diff --git a/module3/.ipynb b/module3/.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4933e016e5d0241312815f3bee24f6b3e7bfad67 --- /dev/null +++ b/module3/.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4933e016e5d0241312815f3bee24f6b3e7bfad67 --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..4933e016e5d0241312815f3bee24f6b3e7bfad67 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -364,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo3/exercice-Copy1.ipynb b/module3/exo3/exercice-Copy1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2ce4238a307544434e15c19dc5df27a6de01abca --- /dev/null +++ b/module3/exo3/exercice-Copy1.ipynb @@ -0,0 +1,1036 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autour du SARS-CoV-2 (Covid-19)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## le 31 aout 2022 - blandine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le but de ce document est de créer des graphiques permettant de suivre l'évolution de l'épidémie du COVID. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Déclaration des librairies utilisées dans ce document" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import des données\n", + "Nous allons utiliser les données de Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) qui ont été mises à disposition sur GitHub. \n", + "C’est plus particulièrement sur les données time_series_covid19_confirmed_global.csv (des suites chronologiques au format csv) disponibles à l’adresse : https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "0 NaN Afghanistan 33.93911 67.709953 0 0 \n", + "1 NaN Albania 41.15330 20.168300 0 0 \n", + "2 NaN Algeria 28.03390 1.659600 0 0 \n", + "3 NaN Andorra 42.50630 1.521800 0 0 \n", + "4 NaN Angola -11.20270 17.873900 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 8/23/22 8/24/22 8/25/22 \\\n", + "0 0 0 0 0 ... 191247 191585 191967 \n", + "1 0 0 0 0 ... 326787 327232 327607 \n", + "2 0 0 0 0 ... 269971 270043 270097 \n", + "3 0 0 0 0 ... 45975 46027 46027 \n", + "4 0 0 0 0 ... 102636 102636 102636 \n", + "\n", + " 8/26/22 8/27/22 8/28/22 8/29/22 8/30/22 8/31/22 9/1/22 \n", + "0 191967 191967 192463 192906 193004 193250 193520 \n", + "1 327961 328299 328515 328571 329017 329352 329615 \n", + "2 270145 270175 270194 270235 270272 270304 270359 \n", + "3 46027 46027 46027 46027 46027 46027 46027 \n", + "4 102636 102636 102636 102636 102636 102636 102636 \n", + "\n", + "[5 rows x 958 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Raw_data = pd.read_csv ('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n", + "Raw_data.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selection des territoires économiques que l'on aimerait étudier:\n", + "à savoir les zones géographiques suivantes : \n", + "- la Belgique (Belgium), \n", + "- la Chine - toutes les provinces sauf Hong-Kong (China), \n", + "- Hong Kong (China, Hong-Kong),\n", + "- la France métropolitaine (France), \n", + "- l’Allemagne (Germany), \n", + "- l’Iran (Iran),\n", + "- l’Italie (Italy),\n", + "- le Japon (Japan),\n", + "- la Corée du Sud (Korea, South),\n", + "- la Hollande sans les colonies (Netherlands), \n", + "- le Portugal (Portugal),\n", + "- l’Espagne (Spain), \n", + "- le Royaume-Unis sans les colonies (United Kingdom),\n", + "- les États-Unis (US)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Création d'un dataframe ne contenant que les zones géographiques listées ci-dessus" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self.obj[item] = s\n" + ] + }, + { + "data": { + "text/html": [ + "
