{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle / Purchasing power of English workers from the 16th to the 19th century\n", "\n", "Le but de ce travail est de reproduire le [graphique](https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png) de William Playfair qui montre l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique est publié dans son [livre](https://books.google.fr/books?id=aQZGAQAAMAAJ&printsec=frontcover&hl=fr&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false) : \"A Letter on Our Agricultural Distresses, Their Causes and Remedies\".\n", "\n", "Les données numériques brutes que William Playfair a utilisées ne sont malheureusement pas disponibles. Des valeurs obtenues par numérisation du graphe sont toutefois disponibles [ici](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html). Pour la suite de cette analyse, la [version en format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv) sera utilisée.\n", "\n", "---\n", "\n", "The purpose of this work is to reproduce the [graphic](https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week.png_of_Labour_by_the_Week.png_from_18_by_the_Week.png_from_18) which shows the evolution of the wheat prize and average salaries from 1565 to 1821. This graph is published in his [book](https://books.google.fr/books?id=aQZGAQAAMAAJ&printsec=frontcover&hl=fr&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false ): \"A Letter on Our Agricultural Distresses, Their Causes and Remedies\".\n", " \n", "The raw data that William Playfair used is unfortunately not published. However, values obtained by digitizing the graph are available [here](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html). For the remainder of this analysis, the [CSV format version](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv) will be used. \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "# ensemble des bibliothèques à importer / set of libraries to import \n", "%matplotlib inline\n", "import os\n", "import urllib.request\n", "import datetime \n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from matplotlib.ticker import MultipleLocator\n", "from matplotlib.dates import YearLocator\n", "import matplotlib.cm as cm\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "## Téléchargement des données / Download data\n", "\n", "Afin de garder une trace des données qui ont été traitées, une copie locale du fichier a été réalisée. Le but de la manoeuvre était de permettre d'accéder aux données ultérieurement même si le lien initial de téléchargement des données est modifié ou supprimé. Cela permet également d'étudier le même set de donnée même si, par exemple, une nouvelle numérisation du graphique était réalisée.\n", "\n", "Dans un premier temps, la présence de cette copie locale du fichier est vérifiée. Si aucun fichier n'est présent, une copie des données téléchargeable à l'adresse URL renseignée ci-dessous (`data_url`) est réalisée dans un fichier local (`fileName`) qui servira dans la suite des analyses. Ce fichier local est normalement transmis avec ce document computationnel (*data_william.csv*).\n", "\n", "---\n", "\n", "In order to keep track of the data that has been processed, a local copy of the file has been made. The purpose of the maneuver was to allow access to the data later even if the initial data download link is modified or deleted. This also makes it possible to study the same data set even if, for example, a new digitization of the graph was carried out.\n", "\n", "First, the presence of this local copy of the file is checked. If no file is present, a copy of the downloadable data at the URL address given below (`data_url`) is made in a local file (` fileName`) which will be used in the rest of the analyses. This local file is normally transmitted with this computational document (*data_william.csv*). \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "data_url='https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "fileName = 'data_william.csv'\n", "if not os.path.exists(fileName):\n", " print('FR: Aucun fichier local avec les données étudiées n\\'est disponible. Un nouveau fichier est fabriqué à partir du lien donné')\n", " print('EN: No local file with the studied data is available. A new file is made from the given link')\n", " urllib.request.urlretrieve(data_url, fileName) " ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Le fichier local peut à présent être ouvert. Celui-ci sera utilisé tout au long de cette étude.\n", "\n", "La première colonne du fichier csv téléchargé correspond à l'ID. Cette colonne est dès lors passée comme index lors de son exportation (`pd.read_csv()`) grâce à l'ajout du paramètre suivant : `index_col=0`.\n", "\n", "---\n", "\n", "The local file can now be opened. This will be used throughout this study.\n", "\n", "The first column of the downloaded csv file is the ID. This column is therefore passed as an index during its export (`pd.read_csv ()`) thanks to the addition of the following parameter: `index_col = 0`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "text/html": [ "
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YearWheatWages
1156541.05.00
2157045.05.05
3157542.05.08
4158049.05.12
5158541.55.15
6159047.05.25
7159564.05.54
8160027.05.61
9160533.05.69
10161032.05.78
11161533.05.94
12162035.06.01
13162533.06.12
14163045.06.22
15163533.06.30
16164039.06.37
17164553.06.45
18165042.06.50
19165540.56.60
20166046.56.75
21166532.06.80
22167037.06.90
23167543.07.00
24168035.07.30
25168527.07.60
26169040.08.00
27169550.08.50
28170030.09.00
29170532.010.00
30171044.011.00
31171533.011.75
32172029.012.50
33172539.013.00
34173026.013.30
35173532.013.60
36174027.014.00
37174527.514.50
38175031.015.00
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40176031.016.50
41176543.017.60
42177047.018.50
43177544.019.50
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45178542.023.00
46179047.525.50
47179576.027.50
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49180581.029.50
50181099.030.00
51181578.0NaN
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" ], "text/plain": [ " Year Wheat Wages\n", "1 1565 41.0 5.00\n", "2 1570 45.0 5.05\n", "3 1575 42.0 5.08\n", "4 1580 49.0 5.12\n", "5 1585 41.5 5.15\n", "6 1590 47.0 5.25\n", "7 1595 64.0 5.54\n", "8 1600 27.0 5.61\n", "9 1605 33.0 5.69\n", "10 1610 32.0 5.78\n", "11 1615 33.0 5.94\n", "12 1620 35.0 6.01\n", "13 1625 33.0 6.12\n", "14 1630 45.0 6.22\n", "15 1635 33.0 6.30\n", "16 1640 39.0 6.37\n", "17 1645 53.0 6.45\n", "18 1650 42.0 6.50\n", "19 1655 40.5 6.60\n", "20 1660 46.5 6.75\n", "21 1665 32.0 6.80\n", "22 1670 37.0 6.90\n", "23 1675 43.0 7.00\n", "24 1680 35.0 7.30\n", "25 1685 27.0 7.60\n", "26 1690 40.0 8.00\n", "27 1695 50.0 8.50\n", "28 1700 30.0 9.00\n", "29 1705 32.0 10.00\n", "30 1710 44.0 11.00\n", "31 1715 33.0 11.75\n", "32 1720 29.0 12.50\n", "33 1725 39.0 13.00\n", "34 1730 26.0 13.30\n", "35 1735 32.0 13.60\n", "36 1740 27.0 14.00\n", "37 1745 27.5 14.50\n", "38 1750 31.0 15.00\n", "39 1755 35.5 15.70\n", "40 1760 31.0 16.50\n", "41 1765 43.0 17.60\n", "42 1770 47.0 18.50\n", "43 1775 44.0 19.50\n", "44 1780 46.0 21.00\n", "45 1785 42.0 23.00\n", "46 1790 47.5 25.50\n", "47 1795 76.0 27.50\n", "48 1800 79.0 28.50\n", "49 1805 81.0 29.50\n", "50 1810 99.0 30.00\n", "51 1815 78.0 NaN\n", "52 1820 54.0 NaN\n", "53 1821 54.0 NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(fileName,index_col=0)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "**Informations relatives aux données:**\n", "\n", "Il s'agit d'une base de données avec 53 observations sur les 3 variables suivantes:\n", "\n", "* *Year* = années espacées de 5 ans entre 1565 et 1821\n", "* *Wheat* = prix du blé (shillings pour un quart de boisseau de blé)\n", "* *Wages* = salaires hebdomadaires (shillings par semaine)\n", "\n", "Notons néanmoins qu'aucune observation du salaire hebdomadaire n'est présente pour les années 1815,1820 et 1821.\n", "\n", "*Remarques*\n", "\n", "* Jusqu'en 1971, la livre sterling était divisée en 20 shillings, et un shilling en 12 pences.\n", "* Le prix du blé est donné en shillings pour un quart de boisseau de blé. Un quart de boisseau équivaut 15 livres britanniques ou 6,8 kg.\n", "* Les salaires sont donnés en shillings par semaine.\n", "\n", "---\n", "\n", "**Data information:**\n", "\n", "It is a database with 53 observations on the following 3 variables:\n", "\n", "* *Year* = years spaced 5 years apart between 1565 and 1821\n", "* *Wheat* = price of wheat (shillings per quarter)\n", "* *Wages* = weekly wages (shillings per week)\n", "\n", "It should be noted that no observation of weekly wages is present for the years 1815, 1820 and 1821. \n", "\n", "*Remarks*\n", "\n", "* Until 1971, the pound sterling was divided into 20 shillings, and a shilling into 12 pence.\n", "* The wheat price is given in shillings per quarter, a quarter being 15 British pounds or about 6,8 kg.\n", "* Wages are given in shillings per week." ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "## Les données / the data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Type des données présentes dans le tableau / Type of data present in the array\n", "\n", "Est-ce des entiers, des réels, des suites de caractères, des dates? / Are they integers , floats, strings, dates ? " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n" ] } ], "source": [ "print (type(raw_data['Year'][1]),type(raw_data['Wheat'][1]),type(raw_data['Wages'][1]))" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "**Analyse**\n", "\n", "Le premier type de données (colonne: *Year*) est un nombre entier *()*, les deux autres (colonnes: *Wheat* et *Wages*) sont des nombres réels *()*.\n", "\n", "Les dates ne sont donc pas considérées comme telles par **pandas** lors de la lecture du fichier csv. Afin que ces dates soient traitées correctement par **matplotlib** lors de l'affichage graphique, la bibliothèque **datetime** est utilisée. Celle-ci propose un format de date supporté par matplotlib. Les données sont stockées sous forme de dates comportant un jour, un mois et une année. Nous avons fixé le mois et le jour comme étant le premier jour de chaque année civil, soit le 1er janvier. La commande ressemble donc à ceci : `datetime.date(année,mois,jours)` avec le mois et le jour fixé à 1. \n", "\n", "---\n", "\n", "The first data type (column: *Year*) is an integer *()*, the other two data type(columns: *Wheat* and *Wages*) are real numbers *()*.\n", "\n", "The dates are therefore not considered as such by **pandas** when reading the csv file. In order for these dates to be handled correctly by **matplotlib** when displaying graphically, the **datetime** library is used. This offers a date format supported by matplotlib. The data is stored in the form of dates comprising a day, a month and a year. We have set the month and day as the first day of each calendar year, which is January 1. So the command looks like this: `datetime.date (year, month, days)` with the month and the day set to 1. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "year=[datetime.date(raw_data['Year'][i],1,1)for i in range(1,len(raw_data['Year'])+1)]# car l'index choisi commence à 1 et pas 0\n", "data = raw_data.assign(Year_date=year)" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Les dates ainsi formées ont été ajoutée au dataframe grâce à la commande `.assign()`, dans une nouvelle colonne nommée *Year_date*.\n", "\n", "Le type de données présentes dans cette colonne (*Year_date*) est à nouveau vérifié. Il s'agit bien d'un **datetime** *()*\n", "\n", "---\n", "The dates formed were added to the dataframe using the `.assign ()` command, in a new column called *Year_date*. \n", "\n", "The type of data present in this column is verified. It is indeed a **datetime** *()* " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print (type(data['Year_date'][1]))" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Calcul de l'intervalle entre deux données temporelles / Calculation of the interval between two temporal data " ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Il est important de connaitre le nombre de jours exact compris entre deux dates de la base de données (`(year[j+1]-year[j]).days`). Cette durée permettra de fixer la largeur des bâtonnets dans le graphique correspondant. Certaines années étant bissextiles, le temps entre deux dates consécutives variera entre 1826 (si une année bissextile) et 1827 (si deux années bissextile) pour une période de 5 ans. Sachant qu'une année normale contient 365 jours et qu'une année bissectile (tous les 4 ans) en contient 366, nous obtenons bien $4*365+366 = 1826$ et $3*365+2*366 = 1827$. Ces nombres peuvent être comparés aux résultats obtenus ci-dessous afin de vérifier et valider le code utilisé. \n", "\n", "---\n", "\n", "It is important to know the exact number of days between two dates in the database (`(year[j+1]-year[j]).days`). This duration will allow to set the width of the bars in the corresponding graph. Some years being leap years, the time between two consecutive dates will vary between 1826 (if a leap year) and 1827 (if two leap years) for a period of 5 years. Knowing that a normal year contains 365 days and that a leap year (every 4 years) contains 366, we get $4 * 365 + 366 = 1826$ and $3 * 365 + 2 * 366 = 1827$. These numbers can be compared to the results obtained below in order to verify and validate the code used. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "width=[(year[j+1]-year[j]).days for j in range(0,len(year)-1)]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FR: l'intervalle 1821-01-01 à 1820-01-01 ne répond pas au critère\n", "EN: the interval 1821-01-01 à 1820-01-01 doesn't meet the criteria\n" ] } ], "source": [ "for i,e in enumerate(width):\n", " if e != 1826 and e!=1827:\n", " print('FR: l\\'intervalle',year[i+1],'à',year[i],'ne répond pas au critère')\n", " print('EN: the interval',year[i+1],'à',year[i],'doesn\\'t meet the criteria')" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "**Analyses**\n", "\n", "Un seul intervalle ne correspond pas à une période de 5 ans : entre 1820 et 1821. Ce qui est normal, cette période ne couvre qu'une seule année. Le calcul exécuté ci-dessus est dès lors validé et les résultats qui en résultent pourront servir dans la suite de l'étude. \n", "\n", "Enfin, entre 1565 et 1821, il n'y a que n-1 période de temps (n= nombre d'années). Cette observation est facilement visible avec la commande `len()`. Une période d'une année (nombre de jours de 1821) est dès lors ajoutée dans la variable *width*. En effet, vu que ce graphique présente l'évolution du prix du blé et du salaire moyen entre 1565 et 1821, l'année 1821 est simplement la dernière année de mesure renseignée dans les données.\n", "\n", "---\n", "\n", "Only one interval doesn't correspond to a period of 5 years: between 1820 and 1821. This is normal, this period covers only one year. The calculation carried out above is therefore validated and the resulting results can be used in the rest of the study.\n", "\n", "Finally, between 1565 and 1821, there is only n-1 period of time (n = number of years). This observation is easily visible with the `len ()` command. A period of one year (number of days in 1821) is therefore added in the variable *width*. Indeed, this graph shows the evolution of the price of wheat and the average wage between 1565 and 1821. Year 1821 is simply the last measurement year entered in the data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lenght of Year_date: 53\n", "Lenght of width: 52\n" ] } ], "source": [ "if len(data['Year_date'])!=len(width):\n", " print('Lenght of Year_date:',len(data['Year_date']))\n", " print('Lenght of width:',len(width))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "end=datetime.date(year[len(year)-1].year+1,1,1)\n", "width.append((end-year[len(year)-1]).days)" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Une nouvelle colonne est enfin ajoutée au dataframe *data* grâce à la commande suivante : `data.assign(nom de la colonne,données)`. Cette nouvelle colonne sera nommée : *period_width* et son type (vérifié ci-dessous) est un entier *()*.\n", "\n", "---\n", "\n", "Finally, a new column is added to the dataframe *data* with the following command:` data.assign (name of the column, data)`. This new column will be named: *period_width* and its type (checked below) is an integer *()*. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "data = data.assign(period_width=width)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print (type(data['period_width'][1]))" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "## Graphique / Graph" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "![](chart_williams.png)\n", "\n", "[By William Playfair (1759-1823) — Edward Tufte, The Visual Display of Quantitative Information, Graphics Press USA, 2001, 2e éd. (1re éd. 1983), 190 p., Domaine public](https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png)" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "**Description du graphique**\n", "\n", "L'axe des x :\n", "\n", "* axe compris entre 1565 et 1830;\n", "* graduations majeures en 1600 puis tous les 50 ans\n", "* graduations mineures tous les 5 ans\n", "\n", "L'axe des y :\n", "\n", "* légende d'axe située à droite du graphique\n", "* axe compris entre 0 et 100 shillings\n", "* graduations majeures tous le 10 shillings\n", "* graduations mineures tous les 5 shillings\n", "\n", "Graphique en bâtonnets :\n", "\n", "* en noir avec la couleur qui tend graduellement vers le blanc\n", "* 5 ans = une même valeur de prix du blé (sauf pour 1820 et 1821)\n", "\n", "Graphique linéaire:\n", "\n", "* ligne rouge pour représenter le salaire par semaine d'un bon mécanicien\n", "* coloration bleue en dessous de la ligne rouge jusqu'à 0 shillings\n", "* les valeurs s'arrêtent en 1810, soit 11 ans avant le prix du blé\n", "\n", "D'autres informations telles que les siècles ou les rois ne seront pas représentés sur le graphique présenté ci-dessous, cette information n'étant pas demandée pour cet exercice. \n", "\n", "La suite de ce document se base sur ces observations. Nous tenterons de les reproduire le plus fidèlement possible.\n", "\n", "---\n", "\n", "**Description of the graph**\n", "\n", "The x-axis:\n", "\n", "* axis between 1565 and 1830;\n", "* majors graduations in 1600 then every 50-year\n", "* minors graduations every 5 years\n", "\n", "The y-axis:\n", "\n", "* axis legend located to the right of the graph\n", "* axis between 0 and 100 shillings\n", "* majors graduations every 10 shillings\n", "* minors graduations every 5 shillings\n", "\n", "Bar graphic:\n", "\n", "* in black with the color tending towards white\n", "* 5 years = the same price of wheat (except for 1820 and 1821)\n", "\n", "Line graph:\n", "\n", "* red line to represent the weekly salary of a good mechanic\n", "* blue coloring below the red line up to 0 shillings\n", "* the values stop in 1810, i.e. 11 years before the price of wheat\n", "\n", "Other information such as centuries or kings will not be represented on the graph presented below, this information is not requested for this exercise.\n", "\n", "The remainder of this document is based on these observations. We will try to reproduce them as faithfully as possible. " ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Préliminaire: Graphique séparé des deux parties du graph / Preliminary: separated graph from the two parts of the graph \n", "\n", "#### Graphique en bâtonnets\n", "\n", "Celui-ci est réalisé avec `matplotlib.pyplot.bar` avec les paramètres suivants:\n", "\n", "* `x=` les dates (*Year_date*). Pour que celles-ci soient bien reconnues comme telles par matplotlib, nous ajoutons la commande suivante `xaxis_date()` dans la suite du code.\n", "* `y=` prix du blé (*Wheat*)\n", "* `align='edge'` pour faire partir l'épaisseur de chaque bâtonnet depuis la limite gauche (et pas au centre vu que nous avons choisi le 1 janvier, qui est le début de chaque année et non le centre)\n", "* `width=` le temps entre deux dates calculées ci-dessus (*period_width*). Ce qui correspond à l'épaisseur de chaque batonnet.\n", "\n", "Nous limitons également le graphique en x (`set_xlim`) de 1565 à 1830 et en y (`set_ylim`) de 0 à 100, comme c'est le cas dans le graphique d'origine.\n", "\n", "Dans cette première version du graphique, nous terminons en labélisant les axes x (`set_xlabel`) et y (`set_ylabel`).\n", "\n", "---\n", "\n", "This is done with `matplotlib.pyplot.bar` with the following parameters:\n", "\n", "* `x =` the dates (*Year_date*). So that these are well recognized as such by matplotlib, we add the following command `xaxis_date ()` in the rest of the code.\n", "* `y =` wheat price (*Wheat*)\n", "* `align = 'edge'` to make the thickness of each stick start from the left limit (and not center since we have chosen January 1, which is the start of each year and not the center)\n", "* `width =` the time between two dates calculated above (*period_width*). This corresponds to the thickness of each stick.\n", "\n", "We also limit the x (`set_xlim`) graph from 1565 to 1830 and the y (` set_ylim`) graph from 0 to 100, as in the original graph.\n", "\n", "In this first version of the graph, we end by labelling the x (`set_xlabel`) and y (`set_ylabel`) axes. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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IeLxj2qvVk++70D3+9zLLV2QM4tWICCAAJA0rNyQzM6uDIglidprFtJWkM8huPXpluWGZmVnVuuxiiohLJB0OPEs2DvHFiLit9MjMzKxSRQapzwO+3ZgUJE2JiBmlRjbAdLcfvCcXhLXqPVYuj5lYqxTpYvoUcIukQxrKPlFSPGZmVhNFZjE9Rna3t2slXRcRXwFUbljWwd/g87XLv0td6+FWikHBJTMi4lHgYGCcpGuBzUqNyszMKlekBTEPICJeBk6V9Elg31KjMrO24bGv/qvIUhtnbLT9jYjYtbyQzMysDjptQUiaHREnSnqAdJFco4jYs9TIzMysUs26mM5Oz0e3IhCzduYuE+uPOk0QEbFK0iBgZkQc1sKYzMysBpoOUkfEBkkvStoyIp5pVVBm1n1upVhfKzKL6WXgAUm3AS90FEbEWaVFZWZmlSuSIG5MDzMzG0CKLNY3S9IQYCzZbKalEfFq6ZGZWb/iLq72U2SxviOBK4CHyJbY2EXSmRFxc9nBtSN/iPoP/67+nP9NBpYiXUyXAYdExHIASbuRdTk5QZiZtbEiCWJNR3JIHgbWlBSPmbWQWwTWTJEEsVjSTcBssjGIE4DfSDoeICKuLzE+MzOrSJEEMRRYTbaaK8BaYBvgGLKE4QRhZtaGisxiOrUVgZiZWb0Uuh+EmZkNPE4QZmaWq8sEIWmXImVmZtZeirQgfpRTdl1fB2JmZvXS7IZBY4E9gC07prQmI8hmNpmZWRtrNovpHWQ3C9qKbEprh+eAM3LfYWZmbaPZDYPmAHMkHRgRd/fVCSXtBFwD/AXwGjAjIr4maRvgh8AYYAVwYkQ81VfnNTOz7ilyodxCSZ8k6256vWspIk7r4TnXA5+JiAWShgPz070mPg7cERHTJU0FpgKf7+E5zMysl4oMUn+H7Nv+B4C7gB3Jupl6JCJWRcSC9Po5YAmwAzAJmJUOmwUc19NzmJlZ7xVJEH8ZEecBL0TELOAo4K/74uSSxgDjgXuA7SJiFWRJBBjVyXumSJonad6GF30XVDOzshRJEOvS89OS3glsSTZO0CuStiCbQvvpiHi26PsiYkZETIiICYM237K3YZiZWSeKJIgZkrYGzgPmAg8CF/fmpJLeQpYcvtewGuxqSaPT/tF4SXEzs0oVWazvyvTyLmDX3p5QkoCZwJKIuKxh11xgMjA9Pc/p7bnMzKzniiy1sZ2kmZJuTtvjJJ3ei3MeBHwMOFTSfelxJFliOFzSMuDwtG1mZhUpMs3128DVwD+n7f8hu15hZk9OGBG/ILu3dZ6JPfmZZmbW94qMQbw1ImaTXdRGRKwHNpQalZmZVa5IgnhB0rZkd49D0gGA55eambW5Il1M55INIO8m6ZfASOAjpUZlZmaVKzKLaYGkg8kW7xOwNCLWdfE2MzPr54q0IAD2I7s4bjCwjyQi4prSojIzs8p1mSAkfQfYDbiPNwang2xFVjMza1NFWhATgHEREWUHY2Zm9VFkFtMistVczcxsAGl2y9GfkHUlDQcelHQv8ErH/og4tvzwzMysKs26mC5pWRRmZlY7zRLEeOCXwMJ09bSZmQ0gzRLEjsDXgLGS7gd+RZYw7o6IJ1sRnJmZVafTBBERnwWQNIRsJtPfAKcB35L0dESMa02IZmZWhSLTXDcDRpDdSW5L4HHggTKDMjOz6jWbxTQD2AN4juye0b8CLouIp1oUm5mZVajZdRBvAzYF/gg8BqwEnm5FUGZmVr1mYxBHpNuD7kE2/vAZ4J2SniQbqD6/RTGamVkFmo5BpOU1Fkl6muweEM8AR5Mt3ucEYWbWxpqNQZxF1nI4CFhHmuIKXIUHqc3M2l6zFsQY4DrgnIhY1ZpwzMysLpqNQZzbykDMzKxeiqzmamZmA5AThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVmu2iUISUdIWippuaSpVcdjZjZQ1SpBSBoEfAP4IDAOOFnSuGqjMjMbmGqVIMjudb08Ih6OiFeBHwCTKo7JzGxAanbL0SrsAPyhYXslsH/jAZKmAFPS5vOPfPnopX107rcCf+qjn1W1dqlLu9QD2qcu7VIPaJ+6vF4Pfbnwe3YuclDdEoRyyuJNGxEzgBl9fmJpXkRM6OufW4V2qUu71APapy7tUg9on7qUWY+6dTGtBHZq2N4ReLyiWMzMBrS6JYjfALtL2kXSEOAkYG7FMZmZDUi16mKKiPWS/gG4BRgEXBURi1t0+j7vtqpQu9SlXeoB7VOXdqkHtE9dSquHIqLro8zMbMCpWxeTmZnVhBOEmZnlausEIekqSWskLWoou0DSY5LuS48jU/lbJM2S9ICkJZKmNbxn31S+XNLXJeVNx21pPVL5p9KyJIslXZzKDpc0P8U7X9KhdalHd+uSyveUdHcqf0DS0DrUpZO/rR82/F2tkHRfw75pKdalkj7QUF7L30mzuqT9b5P0vKTPNpT1m99JnT/vTeqyt6Rfp7rMk7RfKi/vMx8RbfsA3gvsAyxqKLsA+GzOsR8FfpBebw6sAMak7XuBA8mu07gZ+GAN6nEIcDuwadoelZ7HA9un1+8EHmt4T6X16EFdBgP3A3ul7W2BQXWoS149Ntp/KfDF9Hoc8FtgU2AX4KG61KO7dWko+xFwbeNnqeq6dPN3UtvPe2d1AW7tiAU4ErgzvS7tM9/WLYiI+DnwZNHDgWGSBgObAa8Cz0oaDYyIiLsj+xe/BjiulIA7Cyy/Hv8HmB4Rr6Rj1qTnhRHRce3IYmCopE3rUI8UX+G6AO8H7o+I36byJyJiQx3q0uxvK31LOxH4fiqaRPaf0SsR8XtgObBfHeoB3a4Lko4DHib7++ooq7wu3axHbT/v0GldAhiRXm9JukaszM98WyeIJv5B0v2pGbd1KrsOeAFYBTwKXBIRT5It/7Gy4b0rU1nV3g68R9I9ku6S9K6cYz4MLEz/8da1HtB5Xd4OhKRbJC2Q9I+pvM51AXgPsDoilqXtvCVkdqD+9YCN6iJpGPB54MKNjqt7XTb+nfS3zzvAp4GvSPoDcAkwLeeYPv3MD8QEcTmwG7A32R/Hpal8P2ADsD1ZN8BnJO1KgeU/KjIY2Bo4APgcMLuxf1HSHsCXgTM7inJ+Rh3qAZ3XZTDwbuDv0vOHJE2k3nUBOJmGb9x0Hm/d6wF/XpcLgf8XEc9vdFzd67JxPfrb5x2ylvY5EbETcA4ws3FnGZ/5Wl0o1woRsbrjtaRvAf+VNj8K/DQi1gFrJP0SmAD8f7IlPzrUZfmPlcD1qel4r6TXyBbtWitpR+DHwN9HxEMNx9exHtB5XVYCd0VEthCZdBNZv+x3qWldUpfF8cC+DcWdLSFT599JZ3XZH/iIsokEWwGvSXqZbEyilnXppB797fMOMBk4O72+FriyY0dZn/kB14JI/XIdPgR0zBJ4FDhUmWFk32Z/FxGrgOckHZC+1f49MKelQee7ATgUQNLbgSHAnyRtBdwITIuIX3YcXON6QCd1Ibuifk9Jm6cP+cHAgzWvy2FkfzeNTfu5wEmpX3gXYHfg3prXA3LqEhHviYgxETEG+CrwfyPi32tel7zfSX/7vEP2n/vB6fWhQEe3X3mf+VaPzrfyQdakXAWsI8umpwPfAR4gmx0zFxidjt2CLCsvBh4EPtfwcyaQJZKHgH8nXYFecT2GkH2TXgQsAA5Nx36BrG/1vobHqDrUo7t1Scefkn4ni4CL6/w7SeXfBj6Rc/w/p1iX0jCTpOp69KQuDe+7gDfPYuo3v5M6f947qwtZN+t8shlx9wD7pmNL+8x7qQ0zM8s14LqYzMysGCcIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGYVkzSo6hjM8jhBmHWDpH+VdHbD9kWSzpL0OUm/SYtAXtiw/4a0Rv9iSVMayp+X9C+S7iFbjtmsdpwgzLpnJtmaOEjaBDgJWE22fMZ+ZItA7ivpven40yJiX7IrWs+StG0qH0a21v/+EfGLVlbArKgBt1ifWW9ExApJT0gaD2wHLATeRXbvioXpsC3IEsbPyZLCh1L5Tqn8CbKVRH/UytjNussJwqz7rgQ+DvwFcBUwEfhSRFzReJCk95EtFHdgRLwo6U5gaNr9ckRsaFXAZj3hLiaz7vsxcARZy+GW9DhN0hYAknaQNIrsrl9PpeQwlmzFULN+wy0Is26KiFcl/Qx4OrUCbpX0V8Dd6Z5Nz5OtQvtT4BOS7idbxfXXVcVs1hNezdWsm9Lg9ALghHjjFpZmbcddTGbdIGkcsBy4w8nB2p1bEGZmlsstCDMzy+UEYWZmuZwgzMwslxOEmZnlcoIwM7Nc/wvwjTTaiJh7QAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax1 = plt.subplot(111)\n", "\n", "ax1.bar(data['Year_date'].values,data['Wheat'],align='edge',width=data['period_width'])\n", "ax1.xaxis_date()\n", "\n", "ax1.set_ylim(0,100)\n", "ax1.