznfVdS00d/W6K6rCzFRWPLptRGIRydDUEWTHG81u3+yZk3N4+tYltAUjjMtKtWIaQ8wKy0gVHr6unKW2EQbYfajNjXtozj6Gm7xwspMeki2m61FK9QCISJpS6gAwY+SHpRmMcNjk5ser3doGb8aQk+4aiZg0BYLEes0PHGln8T3PcfXaLbxyuI1wOMLyR7Zy6ZpNZPkHlsmOpafXpNpWPa2qa+HVNwP9xO68hXDnTMomJ91am+SkpzBzco6b9RP7XMfYxGvoM9pYVeC1UbuE1451cM2j2+wivm185dJZQ75eT68VtI7qG65dTWctwy0IHalC0mRItk7iN8CngM9iuZhagFSl1IdGZ3iDc7bUSXhXD0rBxx/e3FelbAh/uH0pM6fkohRWDUF9C+kphiWPMb0gKgAdS1aaj55ec9DsJofMVMPtXT2/NN9ud9pKZqqPrlCYyumF7o4i3qonHDZ5vamDcyZlu9LksX+jd4U0EnURyeIt2APL1TanOJ/dDa2uYR5K0NrLgrcVsv6GhZzoCoGd6TSQcq3mzGW4BaEjVUgKo9fj+iP2zW+IyPNAHvB/wxifJgliYw/3XzOPnR73UcRUfOj+l1nwtkLuu3quWyDmSHpX17Xw29uWRPWb9k5wncFIv0Y6A+EYCJ8hPLCinDs21KKUcoPeVQebOd4RZGJuelz/akqKwcwp/WUvEvliT0Uwzxskd6TIx2dbSrlOvUmya7iauhZaunspSE/lY2s3s/dQO9meHhg6FfbsYbhJGOOz/ZSXFFBd30L5SdqJJt3j2kEp9beRHIgmMbGZPiJw3tRcNxAN1oRfdbCZ5s5gv/NnTcnhm0/3aTKWF+cSiii3uhj6xxti25N6yfQb9IRM5hTlIVjxhniKrRuTmPQG87Oe7GBeIsO0YeUijndYlevVdS1k+H1RfTcA0lMMesJ9jqp5HjmSXL+Pud/5C132e+vEZ3Qq7NnFcBc+luNHWTeUQikY7TXTkGISIvKGiPxTRLQi6ykg1rc/LsvP2uvK+1UdRhR87el9ZHviCukpsPdwgCqP0mkwbEYZiHg4BiInLQWBqFjFOROzmV2Uy67GNlZvqKW8xBrbvOJ8t/CsJonYwanwsw6FeFLkhiFMzE1n46rF/H71sn4GAqAnbJKZIgiW1tXD11Ww+YsXsv7GhXxs3RbXQICVLeUzhHI7g0tLiZ89DEfq3hHD9CaDjDZDrZN422gPRJMY76rDyfSpqmuJcnc4KZo1da2uzIZPpJ9UBhAlZOdQWZJPdzjC/iOBqF1BZyjM+dNy2evZtexs7LtdU9/Kpi9eaKe+prL8kW1Jb6FPx+IywxDGZyceY1fYElA88GYnS+59noqSfL522blR72Om38esSVnsbGxHmSbLH9lGTb313jnd+3SsQuNg9SQxOd9u8nWyEh+SzW7SDIHhNJcZ6jmxEhs+sYLHWXbGUFZaCvOKchFg5uTsuNeYW5TbLwYRUfCbm5dSVpQX9XhZUR6vHAnE9b8LMM/WcpqQk4ZhGGxYuYgtX7ooqeDyQLLmY7lRz4ScNBZML0CAjJT+f6sCOoJhIqZi+8EWLrWr1cF67/782WXsOhRwV4XV9Zah3HGwmY88tIlF3/3rmNpZaU4djiDlwrufY2dDG2XTck9ajc1QtZsCxE/kEKz2pcMT3z8DGY4uz2DneI+Xl+RTXlJATX1fQBVwBei6eiO8ctSa1PcdCfTLwJlfms/6Gxdx5dotUdlOuw+1sfWN4+z0yGv4RPD7DCpKrMY884rzCCvYWd+K2IV1+4+0E4koUuxJcjixg0T+2bGucSQi/Oz6BVz64Eu83tTlPh4bk4iHAgI9EU9wPB9EqD7YTLrf58abqg42nxY7K83I443TxQpS7mo4eTU2Q3U35Yz2QM4UhuM6SaaLnNe940yovb0RZk3JYf+RALOm5ES5NBwDIcAf71jGzCm5HGsP9kuHjZiKT/5PdCpxRNmvd+eFtnvFSr/d8s/jXPvodgA6ghFePRbg3KnRO5BkSBS0HutuqHDYpOLuv9IZE5cIDmIgwOohPmNyTpRxVApefTPAJQ/0SbDPKe6vmKs584ldIK2/cSHlJflu//jMNB+FmaknZSza3TTCDNYRbjjnWGlv+fikT6rbCXiFQhHmfvsv7LVbfPpFRbmSMlOtj3hucZ7b/GYoO1RDrBTXitICJuamMS7Lz/GOECLwjgnRbqxxWcOfxAYKWg/nvTyZvN7UEWUgMlIFQyA7bfC117p/m8+Jzl638l0pKwe+INOq5PYZkrDdq+bMx7tAqrJ1zXrDfd+17l6T5q7eAa4wcgw7BVYTn+Gktg10Tp9PHivXTcRNewuHTS7/0ctRqaq1MVLVTk1DbUMb16zbxsZVixiX5Sc7zUrdzEw1mDk5m5qGvt3H+dNy+c3Ni2ntiVCYmcqxQLCfrtL8UitX2zIi0SJ5yTDQbmEsip1B387nnROzyElPIdATJivNx86vvJd/HO/iQ/e9NOD5Ajzw7OtU17dSVpTHxhsWcs2j21zplPml+Wy+80Im5iaX+aI5MzBNhVKKecX57LDjjzf9bAe7DvX9ts+fksPxQA/jslKjClJHA20kRoHh+OXjnROvWZCTWjouy8+V67Zw4Gh0ppICKorz2GnvLLzssP3bYK1EnP9NZa2AZ0/L5bsfKWPWlBwMw6BADK5cu4Xa+lbXbeXoKj150+J+aqnDmcwHKyoaa2JnsW6A6i+/l3+e6HSrx2dMzmGuHcNJxDkTMqiubyViKmrrW5l311/o8ijs1tS3YhiiDcRZiPc3P2tKn5ffayAAXmvq4IP3v2wvTt5HampycjrJkJQJEpErRSTHvv0VEfm1iJSPztDODsJhkwNH2jHNaD+2aSoOHG2PMhAGVrbRuKxUTnSG2J1AZiNoKjbdeQHzS/OjHldArt9nua+K893HHJXXXY3tXP7gJpY/so1w2OTKdVuo8RgIgPKSaF0lbxOdxXc/y9Vrt/BmW8+Qs5Gc3UKyGVGnitidT1swzMwpue5qTkT45U2LmVeSn7CK/e9N3VEG3GsgQGs6nc2c6Ay5v/m9h9rjTtAzJ2e735nOYISPPrx5VDPgkt2nfFUpFRCRZcAHsPpRPzTywzozGCx9MxSKMOfbf+bi+15izrf+QtgOeFrpblv40P0vu5PJ/NICyorz2NXYxvJHtlGYmUql47suzicjtW9G2nuoHUF48qYl/MjOfnLYUd/C/iPt3HpB/NKXiD35vd7UEdcI3b98blT2UVMgyPGOoDtxbj/YwpJ7n0sqdXM4RUWniqHESXw+g1/dvIStX7qIBdML4lwlGiduNGtyFs+sXsr6GxdyvCM0JtN+NaNLYWYq6akePTPPMUPg/Kk5/OamRVHn7DnUPqpFdcm6mxzn9yXAQ0qp34nIN0Z2SGcGQ0lr/djazW7gM9AT5vWmDmZOybUkqj0S1IbAt644j8vWbCJiB7JeO9YR1VvhSGu32wsCoPpgMx84fxIfOG8SPrHqIAyBVT/bQVdv4snHwFrJnjMpm8rSgn59sY046anlpQWUl+Rb9Ruqz9CMtWykkSCZOInz3KZAkNebAlz32I64z3O+F/uPdnLJA5YSb3dvhPkeoUTNmUE8t6z3seauXro9McbsNB9doQiZ/hQ6gmH2Hg7w8XXRwhfnT8sd1Z1nskbikIisBd4H3CsiaegMqbic6Ay5vR7i5bqf6Ayxz5OqmuX3cc6kbExTcfsT1VEunorSAmZMznGFvTJSfXzovpeotNthighT8jNYML2AHQetSuxbN9RiAC9/4QJe+cYHqGpoQZkm1/24v2Ku00woJy2Fv37uX5mYm+5OcMc7gtz2RA01dhc152+ISsuta2HTnRciYmk21SSIL5wpDBYnia5rKQAU1fWtZKdZP/RYYqU9nESEHR6hRM3pT7yFI9Av1XX+9EKq6looK8rjl6sW8XpTZ1Ra9L4jffGJzFSD394yuhlwyRqJq4CLge8rpVpFZDLwHyM/rNOfwsxUMtOszJfMtJSonGYne6Fyel/TmlmTswGhKRCMag3qE+HBFeWYJvRGIkRM5U402w9aqXGT7El9/Y2LuPi+v7mFXSaw7P9/nsrSAkCorm/pV1w3tyiPX6xaxD9PdLnBV9NUHO8IMj7bz8Tc9H5BaugfcHYycTaOwWykk01U3KK+BaUUpsL93DL9Bt0h01WAzfT7XKE/L+YwhBI1Y5d4mXxAX6rrwWaaAkHuv2ZeVFOwGZNzrAZXbzT3u2Yworj2sR2juuMcjrspHbhSRLzn/nnkhnT6EW8L2dzV6wq5dYUibnWkdzUxe1quu4qvbWjjeEewXw1DWVEeBRmpfPihTVFFci7KiguMz/bT0t3LP493RR027a51iPTLdvIZwtpPVNAWjLg1FIlWO7EkcruMtWykU4FjQKvqWshM9bkS6g5O0LErGObp25cBioKMVG55orpfFkvNGeq2OxtJlMlXXpLP9oOWq/Z9P3zR7cli6XdZv6+NjtvyWDvXetyWJ8O1m6yR+B3QCtQA/TWpz0ISxR7GZ/upsP30FZ6ucd7VxO7GNsqm5bGzsc2V195w40IWTC+g6mALc4rz+OVNloRGPAMxv7SA1Rtqqapr4dypufxq1SLmFOezq6GVd07MIjc9lZr6VspL8ukOm1HX8IlQUWKdbz2ngAdWzEMgarXT1BHkjg21cWMr2iDExzGgr74ZiFsz4TR5Ki/O5+q1W+gIRdzdhfscv4+esHlGu+3ONhItrB5YXs6Se58jYvb1ZNnxRjNXrt3C7kNtVqOtlYuYlJfOuKxUV8bfJ4CMfqFpskaiSCl18aiM5DQlUTFYJKLojdiNaTwFcLHNbIK9fatMpwZh46o+986x9iC1DfFTXQM9If7+ZicKK6Np1jf+jFIwtySfX6xcxLWPbQcRxDD49U0LmXeXJSGRnebj2c+9GxFx+0pvP9jMkrufpaK0wNWGqii1xOuc4HWVXtUOGcMQ3j4+iwzPDzqi+goVm7vCvHas3Q1md4VMZkzK4u9vdgLQ3Rvhj3e8y93hac4M4i2sxmf7+7kcM/w+t7hy+xvN7D/SzrhsP3ds3El3b4TzpuRw98dmMykn3Y0hjhbJGonNIjJbKbVnVEZzGuJIZjgibeOz/Zim4sp1W9wPudoOXI/LsoS6nBTH4x09XOJRBi2blsf4bD+9vSavvxmgMLOgn/spzQdOnPPAm51kpBpuYZzjTaqtb+W1pg5q6q3JvaauhbZghF1ffb/bNhSE4x1Ba+x2YZejRurVhjJN5X6BM/0nTy/mdMc0FVet2+oGoSMKzp+ay/4jAVY8tp1wxOxn/L988Qx++Ow/2H2ojXOn5vLOiVnaQJyhxGY0dcW4JLt7o5MZvIFrsILXl6/ZTHaaj9pRLqZL1kgsA64XkTew3E2OCmzZiI/sNMFKZZcoyYzYQrc5xfkUZqb2y3jxprkCPHxdOb29JrO+/n9ElCUD/tfPvYuMFKHb7k8QjODeBwhFVNxeywWZqcwuymN3o7VdLcxMpbmrlxmTc1CKqLFsuvMC7nDdTvkIuNvh5q6Q+wXuCoZPmvLk6c6JzhC7GqOrrvcettx9sZ+7w7/9tIbsNB/pKQZ7D7Uz99t/pfYr78Mw5KxPBjhTME1FU0eQ1etrqLZ/b9+47Ny+GJbd6dBUQ+uh3hGM8JGHN/P0bcvGTOD6g6MyitMQZyWglJXB4g0gjc/2uzUGZUV5PHXzYk509noEu5qt7oOe680vzWdibjpb/3GCiH0gouCC/+rzaTvP7/Y0EooNRgOk++CWJ2rY3dDG3JJ8fnb9fD6+dgu7G9uotHtk96nKtuAzDDauWmx9eTfUsuSe59z4w/hsP5XTC5NuJHS247xvTkZK2dQcdg/SDRCi02EDPWG2HTzBfX99zdV5euqmxfh8Ouv8dMGZJwozUzneEWL1hhqq6lrcXf+Ogy1c8sAmq45AoKQwg1fspmBDLaV85XD72AlcK6XqRmUUpxHuSsAO5s6emktGqmGJ5dnumHgBqsLMVGZPy2VnQ5u7WvCyZkU5IsKCtxW4/uvh0hPB7QtRW9/KFQ9tcjWequpaaO4MUl5aEFXPICIYItTEia+MRZG9sY6TDtwUsDLWlFIsvPu5Qc/LTksBpeiw4xifeGx7lBvx4w9v5le3LNUpsacBXh2mTL+PzmC4Xy9597kACg4c7XDFNwfqM+8lKybFfqRJWuBPRAqAd2KlwgKglHpxJAc1VnE/dLtIDojqy9DZE+a1Yx3MmJzjPr8pECQvLYWPrd3s9pWO1xe5qb2b8dlpNHeF2fu19/PCa02s+9tr1B7q32o0EedMyODVpu6ox9JTJEoEUCnFJQ+8TGVpAZvuvJDx2ZYE+Phsf8IUPZ3FNDwMQ5iUZ/1MlFKcPzVnwN7i937kfD5aPo1jnSF217dy6/rafqvJ2oY2tzZGM7bxdpH0BqUNgcxUHx0xBsAQqJxeyOPXz6eqoYUf/OkA1Q1x0t6xEiD2HWpHMfpuYElGH0ZEbgQ+AxQBO4FFwBal1IWjMrohUFlZqaqq+lcRjwZNgSCL736WcJzlgAFk2cVRFSWWeJ5TKDcU3yJYjWiCEav3cVcw4mbGxCM9BWLrr56+dTGX/2jLkP6WFEN46c4LuPWJGtcN5dRE6F3D6BAORyj71p/7CfpB33fE+7k6NTSxbP/yRf2qsIerwqsZPZSyWo5W1bWAshJDctJS+Mvn/hXTVCy99/l+80J5US4hE/Ydbo86NmNiBn8/1s3sKdk8ev0CxmX5uWrdVnY1tFI5vXBY4pgiUq2Uqhzseck6Nz8DzAfqlFIXAPOApiSvcdriFXebX5rPPLsR0ILphTxzxzK6QhE3NlHtqZqOZyAMYMbErKjHusPYlbkRTIgyEJmpBnOK+rrExinQZUJuej/l13ivK1iy4Lf8vJpaO7PJSW89ncT2TjdSUnzs/toH+MPqpZwb03/c+Y54P9d4BiLL78NUKkr8b6DGTZqTQzwxT8ft/MzqZe5n2RkKIwifeXJn3HmhprGdvTEGAuDvxywPwb6jHeSlpXLtY9vZ1dhGWXH+qPe6Ttbd1KOU6rG6m0maUuqAiMwYlZGNIbyrtNh2k02BIAorw6jC9vOXx+wk4lFWlMsvVi1i1tf/PKT4Q0/Y5AsXz+STP94RN1gNlvjekzctoSnQw0X/9Td3O5vp97nV3yaW1PCuhrYohcmyojwdlD4JpKQYTMrN4O/HOt3HzpuSzb4jid2KBn1qoJ2hCEvufo65Jfn80g5ij/U2r2c6A4l5GobwzonZZNvNqTL9PiKRSMIMt0FfS8Hzrx5z3Vh7Gke/13WyO4lGEckHfgv8RUR+Bxwe+WGNHWJXaUDUSnv1hhoW3f0cC+9+DmUqNt15oeu2Gci27z7UTmtPhH1f/wAzJ0XvKAwhqheBIZDlT+G6R7eTmerDZ4h73Pu81Rt3YpoKwzCo/er7+OMdy9j6xQs419O8JMvv48DRjigDMa84j1/pNpknjcLMVOYU5WEIzCvJ57e3LmWuZ5cYS6xzysSqZ/n42i2YphrzbV7PdGKNdFMgGLWraO7qpdPeInYEI9z4+Ftzj//ohdcoL8k/aZ93UkZCKfURpVSrUuobwFeBx4ArRmNgY4VEolzeYw41DVZHseP24wNtEEwFt6+voS0Y5jXPqtI59i/jsvAZwtziPH5/+1ICwTAKCATDzJyUjbKrd1/8/Lvx2ZN79UGrlH/R3c9y7Y+3M3NyLmIY1Npd0gyBnt7oKccAUlN8DGzSNCOBaSrebO9h+aPb2NXYxpzivsr4nY1WgDLLP3BRlN/zi93daAWxj3dYBZqnS+Omsc5gfWBi8Rrp8pICVm+oiWrANS4rlbklfW7g/QPsGofCnsMddIbCvPgf7+GB5fMGP+EtMiR3k4h8boDDq4EfjMxwxh4Dtdd0jjluJadobfkjW10X0tyiXB66tpxb19f2q7CtqW8lokwy4qTEvn68E8GaCL7yu31Rxxyp4P1HAty+sZaI/WUuK8qLKuU/2tbNbetr3bFUlOQjIlFuMBNLDkRLUo8u8TLj9jS28VpTR5TroXuQlMeQabkPe3qtBAlv7/ENK7WBeKsM1gcmHt6Ud6WUR+rGasBVWVrAkysXcdW6rdQ2WJ0es9N8dPeaVJTk88qR9rgZj2CpNCsUuxqjs5z2HQ7wvv9+kZ5e0006OdXFdI6/YgZW4Ppp+/5lwBmV/uotfnEa+iSqExARtwhNKYUhltS3dxLefaidW9bXkmK7iMqmWW6FPYfaKS/J57b1tVFfEG8mlMKq6N