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Country/Region1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...8/23/228/24/228/25/228/26/228/27/228/28/228/29/228/30/228/31/229/1/22
0Belgium000000000...4471620447162044777704477770447777044777704482315448231544823154488054
1France002333455...33440664334619533348179033493920334939203349392033529669335533913357263133591277
2Germany000001444...31921578319215783200812232041348320413493204135032095854320958543218455332218483
3Iran000000000...7513928751659675189747520993752196975236627525628752749975289617530325
4Italy000000000...21696242217216302174506521767060217888622180650921814856218459432186775721888255
5Japan2222447711...17537636177810181800186518194170183742301853198618627815187803021894979319099600
6Korea, South112234444...22588640227019212280298522898523229838182302696023142479232463982332789723417425
7Netherlands000000000...8377269837853783797378380891838089183808918383706838499283861898386189
8Portugal000000000...5399857539985754060955409185540918554091855413173541710154201405420140
9Spain000000000...13323784133237841332378413332976133329761333297613332976133425301334253013342530
10US112255566...93776222939272769406604394186442941977939420462694306725943782879453185494649469
11United Kingdom000000000...23460787234607872349287523492875234928752349287523492875234928752352179223521792
12Hong Kong02258881010...1471509147939314879721495807150426415139721522460153130815408031551389
13China548641918140120672869550160778131...944631946165947626948009948316952082953856955555957285959314
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14 rows × 955 columns

\n", + "
" + ], + "text/plain": [ + " Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", + "0 Belgium 0 0 0 0 0 0 0 \n", + "1 France 0 0 2 3 3 3 4 \n", + "2 Germany 0 0 0 0 0 1 4 \n", + "3 Iran 0 0 0 0 0 0 0 \n", + "4 Italy 0 0 0 0 0 0 0 \n", + "5 Japan 2 2 2 2 4 4 7 \n", + "6 Korea, South 1 1 2 2 3 4 4 \n", + "7 Netherlands 0 0 0 0 0 0 0 \n", + "8 Portugal 0 0 0 0 0 0 0 \n", + "9 Spain 0 0 0 0 0 0 0 \n", + "10 US 1 1 2 2 5 5 5 \n", + "11 United Kingdom 0 0 0 0 0 0 0 \n", + "12 Hong Kong 0 2 2 5 8 8 8 \n", + "13 China 548 641 918 1401 2067 2869 5501 \n", + "\n", + " 1/29/20 1/30/20 ... 8/23/22 8/24/22 8/25/22 8/26/22 \\\n", + "0 0 0 ... 4471620 4471620 4477770 4477770 \n", + "1 5 5 ... 33440664 33461953 33481790 33493920 \n", + "2 4 4 ... 31921578 31921578 32008122 32041348 \n", + "3 0 0 ... 7513928 7516596 7518974 7520993 \n", + "4 0 0 ... 21696242 21721630 21745065 21767060 \n", + "5 7 11 ... 17537636 17781018 18001865 18194170 \n", + "6 4 4 ... 22588640 22701921 22802985 22898523 \n", + "7 0 0 ... 8377269 8378537 8379737 8380891 \n", + "8 0 0 ... 5399857 5399857 5406095 5409185 \n", + "9 0 0 ... 13323784 13323784 13323784 13332976 \n", + "10 6 6 ... 93776222 93927276 94066043 94186442 \n", + "11 0 0 ... 23460787 23460787 23492875 23492875 \n", + "12 10 10 ... 1471509 1479393 1487972 1495807 \n", + "13 6077 8131 ... 