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax1.set_xlabel('year')\n", "ax1.set_ylabel('Wheat prizes (Shillings per quarter)')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Nous pouvons ensuite améliorer le rendu de ce graphique afin de se rapprocher du rendu initial\n", "\n", "1. Graduation de l'axe à droite et à gauche avec `tick_params(axis='y', which='both', labelleft='on', labelright='on')`\n", "2. Label de l'axe y à droite `yaxis.set_label_position('right')`\n", "3. Graduations majeures et mineures :\n", " * 10 shillings entre chaque graduation majeure : `yaxis.set_major_locator(MultipleLocator(10))`\n", " * 50 ans entre chaque graduation majeure : `xaxis.set_major_locator(YearLocator(50))` \n", " * 5 ans entre les graduations mineures : `xaxis.set_minor_locator(YearLocator(5))` \n", " * 5 shillings entre chaque graduation mineure : `ax1.yaxis.set_minor_locator(MultipleLocator(5))` \n", "4. Grille `grid()` avec les paramètres suivants : \n", " * graduations majeures ou mineures visibles: `which=`'major' ou 'minor' \n", " * axes concernés (x, y, x et y) :`axis=` 'x','y' ou 'both' \n", " * style des lignes de graduations :`linestyle=`'--'ou'-'. Il est aussi possible de jouer sur les épaisseurs des lignes\n", " * couleur des lignes de graduations `color='k'` (ligne noire)\n", " \n", "---\n", "\n", "We can then improve the rendering of this graph to get closer to the initial rendering.\n", "\n", "1. Graduation of the axis to the right and to the left with `tick_params (axis = 'y', which = 'both', labelleft = 'on', labelright = 'on')`\n", "2. Label of the y-axis on the right `yaxis.set_label_position ('right')`\n", "3. Majors and minors graduations:\n", " * 10 shillings between each major graduation: `yaxis.set_major_locator (MultipleLocator (10))`\n", " * 50 years between each major graduation: `xaxis.set_major_locator (YearLocator (50))`\n", " * 5 years between minors graduations: `xaxis.set_minor_locator (YearLocator (5))`\n", " * 5 shillings between each minor graduation: `ax1.yaxis.set_minor_locator (MultipleLocator (5))`\n", "4. Grid `grid ()` with the following parameters:\n", " * majors or minors graduations visibles: `which =` 'major' or 'minor'\n", " * concerned axes (x, y, x and y): ` axis = ` 'x','y' or 'both'\n", " * style of the lines of graduations: `linestyle =` '-' or '-'. It is also possible to vary the thickness of the lines\n", " * color of the graduations lines `color = 'k'` (black line) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax1 = plt.subplot(111)\n", "\n", "ax1.bar(data['Year_date'].values,data['Wheat'],align='edge',width=data['period_width'])\n", "ax1.xaxis_date()\n", "\n", "ax1.set_ylim(0,100)\n", "ax1.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax1.set_xlabel('year')\n", "ax1.set_ylabel('Wheat prizes (Shillings per quarter)')\n", "\n", "ax1.yaxis.set_major_locator(MultipleLocator(10))\n", "ax1.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax1.xaxis.set_major_locator(YearLocator(50))\n", "ax1.xaxis.set_minor_locator(YearLocator(5))\n", "ax1.grid(which='major',axis= 'both',linestyle='-',color='k')\n", "ax1.grid(which='minor',axis= 'both',linestyle='--',color='k')\n", "\n", "ax1.tick_params(axis='y', which='both', labelleft=True, labelright=True)\n", "ax1.yaxis.set_label_position('right')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Enfin, afin de coller au mieux au graphique d'origine, nous ajoutons une couleur dégradée pour chaque batonnet grâce à la fonction `imshow`. Pour se faire,nous créons une fonction `def gradientbars()` qui va permettre de:\n", "\n", "* créer une image, c'est-à-dire un raster 2D régulier `np.atleast_2d()`.\n", "* supprimer les éventuelles couleurs déjà présentes sur les bâtonnets du graphique `bar.set_facecolor('none')`;\n", "* identifier la zone à colorier de façon dégradée qui sera utile dans les paramètres de `imshow`: `extent=[x,x+w,y_base,y+h]`. Dans cette expression:\n", " * x et y sont obtenu à partir du graphique en bâtonnets ( date= *Year_date*, niveau de base=0): `bar.get_xy()`\n", " * w et h sont obtenu à partir du graphique en bâtonnets (largeur= *period_width*, hauteur= *Wheat*) : `bar.get_width(), bar.get_height()`\n", " * y_base est fixé à 15, car sur le graphique d'origine les zones coloriées en noir ne dépassent pas cette limite.\n", " \n", "* recoloriser la zone grâce à la fonction `imshow()` en :\n", " * gardant les axes du graphique. L'aspect est ajusté pour que les données tiennent dans les axes: `aspect='auto'`\n", " * choisissant la profondeur du graphique (utile pour la superposition des graphiques): `zorder=`\n", " * choisissant le dégradé souhaité, ici dégradé de noir. `cmap=cm.gist_gray`\n", " \n", "---\n", "\n", "Finally, in order to best stick to the original graph, we add a gradient color for each stick thanks to function `imshow`. To do so, we create a `def gradientbars ()` function which will allow:\n", "\n", "* create an image, i.e. a regular 2D raster `np.atleast_2d ()`.\n", "* remove any colors already present on the sticks from the `bar.set_facecolor ('none')` graph;\n", "* identify the area of color in which a degraded color will be useful in the parameters of `imshow`:` extent = [x, x + w, y_base, y + h] `. In this expression:\n", " * x and y are obtained from the bar graph (date = *Year_date*, base level = 0): `bar.get_xy ()`\n", " * w and h are obtained from the bar graph (width = *period_width*, height = *Wheat*): `bar.get_width (), bar.get_height ()`\n", " * y_base is fixed at 15, because on the original graph the areas colored in black do not exceed this limit.\n", " \n", "* recolorized the area using the function `imshow ()` by:\n", " * keeping the axes of the graph. The aspect is adjusted so that the data fits in the axes: `aspect = 'auto'`\n", " * choosing the depth of the graph (useful for overlaying graphs) `zorder =`\n", " * choosing the desired gradient, here gradient of black: `cmap = cm.gist_gray` " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "def gradientbars(bars,y_base,zorder):\n", " grad = np.atleast_2d(np.linspace(0,1,2**10)).T\n", " ax = bars[0].axes\n", " for bar in bars:\n", " bar.set_facecolor('none')\n", " x,y = bar.get_xy()\n", " w, h = bar.get_width(), bar.get_height()\n", " ax.imshow(grad, extent=[x,x+w,y_base,y+h], aspect='auto', zorder=zorder,cmap=cm.gist_gray)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax1 = plt.subplot(111)\n", "\n", "bar=ax1.bar(data['Year_date'].values,data['Wheat'],align='edge',width=data['period_width'])\n", "gradientbars(bar,y_base=15,zorder=0) \n", "ax1.xaxis_date()\n", "\n", "ax1.set_ylim(0,100)\n", "ax1.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax1.set_xlabel('year')\n", "ax1.set_ylabel('Wheat prizes (Shillings per quarter)')\n", "\n", "ax1.yaxis.set_major_locator(MultipleLocator(10))\n", "ax1.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax1.xaxis.set_major_locator(YearLocator(50))\n", "ax1.xaxis.set_minor_locator(YearLocator(5))\n", "ax1.grid(which='major',axis= 'both',linestyle='-',color='k')\n", "ax1.grid(which='minor',axis= 'both',linestyle='--',color='k')\n", "\n", "ax1.tick_params(axis='y', which='both', labelleft=True, labelright=True)\n", "ax1.yaxis.set_label_position('right')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "#### Graphique des salaires des ouvriers / Worker wages graph \n", "\n", "Plusieurs points décrits ci-dessus peuvent être repris comme:\n", "\n", "* les graduations\n", "* les limites du graphique\n", "* les labels des axes\n", "\n", "Par contre, les graphiques eux-mêmes sont différents. Nous réalisons en réalité deux graphiques superposés. Le premier en rouge est une simple courbe, le deuxième en bleu est une surface. \n", "\n", "1. En rouge : une courbe représentant le salaire au cours du temps. Nous utilisons la fonction `plt.plot()` de matplotlib.pyplot avec comme paramètres:\n", " * `x =` *Year_date*\n", " * `y=` *Wages*\n", " * `color= 'red'`, colorie la courbe en rouge\n", " * `zorder=` qui permet de choisir le niveau du graphique lorsqu'il y a plusieurs graphiques ou inscriptions à superposer\n", "\n", "2. En bleu clair: une surface située en dessous de la courbe rouge (comme dans le graphique initial): utilisation de la fonction `plt.fill_between()`avec :\n", " * `x =` *Year_date*\n", " * `y=` *Wages*\n", " * `facecolor= 'lightblue'`, colorie la surface en bleu clair\n", "\n", "3. Nous ajoutons enfin un texte sur le graphique au-dessus de la ligne rouge grâce à `plt.text()` avec comme paramètres:\n", " * x et y : position de départ du texte\n", " * `fontsize=8` : taille du texte\n", " * `rotation = 2` : rotation nécessaire pour suivre la courbe\n", " * `bbox=dict(facecolor='white',edgecolor='none', alpha=0.5)`:une fond clair (blanc) pour permettre une meilleure lecture du texte\n", " * `zorder=2` qui permet de choisir le niveau du graphique lorsqu'il y a plusieurs graphiques ou inscriptions à superposer\n", " \n", " ---\n", " \n", "Several points described above can be repeated as:\n", "\n", "* graduations\n", "* the limits of the graphic\n", "* axis labels\n", "\n", "However, the graphics themselves are different. We are actually making two superimposed graphics. The first in red is a simple curve, the second in blue is a surface.\n", "\n", "1. In red: a curve representing the salary over time. We use the `plt.plot ()` function of matplotlib.pyplot with as parameters:\n", " * `x =` *Year_date*\n", " * `y =` *Wages*\n", " * `color = 'red'`, color the curve in red\n", " * `zorder =` which allows to choose the level of the graph when there are several graphs or inscriptions to be added\n", "\n", "2. In light blue: a surface located below the red curve (as in the initial graph): use of `plt.fill_between ()` function with:\n", " * `x =` *Year_date*\n", " * `y =` *Wages*\n", " * `facecolor = 'lightblue'`, color the surface light blue\n", "\n", "3. We finally add a text on the graphic above the red line thanks to `plt.text ()` function with as parameters:\n", " * x and y: starting position of the text\n", " * `fontsize = 8`: text size\n", " * `rotation = 2`: rotation necessary to follow the curve\n", " * `bbox = dict (facecolor = 'white', edgecolor = 'none', alpha = 0.5)`: a light background (white) to allow better reading of the text\n", " * `zorder = 2` which allows to choose the level of the graph when there are several graphs or inscriptions to be added " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "ax2 = plt.subplot(111)\n", "ax2.fill_between(data['Year_date'].values,data['Wages'],facecolor='lightblue')\n", "ax2.plot(data['Year_date'].values,data['Wages'],'r',zorder=3)\n", "ax2.xaxis_date()\n", "\n", "ax2.set_ylim(0,100)\n", "ax2.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax2.yaxis.set_major_locator(MultipleLocator(10))\n", "ax2.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax2.xaxis.set_major_locator(YearLocator(50))\n", "ax2.xaxis.set_minor_locator(YearLocator(5))\n", "ax2.grid(which='major',axis= 'both',linestyle='-',color='k')\n", "ax2.grid(which='minor',axis= 'both',linestyle='--',color='k')\n", "\n", "ax2.tick_params(axis='y', which='both', labelleft=True, labelright=True)\n", "ax2.yaxis.set_label_position('right')\n", "\n", "ax2.set_xlabel('year')\n", "ax2.set_ylabel('Wages (Shillings per week)')\n", "ax2.text(data['Year_date'][1],data['Wages'][1]+4, ' Weekly Wages of a Good Mechanic',fontsize=8,rotation = 2,bbox=dict(facecolor='white',edgecolor='none', alpha=0.5),zorder=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Notons que dans les données, les dernières valeurs du tableau sont absente `NaN`, ce qui ne pose pas de problème dans le graphique, ces valeurs ne sont simplement pas utilisées.\n", "\n", "---\n", "\n", "Note that in the data, the last values of the array are absent `NaN`, which does not pose a problem in the graph, these values are simply not used. " ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Question 1: reproduire le graphique de William Playfair / Question 1: reproduce the graphic by William Playfair \n", "\n", "Dans cette partie, il nous était demandé de reproduire le graphe de Playfair à partir des données numériques. Représenter, comme Playfair, le prix du blé par des barres et les salaires par une surface bleue délimitée par une courbe rouge. Superposer les deux de la même façon dans un seul graphique. Le style du graphique pourra rester différent par rapport à l'original, mais l'impression globale devrait être la même.\n", "\n", "Pour ce faire, il suffit de recopier les codes préalablement établis (ci-dessus) et de les mettre sur un graphique unique. Il suffit d'utiliser ax1 pour les deux codes ci-dessus: entrée [17] et [18]. Nous avons simplement adapté les légendes d'axes afin qu'elles correspondent à celles du graphique d'origine.\n", "\n", "---\n", "\n", "In this part, we were asked to reproduce Playfair's graph from the numerical data. To like Playfair, represent the wheat price by bars and the salaries by a blue surface delimited by a red curve. Superpose them in a single graphic. To apart from these criteria, the style of the graphic may differ from the original, but the overall impression it leaves should be the same.\n", "\n", "To do so, simply copy the previously established codes (above) and put them on a single graph. Just use ax1 for the two codes above: input [17] and [18]. We simply adapted the axis legends so that they correspond to those of the original graphic. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax1 = plt.subplot(111)\n", "\n", "bar=ax1.bar(data['Year_date'].values,data['Wheat'],align='edge',width=data['period_width'])\n", "gradientbars(bar,y_base=15,zorder=0)\n", "ax1.fill_between(data['Year_date'].values,data['Wages'],facecolor='lightblue')\n", "ax1.plot(data['Year_date'].values,data['Wages'],'r',zorder=3)\n", "ax1.xaxis_date()\n", "\n", "ax1.set_ylim(0,100)\n", "ax1.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax1.set_xlabel('5 years each division') \n", "ax1.set_ylabel('Prize of the Quarter of Wheat in Shillings') \n", "\n", "ax1.yaxis.set_major_locator(MultipleLocator(10))\n", "ax1.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax1.xaxis.set_major_locator(YearLocator(50))\n", "ax1.xaxis.set_minor_locator(YearLocator(5))\n", "ax1.grid(which='major',axis= 'both',linestyle='-',color='k')\n", "ax1.grid(which='minor',axis= 'both',linestyle='--',color='k')\n", "\n", "ax1.tick_params(axis='y', which='both', labelleft=True, labelright=True)\n", "ax1.yaxis.set_label_position('right')\n", "ax1.text(data['Year_date'][1],data['Wages'][1]+4, ' Weekly Wages of a Good Mechanic',fontsize=8,rotation = 2,bbox=dict(facecolor='white',edgecolor='none', alpha=0.5),zorder=2)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Question 2 : Ajuster le graphique en doublant l'axe des Y (prix) / Question 2: Adjust the chart by doubling the Y-axis (price) \n", "\n", "Dans cette partie, il nous était demandé d' améliorer la présentation de ces données. Pour commencer, Playfair a combiné les deux quantités dans un même graphique en simplifiant les unités \"shillings par quart de boisseau de blé\" et \"shillings par semaine\" à un simple \"shillings\", ce qui aujourd'hui n'est plus admissible. Utiliser deux ordonnées différentes, une à gauche et une à droite, et indiquer les unités correctes. À cette occasion, ne pas hésiter à proposer d'autres représentations que des barres et des surfaces/courbes pour les deux jeux de données si ceci vous paraît judicieux.\n", "\n", "Pour ce faire, nous avons simplement dédoublé l'axe des y en gardant le même axe des x: `ax2 = ax1.twinx()`. Les lignes de codes décrites en entrée [17] sont dès lors liées à ax1 et en entrée [18] liées à ax2. Nous avons évidemment supprimé les fonctions `ax1.tick_params(axis='y', which='both'labelleft=True, labelright=True)` et `ax1.yaxis.set_label_position('right')`\n", "permettant respectivement le dédoublement de l'axe des y et le positionnement de la légende de l'axe y à droite du graphique. \n", "\n", "Finalement, nous avons choisi d'ajouter des couleurs aux axes pour plus de clarté. Le graphique du prix du blé en noir et le graphique des salaires en rouge : `ax2.tick_params(axis='y', colors='r')`. Pour simplifier la compréhension, la zone située sous la ligne rouge a également été coloriée en rouge (et non en bleu clair). \n", "\n", "---\n", "\n", "In this part we were asked to improve the presentation of the data. For a start, Playfair has combined two quantities in a single graph by simplifying the units \"shillings per quarter\" and \"shillings per week\" to a plain \"shillings\", something that is no longer acceptable today. Use two different ordinate axes, one on the left and one on the right, and label them with the correct units. At this occasion, don't hesitate to use representations different from bars and curve-delimited surfaces for the two datasets if you consider this pertinent.\n", "\n", "To do so, we simply split the y-axis while keeping the same x-axis: `ax2 = ax1.twinx ()`. The lines of codes described in input [17] are therefore linked to ax1 and in input [18] linked to ax2. We obviously removed the functions `ax1.tick_params (axis = 'y', which = 'both'labelleft = True, labelright = True)` and `ax1.yaxis.set_label_position (' right ')`\n", "respectively allowing the doubling of the y axis and the positioning of the y axis legend to the right of the graph. \n", "\n", "Finally, we chose to add colors to the axes for more clarity. The Wheat graph in black and the Wages graph in red: `ax2.tick_params (axis = 'y', colors = 'r')`. To make it easier to understand, the area below the red line has also been colored red (and not light blue). " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "ax2 = ax1.twinx()\n", "\n", "bar=ax1.bar(data['Year_date'].values,data['Wheat'],align='edge',width=data['period_width'])\n", "gradientbars(bar,y_base=15,zorder=0)\n", "ax2.fill_between(data['Year_date'].values,data['Wages'],facecolor='mistyrose',alpha=0.7)\n", "ax2.plot(data['Year_date'].values,data['Wages'],'r')\n", "ax1.xaxis_date()\n", "\n", "ax1.set_ylim(0,100)\n", "ax2.set_ylim(0,100)\n", "ax1.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax1.set_xlabel('5 years each division') \n", "ax1.set_ylabel('Prize of the Quarter of Wheat in Shillings')\n", "ax2.set_ylabel('Weekly Wages in Shillings per week',color='red')\n", "\n", "ax1.yaxis.set_major_locator(MultipleLocator(10))\n", "ax1.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax2.yaxis.set_major_locator(MultipleLocator(10))\n", "ax2.yaxis.set_minor_locator(MultipleLocator(5))\n", "ax1.xaxis.set_major_locator(YearLocator(50))\n", "ax1.xaxis.set_minor_locator(YearLocator(5))\n", "ax1.grid(which='major',axis= 'both',linestyle='-',color='k')\n", "ax1.grid(which='minor',axis= 'both',linestyle='--',color='k')\n", "ax2.tick_params(axis='y', colors='r')\n", "ax2.text(data['Year_date'][1],data['Wages'][1]+4, ' Weekly Wages of a Good Mechanic',fontsize=8,rotation = 2,bbox=dict(facecolor='white',edgecolor='none', alpha=0.5),zorder=1,color='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Question 3 a : Représenter la quantité de blé que chaque ouvrier peut acheter avec son salaire moyen par semaine / Question 3 a: Represent the quantity of wheat that each worker can buy with his average weekly wage \n", "\n", "Dans cette partie, il nous était demandé d' essayer de mieux faire ressortir que le pouvoir d'achat des ouvriers avait augmenté au cours du temps (objectif de l'auteur d'origine). Pour cela, il nous demande de faire une représentation graphique du pouvoir d'achat au cours du temps, définie comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire.\n", "\n", "Pour ce faire, nous avons commencé par calculer la quantité de blé que chaque ouvrier peut acheter avec son salaire moyen par semaine. Pour connaitre la quantité de blé (en kg), il suffit de diviser le salaire hebdomadaire par le prix du blé ( variable *quantity*) multiplié par le poids du blé contenu dans un boisseau, soit 6.8 kg ( variable *weight*). \n", "\n", "---\n", "\n", "In this part we were asked to \"Try to make stand out better the workers' purchasing power had increased over time (original author's goal) . Make a plot of the purchasing power, defined as the quantity of wheat a worker can buy with a weekly salary, as a function of time.\"\n", "\n", "To do this, we started by calculating the amount of wheat that each worker can buy with his average weekly wage. To know the quantity of wheat (in kg), all you have to do is divide the weekly wage by the price of wheat (variable *quantity*) multiplied by the weight of the wheat, i.e. 6.8 kg ( variable *weight*). " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [], "source": [ "quantity=data['Wages']/ data['Wheat']\n", "weight=quantity*6.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons ensuite réaliser une courbe représentant ces poids en fonction du temps à l'aide de `plt.plot()`. Nous avons adapté les limites en y du graphique pour qu'elles correspondent à ce nouveau set de données `ax3.set_ylim(0,4)` et changer la légende de l'axe y.\n", "\n", "---\n", "\n", "We can then make a curve representing these weights as a function of time using `plt.plot ()`. We have adapted the y limits of the graph to match this new data set `ax3.set_ylim (0,4)` and changed the legend of the y axis. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hideCode": true, "hidePrompt": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax3 = plt.subplot(111)\n", "ax3.plot(year,weight,'*--')\n", "ax3.xaxis_date()\n", "\n", "ax3.yaxis.set_major_locator(MultipleLocator(1))\n", "ax3.yaxis.set_minor_locator(MultipleLocator(0.5))\n", "ax3.xaxis.set_major_locator(YearLocator(50))\n", "ax3.xaxis.set_minor_locator(YearLocator(5))\n", "ax3.grid(which='major',axis= 'both',linestyle='-',color='grey')\n", "ax3.grid(which='minor',axis= 'both',linestyle='--',color='grey')\n", "\n", "ax3.set_ylim(0,4)\n", "ax3.set_xlim(datetime.date(1565,1,1),datetime.date(1830,1,1))\n", "\n", "ax3.set_xlabel('year')\n", "ax3.set_ylabel('Weight (kg) of wheat per week that a worker can buy ')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Question 3 b : Représenter le salaire moyen en fonction du prix du blé\n", "\n", "Il nous était aussi demandé de montrer, dans un autre graphique, les deux quantités (prix du blé, salaire) sur deux axes différents, sans l'axe du temps. Trouver une autre façon d'indiquer la progression du temps dans ce graphique.\n", "\n", "Pour ce faire, il suffit de mettre le prix du blé sur l'axe des x et le salaire moyen sur l'axe des y dans un graphique du type `plt.scatter()` et de changer les axes et les limites du graphique pour s'adapter aux nouvelles données représentées. \n", "\n", "La fonction `plt.scatter()` permet de représenter 3 variables différentes sur un même graphique: aux x et y traditionnels s'ajoute une troisième variable c. La couleur des points représentés sur le graphique peut alors varier en fonction de cette troisième variable. Nous avons dès lors représenté le temps par cette dernière variable. Nous avons fixé la taille des points avec le paramètre `s=50` et choisi l'échelle de couleur `cmap=cm.viridis`. Enfin, nous avons ajouté une légende de l'échelle de couleur au graphique grâce à `plt.colorbar()` et choisi d'orthonormé les axes du graphique grâce à `plt.gca().set_aspect('equal', adjustable='box')` afin de mieux visualiser le rapport entre x et y. \n", "\n", "\n", "---\n", "\n", "It was also requested to show, in another plot, the two quantities (wheat price, salary) on two different axes, without an explicit time axis. Find another way to show the advancement of time in this plot.\n", "\n", "To do this, simply put the price of wheat on the x-axis and the average wage on the y-axis in a graph like `plt.scatter()` and change the axes and limits of the graph to adapt to the new data represented.\n", "\n", "The `plt.scatter()` function is used to represent 3 different variables on the same graph: to the traditional x and y, a third variable c is added. The color of the points represented on the graph can then vary according to this third variable. We have therefore represented time by this last variable. We fixed the size of the points with the parameter `s = 50` and chose the color scale` cmap = cm.viridis`. Finally, we added a color scale legend to the graph thanks to `plt.colorbar()` and chose to orthonormalize the axes of the graphs thanks to `plt.gca().set_aspect ('equal', adjustable = 'box')` to better visualize the relationship between x and y. " ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "color_year=plt.scatter(x=data['Wheat'], y=data['Wages'],c=data['Year'], s=50,cmap=cm.viridis)\n", "cbar=plt.colorbar(color_year)\n", "cbar.set_label('Year')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "\n", "plt.xlabel('Wheat price (Shillings per quarter)')\n", "plt.ylabel('Wages (Shillings per week)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Malgré l'utilisation d'axes orthonormés, il ne nous semblait pas évident d'observer l'évolution des deux variables l'une par rapport à l'autre et donc l'évolution du pouvoir d'achat des ouvriers. Nous avons donc choisi d'ajouter des lignes représentant le rapport du salaire des ouvriers sur le prix du blé (*Wages*/ *Wheat*) pour différentes valeurs de 0.1 à 1 par pas de 0.1 en:\n", "\n", "* définissant cent points allant de 0 à 100 afin de dessiner les courbes `a_x=np.arange(0,100,1)`\n", "* définissant les pentes recherchées: `a_p=np.arange(0.1,1,0.1)`\n", "* réalisant un graphique (`plt.plot()`) avec `x=`a,`y=`a_p[i]*a, des lignes en pointillé `linestyle=`'--' et de couleur grise `color=`'grey'\n", "* en ajoutant un texte donnant la valeur du ratio représenté : `plt.text(position x,position y,texte,paramètres,rotation)`\n", " * avec comme paramètres des instructions pour faire tourner le texte à partir du coin inférieur gauche:`{'ha': 'left', 'va': 'bottom'}`\n", " * avec une rotation en degré calculée à partir de la pente de la droite (arctangente de la pente, basé sur les formules trigonométrique dans un triangle rectange):`180*np.arctan(a_p[i])/np.pi`\n", " \n", "---\n", "\n", "Despite the use of orthonormal axes, it did not seem obvious to us to observe the evolution of the two variables in relation to each other and therefore the evolution of the purchasing power of workers. We have therefore chosen to add lines representing the ratio of workers'wages to the price of wheat (*Wages* / *Wheat*) for different values from 0.1 to 1 in steps of 0.1 in:\n", "\n", "* defining hundred points going from 0 to 100 in order to draw the curves `a_x = np.arange (0,100,1)`\n", "* defining the slopes sought: `a_p = np.arange (0.1,1,0.1)`\n", "* making a graph (`plt.plot ()`) with `x=` a, `y=` a_p[i]*a, dotted linestyle `linestyle=` '--', grey color `color=`'grey'\n", "* by adding a text giving the value of the ratio represented: `plt.text (position x, position y, text, parameters, rotation)`\n", " * with instruction parameters to rotate the text from the lower left corner: `{'ha': 'left', 'va': 'bottom'}`\n", " * with a rotation in degree calculated from the slope of the line (arctangent of the slope, based on trigonometry in a right triangle): `180 * np.arctan (a_p [i]) / np.pi` " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "hideCode": true, 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a9CLwIkB+fr7bNYOFjmInPcWRn/7oo48YOXIkK1as8KqjNplMvP/++5w9e5asrCwWLFjQZ0VkVquV06dPk52dTWRkJCtWrCA1NXXACps4VNL8/f3x9/dn5MiRQ9JJO3bRJ0+e5Mknn8Tf35+FCxcSERHhs1y03Xocae9k5yebsDS+opz1DbBarZypKGRY1EcEBrTw5ZnhZKddwBje5rB1RtD57nWsjcgcWpEPT3PWRcDfALeghaS/BLYD9wI7Pb1ZW4j7dNu3XwghjGhh8JeBSqDjb9fx/SWGMM3NzaxevZoLFy70SuzEarWyfv36dvlpb1NRUcHFixdZsGABEyZM6LNQomvI+1vf+paz/3gg4lrdPXv2bGbMmMG4ceMYN25cf5vWZ7ir6M7KyqK5uRl/f3+GDRvmlfvY7Q20th5DJ4Lx8x/rfD1Kew1acM890n7VK/cfTDQ0NHDq1ClKS0spKyujtTWchX+TSu6oUsZnnUEIx34pEML+0ed/+ypn7Z6P0aq5fwHcLqWs9tL9dYAjibgHrX9b55K3ng80AZ976X43HTabjVdffZXq6mqWLFnC6NE9a4lobGzkjTfeoKKiwuv56ebmZi5evEhGRgY5OTmkpKT02W7aZDLx4Ycf8uWXXxIVFcWKFSuIj4/vk3t3l4456ZycnAH7gcJX2O12dDodlZWVrF27loiICAoLC5kwYYJXd9FS2qmr+wWNDS8hhD9gR4gwjFH/S0DALHR+WW25anfo0Rsmec2WmxUpJRaLhYCAAEwmE7/+9a8BCA8PJy8vj6ysLEYMWwiNPyUo8AQgQJcMYf+ALsijzGjPbcN7oihCiNnA99CivinAP0sp/83l/A60aZMdaZJShrStmYu2ge3I466RYyFEIlpBtSNssxF4xpOR054665+hhbt/DjwphNiGVh2+XUpZ48kFhBA/AT5Bm9blD8xG69d+pW3J74HvAi8JIX4JpLfd97dDuRJcr9czbdo0jEZjr6QSAwMDCQ0N5f777/eqvvfZs2d57733aG5u5u/+7u8IDg7uM0dts9l4+eWXMZvNTmGTgRryBli3bh2nTp0acjlpm83GiRMnOHDgAEajkQULFpCcnMxDDz1EamqqT3Zg9XX/Q1Pjy4AZLYMHUjZSU/0QMbEb8PfPxi/wVqzmj3AUlzkRBgwhT3jdppsBq9XKuXPnOHnyJKdOnWL48OHcc889REREMH/+fFJTU4mPj2//Owtai7SbtA8/upg+iaZpcqNey1mHAseBN4Bfuzm/GK3OyoEA9gEfuVk7ES1K7MDkfJA2Tno9mlbJvLbrvIAmODbToTfSGaKL8+0XCxGEVmRWhOa8xwPHpJRdNtQJIX4FLACSATOa0/4T8AeH6IkQYhrwS7QnfA3NkXskipKfny8H0yCP8vJyLBYLGRkZPb6GlJLPP/+c7Oxsrxd5Wa1Wtm3bRnFxMdHR0SxatKjPWrIuXLjAsGHDEEJQWlpKXFzcgBQ2ceykJ02aRFhYGJWVlQghhoyTrqmp4cCBA+00uqdOndpr8Z6ukLKZS5W5SNnk5qyOwKAFREX9HinNNNc+jdW8A4RjwpueYOPv8At0t5Ea3GzevJmDBw/S2trqrKEYM2ZMr7o4hBAHpJT5XjQTgNjRMXLRX+7waO1Lk//isQ1CiHPAy647azdr5qE56ilSyv1tx+ai7ayHSykvdvK4W4EPgWwp5cm2Y2OAo0ChlHLHjWzr7jZE3/YYA1r42g9I8+SBUspngWe7WLMXrfBsSOMQO4mLi2PUqFE9+qTqmp+uq6tj7ty5XrPPZrPxxz/+kUuXLvXplCzXkPc999zD2LFj+2XwR1d0DHdHREQwYcIEEhPdNjUMKhx99UIIiouLOXDgQJ9rdFtby6DTSmE7lhZtnIEQgQRHvYTdegFb6yGEiEAfML0tbD54kVJy+fJlTp48SXl5OcuXL0ev1xMWFsa4cePIzMxkxIgR/TKitjv0o4LZt4DPHY66A7uFEMFotVn/B/zFZcc8EzjrcNQAUspjQoiLwCxgx41u6mmB2U/RCswmA63Ap8CbwJOAO4MVPaSkpISNGzeSnJzMsmXLevTmZjKZeOutt9rlp72Bo7VGr9czduxYCgsL+8RZdtTyLioq8smozt4ipWTXrl0UFxcPuRYs177o++67j9TUVAoKCpg9e7ZXctGN5p3U1r9Eq/U8Bv9sosKeIihgvNu1QheGlJ23Del07dM0Or/h6PyG99rGgc6VK1fYt28fp06dwmTSorPJyck0NDQQERHBzJkz+9lCz+lmNXiMEMI17PpiWydRtxFCJAB3oaVsXalEE/Zy3OcO4CVgFJpMN2gtyO6KpS/RSXuyK57urAvRisx+hCaE0lllhqKHSCnZsWMHu3btIjMzk3vvvbdHn2wrKip4/fXXsVqtXs1Pm0wmPvjgA2bNmkVaWlqf/mGvWbOGsrKyAavl7QgbCiGcQ1CGgpN2V9HtEHEBrRDJG1y59nNqG17GIZ5osZbRaP6YuMh/JzJ02XXr/fxS8fMbjtV6ys3VAgkOfsArdg10HNXbSUlJxMfH09jYyOHDh52vz8zMTEJDQ/vbzB7TjQKzq14MxT+KlsZ9w/Vg2275pMuhEiGEHnhOCPFTD8TDusxHeyqK0vPGXoXHNDY2Mn78eBYsWNBjAYiIiAgSExOZP3++1+ZPHzlyhI0bN2Kz2Whs7JtaP5PJRHBwMP7+/syYMYNp06YNOGETR7j7s88+4+GHHyY+Pp6FCxf2qeZ5f2CxWDAYDEgp2bRpEwaDwWca3S2tJ6lteMlZJKYhkdLM5dp/IixoPnq98brHRRp/S/XVe9D2FW3vkyIIf79MQkIf9KqNAwUpJVVVVZSWllJaWspXX30FwOzZs4mPjyclJYUf/vCHA7oI01O0avC+DYO3FYg9DrwuZUc1Hbd8irarjgUq0Hbft7hZF48H7ck3/2/tJqe1tZWGhgaMRqNTSay7oW+r1crevXuZPn06ISEhrFy50iu2NTc3s3HjxnZTsqKiorxy7c5wDXnPnDmTOXPmMHLkSJ/es7u4a8FyvAEOVkftuou+du0aTz/9NH5+fnzzm98kKirKZ7loU8NbdLopETrqmzcSGbriulMGQx6xcdtoaPgDLeYdCBFCcMgKQkLuRxNjHBxYrVZMJhPR0dFIKfnzn/+M2WwmKSmJuXPnkpWV5WxldGj0DwYkYO37QR7zgVS0XLQnTACaAUfT/h7gX4QQGVLKUwBCiBxgOB7IaStn3Y84xE7q6+v5zne+06NPvK756fj4+F5Vj3fk2LFjHDt2jMLCQmbNmuXzP/SOWt4DUSTEZrPxwgsvUF9fT3Z2NnPmzBnU4e5r166xf//+dhXdEyZMwGaz4efnR3R0tE/vb7PXAO6bQaRsxW6v6/Sxfn7DiYz8dx9Z1n90FCcJDw/nu9/9Ljqdjvvuu4/Y2NibOrztKV7ssw5Fyy2DVjydIIQYDzRIKU+7LH0S2C+lvE73QwjxLHAeOIb2WeI2tF318y5p44+Bg8AqIcTTaK1bz6MpgnYpLqacdT9hMplYtWoVtbW1LF68uEeO2jF/2mq1snTpUq84aqvVypUrV0hMTGTSpEmkpqYSGxvb6+t2xY4dO9i5c6dT2GQghbzNZjNHjhwhPz8fvV7PLbfcQlxc3KB10larFZvNRkBAAFVVVRQXFzs1uvuqottBcOAM6pvX405qQQh/AgN8OYZxYOAo7ATYvn07u3btArSaAEf1tmNNWppHzTk3P9KrYfB82guafKftayfaVEjaJkDegeaw3eEH/AfaLrkVrRr8b4E/Ok2W0i6EuBP4XzSdEglsAp7uqsfacQNFH3P58mVWrVqFxWJh5cqVPRI7OXz4MB988AFGo5GlS5d6JT9dVVXFu+++i8lk4m//9m8JDAz0qaO2Wq1YrVYCAwPJyspCp9MNKGGTjuHu5ORkkpKSyMvL62/TfIJrRfekSZMoKioiIyODZ599tt/mRYcFL+DKtf/AJpvRtCQc+GPwSyfIMKVf7PI1ruIkpaWlPPjgg0RHR5OSktIuvD1UJ4NJvNe61dbffMOLSSm/4gb+Ukr5X8B/eXCvSmBJN02EG93cgdCaDi8CRVLKYz25iaI927ZtA+CRRx7psTRmfHw8OTk53Hnnnb2eP91xStbixYu9OtPaHWVlZWzcuJHhw4ezcOFCEhMTB0wfssVi4dNPP22Xkx7M1d3Hjx+npKSknUa3o05Ap9P1m6MG0IlAUuM/4Kurj2Gxnkbgh5QWggKmkhT9+0HnrGpra/noo4/atLe/FiexWrVWtPT09CEnUdsZShu8A1LKViGEjc4SRwqPcYSqFi5ciNls7nYLkslk4tixY8yYMYOEhATuvffeXtvU2trK6tWr+2xKVkct77Fjx/rsXt3F8fvR6XQcPHhwULdg1dXVOVurjh07Rk1NjU80ur2Bv99wRiR8iKX1NK22Sgx+afj7eWfQR3/iWr3t+FsIDAzk0qVLTu3ttLS0ARNpGkhIlLPujJeBZ4CnfGjLoKakpIRjx46xfPlyAgMDu71zdc1PjxkzhoiICK/Y5e/vT3R0NGPHjvX5lKyTJ0+ydu1apJTd1vK2281UNbzGlYY3sNsbCQucSlL40wQZei/K4gh3nzhxgscffxw/Pz+eeuopn0cX+hqr1cqJEyecfdHf+c53iImJ4c477yQgIGDAVwob/Edh8B84tQw9paysjBMnTrQTJ5k0aRJjx44lKCiIZ555ZtBFDLyNRGC1D+zXq7fx1FknAUvaRmUeANpVe0gph6bqvQe4ip30pADMdf60Iz/dW0dtNpvZvHkzM2bMIC4ujjvu8Exjt6c4wnlJSUlkZ2dTVFTUraiCXZr5suoemlvLkGj9tjVNG7jW/DEZsa8QHtgzrWnXUZWOcLfZbCYkJGRQOeqmpib27NnjrOh2TLpy6MUHBQX1s4WDm4aGBioqKpxqf7t37+arr77qVJxEOWrP6Ee50X7BU2edjlZyDprjdsXzSSBDDLvdzvr16/n88897LHayadMm9u/f75w/3Vsn4piSVV9fT0pKCnFxcb263o1whLybmpp46KGHCAsLY/Hixd2+zpX6NZitZ5yOWsOOXTZztvo58pL2dPsNrrq6mpdeemnQ5qRtNht1dXUYjUaEEJSUlDBy5Mh+qegeargTJxFC8IMf/ICgoCDuvvtuQkNDVXi7N0gVBneLlLLQ14YMRjZt2sTnn39OQUEBhYWFPXqDTEtLIzg4uNfzpztOyXr00Ud9NiWro5Z3QUFBu/aT7nKlcQ32NqnJ6+5lv0Zz6wmCDTldXsdsNlNZWUlaWhpRUVFMnDiRvLy8QeWkXSu6jUYjjz32GEFBQTz33HNOGVCF93EUgPn5+Tn1/UHT3nZUbzs+aA80udybEZWz7gIhRARa8/gRpQ/eOQ7HNHXqVBISEpg0qXuD7MvLy6mtrWX8+PHk5OSQk9O1I+qKffv2UVxc7PMpWVevXmX16tXU1NR4Tcvb7nbUoQM9thueb9+CBfC9730Pf39/br311l7ZNZA4c+YMu3fvblfRPWnSJOdrUTlq79NRnGTBggXk5uaSkZHBggULbnrt7YGOctZuaBv59XvgAbQPNRnAGSHEH4CLN5r9OdQwmUwcPnyYgoICYmJiutX/7JqfjomJITc3F71e32NbpJTU1dURERHBlClTSEhI8Jl0p91uR6fTER4e7hxU7y01tfDAAq40vAm4m6RkJdjf/YcZd7Kgs2fPHvCj/zylurqa0NBQAgICuHbtGrW1tT7T6FZ8TUtLC6+99ppTe9shTuL4W4+MjGTixMEv1tKfSAQ2VWDmlp8B44A5aIorDjYD/wIoZ40mKvL6669jsVjIzc3FaLx+wEBnWK1WNmzYwKFDh5z56d44apPJxPvvv09NTQ1PPfUUBoPBJ47aEfI+duwYjz32GAaDgQcf9O6ghMTwb1Hd+B72DmMPdSKI+LDH0euC3T6upqaGnTt3DqqctM1m48SJExw4cICzZ88yf/58pk6dyrhx43xezT8UcRUn8fPz47bbbsNgMBAREUFGRsaQFyfpT1SBmXsWAw9IKXcLIVwLyo4DA2vKQj9RXl7O6tWrMRgMPPLII91y1DbrsgZkAAAgAElEQVSbjVdffZWvvvrKOX+6N3/8rlOy5s+f77OdZEctb4vF4pOimQC/4WTHr+FM9XNYrBfQXrZ2EsKeJCniGec6R3V3S0sL8+fPJykpiWeeeaZbv4uBit1uZ+vWre00uouKihg9ejRArz7YKa7nxIkTHD58uJ04yejRo3GoQi5Z0iMRKoWXkKrArFMSgAtujvt34xqDli+//JK1a9cSGRnJypUru52j1ev15OTkMGvWrF7Nn25tbeWvf/0rR44c8emUrJaWFt5//32nsElfaHmHGHLJTdyC2Xoeu72eQP90dG3TkzqGu8eMGePM1d7Mjtpms1FRUcHw4cPR6XRUVlaSmprKxIkTVUW3F5FScvnyZU6dOsX06dPR6/WcP3+eiooKpzjJiBEjBk36ZLAglbN2ywlgJlDe4fhC4LBXLbrJkFKi1+tJTk7m/vvvd/auevK4ffv2ER8fz4gRI5g5c2avbfHz86OpqYm5c+dSUFDgM5ELg8FAc3MzRUVFTJ8+vU9bUAL9Utp9X1payrp16wZVC5ZrRbfZbObZZ58lNDSUlStXDnjhkpsF1/C2qzhJWloaycnJFBYWMm/ePPWBaMDS9/Os+xtP32V/ATzfNkpMAHOFEN9CUzW7z1fGDWSklFRUVJCcnExmZiYZGRke/2FbrVbWr1/P4cOHGT9+fI8Gebhea+fOnUyePJnw8HBWrFjhkzeYsrIyduzYwdKlSwkJCeHBBx/stzcys9lMc3MzRqOR+Ph40tPTKSgouOmddFVVFR9++KGzojs7O5uJEyc65V+Vo+4dDQ0NSCkJCwvj4sWLvP76607t7Y7iJGoXPfBRO2s3SCnXCG1i+78CwWjyoxeAx6SUH/jQvgGJQ+zk0KFDPPbYYyQlJXnsuFznTzvy0z2lqqqKdevWcfnyZcLDw5k8ebLXHWhHLe/6+npCQkKuu4/ZeokLdasxtXxOoD6BYeHLiAyc4FVbXMPdycnJPPDAA0RERNzU+cPq6mpsNhtxcXEYDAauXbs2YDW6bzbciZNMmzaN2267jZSUFJYvX67C2zcpUoLNrpy1W6SUrwKvCiFiAJ2U8rLPrBrAtLa28s4771BaWkpBQUG3JkVdu3aNl156CavVyv3339/j/HTHKVnLly/3WpuU6z327NnDrl27utTyrm3ez+dVT2KXNiQWQFDVtIWU8AfIiHqu17Z01oJ1s9JRozszM5Nly5ZhNBp5+umnVei1FzhqFaSUvPjii1y6dAmApKQk5s6d69Qs0Ol0Xv+bUfQtqhr8Bggh0oGctv8fl1Ke8YlVA5SmpiZWr17NxYsXuf3225k8eXK3Hh8REUFeXh6TJk3q1fzp3bt3s23bNp9OyRJCUFlZSXp6+g2FTezSyuHLz7TNG3YgsUsz5+teIza4iMjA8b2ypaSkZNC0YBUXF7N79+52Gt0TJnwdgVCOuvu4ipOYTCaeeOIJhBDk5eUxefJkJU4yCJGoMLhbhBDRwB+Bu1wOSyHEeuCbUspqXxg30Dh+/DiVlZUsWbLE2TLTFVarlS1btjBt2jSMRiO33XZbj+/f0tJCQEAA+fn5hIeHk5eX59U3d5PJxJYtW5gzZw6xsbEsWrSoy+Kxmua92GWr23N2aeFi/ZpuO2vHTjohIYHs7GwmT57MqFGjbkon7eiLzsrKws/PD7vdTkpKitLo9gInTpxwDsUATZwkIyMDq9WKn58f06dP72cLFb5DFZh1xv8Bo4HbgD1tx2YCv2s71/vBygMYm82GXq8nPz+ftLQ0oqOjPXqca346Li6u27KjDsxmMxs3buTKlStOredx48b16Fru6KjlnZ2dTWxsrEdV3hZbNZ3PcrHTYvU8W9Ix3D19+nSys7MJCAi46Ry1a0V3U1MT9913Hzk5OV6p+h+KuFZvT58+naioKKxWK1JKp/a2EicZWsghNkLKU2f9DeAbUspdLse2CCEep72i2U2LyWTik08+ITQ0lOTkZGc+q7y8nPfee49ly5YRFxfnsaMuLy/n7bffprW1tVf5adcpWb0VS3FHR2GT7mp5hwXkILG7PafDQISHu+qSkhI+/vjjm74Fq7m5mbfffvs6je709PT+Nu2mw2KxcOzYMaf2tkOcZOTIkURFRTF27FjGjh3b32Yq+gkVBndPLXDVzfFqwOQ9c/qHxsZGXnvtNcLDw/nqq6/YuXMnixcvxs/Pj3feeQej0ditwRenT59m9erVGI1GHn744R7lp/tqStaZM2eQUvZY2CTMkEmYIYe6lqNI2ofDhfBjeNjSTh9rNpvR6/X4+/s75VBvRiddXV3NlStXyM7OJjAwEL1eryq6e4BDnKS1tZVhw4Zhs9n461//SlhYGOPGjSMzM5O0tDQ1WlLRVg0+tFoZPX3V/w/wH0KIlVLKBoC2nuufAf/tK+P6Ckff7qJFiwgJCWHNmjW8++67SCkZNmyYs7fYU1JSUpgyZQpz5szp1fzpsrIyr0/JcoS8k5OTGTlyJHPnzqWwsLBXb4AT4l/gUNV3qbMcQ6DJXupEAOPjf0eA3/Xzsl3D3bNnz2bGjBnk5eWRl5fXYxv6GpvNxpdffums6A4KCiIjIwO9Xs+KFSv627ybBnfiJKmpqTz88MMEBQXx3e9+1zmTW6FwRYXB3XM7MAWoEEIcbzuWg5asDBVCzHcslFLedHMHAwICiIiI4K9//SuPP/44ISEhSCkJDg722FGbTCa2bdvGHXfcgcFg6FEhmZSSkpIS8vLyMBgMPProo14dZeka8p42bRojR470So+pvz6SyUmraLCcot5yEoM+mqjAKQjRXq/aXQvWzRgePn78OBs2bLiuolvpc3uG2Wx2fohds2YNZWVl7cRJXFuqfCGXqxgcqDC4ey62fbnypZdt6TdCQ0PJz89n586d/PznP0ev15OYmEhsbKxHjrq8vJy33noLq9XK5MmTGTZsWLdtcEzJOnv2LHa7nSlTpnjNUXcUNvGVlneoIYNQQ+e9q+vWrePUqVNkZ2czZ84ct+HuGvMXnLq2inrLWUINqWRErCQ6qHetX73FsYuOi4sjLi6OyMhIVdHdDRzh7ZMnT1JaWkplZSXf//73CQoKYsaMGUydOlWFtxXdQiKUs3aHlPIRXxvSlxw9ehS73U5SUpIznzx27FjKy8tJTk4mOTmZffv2YbVanZXg7nCdP200Glm6dGmP8tOuU7IWLFjg9VGHp06d4tSpU32u5d3c3Mxnn33GpEmTCAsLo6ioiKKiok5z0mWm1Ryt/l9ssgWQ1LWWUdW0h5yob5MZ6d2xm57QsaLboX6VlJTE/fff3+f23IycOXOGDz74wKm9nZSUxOzZs53Tq3w1X10x+BliUfChNzFr48aNHDlyhPDwcKqrq7n99tsZOXIk69at48KFC6Snp3P69GkuXLjAt7/97RuGNj/55BO2b9/unD/dk/z07t272bp1q9enZJWVldHS0sLo0aOZOHEiGRkZREREeOXaXeEYVfnZZ5/R0tJCeHg4EydOvGHhWLP1Mkeqf41dWlyOSmzSzPGa5xkWMo9gf8/V4nrL22+/zfHjx9tpdN+MIfu+xFWcJC8vj5ycHCIiIkhISHCGt1XBncIrSJBKbnTw0tDQQEVFBQsWLCA1NZUPP/yQTZs2ERQURFNTEwsWLGDMmDGYzWaPNLvz8vLQ6XTMnDmz2zthu92OTqcjNzcXKSUzZ870yqAG15D3sGHDyMnJQafT9YmjllKya9cuiouLu92C9VXDls4/KkvJhYbNZBl9F+Cprq7m+PHjzJo1CyEEiYmJJCQkMH78eOVgboDdbmf37t1O7W3QxEkyMzMBiI6OZunSzjsCFIqeosLgg5iQkBBsNhslJSWMHj2aWbNmcfz4cerr61m+fLkzFH0jGdHy8nIOHz7MggULiIyMZNasWd2ywdGSdfXqVZYtW0ZERAQFBQW9fWrXCZs4Qt59kU919L8KIaipqelRC5bFZsKOxe05O6202K55y1wnHSu6HX3RcXFx3f69DhUc1dv19fVMmDABnU7HF198QWBgoBInUfQpqhrcRwghfgAsBrLRxmweBf5NSrm5w7qpwK+AiWj93a8CP5ZS2rxgA1OnTmXz5s1s2rSJgoICoqOjaWxsRK/Xc+edd5Kent5uGIDjTadjfrqpqanbmtyuU7Ly8/Ox2+1eqyC+cOEC27Zt65GwSU9xVHd/9tlnPPTQQyQkJHD33Xf3KEJgDMzFTwRjlU3XnfMTwUQHeretq6qqir/85S80NTURGRlJUVGR2kV3gmt42yFOEhYWxvjx4xFC8OSTT6rJVYo+RWmD+5Yi4E/AfqAZeBxYL4SYI6XcAyCEGA5sAda2nc9oe4wA/sEbRuTm5nL69GkOHjyI2Wxm9OjRlJSUOHs5N2zYgJ+fH3PnziUgIABoP3+6J/lpKSXFxcVs27bNq1OyTCYT58+fJzc3l7S0NJ544oluTQHrKe5asBxv1l05aintWOwN+OuC0YmvX34JwTMI8IvG1tqC5OvPZQI9/voIEkN6PkoUvt5FSynJzc0lJiaGjIwMxo4dqyq6O+Co3o6JiUGv1/Ppp59SXFxMeHh4O3ESx89MOWpFnyMB5ayvRwgxDrBKKY+1fX878AhwDG13bO3qGlLKb3Q49H0hxG1ou22H3vi3gTrgUSmlHTgmhEgG/lMI8TMpZaMn9t6Izz//nGPHjpGTk8OVK1eoqalh3rx5NDc3s2rVKsLDw6mqquLy5cs88MADALz11lucOnXKOX+6u2/sLS0t7N27l1GjRnllSpZryFun05GZmUlAQECfOGqbzcYLL7xAfX19t3LSdmnjSM2rHL/2JlZ7C0LoSA+bT37s0/jrghFCz+ykP/LZpe9hspQi8EfSSphhFNMS/qedY+8OHSu6U1NTyc3NRa/Xs3Dhwh5dczDiTpzkoYceYsSIEUyePJm8vDwV3lYMKFQY3D3/B/wazXkOA94BdqLtfgPpwa5XCKEDwmgvYzoT+KjNUTvYjDYwZAKwu7v3cSClZMeOHezatcu5O9bpdFgsFoKCgti9ezcBAQHce++9HDt2jMrKSqqqqoiPj2fmzJlMnDix2/repaWlpKenExgYyGOPPUZYWFiv3+zcaXk7IgC+orm5maNHj5Kfn49er2fevHnExsZ2Kye9p+rfKW/YgU2atQMSTtdv5GrLCe4Y/jI6oSfIL5a5w/5CvaWcRutFQvySCTOM6LHd27dvZ9euXUqjuxMcaZ7Lly/z8ssvt9Penj17NnFxmvqc0WjsZ0sVio4IVQ3eCVnA523/Xwzsl1J+QwjxN8DL9CxE/SMgEnjN5VgiX++yHVxyOdcOIcQTwBOgSXx2ht1uZ8OGDRw8eJDx48ezYMECZ7jWEc6OjY3l2rVr7Ny5k7KyMqSU/OEPf6CwsLBdX6gnmM1mNmzYwNGjR51zr8PDwz1+fGeYTCbeeOMNIiMjfSZs4krHFqzk5GSSkpLIzc3t1nXqLBcpb9iGTbYvILPLVuos5/mqcS/DQ7+eRhVmSCXMkNptex276Pz8fIxGI2lpaej1eqXR3UZHcZLU1FTmzZtHTEwMEyZMYNSoUYwYMUKFtRU3xGKxcOnSJSorK6moqKC6uppHH32076MuamftFgPQtiViLl9P2ioFuj11QQjxFJqzvktK2VEZrSOyw79fn5DyReBFgPz8/E5/dTU1NRw5coRZs2ZRVFTU7kXl+P+oUaP45je/yc6dOxFC0NzcTGhoKEePHmX69Okev4G5TsmaO3duj8diOrBarZw6dcrZs7pixQpSUlJ8KmxisVjYs2eP00n3dgpWRdNnaGUH12OVzZQ3bG/nrLuD1WrlxIkT7Sq64+LiMBqNjBgxghEjRvTouoONrVu3cuTIkXbiJI6efp1Oxze+0TFLpVB87ZgTExPx9/dn7969fPTRR87NS2hoKImJiZjNZoKCgvrOMKkKzDrjJHCvEOJtYB7wH23HE9Eqtj1GCPF94CdojvrjDqcrud75O76/RDdxhPViYmL47ne/69zdnjlzhkuXLhEUFMSECRMA0Ov1znx1U1MTOTk5XLt2jYiICI931Xv37uXDDz8kKirKK1OyXEPeTz75JAkJCT5VfHKERXU6HYcOHfLaFCzBjYvOdKLzini7tHG+cT9n6ncDglHhcxgePBEhdNhsNn77299SV1fXTqN7qO+iHdXbFRUV3HHHHYA2WU6Jkyi6or6+nuPHj1NRUUFlZSVXr15FSsnDDz9Mamoqw4YNY/bs2SQmJpKUlNS/ryO1s3bLT4C3gP9EyymXtB2/la/D410ihPgp8Cxwu5Ryp5sle4AHhBA6l7z1fKCpO/cBLWS8atUqpkyZ0i4M/eWXX7Ju3TpSU1MpKyvj7NmzFBYWEhoayp/+9CfMZjMxMTGcP3+e+Ph47rnnHo93sSNGjPDKlCx3Wt6+HBvpCHefPHmSxx9/HD8/P5566imv5cKHhcxk/9XfuD3nJ4JIC5vn9lyr3cx7579HTcs5WmUz0ib44ujn+F8bzrfu+Ql6vYHp06cTExMz5Cu6a2trOXLkyHXiJIWFhQQHB3PXXXf1s4WKgYRrKLuyspLc3FzS09MxmUxs3rzZuWPOyckhKSmJ+Ph4AIYNG9aj2Qe+wTt/70KI2cD3gPFACvDPUsp/czm/A3DXjtIkpQxxWZcJ/BYoQPNZ7wDfcy2MFkIkAr9B82sAG4FnpJSXu7LTU23w94UQKWg76S9cTm0F1nlyDSHEr4EngWXASSGEw/s0SykdM7F/D3wXeEkI8UsgHW0M52+7Uwl++fJlVq1ahcViITY2tt25CxcukJmZyZIlSzhy5AjvvfcewcHBFBUVOfN3cXFxXLlyxfkC7QwpJXv37qW2tpbbb7+dhIQE506mp9hsNv74xz/S3Nzscy3vjjnp7OxszGYzISEhXi1aC/GPIzNiMadM72F1FJgBehFAbOAYEoLcpwqKr7zM1ZYyLHV6zGdG0nI2HtliQBdi5tOKv1Aw7DGmTZvmNTtvJqxWK2fPniU+Pp7w8HAuXrzI9u3bSUpKUuIkinZYLBYsFguhoaE0NzfzyiuvOHfMoIWyU1O1GpHExESeffZZr9TY+Bx710s8JBQ4DryBVkjdkcVoqWAHAtgHfOQ8oI2M3ormH2cAUWhtx5HA0rY1OmB9m+Xz2q7zAvCeEGKm7CKE67EXkFJWAVUdjhV7+njgb9v+fbfD8T8DD7dd74IQ4lbgl8AB4BpaTvrHnt6kvLycNWvW4O/vzyOPPHKdw42IiKC4uJjLly+Tk5PDp59+yr59+5gyZQp1dXUcPXqUpUuXXufkO+I6JSs7O/uGAz884fz58wwfPhy9Xs9dd91FTEyMT4VNqqureemll7ySk/aEyTFPE2lI40jNqzRaqzDow8mJuJexUQ+4dShS2jl+bSNNF8Jp2DMGhMSQdJWA9Er8E2o50XScAh7zmb0DEXfiJLfeeivTp08nKyuL5557ToW3FVy4cMEZxq6oqODq1avk5eWxcOFCAgMDiYuLc+6YO4ayHenAAY8X+6yllBvRdrgIIX7h5nyN6/dCiHnAMOAPLoeXAzHAcsfmUwjxHTQtkX+UUp4FbkET+8qWUp5sW/MAmkDYHGDHjez0tM/6xU5OSbTCs1LgLSnllc6uIT2sBpBS7kX7ZNJt6urqWLVqlbNa2p2zy8rK4uDBg7zzzjv4+fk5c9cnTpwgKCiIgoIC3n77baKjoykqKmqnYubAm1OyXEPeixcvJjc312dV3mazmcrKStLS0oiKimLixInk5eX5xElLKaloLuWYaSet9hZGheWTEX47mRELbvi4mpoaDhw4QGxcFLaAVvzjagnKPUtg2iV0QV9Xk7fY693+bgYTUkpn4Y7FYuFXv/oVdrv9OnESAIPB4NXZ54qBj2so22q1MnOmVqT5wQcfcPXq1XahbMfrRAjBvffe259me41uNOjECCFKXL5/sa04uad8C/hcSrnf5dhMoNglSgzaztvedu6s41+HowaQUh4TQlwEZuENZ42mJDYB8EcrNgPIBFrRHPVDwM+EELOklMc9vKbXCQ8P56677iI9PZ3g4GC3ayIjIxk9ejQ7d2op86lTp3L06FHi4uKw2+2sWbOGhoYGjEYj06dPv67CsbGxkfXr1xMXF9erKVkdtbwLCwvJycnp0bW6wlVxDOC5557DYDBw6623+uR+Utr54KtfcrKuGKu0IJEcN+0i3BDLg2n/SZA+tN16m83GiRMnOHDggLOie8aMGQSkhGA21BE8+vx19wjxixmUjrqjOElkZCQPP/wwBoOBBQsWkJCQoMLbQxCr1epMh+3Zs4fDhw+3C2XHxcU5nfXixYsJDQ0d/FEWz531VSllvjdu2Za+vQstXetKIh2KoKWUrUKIGr5uO75uTRuXcNOa3BFPnfXbaAnzlVLK2jajjcBf0MIHr6EVoP03cLuH1/QqLS0tAF32AF+5coWdO3cSFhZGaGgoFy9eJDc3l+bmZt5//31uueUWpJTs2bOHhoYGp7OurKwkISGBkJAQHnnkEeLi4no1JcuhiuZLLW93sqCzZ8/2+Q7sUO0WTtYV0ypbnMcsspmalgo2VvyWe4b/Y7v1b775ptMxuWp0H6i+zP6rr2F1uQ6AnwhkUtRynz6H/mD79u0UFxe3EydxFeIZP358P1qn6Cs6Fn9VVFRQU1PD3//93+Pv74/dbicyMrLTUHZfKBkOCPqndetRtGjyG914jCcfK7pc46mz/iFaq5WzTUtKWSuE+DHwgZTy90KInwAfeHg9r2M2m2943hEyjY2N5Y477mDMmDEEBAQ4B3JUVlby7W9/m+joaGpra/noo4+4dOkSRqORrVu3snfvXhYuXMi4ceN6HDY2mUwEBwfj7+/P9OnTmTx5slc0wjujtraWnTt3kp2dzZw5c3yak3bls+p32zlqB3aslJr283ntAY59oYX9g4ODmT59OlOmTLmuonti1FJqWy5wqn57u+vkRNxGrvFunz8PX+EqTnL69GmWLVtGUFAQkZGRzvC2EicZGrg65rFjxxISEkJJSQlbtmwBaBfKttls+Pv7e2VK32BA9HHrVluB2OPA61LK+g6nK4HhHdb7oxWaXXJZc4ubS8fjQWuyp846Hi0E3hF/wFGJVQX0TvS6F9xoXrPJZOLdd99l/vz5JCQktBMqceh0u34a1el0xMXFcfToUT755BOuXLlCfn5+j8PUriHv6dOnU1hY6MwheRPHTtpsNjN//nwSExN55pln+lwussFac90xWR+A/Wws1vJYPmhZT2RkJLW1tQQHB3f6sxBCxy1Jf89ky0rKG/cjEKSGTiXcv28+dHib2tpaPv30U6f2NmjiJPX19c6ef0ffv2LwcvXqVXbv3u0s/nKEso1GI5mZmeTk5BAdHd3/fcwDGSmg7+VG5wOpaPLbHdkD/EYIES6lrGs7Ng/Q8bUq5x7gX4QQGVLKUwBCiBw0J9+llLanzvoT4HdCiOVtVW0IIUai9Yt90rYmByj38Hp9Rnl5OW+//Tatra00NDQAWt55165dhIWFkZmZ6dRAduy+IyIiEEJQWlpKSEgIy5YtIzMzs0f3P336NJs2baKmpobs7Gy3b8Y1liqKr26m0lxOjCGB6THfID5wuJuruadjuHv06NHO59Ifus5GQyKXzGXO72WTP7YPc0GALsnEvbMfJzsjx+O8a4QhmTxD7wRm+oPGxkZKS0uJiooiNTUVu93O4cOHnWIzmZmZhIaGdn0hxU2Hu1D2tGnTmDhxIqAJHiUmJjJ69OjrBEaMRqPSY/cEL+2s29quHFW9BiBBCDEeaJBSnnZZ+iSa1LY7zY83gH8G3hBC/BPajvp54E2HzwQ+Bg4Cq4QQT6O1bj0P7EWbtXFDPHXWTwDvAaeFEFfRfkyxaD1ljuShH/BTD6/nczrOn3744YeJiYmhrq6O1atXo9frsdvt7NmzhyVLljBixAh0Oh12ux2dTseIESOoqqpi2bJlPVYi27lzJzt27HAKm7ir8j5u2s/q87/CLu3YsFLGUQ7U7uCOpIeZFt11AdipU6dYu3Ztn7VgeUKe351cOvIeNgvoJ5UjglvRTT6Lf3wzeYmzyUka3a/2+QopJVVVVZSWlrYTJ8nPzyc1NZXo6Gh++MMf+lQqVtH3OByzXq8nOTmZlpYWfvGLX1wnyemI4kVHR/O9732vP00eHHgvDJ4PuObavtP2tRNNXpu26Y93oDns602RskEIcQuaKEox2hjod4DnXNbYhRB3Av+L1pMt0aS7n+6qxxo8F0U5B4xv6y9zxIKPu8qFSin/6sm1+oqjR4+yefNmsrKynP2FoO126uvreeSRRwgKCuL1119ny5Yt3HPPPVRWVlJfX8+MGTNIS0sjJCSk247aarXS2tpKUFAQ2dnZzspmd2/QZlsTq8//mlaXARcSO63SwvqKV8gKm4DRcH2/t9lsprm5GaPRSFxcHOnp6RQUFPSrk3bMi/5aozsOkVyLHgNgx29EAynBo7k14Yl+s9EXWK1WqqurnRXa77zzDtXV1SQnJ7cTJ3GgHPXg4MCBA85+ZkcoOysri6VLlxIQEMC8efOIiooiMTHxuml7qpLfS3jJWUspd9CFHJqU8iu68JdtLVk33GFJKSuBJd00Ebq6uZsbbQG29ORGfYUj/Dt69GisVivjx49v98cRGhqK3W5n//79zJ8/nzvuuINXXnmFdevWUVlZSUhICJMnTyYrK4usrKxu3busrIyNGzeSnJzM4sWLiY+Pv6EK2lHT3k5fIVJKDtRu55b4+5zHXMPdSUlJPPjgg0RERLBkyfW/e5u00tBaT6A+iAB9YLeeR0/Ys2cP27dvb6fRLQNbKK3bi1VaGBEynoQg3+ma9yUdxUn0ej3f//730ev1LFq0iIiICBXeHgR0DGXb7XYWL14MwOHDh6mpqSEpKaldVbaD6dOn95fZQwMviiPo/nsAACAASURBVKLcLHjsrIUQ6UARWrFZu54lKeWACH+fO3eOLVu2sHz5ckJCQtzmh0NDQxk7diyHDh0iMzPT6cgrKysZM2ZMu35GT+mo5Z2Xl+fR4xqsJqyy1e05G1ZMlmqg8xYsd9ilnc2X3mN71UZs0obEztiIidw3/BHC/L2jTOQ66WrKlCnOXHxSUlKHiu4w8qPv9Mo9+xNHhEoI4RzWArQTJ3E8594Ob1H0DxaLhStXrjh/f5s2bWL//v3tQtkpKSnOzcDKlSvx9/dXu+R+pK+rwfsbTxXMVgKvoPWXVdE+ACEZALnqxsZGXnvtNYxGIy0tLc78UEeEEBQUFHD58mXefPNNrFYrQgjy8/O57bbb8PPzo6KigujoaAICArpUySotLeWdd95xCpt0FvJ2R2JgKv66AFrszdedM4hAhgdrbV0lJSXs3LnTo5z0m+f/REnNHiwuofUj1w5woekcPxr9Cwy6nvdYO+ZFHzp0iKamJiIiIrDZbACEhYUNqsrVjuIkCxcuZMSIEaSkpCjt7UFATU2NczKZ63SpH/zgBwQHB5OSkkJgYKDbPmZAqcUNBJSzdstP0ARPfiyltPnQnh5jMpnIyMhol5++EXfffTdbt27l0qVLhIWFUVRUhN1u5+233+bkyZMUFRUxY8aMTt+MHcIVSUlJZGdnU1RU1G1hk4ywcQTrw7DYW5CuqvQWHfozMQTpYiEaJk+ezKhRo7rMSddaqtlXs/u63boNG/VWEwdri5kW7W54TOc4PqxIKXnjjTeora0lKyuLSZMmDcpJVw6FOof2tkOcxPEBrGO4UzGw6RjKLigoIDo6mnPnzrmdLuXobR8zZgxjxozpZ+sVN0LtrN2TALw0UB01aDu7+++//4bOwzEla+vWrdx3333ce++91NfXOz81Hz58mIqKCp544ol2OUfX3bUj5N3Q0MAjjzxCaGioM4/VXXRCz+Mj/z+eP/0jGqwmpEVgKIvF70w0tOqpjKwidwwEBAR4VDx2qv44eqHD6uZFbLG3cPjafo+dtWMXXVpaypNPPomfnx8LFy4kMjJy0OygXcVJAgMDmTJlCoGBgdTU1JCXl0dWVpYSJ7mJsFgs2O12AgMDuXTpEuvWrbtuulReXh7R0dGMHj1azfW+2VE5a7dsRdMGP+NDW3pFx4rLjtTV1fHee+9x9uxZsrKynLkp1z/WkydPEhkZSVxcHKWlpTQ3N5ORkUFwcPB1Wt4FBQVeGSSxvnIzF806gs7FE3EiCp1VT3NCEzH/j70zD4+yPPf/55k1M5nsCSEhIXtCIEBYQ4gsARREEEVF2qpV61JrlYo9/rp4erqv1lpr7anWWluXU4sioqDs+xoiyE4SEkjIRhayzv4+vz8m85rJRoSwiPO5Li4yy/u+z0wmcz/39r2zo7h+Ys9znntDK3T0VdSoE30bne4V3YJhw4ZhtVoJCgoiPr7/vd9XM6WlpRw5csRHnCQrK4uJEyei1Wp55JFHrvAK/ZwPRVGoqKjw6WOuq6tj5syZ5OXlYbFY1DkAXfuYAQICAvoVgfNzlSLxh8F74V/Ab4QQccABwNH5QSnljoFe2EBy5MgRVq5c6TMlqzPe8ZaDBw9m+/btvP/++5SUlOB2uzlw4AAzZszgvffeo76+vkct71ZXG+trtrG7vhCt0DEtahJTo3IwaPvOa5WfK2d7zXYcGid6rQFrhI1zaedwBjs5p2miuLWYtKD+y5FmBo9C6SX4YdQYmRh+XY+PeXvLKysreeedd3wquq8Fz6O1tZWysjKysrIAKCws5Pjx46SkpPjFSb4AdA5lm81mRo4ciaIovPbaayiKgsViITY2luHDh6tqeBaLha9+9drTj/fTCb+x7pF/d/z/hx4ek8CFD3K+DNjtdiIjI9UpWdu2baOxsZHo6GjVmwLU3ffJkyeZP38+Z8+eZePGjej1ekJDQ5kzZ043YZN6eyM/OPgr2t1WHIonV3yqvZyPqjfys5FPYeqhbcpb3b1t5zbMyWYcyQ7ahrTRNqRNfY5DcbCnYc/nMtZmXSBzY25ndfU7OJTP9lN6YSDOlMSIkM82KZ0nXUVERHDTTTcRFxfH17/+dRISEr7QuejexEliYmKIiIhg9uzZLFiwwN/zfBXi3TgCfPzxx5SUlPiEsjMyMhg5ciQ6nY67776biIiIa2JD6efzI5TzP+daor/fVgMvZH2JKS0tpa2tjaysLLKzsxk9ejRCCLZt20ZBQQGJiYmsXr2axsZGpkyZgtlsJi4ujuzsbNavX8+2bduorq4mMjISk8nEXXfd1e0abunmd8f+l3POZp/77YqDalstyytW89WEW9X7u7ZghSWGcSbqTI/rl0gUPv+ncdbgeUQFRLO66l1qbVWYdRamRM5iRvRNaISmW0V3aGioKqUqhCAxMfFzX/NqwOVyoSgKBoOBo0eP8p///AfwbMC81dvecaZ+L/rqoCdJTikljz76KABWq7XP6VJf1M+qnwHC71l3R0p51Wl+94bL5WLDhg3s3LmTmJgYRowYgRBC9RRPnz5NcnIyN998M9HR0axbt44hQ4aQmZlJQEAAgwcPxmw2c+rUKcxmMzfeeCPBwd37k2ttDfzX/uewKrX05IQ6pYsNtdt8jPXy5cs5ceKE2oLlDnHz0yM/pSebbNQYGRs69oLeg9GhExgdOkG97Xa70QiPt7Jz504KCwuviYruruIk06dPZ/LkySQnJzN//nx/ePsqwmuYq6urmTBhAkIIVq9ezf79+4HPJDljY2PVWpBbbrnlCq/az9WKkP5qcBUhxGRgt5TS3fFzr1wtOeuamhreffddamtrGT9+PNdff303QxQeHs7BgwdxOBzk5uZy5MgRCgsLSUtL4/XXX6esrIzw8HDmz5/vM0u4Kz8+9FcaHOcw9bHdsdscbNq0ibFjxxIcHEx+fj75+fk+ld2jQkbx6blPffqidUJHoDaE/y3aAGIjM6OzmT14Aiad8XO9H5296MWLFxMfH8/UqVOZNm3aFzp0qCgK//jHPygvLwc84iSjRo1i6NChgKd4yDswwc+Vo7y8nH379nWbLpWamkp4eDjjx49n2LBh/ulSfi4MfzW4yjY8LVu1HT9Lei41vipy1m63m5dffpmAgAC++tWv9jonOjMzk0OHDrFu3Trmzp3LqFGjWLNmDTU1NZSXlzNs2DBuu+22XrS8HeysO05payVVtjqUXuIwGqcg4lQgkeVBbHZuJjg4mLFjx/bYfvXNlG+y4swK1tWuw6k4EWiwu80caQWbUgZAcUsFb5/ezF/GLyHE0Len2DkX3bmi29t+1FOU4GqmsziJ3W5n4cKFaDQaoqOjSUlJ8YuTXGF6CmXPnz+f+Ph4Wlpa1OlSPYWy/Wpvfi4Kv2etkgSc7fTzVY1Wq2Xu3LlkZGT0ql4GMHToUEaOHMmuXbsQQnD48GGGDh1KeHg4S5Ys6XWHv776U35xeBkCgcCOQedECIFd0WDUKJ5QuITIkxYiTgWidWmISRnCzbPm9dkjrRVaFsYt5JYht2B1W/lr8Yd8VFWAU7rU59gUJ2ft53ixeCXfH/6VHs/jcDgwGAxIKfnwww8xGo3MmDGD7OzsC/JaXIobh+LCpDVcEUNYXFzMvn37fMRJUlNT1RDpTTfddNnX9GXHa5gDAwOJiIigsrKSv/3tb92mS3k/L5mZmWRm9n8Uqh8/nwd/GLyDznnqL0rOuj+hTyEEY8eOpaioiD179qDRaEhNTfXJbZ46dYq9e/cydepUT891cyU/O/Qf7B3V3lqh4G3KcilaNG7Q6z2JZ0O7lvZwB9OmTWf+8Nn9XrtGaAjUBbK2+hOcPbRfuaTCxppPeCrzTrQd+WdvX/S+fftobm7m29/+Njqdjm984xuEh4df0Jdknb2F3x9ZxYbqIyhIIo1BPJI+k5vjLl1YubM4yYQJEzCZTNTW1lJZWekXJ7mCuN1uCgoKfPqYpZTk5eUxa9YsIiMjmTJlSq+SnH4j7eeSIf3V4CpCiH5rKkopKwdmOZcWKSXbt29ny5YtSCmZPHky+fn5asi7oaGBZcuW0dDQQG5urtpL/XrZZpzKZ56uWwrcUmBwQswZE9EVARzLbsIe7OJUpp1fjf4OGcGJn3t9ilSwKY5eH3dLBZfipqWlmb179/pUdI8dOxa3241OpyMiIuJzXxugxWnjru1/od7eilt6/hJqbE38+vBKmhzt3J3cc5/2heANb3vbq7ziJDExMaSlpZGTk0Nubq7/C/8y4PWYvTrZYWFhTJ8+HY1Go7Yudg5le8PXBoOB/Pz8K7x6P19a/J61SgXnfzs6gr9XPmfdH4QQ1NbWkpqayg033NBNy7umpoaqqioefPBBGhoaKCkpYciQIRxrPoMipZqx17kE0eUWEqp06NwaGqLsaHQ6tELDgym3+RjqAw0VvHxiK8eaqokKCOKe1EnMiR3RoxHSCA1DTJGcsdZ1X7sCUfpgDBodJ6uq2Llz54BXdC8v30uTo1011F5sbid/KVrP7UMnYtJd+ACD1tZWnE4nYWFhNDU18cYbb6ja213FSby9734GFofDQVNTE1FRnjnpb731FkVFRWooOzAwUNXEFkKwZMkSTCbTFVuvHz+94jfWKtfElrmpqYk1a9YwdepUoqOjWbBggWoIusqFDhs2jOjoaF5++WXCw8Npa2sjLCyMqLHBlLd7DKhQIO+AEZNDQ3W4i8qhDqYPG8W0wGimRY0jwhiinu/90wf48YGV2N0uJFBpbeLpwhVsrj7Br8be2qOBvT95Dr89+m815G6yaoip1hNTYyBhZDJCCNLT03niiScGvIJ2TdUh7J0iCJ3RCg0HGk8zKSq1x8d7oidxkqysLG677TYiIiK4++67iY+P94e3LyE1NTWUlZX5hLLNZjNPPvkkQggSEhIYPHhwr6Fsv6H2c7Xiz1l3IKXcfDkXMtB01fL2GmKtVktVVRVbt24lKiqKtLQ04uLiVMM9e/ZsysvLmThxIkePHuX9999nSvs4ztWe4WSkHamBEwkuWk0KrYGQFDiIJzK6F321uez8+MAH2Ny+xs/qdrK28ii3J4xlfGRit+NmRI+hzt7Me7s/JqZST/A5DRKJZWgE14/0hKG1Wu0laXU5n2/eH++98wboX//6F6WlpYCn8nf69Ok+7XDJyckXvFY/vnStyp43bx46nY79+/eza9cutfjLq5XtZfLkPrsy/fjxc5VwTeotlpSUsHr16h61vBsbG1m5ciVBQUGcPn2aPXv2cO+99xIdHQ2gzizWarVERUWh0+ko3rqfdLeGlkA9dYEuqiLdBGj0BGv1/DK7u7IZwJbqIrS9GDeb28m7pz7pZqybm5sJDg5m0dBpuHdUctpdQVxOMtfnTCMkOJS1lcf46Ng2EizhzIxJx6Ad2F/f3CHZlLTUYlOc3R6TUpIdNrTH4zqLk1RWVvL444+j1WoZNWoUWVlZfnGSAcbhcKDRaNDpdBQVFbF27dpu06Wam5sJDw8nNzeXyZMn+/uY/Vx7+D1rD0IIJ/18O6SUV9Uk9lOnTiGl7LHfurW1lerqau688040Gg1/+9vf2L59O3PmzMFsNgNQVVXFtm3bKCoqQlEUkpOTmTVrFrUBdt4/s5cmRxsTIlK5KXY8Fn3Pk3vaXHZPnrsHJNDstAGeCMCxY8fUSVff/va3iYiIYMHNCwgICECj0XCg/gxzP/gDLqlgczkx6fToNVpenfI1RoTF9HiNC2FB3FjeKttJjbXJpyI9QKPnO8NmY9T6hqtPnjzJhg0bVO3t4OBg0tLScDgcmEwmsrOzB2xtX1ZcLpda+NU5lL148WLS09MJCAjoU5Lzi9ZX78dPv/BXg/vwIF+gvUtrayvFxcWkpKQwZcoUpk6d2qOwiclkwmg0cuTIEXJzc8nJyWHTpk1MmzYNk8mkeiclJSWYzWbmzZtHRkYGADHA6LDEfq1nTMTQXo21SasnJziOtWvXqhXd3klX3hyhd+PQ5nJw39Y3aHXZ1ePbXJ6K8Xu3vM62eU9gHCAP26wz8nreI/z5+Fo+OLMfm9tBkmUQj6bPYkpkOsXFxRw/fpzRo0cTFxeHRqNBSqlqb/vFSS6OzqHs6OhoEhMTaWho4NVXXwXwCWV7dc7j4+P906X8fDn5wlingaGvnPU/LuM6Lprm5mbVWPdVsBQaGkpcXBx79+4lNzeXESNGsG7dOtavX8/p06cZMmQIixcvZsmSJRcVuk0JimJCZCJ768rUoi2NAmaXwGAxcGPcSF5e8RdSUlIYO3ZsrxXdH5Yf7lad7cUpFT4+c5Sbh4684HV2JVhv4vtZN/P9rJtxOp0cOnSIE5sP8tuS91RxktjYWOLi4khMTOTBBx/8XOe3u13U2doINZgI1F9VAZnLije3rygK77//fjdJztzcXBITE4mMjGTx4sV+SU4/fjoh8BeYfWGJiIhgzpw5532eTqcjJyeHd955hxUrVuBweLzUo0ePMmTIEPLy8hBCDEiO9Y8TF/Gj/e+z4+RRUhogrl7BbdLz7Ye/QWRgKEuXLsVo7Fvvu7j5LFZ39xwyQLvLQWlL/UWv04tXnKStrY3k5GQ0Gg1r1qzBYDAwevRo0tPTL1icxOF285sDG/i/Ys/gBrdUuH5IOj+fOIcQw7Vdcdy1j7mqqorIyEgWLVqERqOhrq6u11C2RqNRIzt+/PjphN9YexBCnAAmSSkbhBBF9PHWSCnTL8XiPg/nM3qdSUlJIS8vjw0bNiClJCQkhNtvv524uDj1OZ3n6l4olafKGXmkHUupghCC+JRkpuTkEmcO6/ea482hBGh13arKwRNOjzWH9HBU/+msvV1UVERTUxMRERF8+9vfRqvV8s1vfpPg4OCLDm8/vmM5W6pO+ryONWeOc3xtLR/e+AB6zbXRV+10OqmurqapqYmsrCzAUxVfUVEBfBbKTkhIUI954IEHrsha/fj5wuKfuuXDG4Ct4+fXL8NaLjk2m43KykqSk5PJy8ujsbGR4cOHk5KSoj7Hbrezbt06GhoamDNnDlFRUd36sfuivr4ei8WC0WiksbGRxsbGi9Lonp8wkmcObejxMQHcGDf8c5+zvb1dzYmvXLmSTz/91EecpHNRXkjIxW0GAIqb6roZagCnolDV3sK6M0XcGN/7hLOrnRMnTnDkyBGfULZOp2P48OFoNBqmTJmClNIfyvbjZyDxF5h5kFL+pKefv4jYbDZ27drFrl27AFi6dCkGg4H58+d3e25dXR0FBQVYLBaqqqqIioo6r6H2anR7K7rnzp3LhAkTGDNmDOPGjbsorzTUYOL5Sbfz2K5lICU2xUWAVodA8OLkO7Hoz++dd9be9oqTeKvOJ06cSFZWFklJST0W5A0EO2rK6KXWjjaXg3UVJ656Y93TdKn77rsPk8lEVVVVj9OlvJGZ9PQrHnjy4+eaw+9ZX0N0NtJ2u53MzEymTp2KwdB7YZPJZCIqKoq6ujpKS0tJS0tTq8S7Gl1FUVi/fr2PRveMGTNU4Y8Llcx0Km7WlhfxfukRFCmZl5jJmtnf4sPyw5S21pMaFMWChJGEGc3nPVdlZSVvv/22qr0dGxvL9OnT1ffgcowp1Gu0aHrZsAggQHt1KZh5DXNkZCRms5lDhw7x7rvvdpsuZbPZMJlMTJkyhWnTpl3hVfvx8yXDb6y7I4QwAU8BNwDRgE8yV0rZLykqIcRU4EkgGxgK/LeU8uddnpMD/AEYCzQC/wCelrKHUVTnobGxkc2bN6tGevDgwSiKws6dOykvLycjI4NRo0b5GOGTJ08yYsQImpubOXbsGLm5uZhMJvU5brebyspK4uPj0Wg0nDlzhqFDhw6YRne708GdH79JSXM97S5PYdn2qjLiLKEsm3MXQYbePenO4iTJyclMmDCBsLAwYmJiumlvX05mDUnjJ/vW9PiYSadnQeKIy7wiX9rb2/n0009Vr9kbyr711lsZNWoUMTExfU6XutjaBj9+/HxOJH5j3QsvArcA/wec4cLfJgtwBHgTeK7rg0KIeGAt8A6ePu804O94HLDvne/kVquVXbt2YbVamTt3LjExMTz++OOEhYWpzykoKOCTTz4hNjaWVatWUVdXR35+vs8XbktLC/Pnz6ewsJA1a9YwcuRIYmNj2b9/P/v378dms7F06VICAwO55557BvTL+tn9Wzl+7iwO5bO9SZvLSWlzA78u3MgvJnWveN++fTtHjx71ESfxFjCZTCbuvPPOAVvfhRBlsvDN4bm8fHS3T2W7SasjLzqJCVHxl2UdnUPZ3tqF0aNH43K5+Pjjj1WP2RvKjo/3rCsiIsI/XcqPn6sMfxi8Z24G7pRS9uwe9RMp5SpgFYAQ4jc9POURoBn4hpRSAQ4LIYYAvxVC/ExK2dbbuVtaWvjjH/+I3W5n+PDhati6s6EGj9hJWFgYt9xyCyEhIezevZv09HT1i9lms9HW1sbrr7+OVqulpKSEpqYm6urqEEIwbNgwxo4dqxZoDbRX9X/FB3wMtReH4uadkkP8z7iZnD51irNnz5KbmwtAcXExAPn5+aSnp1+V4iTfGTmV9JAoXji8ndOtjUQYA7kvYwJ3p11cTr83HA4HVquVkJAQpJS89NJL1NTU+ISyBw0aBEBQUBBLly71F3/58fNFwm+se8QBlF3CdXjJA9Z0GGovHwEvAGOAbZ2fLIR4CHgIPHOQvdXMgwcP7vHkUkosFgslJSUATJgwgV27dlFaWkpcXBxCCEwmE7W1tQwePJicnBzKy8tpbm6+qIruntZR0tSAU3GTFhqJrsPgK1LS6uw+zzrALYlvV4hvd/LM736H0+nEaDQyfvx49Ho9d9111xdipOTcoZnMHZp5Sc595swZKioq1F7muro6EhIS+PrXv+5pm4uPJz09vcdQthDCb6j9+OkDm83GuXPnaGlpoaWlhdbWVlpaWpg7dy5CCNauXUteXp7qxFwO/HKjPfNn4GE8+eZLSQywvct91Z0e80FK+RLwEsDo0aPlokWL+jy5EILExEQOHTrE8ePHycjIICYmhjNnzuB2u/nwww9paGigoaGBQYMGcf3113uvM2De39YzZTy17SMa7VY0QqATGv7f+Kl8bVg2GiEYEhjMmdYmwhySFr3ApRGktLqZ2ODGqtMwOtsjTtK5evuLYKgHis6h7JaWFmbNmgXAxo0bKSkp8Qlle6MlAHPnzr1SS/bj56rEG2USQtDQ0MCpU6dUY+w1yIsXL8ZisbB79242bdrkc7zJZCI/Px+z2Ux8fLx6vsuzePyetRchxEtd7rpDCDET2I/H01aRUj50Cdamnr7L/z3SX1Wt1NRUBg8ezPr164mMjESv12Oz2fjd736Hw+EgKCiI/Px8xowZox4zUIZ6/9kqHlj3brd+45/t3oAeGKuzsMhu4UxFHYEuyaYoHaUWLSUWLQ2BBh6clM9NwycMyFq+CDgcDvR6PUII9u3bx+7du30kOb2/K61Wy5w5czAajX4P2c+XHikl7e3ttLS0EBISgslkoqamhoKCAtUj9hrj+++/nyFDhnDq1Cnef/99wGOEg4KCCAoKwuXyfFdlZmYSFRWl3m+xWHxaPTuPvr0cCM4/0vdaoy/POq3L7f0d/yd0uX8g9zdVQNcYtvd2NQOAyWRi5syZvPvuu7z++uu43W4yMzMxmUyMHz9eldkEVC3sgeKZfVt8DLWQEikEOJwcfHsFR6Vn0xEUEc4OpYUmi45AnRaXxs3cjLHclzl+wNZyteF0OtVq7M7TpR577DHCwsLQarV9TpeKjIxUf7a5XPzlk928fuQATXYbCcGhLBk/mZtTr+5ebj9++qKzEfYa27i4OKKioqipqWHlypXq/YriiREvWrSIzMxMWltbOXz4sGpsIyMjCQoKUsPWmZmZJCUldTPCXgYNGqTWeFw1DJDl6WeXkhn4EbAYiAXOAn+TUv5Px+PTgY09nP5BKeXfOp0nBvgj4K0UXgU8LqWsPd86+xJFuRLlr9uBu4UQmk556zlAO/DJxZ68vr6ewsJCtS961qxZ5OXl+TynvLyc7du3U1VVRUJCAjk5OQwZMmRAQuEFNWcIcygMtbmJt7pp1mnYEmHAqhUcCzbwXzPnMjZzBDqdjjprG1urSpESrotNZJDp2pkH3TmUnZqaSkREBCdOnGDZsmUAPqFs7xdHdnZ2v0ZuuhWFuz74D4fOVmNzewr1Ss418P82fURZUwOPj5t86V6YHz8XiKIo1NbW+uSDW1paSE1NJSMjg4aGBv785z+rRtiLV2VRr9cTEBCgGmGv9+uVUE5JSeGpp57q9foBAQEEBPQ87vdqZQCrwc/XpaQFPgSC8aSDjwORHf+6MhaP0+mlqdN5NMAHeLTXrscTHHgReE8IkSfPk0e4IFEUIcRQIArY/3n6n4UQFiC146YBGCyEyAZapZTFwF+AbwMvCyGeBVKAnwF/6qsS/Hy0t7ezbNkySktLEUKQkZGh9kV3RkpJaWkpWq2WMWPGsHPnTrRa7YAIh2zdupUFFW2YXZ4/trMGQZ3hs0ryT0L1ZKRnqMYp0hTIrclZF33dgaCypZnXDnzC7soKwgNMfG3kaGYkJvdr8+Ld5LS0tLBu3TqfPmaAefPmERERQWJiIosXLyYmJoagoKAL3hitP3WSI/W1qqH2YnW5+HPhbu4eMYawgGt7cIifqwMpJU6nE4PBgJSS/fv3dyvQSk9PZ+rUqbhcLv7617/6HG8ymdRuFovFQm5urmqIOxtkgPDwcO66667L/hqvKANkrPvRpXQPMA5I7eQBl/VyurNSyt6iwLPwGPNhUsrjHde7GzgETAM29bXOPo21EOJOIEJK+WKn+/4EfKvj5kkhxHQp5Zm+ztOJ8fiGCh7t+LcZmC6lLBdC3AA8C+wDzuEpIHu6n+dXqa+vp66ujoyMDFXUxJuLdrvd7Nmzh7KyMkaNGsWgQYNU4zFmzBg1vFpUVERERIT3dff72l5x0NmTDQAAIABJREFUktLSUhYsWIBWq8XlcmEOD2Wnq5WyAA1W7WfnE8DoyBhCjFffzvaT6kruem8ZLkXB0WEAd1dWMCsphedumOvzvnTtY66qqmL48OGqYlppaSmDBw/uMZQdGBg4INOlVhQfpd3Z85QyrUbDptOl3Jr++fXU/fjx0jkcLaUkJsZT+7pu3Trq6upUQ9za2kpmZia33347QgjWrFmjqt55ja03DG0wGFi0aBEWi6XHnLDBYFCLKf3gGeRx+arBbwP2AEs6jKsLWA98T0rZdezhto6QeTHwV+CfnTzmPKDUa6gBpJSHhRAVwHVcjLHGY0jf9N4QQuR33PffwDHgl8APOu47L1LKTZynLkBKuQu4oFhlV41us9nMk08+iUaj4e677wY8BuXdd9+lvb0dvV5PYWEh3/rWt9QdalBQEGfPnuWDDz6gsrKSiIgIqqurGTx4cLdQ+MbSk/xhxw6K6uuJ1GqZGxJKhNVKVWUl4BEnaWpqIjw8nPz8fLJzJ/HxitdQrG3Q0UutExoCdDp+lTf7Ql7yJUWRkm+tXtnN+LU7nWw8Wcy7u3cyZtBgkpOTkVLyhz/8AZvNM/vFG8oODw8HPBPGli5desnX7HL3EeiR4FK+ZP0efvpN15xwS0sLAGPHjgVg+fLllJWV+eSE4+Pjuf/++wGoqKjAarUSFBREVFQUFouF2NhY9fyPPPIIZrO5Vw3+zMxL09Z4zdJ/zzpSCFHQ6fZLHZ1E/SUFSMJjpBcBgXhUNlcIIaZ0GOMqPDoh3uvcBLyMJ5L83x33xdBz7VU1PXQ7deV8xnoYsLvT7QXAWinlLwCEEHZ6iPFfCWw2G88++6yPRnd2dnY30ZKWlhaOHz/O/fffT3BwMC+99BI7duwgPz9fLSYzGAzk5eWRlZXFqlWrcLlcdG0Le/2TT3h1wwbqpYJdq8Vlt+Ooq6fYaGTO1KlkDhvG4MGDfYx7WICJ1bfcy98PF/BO8WGciptZQ1N5ZFQOcZaLn25V0dTE2wcOUd7UxIjoQdw2cgRhpgsP+R6oqabFbldvJ9udxDrdRLgVQtwKhz5eS93gwTz88MMIIbjhhhswm81XdLrUjcnpbK04pUq1dsYtFa6L61of6efLQmNjI3V1dT5haJfLxYIFCwD497//zfHjx32OCQ4OVo11SEgIycnJWCwW1QPuLLp077339nn94ODggX1BX3I+R866Tkp5MdW5WjxO5mIp5TkAIcT9wF48+h+FHd5y5w9PQUeue6kQ4qdSyp7DfZ9x3ldzPmMdDNR1up0HLOt0+yD92BFcDrRabb80uqWUBAQEUFtbS3x8PKmpqZSUlJCTk6OqXRmNRtLT06muriYqKoqoqCgA2traKCoq4sixYxw7cYKpwCGDnn1aLbVaLW9bAhFGI7nR0WporCshxgCeGHsdT4y9bkBf/zsHD/OjtetRJDjdbtYWlfCn7bv4+x23Mi6u/zn3zqHswqITTGtsYZXFE56Pd7oZ7HRTp9NwSq/HFB7O0sVfVY/t3O4GHi/2YGU1dpebrJhoLMbeB6gMFDempPP8vp2UtzTh7ORFm3Q6bkkbTozF39p1reD1hM1mM0IIysvLKS0t9THGra2tPP7442g0GrZv386+ffvU400mk/o3L4Rg9OjRJCUl9ZgTBpgxY8aVeJl+euPy9VlXAgavoe7gcMf/CUBhL8ftwONVR3WcowpP3ror0fSj2+l8xroSSAfKO+Lwo4D/6vR4KGA930UuB3q9vl8a2EFBQYSGhnLixAnGjRtHcnIyhw8fVnNHUkrKysrYvn071dXVxMbGkp2djaIovPjii1itVgxmM6cDjJQKDVU6jyCJFAKrEOB0suzwYW4dfnF50cY2KzuKTyGB3JShRFh6VwYqP9fE/6zdgN31WQjY1tEf+eA7K9j56EMYdToURfLhoWP8c9cn1Le1kzU4koUZiUwZPRKtVsvWrVvZuHGjmr83mc04Aa2UuIVgW6ARd8cmSK/R8JX0tF496HXHS/jBB2twuhWEAJdb4f5J41gyLbff+X+ny82ZxmYsAQYigwL7dYxRq2P5wq/x423rWXXyhHrfg6PH8+jYSf06h58rS9dw9NChQzEajZSUlFBQUOBTpKUoCk888QTBwcGUlZWxceNGn5xwVFQULpcLg8FATk4Oo0eP7jEnDP4w9BeNy6gNvhWYJIQIllI2d9znLbAp6+O4MXjso9fh3Q78SAiRJqUsAhBCZALxdFHn7InzGesPgN8JIZ7GEwJvwldhbDSeRPoXBqPRSGpqKtu2baO+vh673Y7ZbKa6upoVK1Zw3XXXIaXEYDAQEBBAW1ubGuqaP38+YWFhfNrawlsff0yro7s0KHhGXF4oUkr+tH4nr24tQKfVIDvyrHdNyubJOVN6NHRvf3oQdy+5WEVKNp0s5Ya0VJ58611OnzxJkNtJPG6Cms+w+cQB6ttt3HbdJOLi4rpNl3p09Up0pSdxu12qofYsFGbHp/pcS0qJ3enmcHUNS5evUjcMXl7dvQ+zQc9Dk/sWdpFS8tKGPfx9UwFSSlxuheFxg/j5otkkRoX1eSx4ohd/mHkTv54+mzaHgxBjAFr/ZKwrTk99wi0tLWRlZREeHk5xcTErV670yQkD3H///cTHx2Oz2WhoaPDpE7ZYLGr6Kicnh9zc3F5zwt4ImZ9rAImnAWoA6EeX0ot4upReE0L8N2DGo+q5mQ79ESHEE8BpPB63BGbj8ar/LKX0Gop1eLzw14UQj+EJrf8Z2NVxrj45n7H+MfAuHqPdAtzTJfZ+X8cCvlDk5uZy+vRpli1bxtmzZ5k5cyZhYWEkJiby4Ycf4nK50Ov1JCcnk56erobJvDtvozUEZy+FTAE6HXPT0y94bcsLD/Patn3YXW4fT/mt3QeICDQzKyOFiJBAzAGfhZRPn/MN+eqkJExxEyHdRDptlJ06xTaNnsMnT5GntGNF0ICW01JHI1o+3LyHeTnjSUpKIikpieY2GxsLi2lus3FXwkgUqbCxrBS3W+JSFISEgDrBkv9dwdxxw/jR7bN4d8dBXvpoN/XN7djCJK4eIt5Wp4u/bt/LfTlj0fchkfrHj7bzxrZPsDo/M/YHTlfx1Rfe4v3vfv1zedlGU/+7E8uqG/jHx3vZd6KCIFMAd0wfxbzc4X2u1c9nOBwOVbKyc6/whAkTSElJ4dSpU7z22mvdjouKiiI8PByLxeIThvbmhaOjowEYMWIEI0b0Pk61rzn1fq4tBAPqWZ+vS6lKCDEDT5fSHjyjm1cDT3Wq9NbhKbiOB5x4nNglwCvek0opFSHEPOB5PNXksuM8j52vx9p7gV6RUjYA04UQIXh2GV0t1G1A6/kucrVhMpmYNm0ahYWFuN1udc5zeoeRzcjIIDExsVf1sjCTia+PGcO/9u/H2sl71ApBiDGA24Zf+HzmFzfs9jFSXqxOF8+s2sorr3qiJTfkDuO/7pmJViMZFhbCep0O4XQwy95OsFTUgeM2IYjQaVlWeIgzbngPC1bfceQECi3bT55iRkYKq3Yc4ZevrUOjAadLQa/TEjcolDtHjuC9g0dwOiVaG4DEjpvVhceprm/hQHElNodn3a4+PlVuRaGyqYWE8FDarA5WrPuUj7YcQVEU8ielc2P+CP61tdBnowIgJdicLt7cvp/H5+T1cvYLZ9+JCh7/03s4XC7cigSa+d2/N7Fq9zFe/M7CL6XB9nrC4Gmtczgc7Nq1q5sxzs3NZdKkSbS2tvLmm2rziBqOtncUKUZFRXHjjTeqRrhrOHrw4MHccsstl/+F+vliMnB91ps4f5fSJ0CvQmFSyt8Bv+vHtaqAOz7nEoF+iqJIKZt6ub/hQi56pWhpaWHLli0UFRXR1OR5SbGxsVitnrR7enq6arCdTicOh6PX3fr/u24KESYzf9m7B5vLhSIlUxMS+dnMmQQZjRe0PkWRVJ5r7vVxqVEI1CmEGN2UH9/Hz39diFHjYmT2GDRCYEXQIjSc0upo0Ghp0GgJCwnlF9On8+E/38WN5zndzoukze7k+OlafvXPddg7bRZcboWTlXUcba1HSomuy/E2p4udRafROH1O2CsuRSHIaKC51co3vv8GdY1t2DuMfEX1Ht7YUIguQoOdHsaEutxsOnJywI21okh++MoqrA7fgk2bw8WRsho+2nOM+bkXvgG72ugajjaZTMTFxSGl5O233+6WE87JyWHOnDloNJpuOeHIyEi1PS8kJIT777+/15xwYGAgEydOvBIv2c81iLicg0OuAvoa5LEUeKFTvL1XOuL7MVLK1QO5uIvFK05iNpvJyPCogx08eJDExESmTp1KWppvgZTD4WDHjh0UFBTgcDgYPnw4U6ZMISIioluPtRCCB8eP574xY6ioO0dYkJkQ88UpY2k0gkCDnjaHEy2SMOEmTLiRCIoVA0jBmGgrJr3E5hKca9dw3cRsxo4exYwiO6vc5WzWmpDCEyLSOuCuqAw0QnBdagKfVFRi68FrdysKY+JjeOk/23E4uxtJlyJxK/S+9+xyv64dXJbu9wsgKyaa8EAzv39lPbX1LThdn4XvHU43SruCPaj3/HKA4YJE9/rk2OkaWq09f8ytDifLNn/6hTHW7e3tNDc3+xhcs9nM+PGezhXvXO/OOeHMzEwWLVqEEIL29naMRqPaJ2yxWFQFP51Oxw9/+MNec8JardZn0pkfP5cM/9QtH6YD3xVC/BN4D08vmfqNJoSIwyORdg+eQrOrQuuupqaG48ePc+LECc6c8QirDR8+XFUy++53v8vZs2cpLi6msrKSlJQU9cunvb0dl8vFDTfcgNPpZNWqVQQEBDBnzpxu15FS8sGyvbzx8mZamtoRQpA3I5NH/msuoWH9y6l6cbvd6pjLuYPMtFSfIUgoaDqMXb2ipdilR98Ghe0mbC6B3a0BKQmpgNFZeo5uL2eww4ktQuA2CnTtEmOj5O3CPdx5wzhuH5vFy9v2Yne6fD7jRp2WaWlJxIWFUFRRh9LTbvU8fxRdbbi+HdwBILXgjbgbtFoC9Dp+Od8zdvSjLUd8DLV6LqvE7ZaezsYumPQ6bps4su/FXABtdicaTe9RsN4M+eWi80axvLycs2fP+rQmGY1GNXz82mv/pLa2xuf4xMQk1VinpaWRkpLiE4oODQ1Vn3vffff1uZbeDLUfP5eby1gNflXQ1yCPmztGYj4BfBdQhBD1gA0IxyN+Xo1HUm1Rb6Hyy83q1as5deoUQ4YMYfr06WRkZKgFKgDV1dV8+OGHSCk5ePAgp06d4oYbbgDAbDYzYcIEQkJCaGpqIjo6Ws23deVf/7uRZa/vwG77LHS6dd0Rjn5awV/f/hYmc8+h8J4kOZubm3nqqafQaDRoztppdwkqhIEGtDRILTZFoHGB8Rw0SV8rVlpWx8cbDmF3uBASTHW+W06tRrBtVzFzrx/JW99YzNJlH1Ja14hOq8HpdjN3RAY/njcTgCGRIRRX1NEVARhcIAM0ON2+Btao1+Gyu5GdrikkBNSDCNIQEWMhwKhnRloy90wcQ5TFs5Gx2XvWCBCApREcMTqfcLxOCMK0BsZHDvzkn4y4KJyungsGdVoNE4ddGm/RG45ua2tTJxodOXKE0tJSn5wwwBNPPAHAjh07OHbsGPBZTtj7+T5zqo69H5/F5VZob3HjsEo0Qk9DchtfWezEYNSTn38l5vP48TPwXEa50auC8xWYrQfWCyHC8WiXJgEmPOPBCvEM8rgq9jfesN7cuXMxm80+YgadOXHiBFarlccee4zNmzezZ88exo0bR0REBAaDQc1RNzc3U1tbqwohdA6Bt7ZYefuf23E6fEPKbrfCucY21n14gPl3TPQxzNnZ2RiNRnbs2MHmzZ4q/cDAQGJjY8nMzMTtdiOEYOfWZhwuDbYIDdZIDYpOEuAEXbvoMQqttSu0ttl7HfzuditYOzzDpMgwln/zLk43nKO+rZ2kiHBCzZ/pkX9l9lh2HzmlFop5EUKQFBKGKdrEsTNnsTqcCAF6oSGsTcFxspmmoYFI3Wfha71GQ1JwOG9+66vodd3d5JSEKE6U9jwVbpDBzE8enM+La3dScKIcxeYi6LQNQ9lZHlv+R66/dRyP/vgWVZ2usuwsb/1hNQUbDqPTacm/bSK3f2sWweH9m1QWHBjArdeN5L3th7q9doNOy93Xj+vXebz0JFs5evRotFothYWFFBYWdhtl+PTTT6PVaikrK1NHGVosFiIjIwkODla96xtuuIHZs2f3mBP+/dPvUFVqRyqdPwtOSk/UsOKNndxx/9TP9Tr8+LmquSosz+WjvwVmDcD7l3gtF4X3i7uvmatSSqxWK1JKtFotI0aMYNu2bVRVVakDO8Bj+FeuXMmwYcNITU3tdp6DhafQ67XdjDWAVu9k197NlJ8t8JkuNXjwYBISEsjKyiImJqZHSU5Fkbhc7g4P2Y2pzo2iFdgi9Sh6wKfPWaJxSOJCgpg4Lpntu4qx2rp7q0IjGJ3l6xkODQ9laHhot+eOy4jn7jnj+efqAlxuN25FYjLqMRn1PPv4LQyJCmFvcQVvvLedgjX7Cah2ojnnIkAIZJOdlowQFJMO4ZKYTjUyIzSmR0MN8NCiyTz1m/e6tUpqpOT+hZPIToxlhjuM2o8/UaMX7o5/61cUMjQ1mgX35FF6pIInb/49dqsDxe15r997aQMb39nDC+t+QEhE/wz2k4umodUIlm05iE6rQVEUIkMs/PKBG4mN/EwK1uFw0NDQ0K0ieurUqQQFBVFQUMDq1au7jTJMTk4mNDQUIYTPKENvONrLjTfeyNy5c3tdZ2d5y8401rVSdORMF0PdsWa7kw/e3uM31n6uHaQ/DH5NI4QgJCQEu91Oa2srkZGRBAQE0Nrq2322adMmtSVly5YtJCQkkJCQoHo3ilQIDFEIHgSWUIElBMpPSOorQaMFXYCN0NAhPU6XioyMJDKypzGongKzpKQoTp48+9ma3RJdsxt3oBa3AU8OWAGtQ6JtczNpXDLTr8vgb//cgt3hQun0ZW3Qa8nKHEJqcv9Dxw/dMplZEzL4YPthGlvaGZsRz/UT0wkweNrYQpsdlPxkJYFugSYiHNGxSTKXt2Eub/NsdqWEtnY+OljH9PnjyJyQ0u06ZzYfxXyogtb0wZ9tQgQElNRSsfoA8vpRvPvKFp80gxe71cl/Xt7Egnvy+ON338Ta6puqcDpcnKtr4a3nVvPNn52/S0JKiQCeXDSdr+WPZHfhAaTTgV64ObhrEzvXtTJnzhzi4uI4ceIE77zzjs/xJpOJsWPHqiHpyZMn+xjioKAgVRd6zJgx3WRZO3Oho0FbW6zodFqcjp7D+W0ttgs6rx8/Vy1+Y31tk5iYyObNmykuLlanRWm1WhobG2loaCAuLo6TJ09is9nYsGEDWq0WIQQGg4GYmBhaWlpYv2UZaeMloMFhk7SeA3eHTXHZdUweP4/ZN/f+hdwXDzw8g5/86B3sdo/XLgC9zY1WkSg6DVIrEG6JxqVgdkhmzRmJ0aDjL7+/i58/8yGHj57xeP0uN9PzMnjy259/mlfykAgeX9SzF/bWr5bjsDoRIcG+nn4Hgo6/IZ0Oh83Jh3/f1KOxfvdPHyOqz2Epq8MdGggCtOfaEYrk44rNfP3Ht9PW2nO9AEB9TTNN9S2UHCrv8XGX083GZXu4+3tzaWlpwWw2ExwcTHNzM1u3bu2mH71gwQJGjRqFw9rG/l1bgc9ywp1TKkOHDuWOO+7osU8YPFOYrkRFdPSQvpXdMrIufia7Hz9XCwMsivKF4EtnrGNiYhgxYgS7du1i9+7dxMbGEhMTw7p16zCZTAwdOpSUlBTCwsKoqamhrq6ODRs2cPbsWRYuXIjFYiE/P5+Sow2s/L9PaW36LBSu12uJjg0nf3bWBa8vJyeFJ//rJl54fg1OlxtBR3jc5gSHxKlI9EKgB57++W1Ygjw558iIIJ771WLq6ltoaGwjJjqUoKCBn499oqBE9UTP5wVKRVJfda7Hx86d9fSTCwm6xjafxxS3gsvhwmDUYWvvuRI7NMpEVVU1ofFGhF7Bds5NS7UTnVEw7KYw9IEaDGYNzzzzDAAzZ87kuuuuQ1EUNSfs7RO2WCyqFGVMTAxLlizpMScMnslJwy9S9/1SYDDoWHhPHste24bd6huNMAbo+dojAz+Ewul0UlFRoY5F9fPlJSAggLi4uF6FpC4FooeUz7XMl85Ya7Va5syZwyeffEJNTQ0Wi4WCggICAgKYN28eAMeOHaO9vZ2YmBifUDZ4DNSUKVOYMgVSUtL45182UH6qDrPZwOwFY7n74XwMxov7wM6cNYLp0zMpKalBSkhNjaa+roVVKwo5faqOxORBzL15DJGDuo/ci4wIIjLi0k2WCo4IovZ0HdJqQ5rNahi8Gy6Psc3M6Z7zB4iMDaO2vOvcdg9BMSZq6quYelsyxw6eRKOXtDdKaotdIGDinSY0WsG/33mT1Bs8r7X6YDst1U7cTonLrmA95yIiMpwbvzaNoKAgdQpaaGgoTz31VK+vT6/X+7QyfZH42iMzsLY7+ODfu9HptSBBoxUs+Z9bGZ498KNBKyoqCAoKIjEx8YLD936++Egpqa+vp6KigqSkpMt0Ub50YXBxlRRzXzTjx4+XBQUFPT7mcDior69Xv7Dff/999u/frxZ/WSwWkpOTufXWWwGw2+0YL1CF7Frn439s5IXHXsHWZkcTFYkwGHy+qKWU4HKBzY4p0MjfCn5BeHQIDodDfU8PHz7M9o/2cGDHEbQBYLDoaD/roGR1LUaTgYmPJ+MWnVTUHJK6UhdlBU4CzAZGTA1l7uLJhIaGcKLgNG/+bg2t9TZkp5ouo8nAMyuWkjpq6GV7b64WWpqsHPv0NIYAPSOyEzyG+xJw9OhRhg0b5jfUfpBScuzYsW6Ty4QQ+y5ylnSPWMLj5ajrv9Ov5+58+7uXZA2Xm3551kKI64F2KeX2jtsPAg/hmTDymJSy5dIt8fNTV1dHSUmJ2sdcV+fpHf7+97+PXq8nISGB4ODgXquyjUYjNpsNo9Ho/yLqwqy7p7Jt+R72bziE7WwdhsGhBERZ0Oo1tNS40KEQk20mZEgoCVkx/POtV2ltbWXQoEE8/PDDAOzcuZOq1jNEZwXT1mDD2erG2erGaDIwdmYWd351HgGmAIKCgjCbzRzcXcr2tYcZkSaYcuMoRuUkq7+X4cOHYzaE8MpPl+OwO5GKQnh0CN/5/V1fSkMNEBRiYsKUjPM/cQDw/334gSv0Obg2/Mx+098w+G/xTOBCCJGOZ6zXK8AUPOLl37wUi/s8nDt3jubmZoKDgykqKmLNmjU+fcyxsbGeSm5FIS4ujqCgIAYNGqQWD3krvVeuXMmBAwdwu908+uijREZGdpMavdbpqU/YarWSl5eHVqtl2mPjCMwBm8Om1pgFBARw5813UXq4grKG41hdrQQHB6uSlZ1b477yla9gNBrR6XScKa5mx8p9KCMkE34/iuSR3Q3suCkZjOvD+NywOJeZd+RQebIWnUHH4KERX6rflx8/X0b8BWY9kwIc6vj5VmCdlPIRIUQu8J9LsrLPid1up6mpieDgYEaPHk1WVlY3jxmgsLCQbdu2YbfbSUhIYNasWYSHh6sGOT4+HqvVSllZGW1tbb22WX2RaW5upqampltF9MKFC9Hr9axdu5adO3d2Oy4nJwedTkdoaCjDhmd0a0+Kjx9CYuYQIKfP6wcGfibHOiR1MHc8cdNFvyatVkN82uCLPo+fa5PnnnuOhx56CLPZDHjEk958881+1Sf88Y9/pLS0lOeeew6Ahx9+mJKSEtat80wH/tOf/kRRURHPP//8pXsBnZgzZw5f+cpXWL58Oe+99x4Av/rVr3jllVcoLi4GYOXKlbz88su8//77WCyWbu2pF8L+/fuprKzsUwfgsiHxtIh+ifg8BWbed2YasKbj5zNARM9Pv7xER0erLTPeP8iuOBwOtm7dysiRIxkxYgSvvfYa+/fvZ8aMGWg0GqSUZGdnq0pSXqnHqx2vJ+z1Vmtrazl27Fg34Y577rmHiIgIDh8+zJo1a9TjvS1KNpsNvV5PRkYGoaGhvY4yHD9+vKo17cfP1YCUEimlKo7Uleeee4677rpL/W5YtWpVv889efJk3njjDfX2/v37URRF1fTfsWPHZRvtabVaaWhoYO7cuT6Fkjt37iQ4OJja2loGDRrEjh07yMsb2Ol0+/fvp6Cg4Oow1vjlRnvjU+ARIcRKYAYerXDwDNo+2+tRVxneQR2BgYGqca+oqKC9vR2z2ax61yaTCa1WqxrrKxUG7xqOjo6OJjg4mOrqajZt2uTjFSuKwj333ENSUhJ1dXU+owy9spXeL7IRI0aoqYCeWpS8IjB+/FzNlJWVceONN5Kfn8/OnTt57733+PWvf83evXuxWq3cfvvt/OQnP+H555+nsrKS/Px8IiMj2bhxI4mJiRQUFBAZGcmzzz7L3//+dwAeeOABvvMd38KlMWPGqDLFDocDs9lMamoqBw8eJDs7mx07dvDb3/4WgFtuuYXy8nJsNhtLlizhoYceAuCVV17hN7/5DbGxsaSlpWE0GnnhhRc4e/Ys3/zmNzl9+jTg2VTk5eWxefNmlixZAnjywVu2bCEoKIhNmzYxffp0oqKiCAkJobi4mNTUVM6cOcNtt92mbhx27NjBz3/+c/U1/PCHP+SDDz7AZDKxYsUKoqOje732nj17+M53voPVasVkMvHqq6+SlJTEj370I6xWK9u2beP73/8+d95556X9BfeBv8+6d74HrACWAq9IKY903D8f2HspFnYp0Gq16HQ6NSQUHBzM6dOncbt9VZ9MJhN6vX5AQkc90dUIe41uUlIS8fHx1NbW8sYbb/hoRwMsWLCA7OxsFEWhsbHRp084ODhYnSucnp7e5yjD4OBgVVHLj5+B4h//+Ee3+0aMGMGECRNwOp0+3qmX7OxssrOzaW+YAQTXAAAfG0lEQVRv5+233/Z57N577z3vNY8fP86rr77Kiy++CMAvfvELwsPDcbvdzJw5k08//ZTHH3+cZ599lo0bN3ZLa+3bt49XX32V3bt3I6UkJyeHadOm+ajM6XQ6srOz1U1ATk4OaWlp7Nixg0GDBiGlVKN6f//73wkPD8dqtTJhwgRuu+027HY7P/vZzygsLCQoKIgZM2YwevRoAJYsWcITTzzBddddx+nTp5k9ezZHjx7lmWee4c9//jN5eXm0trYSEODRTFi9erXqxU+ePJkdO3bgdrtJS0tj0qRJfPzxx8ybN49PP/2UCRMmANDW1sakSZP4xS9+wVNPPcXLL7/M008/3eu1hw0bxpYtW9DpdKxbt44f/OAHvPPOO/z0pz+loKCAF1544by/l0uOlP4weE9IKbcJIaKAYCllZ5WLl4G2Xg676jAajVgsFmprPQMkXC5Pe5A3t+31PL0iD2VlZRQXFxMXF6f+sfQHRVEoKSnplhNOT09nzJgxtLa28uyzz3Y7TqfTER8fT2BgIElJSd1ywt4vmtjYWB555JFer+8fY+jny0JCQgKTJk1Sb7/99tu89NJLuFwuqqqqOHLkCKNGjer1+G3btnHrrbeqdRQLFy5k69at3SRh8/Ly2LFjB1arldzcXNLS0vjlL39JVFQUkydPVp/3/PPPs3z5csAzzrSoqIjq6mqmTZumbqbvuOMOTpw4AcC6des4cuSIerx3FnleXh5Lly7la1/7GgsXLiQuLg6A7du3q0I/3jW53W5yc3OZOHEiP/3pT/nkk0/IyMhQv7MMBoOqITFu3DjWrl3b57Wbmpr4+te/TlFREUIInM6eJ+RdafyedS9IKRUhhBRCjAMOSikdUsqSS7i2AcdgMDBixAi2b9/O+vXrqampIS0tjYMHD1JQUMB9991HbW0tr7/+OgEBAWqOe+HChbjdbtxut+qRbtu2jaamJh+DnJ6ezk033YQQgrfeekvt4/aGo73jNgMDA5kzZ45qhLuGowMDAy9bDsyPn4GiL09Yr9f3+bjZbO6XJ92VzsWKpaWlPPPMM+zdu5ewsDDuvffe86qr9VdnYvLkyfz1r3/FZrPx6KOPEhUVxZEjR4iKilJzw5s2bWLdunXs3LkTs9nM9OnTsdlsfV5DURR27tyJyWTyuf973/seN910E6tWrWLSpEmsW7cOg8FAfHy8Ohlw8uTJ/OlPf8LtdvPggw+qdSebNm3yyVfr9Xo1jafValUnpbdrP/bYY+Tn57N8+XLKysqYPn16v96jy86XzFj3Ij/lixDCLIR4DWgE9gBxHff/rxDi6Uu4vgFn/Pjx5OTkUFpaSlJSEmPGjMHpdDJkyBCKioqIioriBz/4AVlZWYSGhnLu3Dmef/55nnnmGVauXKmep6CggMOHD9PY2IjRaCQpKUnd/QoheOCBB1iyZAk//OEPeeqpp3jkkUdUD0Cj0ZCTk8Pw4cOJj48nNDTU7w378fP/2zv3OCmqK49/f7AoAQySAApqFBFRiIiCIKAoPhB1jTExbmJ0JSTRmGTxlagxrmI0CXmg+AxB18fmRUwUfEQUUERFiaCSoCskIhgFVEAEEVAGzv5xbg9FTc90DTA9NTP3+/nUp7tv3ar6dXdV3brnnnvOdrJmzRpat25N27Zteeedd5g8eXLlul122aWow+jgwYOZNGlSZV7xiRMncuSRR1apN3DgQGbNmsXy5cvp2LEjkujQoQMPPPBAZc969erVtGvXjlatWjF//nxmzZoFQL9+/ZgxYwarVq2ioqJiq0QwQ4cO3cqsPHfuXAAWLlzIQQcdxGWXXUbfvn2ZP38+kydPZtiwYZV1e/TowdKlS7eyBPTu3Ztx48Zt1duvjuqOvXr1avbYw2PJJ4c2qvsN6wtZtqWxkLWFuBY4GPcEn5wofxS4Criu2Eb1yfr16yt7voXer5lx1FFHccQRR7B06VJmzZrFs88+W7nNwoUL6datGy1atGDjxo1VUhkW4keDP302b159ZKhCeNJIJFIeDj74YA455BB69uzJvvvuu1Xv8txzz+XEE0+kU6dOTJ8+vbL80EMPZfjw4fTr1w9wB7NiWdHatWtHhw4d6NmzZ2XZgAEDmDlzZuX487Bhwxg3bhy9evWie/fulQ/ne+yxB1dccQX9+/enc+fO9OjRg7ZtPe3qTTfdxHe+8x169epFRUUFgwcPZty4cYwdO5bp06fTvHlzevTowYknnsjpp5/OzTffXHl8SfTv35/Vq1dXxuQeMGAA48ePz9RYV3fsSy+9lHPOOYfrr7+eY47ZElN+yJAhjB49mt69e9e7gxkGbGpELXEGMoUblbQIODuMXX8AHGxmr4cAKc+bWb0HU+7atatdddVVnHPOOQDcf//9zJs3b6s67dq1Y+TIkQDMnj2bNWvWVEljWLiIIpFIzbz66qtVwktGirN27VratGlDRUUFp512GiNGjKgMb5yFjz76iEGDBlFdSOU8UOx8qKtwo7u03dP6DByZqe6MRy9rOuFGgd2BYrkIW9RiH3VKRUUFzZs3r5z72LdvXw444IBqUxkWPCUjkUikrhk1ahTTpk1jw4YNDB06tNY+KTvvvHOuG+p6IXqDF2U+MAh4I1X+eeBvO1TRNtKhQwfOOuusys+f+UzTjAsdiUTyR8GDO7LjaEzj0VnI2lj/DLhVUht8PvrRkr4FjATOqCtxkUgk3zS1uPmR4pQ9e2MTTJGZdZ71BEktgauBVsAduFn8G2b2YB3qi0QiOaVly5asXLmST386Jk5pyhTyWdcmFsX2IkBNzMGsNvOs7wbultQeaGZm79aZqkgkknv23HNP3nrrLZYvbzARhyN1RMuWLSunrpYLxTHrmjGzFXUhpICkk4CfAAcCy4CbzKxquK9IJFKvtGjRgi5dutS3jEhTJJrBiyPpnxT/aQzYAPwDuN3MphSpkxlJffEY5GOAr+C5FsdJWmdm47Zn35FIJBJpLDS92OCZIpjhOas7AiuBh8OyIpQ9AXwKmCzplO3UczEw28wuN7NXg+n9ZuCy7dxvJBKJRBoRTS2CWdbGui1wm5kNMLOLwzIQuBVoYWbHAr8Etjf06CA8KlqSR4F9JJV3QCQSiUQi+aWQeavU0kjIOmb9ZeDwIuX3AH8Fvgv8BvjWdurpBLydKns7se6t5ApJ5wLnho8fSXp5O49fbtrjFoqGREPT3ND0QtRcDhqaXmiYmrvXyV4teoNXR3Ngf+CfqfL92dI7/wjYTN1R5Z8xs/HAeABJcxpaSLmoue5paHohai4HDU0vNFzNdbbzptVWZ26s7wXukPQDvCdtwAA8gceEUGcAHulse1iGhzZNslt4Tfe4I5FIJNJEaWpTt7KOWY/EnczGAS8D/wf8CvgzcEGo8yLwje3UMxM4IVU2DHjDzN4qUj8SiUQiTZEdNGYtabCkByS9IcmKpX0OaaJHS1os6WNJSyRdk6qzv6THJK2TtCKkkG6dqtNJ0r2S1oRlgqSOWb5u1ghmG4CRki4H9gvFr5nZukSdHTFefAPwrKQf42Pg/YD/Ai7KsO34HXD8chM11z0NTS9EzeWgoemFqHkLxo4cdG2Dd0B/D4xNr5TUHPgL8EngPGAB7j/QPlGnDfA48HdgID5D6k5gV9znC0nN8JlUm4Hj8UBstwGTJA2yEjFbM6XILCeSTsaDohyAm75vjEFRIpFIJFKgbevOdniP8zLVnTJnVOYUmZIWA3eY2XWJsq8BNwL7VRe5Mzg73wjsbmarQ9nJeOO8r5ktkjQUeAw4wMwWhDo9cWv1EDN7siZtmSOYSToKOBPYG9gpuc7Mjim60TZgZn/Bn2IikUgkEinO5sxd6/YpR7fxwTk5K18EngcukHQ2UIH3oi83s5WhziDguUJDHZiC96IHAYsKr4WGGsDMXpH0FnAE8GRNIrJGMDsL79I/AgwBJuOe4J3xsexIJBKJRMpD7czgK7bTi74r0AVvpM8AWuNDtg9IOjKYr6tMOzazjZLeC+soVifwdqJOtWTtWV8KXGRmt0r6AI80tggfj4iOX5FIJBIpK2X0Bm+Ojy9/2czeB5A0ApgNHII7V9dEFqEl62T1Bu+K96oBPgZah6eJG9gSlKROkfR9Sc9JWiXpfUnPSBpWpF5/Sc9K2iBpmaSfBgeBsiPpbEkvBM3rJb0q6RIl8gnmSW8aScdI2iTptVR5bjRLGhU8ONPLfok6udGb0NRe0q8kLZX0kaRFIUd8sk5udAcv2GK/8ys51dtM0lWSXgvX3r8k3VTEOzc3moOe1sHr+PWgaZ6k0/OgWdm8pktqUwav6UyUL4LZUmBZoaEOFM77vcNrlWnHklrgjmZvV1cnsBsZpiZnbazfx/NYFw5YiErTGveQKwfH4Kb4IXiCj1nAw5IGFSpI2guYinvr9QHOx733flwmjWneBa7FvQN7AqOBH+FT4fKotxJJu+ER6qamyvOoeTFuRkouiyCfeuWeo0/hMyu+gl9PZ+IeqYU6edN9GFv/vvsB6wlxFnKo9xLg+3hegQOBbwKnA5XOqjnUDG6t/FLQ0TN8niDpBKh3zQWv6Usp0rhk0aYtXtMV+H3xDHx67v/UTkrGhnrHNNZPA7tLSrZ1hTZwcXidCQxI1Tkeb2NnJup0kdStUEHSgcBewDMlVZhZyQW4Dzg/vB8N/Av4Kf7HTc6yj7pYgHnAmMTnn+Bm+WaJsu8AH+LWgHrRmdI8EZiYZ73hBJsGXA6Mwqfp5fI3Tusrsj5XesPxrwkX+c4NSXdK3zeBjUDnPOoFJgH3pcrGAC/l9TcGWobf9Mup8geAGXnSHM7fK1NlJbXhltj1QNtEnZNxM3CXrMf/ZMvd7YQeV2RagDklvksboHdYlgK3hPf7hfWdgFXh3v1ZfErxbNwhTIl9vIl7fx+MdyoXARMSx2kGvIAHFuuHdzrnAM8V9lPTkrVnfQn+dAF+o3ko/MCv4Bdt2ZHPWduFrWPlDgKmmFnS9eBR3CpwSBnlVUFOP1zj9FCcV73/jV88Py+yLo+a95T0VlgmSxqYWJdHvV/En6RvCKbC+ZJ+IalVok4edSc5D3jIzJaGz3nT+wwwSFIvAEn7Aiex9UyTvGlugY+PbkiVrwcOD2bVvGlOkkVbKa/pzMgs05KBvsBLYemEP2C8BNwBYGbLcMvurrhX+ETgb8AXLLTCZrYWOA6fKfUcHjBsCvD1wkHC7/LveGf3cdwKsRA4tbCfmsgaFGVx4v368GXqmyvwH+83ibJObDE5FEgmAik7ktoCS/A/sTlwjZndlNCUN71D8IQsh5jZZm0ZXi+QN81/Bf4TD3XbFje9PS1pmJlNJX96wX1A9gP+CJyCz6q4Jbx+NdTJo26gMu98H+CHieK86R0DfAJ4UZLh97rb8QfRArnSbGYfSJoJ/FDSXPymfgJwKn7/aE/ONKfIoi2L13Q2sjXEGXZjT+IOZDXVeQnvLddUZwEwtESdZfgwR62psbGWdBduDn0iHCQXSPo23lh/zkqHIbXUa7n5ADeptMLHaH4qaamZ3VFN/XrTK6k98FtghJnVJhZ7vWk2s8mpoqcl7YGPV04tsgnU/znRDLcIfd3MKgAk7QT8SdJ/mdl71WxX37oLnIeb+KaUqFefek/HH9y+BszFxxhvwPMZ/LCG7er7Nz4LH799He9tLsB7eN8FNlWzTX1rronaaMuu34DNefy6dUepnnUfvNeCpPl4wz0VHz/5oI61FUXS93BT/OfMbFpqdTFvu8LnekkEEkwfBW/qv0tqh98w7iB/ej+L9+4eSvSom+FW/Ar8XMib5mI8B3whvM+j3mXA4kJDHUh6l75HPnUTHGi+AlyXMt3lTe8YPPphwfI2T9IngDslXWseQjlvmjGzN4DjwpDIrma2VNLPgTX4A17uNCfIom0Z7lBVSRGv6Qw0rlzVWahxzNrMegEd8YvzKdxr70FgZXDPv1Ye2awsSPoRcDVwUpGGGtwEc3wYzy4wDFiHj0HkgWbAzuF93vTOBg5ii7NFbzx5y5vh/V/In+ZiHIJrhnzqfRromprSUsy7NG+6wXt+OwF3pcrzprc1VcNmbMLNnYUn0bxprsTM1oWGeifcSjApPPjnVjPZtGXxms6Glc0bPBeUdDAzs5Vmdq+ZnW9m3fGnom8A/8CnID1exxoBkDQWN22eDSyQtHtY2iaq/Qoft7xdUk9Jn8OnTt1sZh+WQ2dK8zWSjpO0r6Tukr6JTyX53zzqNbMPzezl5IJPP/s4fF6dN82SrpfPB99XUm9Jt+IXfyEgf670Bn6JPwTfEs6LIaHsf81sVaiTR93gJvBJZvZOqjxveicB35N0mqR9wtSn6/DZK+tzqhlJx0s6OZzPR+GWzE/gw371qllSm3CN9cYf2HYPnwsxDbJo+z1uIfi9pIPDuX8r8EczW5RZjAGbNmdbGgu1dNdvD/wHPvfvdXyawaza7GNbF/zvKbbcnap3OPAs7lH5Nj7FrHk5NBbRfANuAl+Pu/6/gDvnNc+j3mq+wyhSU6PypBn4Az5d5CP8wWIacExe9SY0HYtbMjbgvelfAK3yrDvoMeDYGtbnQi/es/5FuE9twJ21bgM+lVfNQc8XgX+G83kl3rjtnQfNwNHV3IOfrI023Io0Be9xrwR+TS2nnX1yp442bJ+LMi2UmLrVUJYas27Jo8ocFW4sxwE98BRg08PylNXT2HUkEolEmiZtd97NBnY6M1PdR98YmznrVp4p5WBWcHR5AB8rnmFbzHSRSCQSiZSf6A1ehQV4b3owHh5uo6QZ5hPAI5FIJBKpH2qwCjdGamyszayXpE/j0VuOwePqdpH0IvBEWJ4xnwYRiUQikUh5iI311pgn1/5TWAgBJ47FG+8/49OQPlGHGiORSCQS2YIZbKouRkzjJGs+a6AywtURYRmMZ9zaWAe6IpFIJBKpntiz3kLKG/xYPMKV8CDm9+FzrJ+qY42RSCQSiWxNE2usSwVFWYVn2Pocnj/6K0BHMzvUzL5vZo+a2bq6FhnZNiSNkvRa6ZoNg3J+H0lflPT3VDSmUtsMD2FZa6qzjySTdESizCSdlfi8WNKVic9PSqoulnykgSHpB5L+XN86Gjbm3uBZlkZCqRvRt/Aco93M7Ftm9qcwhh0pE5K+LmmjpF1S5X+vofye8qoESdMk3V3Hh/klHnShTpH0b3hAjastke5P0tckvSBpjaQPJL0q6fZa7v5NPLvQX2uxzReAi2t5nMgOQlKFpOE7cJc3AkclH9gitcTAbHOmpbFQKjb4nWb2r3KJiRRlGj5cURmDPfgO9MTnwKfLP0uZQsCWC0nNJDU3s7VmtqL0FtvNaUBLPA5+QcNwPE76XXj+2z7A5Xja08yY2SYze9vMMvt6mNl7ZramNsdpSMhpUd860oS43Dtyf4XzeB0eee/CHbn/JkcTCzea2cQXqR/Ms/AsxH0GChwDvIwHq0mXi1RjLelUSfMlfShpuqSuqfV9JE2RtFbSckn3S9o7sb5LKFsqaZ2keZLOTqy/O+g4J5h0TdLRxb5PwZQt6UxJr0vaEHrlXYrU+Q95trePgQOLmcHlsdefDrpWS5qR/H6SvixpbjjOYnks8dY1/OTgOaUfNrOku+nngYfM7BYz+0dYHjCzEUW+4yBJLwZNsyX1SayrYgYvRdoMXvgs6b8lvS3pPUl3J79XaBh+Ev7PtZImSLowaaaXtKek+yStkLQ+/B/fr0HH0UH7KZKeD7/pK5KOT9XbL+z3fUmrwrl1UGL98NBbHSLpJTy05gnVHLOdpD+Gc/cdSddJukfStESdKsMEkq6UtDjx+VBJkyW9G36P2ZKGpbZZHPZ/m6SVwMywj+bAXYVzO1G/1HVT9DwOqycCpyplGYtkxAw2b862NBJiY90weJyqjXJhnnu6fL6ZLUmUdcLz+n4Vz6e9K3BnYaWkHsAMPK1k37CPTcBUSS1DtTZBwzA8K9d4/OZVSMZ+AZ5J6t5wvE54fODq6AR8G48zfySwCzBJUjIBfOdQZzgemOeN9E4kHQc8hsdcHwD0x5OktAjrh+PJBcaEffwnHjZ3XA3awK0Vz6fKlgF9Je1fYttmeDzkC4BDcb+Pe+Wm9R3J6XhawaOBM/GHiUsT6y/EE+1cjGchex64KrWP2/DEC8fhjcjX8Tjrpbge+FHY7yzgQfmUTiTtBjyDx2k/Eh+2WAA8KalDYh/NgJ8DlwAHUP2wwJ24FeMU/NzcB7d81JZPAhPw3+tQ/Lx5sMj/OTJoHwCcAxyGXw8XsuXcznrdQPXn8fP4Q0A0hW8r1rSybu3oG0ikbngc+Kakjmb2Ln5j+B5+U+yRKn8ste3OwNlmthxA0s/wjDctQzCbS/Fe5NWFDeTOTqvwxnmSmc0D5iX2eXNoKM8EppvZakkfA+vNLEtO2lbAcDN7LRzvbPyGfixu9gc3Q5+dHIbZui0HPATuZDNLmhPnJ96PAn5gW3Iavy7pu8AMSSOLhc6VtCv+QLMkteoa/EFlgaQ38MblceC3KSdLARea2Ythf1fhN/Su4TvuKP5lZheF9/MlTQCG4r8JeCN4Q+K7Xy+pH97IF9gbmGhmc8PnxRmPPdrMHgaQdB7e2J8PXBleF5vZ+YXKkkYCJ+EPjIVsaAIuNrOnqzuIPJvT54GhZvZEKBsBZM/OFDCzJ1NFV0o6BfgS8ONE+WwzG5XSAbA6dW6XvG5CcZXzOOj5UNL7wL61/S4RxxpRrzkLsWfdMHgivB4raU/8Ap9hZu/hiVUK5d3Y0tgVWFpoqANL8Btlx/D5MOC0YMpbK2ktngmnZdgfklpJGh1Mnu+FOifhN/ttYXmhoQYws3/gafN6JOq8k8Ffog+evacKoRe3N95IJb/b5FBlv2LbsSXAz1ZR+cI48xFB40+BD/Ge4SuSOiar4lMbCxQa/d1KfJfaMjf1eUnhGPJcwZ3xXm+S51KfxwJXSPqrpJ9JGpzx2JX7MbMKvJdY+O8OA/qkfvMP8B5xt9R+Zpc4TmGflVYaM/s4w3ZVkNQhmLfnB/P8WtzvI30Opy0q1VHyugnUdB5vIAaU2kYy9qpjzzpSTsxshaS/4T3PnYAXzXNLg2c/K5RvAp5Mbf5xenfhtVni9TfA6CKHLnj+/wI4Fe+tzccbqjG4CXVHke42Z83NW93VWPh+F+C/UZrqzL0rwj4/VfRgZq8CrwK/lnQtntf9fLznDbA5Ndad/r13FMX+18IxlCirFjO7S9KjeE9wCDBZ0kQzO6um7YqQ/O+a4RaH7xaptzrxflOGMMVVTCnVsLlI3bTD2t3AZ/Ae8SI8be0E/LpJkvW8y3LdlNrfp4DlNayPVEdM5BHJMY/juW5bsKWnDd4Q3RzK5yQa8azMAXoBC636fKmDgd+Z2R/BnZeA/YF3EnU+JrtndAdJXc1sYdjf/sCn8UawNryAOybdnF5hZu9IehPobmaZp1eZ2UZJL+O9rvtKVF+M5+TtWKJeWQnDEkvxcddHEquqTHszs2W4h/tdkh4B/iDp2yW8zw8H/g8qp7kdBvw2rJuDj88uMbP12/lVXgmvA4Gp4Xg7heMlz5V3cUtCkkNTnwcDl5rZg2E/rXEL1csZdBQ7t7NcN9UiqRs+RDWntttGQiLtJhZuNJrBGw6Ps8W5JtlYPwXsFcq3ZcrWT3Dnot9K6if3/B4i6UZJhfG0Bbjnar/gWDOeqjfHRbj5s6uk9qp5Ks46vHHoI6kvcA8+Jp424ZfiWuBESWMl9ZLUXe5p3D2s/yEwUu4Z/Nmw/vOSfl1iv4+QmBIHIOlXkq6WdKSkveUe3vfgjkuTiu2knhkDXCjpq5K6SboQH9NOejPfIumk8J/1xOdzv4mbrWvi8rDdgbgD327hFeAWvGGbFH6rfSQdIenHkgbW5guEoZIHgVvDOdkDuAN3SEwyDThO0hlyT/TLcee2JAuAr0o6SFJvfOpU1ofLRcAQSZ3l0yMh23VTE0cDb5jZK6UqRopgBrY529JIiI11w+EpPA57S9yxDIDQA3oBv4HVtrErmHUH4h7fj+E9ptvxsbT3Q7WLcC/W6fgDwRI8iUuSMbgJ+W+4aW9QDYddhjf49wEzcZPkabXtoZjZFHzsvD/u8PU87sG7Maz/DXAGcHJYNxt3Oks7j6UZDwyWtFeibCo+Rv4H3PT9CO4ZfJKZTa2N7jIxFm84bwRewnvDY9h6LF6h3sv4+dUaODHD//A9/EFpLv4/n2pmb4FbNPAe/QrgfryR/B0+NrxsG77HiHCch3Hv6yX4tKck9wC34t93Dv7welOqztfw+93z+MPVo2Qf+74E/+8XEczWGa+bmjgLKPXQGKkB22yZlsaCtsGCE4lsM5JGAWeZWXUOXrlA0v8AH6Q8zRs0ku4EDjazPiUrF9/+aPyBba9C41wfyOf172lmx9WXhu0heOU/COzfmIPd1CXB16J9yYrOCjMbVrpavolj1pFIcX4AjJDUzBpgzEJJnfGhkem44+Ep+DzzYo5fkfKyG/7AGhvqbaQxNL61JTbWkUgRwrz1Yp6+DYVN+Bzia/Ghk9eA82vjbBepG8zsofrWEGl4RDN4JBKJRCI5JzqYRSKRSCSSc2JjHYlEIpFIzomNdSQSiUQiOSc21pFIJBKJ5JzYWEcikUgkknNiYx2JRCKRSM6JjXUkEolEIjnn/wGE5oZVvd3QXgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## FR: instructions pour avoir un graphique plus lisible (taille du graphique, taille du texte)\n", "## EN: instructions to have a more readable graphic (size of the graphic, size of the text) \n", "plt.figure()\n", "params = {'legend.fontsize': 'medium',\n", " 'figure.figsize': (8, 4),\n", " 'axes.labelsize': 'x-large',\n", " 'axes.titlesize':'x-large',\n", " 'xtick.labelsize':'x-large',\n", " 'ytick.labelsize':'x-large'}\n", "plt.rcParams.update(params)\n", "\n", "x=plt.scatter(x=data['Wheat'], y=data['Wages'],c=data['Year'], s=50)\n", "plt.colorbar(x)\n", "\n", "a=np.arange(0,100,1)\n", "a_p=np.arange(0.1,1,0.1)\n", "for i in range (0,9):\n", " plt.plot(a,a_p[i]*a,'--',color='grey')\n", " plt.text(22,22*a_p[i],str(np.around(a_p[i],1)),{'ha': 'left', 'va': 'bottom'},rotation=180*np.arctan(a_p[i])/np.pi,color='grey',fontsize='small',fontweight='bold')\n", "\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "\n", "plt.xlim(20,100)\n", "plt.ylim(0,32)\n", "\n", "plt.xlabel('Wheat price (Shillings per quarter)')\n", "plt.ylabel('Wages (Shillings per week)')\n", "plt.legend(['ratio Wages/Wheat'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "### Conclusion\n", "\n", "**Quelle représentation des données vous paraît la plus claire ? Which data representation seems clearest to you?**\n", "\n", "L'ajout des courbes de ratio sur le graphique représentant le salaire en fonction du prix du blé permet déjà une meilleure visibilité de l'évolution du pouvoir d'achat des travailleurs à cette période toutefois, le graphique précédent (poids de blé qu'un ouvrier peut acheter en fonction du temps) semble représenter les choses de façon beaucoup plus visuelle et intuitive pour le lecteur. Elle serait donc à privilégier pour montrer l'évolution du pouvoir d'achat des travailleurs entre 1565 et 1821. Il est d'ailleure important de noter que ce pouvoir d'achat n'est pas stable et après une tendance à l'augmentation, rediminue à partir de la seconde moitié du 16ème siècle. \n", "\n", "---\n", "\n", "The addition of the ratio curves to the graph representing the wage as a function of the price of wheat already allows better visibility of the evolution of the purchasing power of workers during this period, however, the previous graph (weight of wheat than a worker can buy over time) seems to represent things much more visually and intuitively to the reader. It would therefore be preferable to show the evolution of the purchasing power of workers between 1565 and 1821. It is also important to note that this purchasing power is not stable and after an increasing trend, decreases from the second half of the 16th century. " ] } ], "metadata": { "hide_code_all_hidden": true, "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 }