5Z3xoVV3CYPTU3KgX321ecx6VrNrv3P/WT7a4WkM8Q7r9mHresr0GAOUW5pKb42HHQ8m3evr6GjasW6/z7UcJNh7Q/XJ+96vza7/b26x2y73DrgI2hukMR/rB6KYYIl9qNqHQsYmQYbnzHSfhQSlnV0/ZiwEkKae7q5a6PnM+l97+MCXQFIzxzxzJmTsmlt9fkww+97BbReXnounKUCSsf38G+Ix1kpArd9nfDaVg2JlRglVLfBBCRPwPlSqmAff8bwC9HckAicjFwH+ADHlVK3TOS1x8I72ovM81KZ3WsdLwPwA1oZ/lZ8eg2W/47L/o5qq+wDXDdCudPy+W+a+ayzNNNDuJnQmX4DYLh/mmTq/71bdy6fqd7f0JOutt4KMPvcw0EWLpQt66vccey53CA39++lMseeNnVbNKTzOgRLeyYz5oV5ZimYtE90QV237j83Cg9r3go4Op1W+kKRvrSrnUsYkQYyHMwlDRjZ1dxLNDD+374IoGeMOkpBrc9UUVtYzsZfoPOkIkJfP33r7Bx5SJaenrjGojy4lw+s3Fn1KKzu1cxe1ou+w63k5V2cj77ZLObSgBvM9UQMH2kBiMiPuBBrM53jcAOEXlaKfXKSL3GQHhXe07xSyIr7d2WlhVZvacjpmJXYyvnTsmO+6F72XuonVufqKWitK+bXCK6QiaZfoOukLeNKNy2fic5aSl09Ubsxj/prL9xER95aBN7PLLgs6fl8vB15SzxTEhlRXnMnJyjJTdOEvGq8I+190Q9Z05RLgUZQ6ucdXafncGwVosdQRLJeSfjhjIMwWcYbrC6MxShqr7Nvt33G97xRjPb/nmCt43P7D8O4K6PlHHpmugFQ3aaj1/fvITWnnCUt2MspcA+DmwXkd9gLWg+Avx0BMezAHhdKfVPABHZiBUYPylGwm0W49lJOJNn7CrCuy3d1dBKWVE+tQ2tmIp+BkKwegh0hSJkpForCYDdh9rYfOeFNHcG+eD9L8cZUR+xBVhOFmxXKMwznkniRFcwqi3q7Gm5/O62pYitO+/0qfjVzYsxDENLbpxEvJXrTi1DZUmeO4Gkpfi4ZX1NUtfM8Pt4xwStFjuSxFMY8P7eqwZoK+vMEwUZKZw3LTdqsRaLAMsf3UaW30dFSR7V9X0eh8rS/Lgd6bp7TVp7wu74TsbOP1ntprtE5H+Bd9kPfUopVTuC45kGNHjuNwILY58kIquAVQAlJSUj9uLeVYTXSntVU73Cd45BKSvK49tXnMtlazZH7QgMoHJ6AWtWlDMuy5K/ME2TW9fXsvuQVeU8MTeN8dl+ctJS+nUwS1Rx6+AzhEq7/7XTVW71hlo3ZXJuUS6/vnWp25IUBDEkKptJS26cfKK7E+bhM8SOK7RiJqGAAJZf+sq1W/nVLUt0PGkUcVoCOB3k4rWVjeeujhdLdJ9v/98ZitDTa7LZVkDY1dhGd69JOGzywPJ5RMIR3vffL9EZipwSuf6ktZuUUjVYnelGg3jf8n6/GqXUOmAdWLIcw32xeD5G76Tp/H+8Ixg3mPX4pxbw0bWbqW1o49I1m8ny9+0SAMqK89iwchE+nzVJ37HREgWcV5zP729fyjkTs13D0WXrx/tE3GwlU/UZCieg7WRAxhqf8dn+fim5Yjj11dZKyNtfIt1dtLwAACAASURBVJELTe8qRp/o7oR9u9CIUrZb0STNgGD/MFRcahtaaQoEmZCTpj+/UUJEojrIeX9DXkXoqhh3dSIDEcu+IwH+ebyD3YfarCZih9uZ+bU/YQJzi/PcrLfOnjAnOkMnNRNxrGkONwLFnvtFjFKxnmP1F939LB99aDORSOJfpLNr8Inlyx+XlUo4bFqifZ7tZHevyds9/kWnGhKsyuwqe2LYUdfCh+5/mXnf/ivz7/orF3z/ebeKurw0n4xUz+pEwTsnZEdlPD1x40I2rlpEYaafjz+8mYV3/ZWr126hMDOVsqK+wPnuhla3rmOwgist7XDy8H4WldMLedBuGwuWW9GQgQ3E2wr7TxAK/fmNNhNz06j01EMopejtjfDxhzez6Lt/5bYnasi0m//EFsQOhese24Hf5/nt2//vbGhj1pRs97GbHq8ecL4aaZIS+BttRCQFeBW4CDgE7ABWKKX2JTpnuAJ/TYEgi+5+tm9yLsnnqZutLbt3Re1Ib0SUyW1PWG6iipJ8OkNh9sUoesYK+c2YlMUfbl/G8a5ebvl5dVSWUyL+cPuSqDTWTL9BMKzccTr9kStK8gmFzagU2G1fvojxWX6uXLuln/CXUyAkQlxtJq94YYohbPnSRdoNNYp4U62XP7J1QAmXipI8/n60o59qqENmqsEvb1nC5XY6rP78Rg/nd7R6g9U0KD3VcFNRDaFfn5jBxD1jvQ+JiL1OeUk+v1i1mJbu4QeuhyrwN2R3k4jMxIoZbFNKdXgev1gp9X9JjzAOSqmwiNwO/AkrBfbHAxmIt8L4bL9VfGZXI++yV93jsvyuBnzZtDx8AlX10R3GEv2g+4lyvdnJO7/6p6TGdZnHQGSkCDvuvIAF33uBTrtfRXfI6loVt6JbKUSEh66tiDIG8TIzYr9TA6X+aUYex63ZFAhS4/l++eyZJuJxNRoi/On/+1eWxVENBejqNbns/pd1OuxJoqUr5Lb87fTUOKWl9NUwOCgsrbRX37SmzNgNXmfIJD1F6AkPvFiPPbqzoZWr1m1xO0+e8mI6EbkDuA3YDzwmIp9RSv3OPvxdYESMBIBS6o/AH0fqeokQEZ66aXHUqtvprVBli2c51ctDJcv2Jw91bzZ7SjZ7YjKhvOd2hxXz733B9Wt2hSKcPy2X/YfbqSgt6LeTUPQPsIsMrUAoUeqfZnSJVz9RkJHK9rpmrn10OwA76lrxGcL8txVGZbpk2UFRBXaBVnSmm4OONY0McQPTqT434STWQID1Gf3k+vlEIor3/vBvUV0lHeIZiD+sXkphlp/Wrl6+/ru91Da0Ma8kn3DEZLet/rqroZWIGiPFdMBKoEIp1SEi04GnRGS6Uuo+TmPRH5/P4Kmbl0S1FxyXlRq1w/ByzoRMXm3qSni9cybl8uC181j1s2q3n3Eizp2Sza9vXcaVdltRRV9g2rv59Aa+DLGkOMqK89mwchGmqfjwQ5t45XCA+dMLEHANXFVdC6++GWDG5Jwh7xJ0ptPJJ15GXUqKwTsmREuJC7Bx5SIOHG3nMtut1N0b4cEV83j4hdfYe7iDCk+mm0M4bHLlui1RPUN0FtTw8NZROQY5PzOVpfc8l1DJ+e3jM1ly7/OkpRj9DER6ikFPnCJZsHpReN3bT9++lJmTcwCxPR6pLH9k20nZ+Q/VSPgcF5NS6qCIvAfLUJRyGhsJsCZG18V0sJk5xfk8eeNCPrp2S1RQOtPvG9BAgFX3cPv6WvYdbu+XvhprYB79ZCUnOkNRO4EHV8zjvTMm8PrxTr7y273sPtQe5fM0bZ2OPY1tHO8IccfGWg4cCTCvJJ8nbljItY9tc2MXmak+Lrn/JSqnF7Jh5aKEBUJ6hXnq8X4HnR/9fdfMjXrOCTsvf8akHMqK8thpL2JuXR+dga4UrivRNBVXrdviLnicniHjsvz6cx8Gbtq7XUB7zqRsRKzkg6qDzWT4U+iISWN3+pp39/Y3Bl4DkZEinD8tj+r6VjJTfVFu5+0HW7jsgZfd37KzkDtZO/+hGomjIjJXKbUTwN5RXAr8GJg9aqM7SZzoDLHjjWZXgvlja7fw65sWc/UjW9llZxYMpPfvMHNyNrX1ra7mkpdYA+MzDP7RFH3NOzbsJDPN6oWb6fehTMWsSdmEzeg2qeUlBYjgrmp2NbbxalOH1QELy2p3BsOueJ+zFfXuEoYjZKYZPWJdgoYI80sL2GF/ph96YBMLphcAVq/rNJ/VxdBLVUxa5qtvBlxjAlZmnhModya6p+yeFJrBUQruu3outzxRza76Vj7+8BZ+edNid7LO9fuY+60/0T1I1mtWqkFXb7Rb+vl/fw8TczN49c0AH7zvpX7nxHMrnayd/1C/HZ8EjnofUEqFlVKfBP51xEd1knAyFQoyUshI65No3nOonY8+vBmf1VyJgycG3kE4eLOdDEn85p4/NZfb1tfwicd2RKXJRZTVC9fbnW5nYzvf/ehsK6AJ+ATWrJjHhJw0ykusSSNiKq56aLO7c8lOS6GidGC9+YEk0DUnn9gU5XFZfr55xXlR2/TqulY3aSKeaOic4nxXHeCadVv40P0vkZnmw8DKhvnVzYtp7urti7nVt3Kl3ZNCEx9njohETJY/spWl33ueWrthV019K1f8aBNKKSbkpPGPE50JDUR6St8n2RljILL9PibkpPc1KErrW7vPLcpl/iC/5dFmqAJ/jQAi/6+9846Pqzrz/ve5I41kdbkXSRabgG1srGK5kwaENxtKyL5AbEySDcU029nkTYEkmyUV2OyGxDYJsSHdBQghkLIJYJIQ3FVsuWDa4m7kot41c8/7xy2aGc2MZozKSDrfz2c+9ty5c+cczZ3znHOe5/k9kgq8F2v7/C2lVLtSKroaWYLi85ncuG47e47WM3NKVg+J5sCC9YF6K73hnOkO2N7gguejkqSHv+KXt5Sx9qW3qDhaz6hkI0gVtrQgx9JYCvApOFFLa24qYdEDW/Ar68ZzaO3ys/amORiGRFyK6mimxCLUN3HTYzspP1xLekq3jPycqTnsO1Ef1kE6fWIGTy1fwOmmDs42tbvGpLnDz59WXcqMSVmISMSoPu2L6kkkfbZA9p9o5Nq1r/C7uxdHvVa06KXmTj+Hapq4eFIWta1d3Ym1hrDuU3MZmzG4SZKxRjclYUUx3QIcwZok54nIz4CvKqW6+q+JfY9pKm4I2Kvdd6KR9BQrvDQ1SaLKNAfSWww0WCn3geeFi25IEktDqba1C5/Pz6X/+Tf8SuExhLU3lYbVWDJNu2Rq4WgqDtcG1aSYY8t9RLuhdDRT4hEYFutsJbZ1WoP8mHQvtS2dXLWmp8ZXutfD45+cw/WPbnNVhgMZm9F9L0SK6tN0E5hB7ay29xyrZ+bkLA6eamL2lOygyMcDp5oo+ubzdPgVafZWUjScrPpArl79CvMuGM3G2+aHnRAOphGP1SfxPayaEhcEyIRnAf9lPz7bP83rH861dAbt1YKj0X8p9z93IGpiU7xIBP2lQJmNZY/voqQghyduX8BNj1W4shxpyR7GpFs/4FBxOGeGU1qQw7Z7L7fCd1s6EcIny4VDRzMlJo5OkDNQTJuQaQdW1JFmC0VePDmLp5cv4K1zrfzHs/tZ/L2/97iOR4Q5hbk9vuPAqD49QQgm8LdVkp/NJVOy2Xu8nvSUJA6caKS4IIcnly/oYZAdw9DeS3JyahLs/fcPc7qpg0v/829BSgoVdt2JRJu8xeqTuBq43TEQAEqpRuAu4KP90bD+JCc1KcgXIEDZ1O4Q0lhJsfcZQ7/G947tlk0INBCBn1k8JYNf3ToXE+sGqTxaz788uo2Ko92f39Lh42xzZ49SioH+hMqjVslUj8dgQlYqYzNSONvcGXPpRU3iYX11Ys0wRDjdbBWyMrG2j351yzx+d9ci6tp81LdYiV2hFE3JYtu9H+KJCOVMnQlCIgxCiUTgb2v3kXqqTzQwc3IWLe1WIMjeY/XUtnbx9F2LmTW5Z13yOQU5ZKRELkFbOCYDwzDwJicFBYp4BHfbN9G+m1iNhFJhRh2llJ/ed1wSjrfOtgTFNf/61nko4Oo1W90BP1ygT2j94Xaf6rHlZACpKeGX74EGo+pEM//9l9eDXj94qomiAO2lOVO76+UG6vFE0mHS+kvDg1AxxtrmjqDXP/nTXRR/60UWPPgSNz2+q0ckHVhbqKs270Epy/926FQjpjlwej9DFee35fz+/aayfpf51u/SryztJIBn7wk2FAJ882OzqPjKFfwoQI8rkEM1zdzwk+3kpCaRZo8nmSlJbP3yZQlbnzzW7aaDIvIppdQvAw+KyM3Aob5vVv9y0YQMV5o73eshK8UTVGcYwm8RtXT6mTkpg7fPtbp7iqGnFRXkUH28d40msPIqAnF0d3bce5k7y3Dq5QaGv0XyJ5xv6UVNYhGahf2N3weXU7Gi37rj8RUwbUIGr9V0h1SbWKviV0818ol122nu8JOR4uHFz32ACdmpCTkYJQIiwoZb53ODre4MVlXHHy8rYfF//s1VYrjhJ9t5cvlCAhcNo7werlrzCukpSbS0+8iwZVJmTcqkw2dyyP5+9hyt562zLbR2OEqxPjweI2G/k1iNxD3Ab0XkFqAC676cC4zCKjw0xBCmTUin/GgDLZ1+rvnR9pjf+eqp5h5fZnFeNo9+cg6GCKPTkvn4j7dFLTYCMK/QUpGsPFrPrClZ7DveiAlUHWvA4zGC6uWGi0AK50/QEUtDH8dpuvG2+dS2dqGUcisKegxhxsQMDp5q6jGJeb2mZx5Pike4es0r7kSmucPP4of+SlmhzosJJTCxtK6tK2iiV3WsnpWbqoIc1nuO1nPwnQYqjgX4JewoRkcmvKXdx58+a8mk+Hwmxd9+gRa75Ox7x6UPmaqQsYbAngDmi8hlwEysldX/KKW29Gfj+otzLZ3uLCEcXoHQqNdRyUKHT1E2NZcuvxn0/m9/bCYAPtPkhnU72H+ikXSvh5aQsNqS/Bx+fHMphjgZtjtBhJQkD2WFuVQerQ+6YeKNQNIRS0MbJyzbiTpytLecLN/UJIMDJ5sozs/Bb/qpPtEdph1J+C8Uv9KrzFBCE0s33jafovycIOHFiiP1QTL8JvDFJ/eGvZ6jtpCW4iE3LRnTVLx1toV2+/to7fRT1+YbMr/VWENg3wtMUEq9BLwUcPx9wEml1Fv91L5+wZlxR4piCjUQ6V4Pe/79CmrbfAjQ5fez+KG/ua9f/cg2QnEMRGZKEi2dPorzc/iNXTIUoKaxnYojtfiV5bTe+mVri8nRkHJunHgjkHTE0tAkNCy7/HCtO5BvuHU+1/1oq5tfU3Wsnu1f/hCf+UU5h94Jlqv3GhBOeTqwQlppQU5Cz1wHmtBt2trWLp66YyHXP7rdXTnMzstmX0h+02unW8Jez1nlNXf4WfTQX0lL9tDc4SPDrkdfZk8EBzu0NVZi3W76AfCVMMfb7Neu6bMWDQAiwublCzl4qoFr126NWiIUYPqEdEQMVm6spPxInetwioWWju4lZ6Bm0sqNla7zvLQgh/FZKWHLpOotgZHBuZbOoC0OJ3saoK6ti1dDjIHHY/CHFYu55P6/BOXedJowbXwar50OVglwDISTe5PIM9eBJtw2rYjw9F2LrGxr0+TuDRXdNV3C7BIEMn1COm+ctoJj/KZyVWKbOnxBiY1DhVijmwqVUtWhB5VS5UBhn7ZoAPnmH151DcSMCWkRz6s41siBdxqsMERbMgOsOPRZkzOjfkZxQU4PZc4zTR1Bq5g1S0sQES2VMYIZm+GlbGouHkMoKbBWnc494wxiDmUF1raHx2NQ9bUP94jEe+NMKyX52RRN6b430215jjkFOQA6RDoAZ5t2+32Xs/G2+Zxt7sTvN92toHs2VQXlREQzEADp3iR+d88iSvKzewywgYmN0C37kcjfR6wriWgFVUf1RUMGmnMtnVQG5EQcqgmeeYVKdn/pyT09ruFXitQkg5L8HKpPNJCSJLR2mmTYCU9F+cE/dofQSUToYDAUnFmaviWaP0lE2Hz7AmswQbFq8x4WPfgSpQU53H/tzB7XMpUVALH9yx8CQ7h7QyV7jtYzbWIGpmmy6MGX9Eo1BEeJd8m67ZQfrmOU16Cty6QoL5vqXoJQQqk41hCk4pqebNDuV24GtcNQEdmM1UjsFpHblVLrAw+KyK1Y0U5DDier1ZnRO3ZcgLLCXNYsKeby77/szhpCl+8Oe443svXey9zIptrWLvffSA6pcZkpzCsMTr2H7vC7N880uzLEmpFDNH+SYQgTslOtSnb2anPX4TqutkMuWzv9TJ+YESQyufxXu3no+mL22MrEh97pjoDSzuuenGnuXuE7JUWrjjVQnJ/NvhONQbpN08aOorXLz7GG3lf7bV1mjy1nGDoh67EaiX8DnhGRZXQbhTLAy5AMgbUG5DVLS1n44JYgn8QD183k+rJ86tp8VH3tCj7+420cOGX98FKToD1ALt7A1knKtPwJgbPAaF+2iLDxtgU9jIFpKpY9vjPhZxaagccJ0RyTnkxpQQ7ldga2aRfA+f3KSzH9ZlAQxb6TzVy9+hVGhegJCd3Oa11TpJtIvf/uxy+htqWDmx/f7R577Wxb1GsEbh6VFfYsBgVDZ+cg1hDYGmCRiHwImGUf