944631 946165 947626 948009 \n", + "\n", + " 8/27/22 8/28/22 8/29/22 8/30/22 8/31/22 9/1/22 \n", + "0 4477770 4477770 4482315 4482315 4482315 4488054 \n", + "1 33493920 33493920 33529669 33553391 33572631 33591277 \n", + "2 32041349 32041350 32095854 32095854 32184553 32218483 \n", + "3 7521969 7523662 7525628 7527499 7528961 7530325 \n", + "4 21788862 21806509 21814856 21845943 21867757 21888255 \n", + "5 18374230 18531986 18627815 18780302 18949793 19099600 \n", + "6 22983818 23026960 23142479 23246398 23327897 23417425 \n", + "7 8380891 8380891 8383706 8384992 8386189 8386189 \n", + "8 5409185 5409185 5413173 5417101 5420140 5420140 \n", + "9 13332976 13332976 13332976 13342530 13342530 13342530 \n", + "10 94197793 94204626 94306725 94378287 94531854 94649469 \n", + "11 23492875 23492875 23492875 23492875 23521792 23521792 \n", + "12 1504264 1513972 1522460 1531308 1540803 1551389 \n", + "13 948316 952082 953856 955555 957285 959314 \n", + "\n", + "[14 rows x 955 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tout d'abord on ne selectionne que les pays qui affiche des données au niveau national\n", + "Select_data= Raw_data[Raw_data['Country/Region'].isin(['Belgium', 'France','Netherlands', 'United Kingdom', 'Germany', 'Iran','Italy','Japan', 'Korea, South', 'Portugal','Spain','US'])==True]\n", + "\n", + "# Le fichier présentent également des données pour des colonies/departements d'outre- mer ( cas de la france, holande,royaume uni) \n", + "# Pour se faire, nous ne gardons que les lignes où l'identification d'une province/state est absente (on exclut les colonies/departements d outre mer)\n", + "Select_data2= Select_data[Select_data['Province/State'].isnull()]\n", + "\n", + "\n", + "# cas de la chine \n", + "#les données au niveau national n'ont pas été aggrégées, il faut donc sommer toutes les provinces (sauf Hong Kong qu il faut traiter à part)\n", + "#pour obtenir le territoire national. Par consequent on va traiter separemment Hong kong et la Chine \n", + "\n", + "#Hongkong : selection puis renommage \n", + "HongKong =Raw_data[Raw_data['Province/State']==\"Hong Kong\"]\n", + "HongKong.loc[71,'Country/Region'] =\"Hong Kong\"\n", + "\n", + "#china : selection puis aggregation \n", + "China = Raw_data[Raw_data['Country/Region']==\"China\"]\n", + "#ChinaSum= China.loc[China['Province/State']!=\"Hong Kong\"].sum(axis=0) \n", + "ChinaSum= China.loc[China['Province/State']!=\"Hong Kong\"].agg('sum') \n", + "\n", + "#on transforme la serie obtenue en dataframe et on fait pivoter les index en colonnes (.T)\n", + "ChinaNat= pd.DataFrame(ChinaSum).T\n", + "ChinaNat.loc[0,'Country/Region'] =\"China\"\n", + "\n", + "# On met ces 3 selections de données dans un meme dataset\n", + "DatasetConcat =pd.concat([Select_data2,HongKong, ChinaNat])\n", + "DatasetConcat.reset_index(inplace=True)\n", + "# et on supprime les colonnes inutiles\n", + "Dataset= DatasetConcat.drop(columns=['index','Province/State', 'Lat','Long'])\n", + "\n", + "Dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Creation du graphique montrant l'évolution du Covid en cas cumulés au cours du temps pour cette selection de pays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### D abord mis au format date de la serie temporelle" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2020-01-22\n", + "1 2020-01-23\n", + "2 2020-01-24\n", + "3 2020-01-25\n", + "4 2020-01-26\n", + "5 2020-01-27\n", + "6 2020-01-28\n", + "7 2020-01-29\n", + "8 2020-01-30\n", + "9 2020-01-31\n", + "10 2020-02-01\n", + "11 2020-02-02\n", + "12 2020-02-03\n", + "13 2020-02-04\n", + "14 2020-02-05\n", + "15 2020-02-06\n", + "16 2020-02-07\n", + "17 2020-02-08\n", + "18 