/qMd7TQkMU2FQlGUlx0Uznrv7w7wnf95jdZOH3Om5uJN6nZSt/u6o0QyU5J48fPvZ3xWatwO50jGYKjMLDQDS6hWF4AYQqbXQ2uHdZ/e/9yBsJIyJlYorCMqZ5fQRgF+f/B9uOHW+dS1RV4BD3esFf5oKo7WBUUkXbN2K7On9JTgCCUjxcMTyxdwzdqtKLtG+R9XXsr0SVkoBWebO4L+tkMlZD3WlQQASqm/An/tp7YMGI7evrO0vHhSJgdPdS/TnWiEiiN1QTOCUluE762zLVw0IcMNZz3b3NFjcI9W/SuSMRgqMwvNwBJ6vyCC31S0dvr546r3kZuWzPwHos/XpuaO4lBNi7tq3n24jkM1jUHlbm+0y5yO1FWsFfVoDdo5qUlBdcarjjUwbWwqr51tj/j+1k4/YzNSmBuQJOcYiEiTyKEQsh6XkRguOD86h9BY87Rkgw6fSWlBDodqmmlq95Ge4uHJ5QtISvIwfVLwrGJ0WjKX5GW7PzCn+leklUUkYzBUZhaagSVUpgMRKh2F2ImZnGnq6PGe0PK5r9b0jOn/2jP73H322VOy2XusPmwFtKFKPFtpbpQRlh7b0vU7gpLpAI7U9/w7A6R7DVo6TUwFKzdVsfG2BUErsnCTyKH0tx2RRiI0mS50y+mFf3sfXm+yFQnykLVwau8yqWvzMS4zOEfCNBU3PbaT6mP1FOXnuHIK0W6KaMZgKMwsNAOLc7+cae5AwKotERAYMSbdS3FeVlCYpojw4HUz+coz+4Oi9NKSDTr8itlTsqk+bg2CHoFHby5l1eY9w2YVG0/kkLWzsINdh2ujXjO0cNDsyZms+9Rc7vh1OXvtv33FkTrq2rqCfsNDfYdgRBoJJ5nO+dEppYKW6/XtPsYkeVi5qcqdac0pyA3r6HNWJX4F1ccb3B9vbzeFNgaaeFm1qSpo0BMRfD6TG9Zt7xGm6TcV9z6z38qhCBjbZk7JZu1NpYzLsGRhnOuNz0odVqvY3vx7gb/jcy2dQRL9sZLqTUIJQTptMyZnMSY9Oei8ob5DEJeREKt3y4B/Ukp9U0QKgIlKqV390rp+xDCECVlW+odSKqjo/FVrgiuyOnWlw+0tOgah/Egdl+RlMyY9ecjfFJrEI3DQKz9caztBU7gxQMojHKFqArsP19lGQ1i9pAQRbDFJK8lzuNyq0SZqoauMX/7rXFI8QmsE6YUUT/ia4hVH67l7Q6X7N073Ghw80cjS9Tt7rFyG8qQw1oxrhx8BC4Gl9vMm4JE+bdEg4PcrVl3x3oivl9ha8uH2Fp3chqK8bKqP1bN0/U5MUyVc4RDN0MbJ6wGrpsGKTVXUNLX3qLAYC6ZpsmTddhY+uIXlvyznVEMbn1i3nfkPbGHed7fwiZ9sD6pFMhSygkMJzKJ26jQ4/Qj8HZcfruX/rtseteRoOAMBcMmULKqPOVt2QluniUm3T2e4IPF88SJSqZQqFZEqpVSJfWyvUqqo31rYC2VlZaq8vPy83muaipqmdq58+GVX3jcc6Ske2rtMW9JAqDxqzUCcm+9MUwcLH9iCz64Hsf2+y4fsrEGTuNQ0tLPooZfwmwqPwOy8HFeAbuakDA6c6ikXHo7ivGz2RKl5YgA7v3oF4zJThkxWcG8EhRFPzQVbpv+SKVlRFaEj4RFh270fZNXmvT2uGTg2JDIiUqGUKuvtvHh9El0i4sFZsIqMI1i9Ysjg3DS7367ttQMt9lSi4mg9v1+xmLEZKUGrhKHumNIMDcZnpVBm32ez87LZaw/0HkP46b/OY6Xts5g1OYvqE40RS0ZGMxAARfnZ7j08XHJ3gkr+HqlzVZe7fD4WBSg6hzIqyaDN1z1CzJqcyf6TTfiVYuWmPUGRTE5CbaiS81AnXiOxGngGmCAi3wGuB77W560aAM61dFJ+ONhAZKYk8cLn3seqJ/ay++1aRtkJSJl2hak0r4erV79CcUEOT92xEI/HugGUgtVLrXKFeotJ018E+rrGpCf3cDxvXr7Qfe36R7f3COEMpCgvy43ICWT6hPSw4oKxToASKYM7sC3h+lHT0M5nfh7dnRpoINK9nqB8qsqj9Zxr7cSQ7pwHq07M0F95BRLXdhOAiEwHLrefvqSUerXPWxUH57vd5PebFH/rBSsHwmvw1J2LmD4xE8MwuiNGjjcwOy+bJ+0Euo/+8B+uUSkpyOHpOxcBWt5bMzhEG5D9fpPrH90WcSulaEom1SeamDU5gyRPEtUnGiz5jg4/cy8YzYZb59s+t54ht9Hakyi/hXBtge6Z/tL1OyLWk4kFj10MKjBnxQlTXvTAFvyKhN967tPtJhH5fISX/llE/lkp9f24WpcA1LZ2uRr77T7FuMxUN4O6rq2Lfccb8JuKfccbqG/3MW1iJsUF3dWq9h6rd51Tw2E5rhl6RIuYCZz7zZiQjggcfMdKqEtLNthrV7Xbd7KZ7fddRkNrF1et/odbGzuwyNG8wlw2L++pZhwpHDwRfguR2jIuM4UzTR1UHInf4Q/W1t6cglzW3lQCBNegr2ls5+4NwXVihsPWc6zRTZn2owy4C5hiP+4ELu6fpvUvYzO8zCnIwQBmTwmObXaWpkmGBBUh2XTrfFe7X4Cc1KSw52o0g4lb5c5eRbx+uoX/urHEfT00kscjwrSJmZQVjsZjCKnJhmsgIHy0jjNTX/jAFpas24FpqoT6LYRrixPdlDsqidl5vWsxhVKSn822L3+IJ+5YwPisVMZlprifUVqQy90bKtxAguFU3ClWgb9vAIjI80CpUqrJfn4/8FS/ta4fUcp6mFi6LEvW7WTzcmt5HCnP4e3aVjcm2q/grbMtTJ+UpXMiNAlFuCp3F43PIC1EDRYgIyWJMele954/9E4jV61+JeiccAN+pJl6ovwWQn/Dfr9yc0rSAirLFeamcLguWG4j3euh3WcyKsmg2T4v3euh+kQjqzbvYdPtC1DKWkU5CgtKKRY+sMW9xuy87GGzoxBvnkQBEDil6GSIVqY719JJZUCWZcXR4NmS44Q629zpxodfNCGDzFTLrmamJnHRhAz3XO2w1iQK4arc1bX5wuYCNHf4ONPU4eZB3P/7g25UVHFeNjvvu4wn7ui51eTolXlCVg2J9Ftw2qIU3LDOcuQrgivLhRoI7NdnT8li0/LunYOWTj9+2yCeaepwV1E3PbaTMelexmWmWCsxsbaZng5TbGyoEm9006+AXSLyDFYY7MeBX/R5qwaAUP2m3rIyLceX8Pzn3k99SyfTbCe3RpNohFsJ56QmEaLQ4XLXhgqqjzdw8eQsDtjbTB5DWPepMsZn9SxKGU6vLJEHxNCVVSxUHWvg2rXbyEhNoqXDR7o3idYuP6VTcxEJ74dMlFVUXxOvVPh3ROR/gPfZhz6jlKrq+2b1P6H6TaGzn9Dl9JmmDlZtruoRLaHRJBrhop7eOtsS1kDMnpzh+i72nWgkzeuhrdNPaUGOu13i85lugSzDMNzwcb+yAjhqW7sSdmvFNBVKWaVDd79dGzF3JBwKaG738YeVi7n/9wetcsdKMSY9fFjwUJbeiEbcAn9KqUqgsh/aMuAE6jeFEhpXHWn2oNEkEpHCUJ2t0lBlgf0ng7O0nYi/gycbqGloZ0y6l9LvvEhTu4/M1CSqvvZhRqclk5ZiXSvV6yF3VGLohIYaR5/P5MZ129l7zMqC/sOKxVy1dmvvF6Jbal0BX/ltNftPNeNXVm5EbWvXsF01hCMxvl1ARL4HXIPl53gLa5VyfnFqfdOeoBsB0FnVmoSkh6JpyGQmd1Qyb55ppuIrl/PCqzXcs2lP93sjXLOl02TxQ39lxuRM17A0tft480wzYzJSXGPS0uHnxnU7+M2diwY1PyjUOG64db4V4WWHrJcfrqO2rXc9pVQPdJrBwoh7TzRRkp/DvhMNQdGOI2WSmDBGAngBuE8p5RORh4D7gC8PZoNCl48jafagGRqEDo4bb5sfNJnJ8noo+tbztHT4yUxN4i+fvTTma/uVYn+ADHZasnDh+HQMw2B2XrY7ADs5Q4M5aAap5B6pY/fh2iA/RKrX4JMBNaoj0R5BzO/b181kXGZqwjjlB5KE8bwqpZ5XSjlr4R1A3mC2JxyJFLmh0UBP35mzFbL9vsvZeNt8rl+33dUea2r30dDmozgvO+o1Uz3h7+/WLsXS9btQCn5zx0JKC3LwCJQVjg67su4P9dhI13S2hz2GkOb1sPSxnaR4BAO4eGIGrZ3h10zvGZtGWUEOhsDcqTnMK7TyHuYW5lKcl40hllzPtWu3snJTFUNICLfPiDXjuongwAgnUMKu2aPiz0yJzi3AE1Hadk4DawAAG+ZJREFUsxxYDlBQUPCuPyyR9GY0mngIp0nkbIWcaergQMBKIN3rYdrETJ66YyFF33o+4sDZ7o88Ejqh4uMyU/jNnYsi/m76Q6KjN6mN1UtLONvc4eZ5OCG/B98Jr45rCLx1tpW0pDZ+8Zm5LHrPGEQMt09Kwes1TVy1+h/DqqxrvMSaTJfZFx8mIi8CE8O89FWl1LP2OV8FfMCGKO1ZB6wDS7vp3bQpkfRmNJp4iVbgamyGl7LC7jDvGZMyAaG+vYu2CAYilNAEvNlTsmOK5ukPiY7Qa9Y0tnP3xkqqjzdYCXLtPtK8nt4vZOP4HVp9ik/+dDcZKR72/PuVbjtFcDPRR7IvMm6fhIgU0R0C+7JSqjrW9yqlrujl2p8GrgYuVwNU4SSR9GY0mvMh0mAtIqxZWurWoNhzrME1JiUBOmShXDwxw519t3aZFOdlU32igaK8bB69uTRqW5xV+Zj05JgDPWJdyQeumkoLcrnr1xWu7LnjXG/ujOBUiIHmDj+7D9ey4D1j3HboKpPxly/9LHA78Fv70AYRWaeUWvNuGyIiH8FyVH9AKdX6bq8XK7oWhGY4E1iDInA76onbF1D87Rdo6fC74Z4OoePgj5eVYhgGKzdVsujBlyjKd6TyLZemM8jnpCZx4/odVB9voMyOMHJqLUQaXONZyQcO2H6/yYIHX+pxTrrXoMVeJV08MQMR4UCAvHcoaV6PG6llAMse28ncC0YHtWO45j/ESrwriVuB+UqpFgA7Cmk78K6NBLAWSAFesG+oHUqpO/vgulHRMwXNcCbS/V3f7qPdHhxNBQ9cN5P7fncAoEeFu3OtnTS2drH7cB0KK1fg+p9sD5LKLz9SR2qy4TrJy4/UUdfWe5JdLCv5wJVGd7/CX+8946zoq33HG0lJ9kStOifAi59/P3f/uoI9xxvdcODAdmh/ZfxGQoDA9ZzfPvauUUpFLjLdz4z0mYJmeBPu/g6VpXEMRCijkoSr1/RMQKs+3hAkle83lWsgINh3EY3eVvJBZUcLcgGrRGhpQQ5zp+ZSfqQuKKKm+kT3qqG3sqQzJqYjWJnmDk6dCEc1Vvsr4zcSPwN22tpNANcBj/dtkzQaTX8QOiv+4ZISFobZsnHwCrT5gl2Do5I9dPqteu9KdcuDlx+pIzXJcMXzkgxLZbm3yXe0lbxpKl6vaXIlQCqOWrIYTubz1nsvQynFFd//O822gZo9OYPqk7HV+j74TgtXPvwPt/7D3Km5PLKs1A1zP9vcof2VxJknYRcXugWoBeqwsqJ/0B8N02g0fUe4+g+eXmbFnWFCR9q6LDXUAycbWPBd61q/+PRcZkzMCFJXrTzaXZSrN8LlH5mmYsm6HVy15hXSUpJcddXZednubH98ZgqCuAYC4BvXzYr+WSFdbuqwHN4eQ3hkWSnjs1J7lG5NhPoYg8n5aDdVABX90BaNRhMnse6Zh9v7H5PupSQ/h+oTDRTkjuLtc7HHizjO4V2H67ju0W0ceifYOXzx5OBCXpHaGe64aSoOvdPIrsO1gBW59IeVi/nmH16l+kQjswOUZ1WIZN+Sn+yI2m4zjOHzGELZ1NweqwTtr7SIayUhImUi8oyIVIpItYjsE5GYQ2A1Gk3fEW51EInQWXFOahLXP7qNPcfqmT0li/9Zufi82xFqINK9Hg6eamLp+p2YprJXBduZ/90X+fiPtuL3m2Hb7/OZ1DS2s2T9Dq4JEeLz2LWk/aai+ngDb5xuxu83WbW5W4fKA3TEGAFriOVMnTs1h+33XsbG2+YH1Y5xz9MqC3GvJDYAXwT2EVkbTKPRDADx5PgEzopzUpO47sdbOXDSGtyrjjXw1rm2Hu+ZPTkDE4NXTzWSmuwJ2k6KhnNexZE6zjR3cK65w3WQ7znWwPWPbuPpuxYHSY7vfruWG9Ztp/pYPYEJ3wZQVpjLheMzmJ2XzZ5j9YxKNvjoD/9BUX42ewP0meLJkDAV/GnVpcyYlIVSaAd1FOLVbjqjlHpOKfW2UuqI8+iXlmk0mqjEu2fuVFu8cf0O10A41LcGV2ibNSWL3959KWleD35FzAYikJmTM1m5sbLHqmCvndTnSI4DpKV42BtgIDyGMK9wNDu+cjmbbl/Assd3sdcujNTc4XfLDufnjoqpLb9fsYg/rQoWNxybYa0QwhlbTTfxriT+Q0QeA7YA7l2llPpt5LdoNJr+4Hz2zMNVactMSeJTPysnI8VDc4efmZMyeObOhbx5poXdh+siXCmY0IQ8gOrjjWGL/MyZmuPWiHYS2dq6TIrzc6g+3kBJfjbf/Ngst/pjTUM75fZW08GTjUHXOlLbcwUUjvGZqYzPSmVeYS4VR+qZM7W7qJJOqI1OvEbiM8B0IJnu7SZFdwa2RqMZQOLN8XHqX5cfqeOSKVl89+OXcM3arfhN5UYJHTjVTOm3t9Da5Sc1WWjr6jnUGwTvN5fmZ7Py8gv5zz+/xqGaZvxmt0vZYwhzCnJZvaQYBFZuqmLhA1soyc9m9hRL8qPMljk/29LJyk1VXL3mFYryc3ji9gWs3FSJ37ZAUdwuUVmxsYrVS0tsn4N1ESdEVzuooyPxSCSJyD6l1CX92J64KSsrU+Xl5YPdDI1myBCawbxk3Q53ph5KpLrY4c6zVgL1zM7L5vWaJlo6TTK8Hp64cyEznFVBYzvzv7vFfZ8h1vs23Tqft2tbyU1LZtGDL7nbTrOmZPHqqaawbQvEa1jFgqIRuNpJMoTt910+IvMeHESkQilV1ut5cRqJ9cDDSqmD76ZxfYk2EhrNu8M0FWebO7hnY2WP7aWygmwO1bTQ3OEL0jmKhscQlFLugOzUnNh0+wLONncwL8BIOOc7iXjpXoMLxqaz3/aZGGLpKzXHGrZkk5YstPus+qOhtsNpz+blC0b0qiFWIxGv4/pSYI+IvKZDYDWa4YFhCOOzUnli+UL+tOpSN8nOYwjfuu4S2jqthLP2rtgG6lHJRtC2kBO9dLqpHbAymw2BzNQkDIELx6e7jvGWTpNXTzWT5vVgCBTl5wTJfcQ6pLd2WUYq0EB4BOYVjmb7fZePeAMRD7EWHVqIVS3uI/3bHI1GM1gYhjBjUlaQauy0iZnMmTqaXYdrI/oDRiVJkHxH4Kw/xYAO0xqsL//vv9PW5adsai7PrVhMbloyKzbuofpEsCPdrxStnX5mTc5kzY1FfPiHL9PWpRjlsa7V2+ZHcV4We453O7itMNrRrL2pZMTnPJwPMW03icijwDzgdeDPwJ+VUu/0c9tiQm83aTR9S2gWdE1jOwu/uyVoVj4qxKF90fg0Xj89YAr/YUlLNnjx8x9gYnYqS9Z1iwIG6jFpuol1uynWynR32hedDvwz8HMRyQb+imU0tiqlzr/ah0ajGVQCDUNoxNT4zBSKA4oUeQxh/afKuPnx3e45b545fwNhiKUau+d4dNXWcCQLOLaqtcukrrWLyblpbF6+UEcr9RHxCvwdUko9rJT6CHAZ8ApwA7CzPxqn0Wj6n97kPUSEp+5YSElBDh5DSPN6+NTju8lI6Z5jxhKaKsD0CWk9jqckGRw6Fb+BAKckazdffWYfpqm0nEYfEq/j2kUp1aaU+pNSamUsSxaNRpOY9JZxbJqKsy2dPLqslD+sWExrhw8TaLYVVCMhQJrXGmLSvR6evms+r9X0XHG0dZm0n+c+RHVI5vi+k406Y7qPidVx3UR3uHSoaVZKqaw+bZVGoxkwomUcO+J8jvbSvMJciqZkUWk7hj1iRS+lp3ho6/STmmTQ2mV5LxTQaicvtHT6+Zcf9++GgwBzCnJ0xnQfE6tPIrP3szQazVAkWsaxs8pwKD9SR0pS9waEX8HG2+Yxd+poPrF+B1W23yJWUj1Cu//80qgvHDuKN852y3KI1ZmYih1pYifu7SYRKRKRFfZjdn80SqPRDCyRCv8opSgtyHGPpSYZtHUFp6c99OdDriZULMO9N2DUafcrnr17wXm1+Ze3zmdeYa47iJlApRbo63PirSfxWSy58PH2Y4OIrOyPhmk0msHD2WZa+MAWOv2mu8fc1mWSnuIJOnfv8Ubu2lBBcV62HakUfeMhVD4jyeMJf2IviBhsXr6QHV+5nHkXjA5SwzVNxZmmjh71ITTxE6/A363AfKVUC4CIPARsB9b0dcM0Gs3gUdPU7voh9h5vpCQ/m+rjDczOy+aRm0p4+2wLywJCYKuONZCWLJgK3jzdwpz8bCqO9R6xlO714DfPrzSNYQiGIYzNSGHN0hIEGJeZ4taHKD9cS1F+Dk/dsRCP57xjdEY88f7lhODaHn5iz5TXaDRDANNU3L2hMujY2iUlzJiUSdWxBhY99Dd+uOVNiqYEx6u02gkLrV0m+0/GFtLa0unn2ke2x93GeYVWuVEnfHfxgy+xclMVShFUzKjyaD3X/2R71Kp9mujEayR+BuwUkftF5H4sqY7H+7xVGo1m0DjX0kn1sW4HdEl+Dis2V7miewAVR+tZ96kySvKzMYDivOyga8SpxxcXf1y5mCfuWBixYNDYDC9F+d1+lOrjDdpP8S6IN5nu+8AtQC1QB3xGKfWD/miYRqMZHMZmeCkrHI1HoLQgh0dvLu1RqGjO1FzGZ6Xy1B2L+NNn38dTdywgI8BXkZbc+wbDK194H0VR/BfhEu8AxmWmug72cNX5QpP/ynQhoXdFvD4JlFIVQEU/tEWj0SQAoSGxYAnklR+uZXZeNo/ePIfxWakoBcse30nFkTpm52UHRT1Nm5jFtz9+CVetfsU95oj9OdyzeS8i4eepZQXZGIaBx2jjkslZJCcZVB6tZ87U3CDJkEjhux6PwdN3LtLSHH1AXEZCRMqArwJT7fcKVjKdDoXVaIYRofpN4Qbis80d7lbP3mP1zJicxf4TVpJd9fEGxmWkUJKfQ5W9ddUR4p/eezy4FCnAr2+dR86oJAzDcCvm7TvZyCtf+iANbT4umpDRY8CPVJ0v3qp9mvDEu5LYAHwR2EfPWh4ajWYY4YSRilhRQ6EDbmCmdmlBTlC46ZypuYxJ9/KNj13MtWu3Bb0vUrW7jJQkHn7+EBXHLOORmZJEU4cPv6n48MMv09bpd4sXGYZeGQwU8RqJM0qp5/qlJRqNJmGw8iR2sOtwLWBFE21evjBocBYRNtw6n9dPN6GU4pq1W63jwA8+UcTS9TvcMFqHjbfNY9lju8J+ZluX3zUQAE0dPtegODUqHOe0XiEMHPEaif8QkceALUCHc1Ap9ds+bZVGoxlUzrV0UnE0WI7j9Zompk3MdLd7TFNx02M7XUPioLALDPmC1wsl+Tks+KcxzC3MdY3HtAkZZI9KpuJoPTMmZbrbVRBckzrD1oYK1ZbS9D/xGonPANOBZLq3mxSgjYRGM4xwtpJ2vW0ZgDSvh6tW/8Pd7jFNxe7DtewOMRAOoQZi5uRMnr5rIYZhZUmfamjjyodf5rWaZtK9BrOnZLH3eAPpXo9bytStkW0IWz7/AQzD0E7oQSBeI1GklLqkX1qi0WgSBhFh8+0LONPUQW1LB1eveQW/srZ7apraufLhl2lq97kqsNFI8xo8e/diDMOKZDIMob61K6iudfXxRkwF7T6TWVOyePVUE2leD60dPsrscFttHAaHeI3EDhG5WCl1sF9ao9FoEgbDECZkpzI+K4WywtGulHhtSydN7VYtCb+CX90yl4f+/BoHTzUye0oWnX6TQ+80uyuBji6TujYfY9INzrV0kpOaxCd+0p1lnZZsMGtKthviuvG2+dS2djE6LZna1i69ehhk4jUSlwKfFpG3sXwSOgRWoxnmBOYijE5LZsn6He5rGSlJrN7yBq+eaqTY1klSCt443cTXn91P1bEG5kzNZXRaMkvXW3Wnp03MoLmzOyXbya/Y+uXLGJ9lKdE6jmntoB584jUSH+mXVmg0moTGyTk409Th1owwBJ5cPp9rH9mGX1m5EWdbOlm1qcoOi81l672XMT4zhdNNHew+XIup4EBINTmFJfMhgl4xJCDxynIcCffor8ZpNJrEIlAGY27haKZPygqSxRBwE+wqj9Zh2EWAVm6s7FEH2yNCcZ4lEug3FSs2VmohvgQkblmO/kZEvgB8DxinlDo72O3RaDTdhJPBCJXwCC2Fera5k4qQinWOptIPlxSz+MGXXMVWnQOReCSUkRCRfODDwNHBbotGowlPqNxFNAkPpUApxZyCHDc3Yu7UHB5ZNocx6V7OtXQyp3A0lWHqa2sSg3i1m/YB1QGPfcCnlVLf6aP2PAx8CXi2j66n0Wj6GNNUUYXzHKPh1HpwZDt23HuZ+5pTGKjiSB0l+dk8t2Ix0wMS9TSJQ7z1JD4ArAfagCXAfuCqvmiIiFwLnFBK7Y3h3OUiUi4i5WfOnOmLj9doNDHgDPwLH9jCknU7ovoQQms9GIa4+Q6Br+0+Us81a7eydP1O7ZNIQOJ1XNcqpf6mlFqtlPo0MBd4I9b3i8iLIrI/zONjWOqyX4+xHeuUUmVKqbJx48bF0wWNRvMuCFfkJxJjM7yUFljFf/wKVmyqco2A4wD32AsHfwzX0wwOcRkJEbkw8LlS6g0g5hwJpdQVSqlZoQ/gf4ELgL0ichjIAypFZGI87dNoNP1LuCI/kRAR1iwtxWOLAlYGGAHH4b39vsuZd8HomK6nGRzidVyvE5H3ACewfBKpwH4RSVNKtZ5vI5RS+4DxznPbUJTp6CaNJrGIVOQnEuOzUigLiXZycLafNsdxPc3AE5eRUEp9CEBECoBioMj+d6+I+JVS0/u+iRqNJpGIp5hPLEZFFwdKbM4rBFYpdRQrTNWtLSEiGX3VKKVUYV9dS6PRDC7aCAxt4o1uiohSqrmvrqXRaDSaxKDPjIRGo9HEilMaNbDkqSYxSaiMa41GM/wJTLKbMzVX16xOcPRKQqPRDCjx5FpoBh9tJDQazYAST66FZvDR200ajWZAiTfXQjO4aCOh0WgGHB0WO3TQ200ajUajiYg2EhqNRqOJiDYSGo1Go4mINhIajUajiYg2EhqNRqOJiDYSGo1Go4mIDHXtFBE5Axw5z7ePBUZCzYqR0k/QfR2ujJS+DmQ/pyqlei3tOeSNxLtBRMqVUmWD3Y7+ZqT0E3Rfhysjpa+J2E+93aTRaDSaiGgjodFoNJqIjHQjsW6wGzBAjJR+gu7rcGWk9DXh+jmifRIajUajic5IX0loNBqNJgraSGg0Go0mIiPSSIjIR0TkNRF5U0TuHez29CUi8lMROS0i+wOOjRaRF0TkDfvf3MFsY18hIvki8lcReVVEDojIZ+3jw6q/IpIqIrtEZK/dz2/Yx4dVPwMREY+IVInIH+znw7KvInJYRPaJyB4RKbePJVRfR5yREBEP8Ajwz8DFwFIRuXhwW9Wn/Bz4SMixe4EtSqkLgS328+GAD/h/SqkZwALgHvu7HG797QAuU0oVAcXAR0RkAcOvn4F8Fng14Plw7uuHlFLFAfkRCdXXEWckgHnAm0qp/1VKdQKbgY8Ncpv6DKXUy0BtyOGPAb+w//8L4LoBbVQ/oZQ6pZSqtP/fhDWoTGGY9VdZNNtPk+2HYpj100FE8oCrgMcCDg/LvkYgofo6Eo3EFOBYwPPj9rHhzASl1CmwBlZg/CC3p88RkUKgBNjJMOyvvf2yBzgNvKCUGpb9tPkB8CXADDg2XPuqgOdFpEJEltvHEqqvI7F8abiCujoOeAgjIhnA08C/KaUah2PNZKWUHygWkRzgGRGZNdht6g9E5GrgtFKqQkQ+ONjtGQAWK6VOish44AUROTTYDQplJK4kjgP5Ac/zgJOD1JaBokZEJgHY/54e5Pb0GSKSjGUgNiilfmsfHrb9VUrVA3/D8jsNx34uBq4VkcNYW8GXicivGZ59RSl10v73NPAM1nZ4QvV1JBqJ3cCFInKBiHiBJcBzg9ym/uY54NP2/z8NPDuIbekzxFoyPA68qpT6fsBLw6q/IjLOXkEgIqOAK4BDDLN+Aiil7lNK5SmlCrF+my8ppW5mGPZVRNJFJNP5P3AlsJ8E6+uIzLgWkY9i7Xt6gJ8qpb4zyE3qM0RkE/BBLMnhGuA/gN8BTwIFwFHgBqVUqHN7yCEilwL/APbRvX/9FSy/xLDpr4jMxnJgerAmdk8qpb4pImMYRv0Mxd5u+oJS6urh2FcR+Ses1QNYW/8blVLfSbS+jkgjodFoNJrYGInbTRqNRqOJEW0kNBqNRhMRbSQ0Go1GExFtJDQajUYTEW0kNCMWEUkSkRUikjLYbdFoEhVtJDQJiYgoEfnvgOdfEJH7+/D6ghUGXa2U6ujD6/5cRK7vq+v1JSJy7XBTPdb0P9pIaBKVDuBfRGRsf1zcFs1bYQsi9kBEhp1kjVLqOaXUg4PdDs3QQhsJTaLiw6r3+7nQF0Jn6yLSbP/7QRH5u4g8KSKvi8iDIrLMrsWwT0TeY583TkSeFpHd9mOxffx+EVknIs8Dv7TrOPzMfm+ViHwoTFtERNaKyEER+SMBYmwiMsduT4WI/MWRWgh5/w0isl+sWhEv28c8IvI9u23VInJHnP27RkR22m1+UUQm2Mf/VUTWBvwNV4vINhH535C/5xcDPvsb8X91muGENhKaROYRYJmIZMfxniKsWgSXAJ8ELlJKzcOSnV5pn/ND4GGl1Fzg/xIsST0H+JhS6ibgHgCl1CXAUuAXIpIa8nkfB6bZn3c7sAhcTak1wPVKqTnAT4Fwmf1fB/6PXSviWvvYrUCD3b65wO0ickEc/XsFWKCUKsHSP/pShL/VJOBS4GrgQbvdVwIXYmkIFQNzROT9Ed6vGQEMuyW1ZvhgK7r+ElgFtMX4tt2OzLKIvAU8bx/fBzgrgSuAi6VbLTbL0dABnlNKOZ91KdZAj1LqkIgcAS4CqgM+7/3AJlul9aSIvGQfnwbMwlL2BEtS41SY9m4Ffi4iTwKOQOGVwOyA2X021sDdGWP/8oAn7JWLF3g7wt/qd0opEzjorDbsz74SqLKfZ9ifHXZbTjP80UZCk+j8AKgEfhZwzIe9CrYd0N6A1wKd0GbAc5Pu+90AFgYYA+xrAbQEHoqxjeG0bQQ4oJRaGPWNSt0pIvOxiuzsEZFi+70rlVJ/CWnfB4mtf2uA7yulnrPfc3+Ejw+8lgT8+4BS6ifR2q0ZOejtJk1CYwubPYm1BeNwGGtbCKwqXslxXvZ5YIXzxB6Yw/EysMw+5yIswbXXwpyzxPYjTKJ7Nv8aME5EFtrvTxaRmaEfICLvUUrtVEp9HTiLJWP/F+Aue8sKEblILJXQWMkGTtj//3S0E8PwF+AWsWp0ICJTxKp1oBmh6JWEZijw3wQM6sB64FkR2YVVA7gl7Lsiswp4RESqsX4DLwN3hjnvR8CjIrIPa/Xyr2HCZZ8BLsPa7nkd+DuAUqrT3i5abftUkrBWRQdC3v89EbkQawa/BdiLtZ1VCFTaK6UzxFfC8n7gKRE5AewALoh+ejdKqedFZAaw3V5ZNQM3M0zqN2jiR6vAajQajSYiertJo9FoNBHRRkKj0Wg0EdFGQqPRaDQR0UZCo9FoNBHRRkKj0Wg0EdFGQqPRaDQR0UZCo9FoNBH5/5LYiP5qO1xFAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "co2_merge[\"oscillation\"]=co2_merge[\"average\"]-co2_merge[\"yearly average\"]\n", "\n", "co2_merge.plot(x=[\"week\"], y=[\"oscillation\"], kind=\"scatter\", s=5)\n", "plt.xlabel(\"Numéro de semaine\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n", "plt.title(\"Oscillation pendant l'année\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La moyenne annuelle supprime cette oscillation. Donc, en tracant seulement la moyenne annuelle, on voit clairement l'augmentation de $CO_2$ dans l'atmosphère à cause du changement climatique par nous humains." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'La moyenne annuelle de la concentration de CO2')" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_avg.plot(x=[\"year\"], y=[\"yearly average\"], legend=None)\n", "plt.xlabel(\"year\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n", "plt.title(\"La moyenne annuelle de la concentration de CO2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant, on peut faire une extrapolation jusqu'à 2025 comme indiqué dans le sujet. Le module scipy nous permet de faire un fit pour la courbe. Car il est déjà 2024, nous allons faire la extrapolation jusqu'à 2050." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def exponential(x, a, b):\n", " return a*np.exp(b*x)\n", " \n", "param, cov = scipy.optimize.curve_fit(exponential, yearly_avg[\"year\"], yearly_avg[\"yearly average\"],\n", " p0=[30, 0.001])\n", "\n", "yearly_avg.plot(x=[\"year\"], y=[\"yearly average\"])\n", "plt.xlabel(\"year\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n", "plt.title(\"La moyenne annuelle de la concentration de CO2\")\n", "\n", "year = np.arange(1974, 2051, 1)\n", "plt.plot(year, exponential(year, param[0], param[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les valeurs estimés dans les prochaines années." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimation de la concentration en 2025: 421.2129053519808 ppm\n", "Estimation de la concentration en 2030: 431.76476793366294 ppm\n" ] } ], "source": [ "print(\"Estimation de la concentration en 2025:\", exponential(2025, param[0], param[1]), \"ppm\")\n", "print(\"Estimation de la concentration en 2030:\", exponential(2030, param[0], param[1]), \"ppm\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En conclusion, cette courbe montre très bien comment le $CO_2$ monte dans notre atmosphère chaque année depuis les années 1970. Ceci implique l'effet de serre qui augmente la température globale. Pour que nous humains peuvent toujours habiter sur la terre, il est nécessaire que le niveau de $CO_2$ finisse d'augmenter." ] } ], "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 }