2020-02-09\n", + "19 2020-02-10\n", + "20 2020-02-11\n", + "21 2020-02-12\n", + "22 2020-02-13\n", + "23 2020-02-14\n", + "24 2020-02-15\n", + "25 2020-02-16\n", + "26 2020-02-17\n", + "27 2020-02-18\n", + "28 2020-02-19\n", + "29 2020-02-20\n", + " ... \n", + "924 2022-08-03\n", + "925 2022-08-04\n", + "926 2022-08-05\n", + "927 2022-08-06\n", + "928 2022-08-07\n", + "929 2022-08-08\n", + "930 2022-08-09\n", + "931 2022-08-10\n", + "932 2022-08-11\n", + "933 2022-08-12\n", + "934 2022-08-13\n", + "935 2022-08-14\n", + "936 2022-08-15\n", + "937 2022-08-16\n", + "938 2022-08-17\n", + "939 2022-08-18\n", + "940 2022-08-19\n", + "941 2022-08-20\n", + "942 2022-08-21\n", + "943 2022-08-22\n", + "944 2022-08-23\n", + "945 2022-08-24\n", + "946 2022-08-25\n", + "947 2022-08-26\n", + "948 2022-08-27\n", + "949 2022-08-28\n", + "950 2022-08-29\n", + "951 2022-08-30\n", + "952 2022-08-31\n", + "953 2022-09-01\n", + "Name: 0, Length: 954, dtype: datetime64[ns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "import datetime\n", + "import time\n", + "Dat = pd.DataFrame(Dataset.columns[1:])\n", + "\n", + "#D=time.strptime('1/2/20','%m/%d/%y')\n", + "\n", + "#D=[time.strptime(Dt,'%m/%d/%y')for Dt in Dataset.columns[1:]]\n", + "\n", + "#Dat[0] = Dat[0].apply(lambda x: time.strptime(x, '%m/%d/%y'))\n", + "\n", + "Calendar = pd.to_datetime(Dat[0], format=\"%m/%d/%y\")\n", + "\n", + "#D['period'] = [datetime.time(yw,'%m/%d/%Y') for yw in Dat.index]\n", + "Calendar\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Definition d une serie par pays\n", + "X_ax = Calendar\n", + "Belgique = Dataset.iloc[0].squeeze()\n", + "France= Dataset.iloc[1].squeeze()\n", + "Allemagne = Dataset.iloc[2].squeeze()\n", + "Iran = Dataset.iloc[3].squeeze()\n", + "\n", + "Italy = Dataset.iloc[4].squeeze()\n", + "Japon = Dataset.iloc[5].squeeze()\n", + "Coree = Dataset.iloc[6].squeeze()\n", + "PaysBas= Dataset.iloc[7].squeeze()\n", + "\n", + "Portugal = Dataset.iloc[8].squeeze()\n", + "Espagne = Dataset.iloc[9].squeeze()\n", + "EtatsUnis = Dataset.iloc[10].squeeze()\n", + "RoyaumeUni = Dataset.iloc[11].squeeze()\n", + "HongKongs = Dataset.iloc[12].squeeze()\n", + "Chine= Dataset.iloc[13].squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph -- Evolution du nombre cumulé de cas covid par pays" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# On indique quelles series on montre dans le graphique\n", + "plt.plot(X_ax, Belgique[1:], label='Belgique')\n", + "plt.plot( X_ax, France[1:], label= 'France')\n", + "plt.plot( X_ax, Allemagne[1:], label= 'Allemagne')\n", + "plt.plot( X_ax, Iran[1:], label= 'Iran')\n", + "plt.plot( X_ax, Italy[1:], label= 'Italie')\n", + "plt.plot( X_ax, Japon[1:], label= 'Japon')\n", + "plt.plot( X_ax, Coree[1:], label= 'Corée du Sud')\n", + "plt.plot( X_ax, PaysBas[1:], label= 'Pays-Bas')\n", + "plt.plot( X_ax, Portugal[1:], label= 'Portugal')\n", + "plt.plot( X_ax, Espagne[1:], label= 'Espagne')\n", + "plt.plot( X_ax, EtatsUnis[1:], label= 'Etats-Unis')\n", + "plt.plot( X_ax, RoyaumeUni[1:], label= 'Royaume-Uni')\n", + "plt.plot( X_ax, Chine[1:], label= 'Chine')\n", + "plt.plot( X_ax, HongKongs[1:], label= 'Hong-Kong')\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph - Evolution du nombre de cas covid cumulé - échelle logarithmique" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# On indique quelles series on montre dans le graphique\n", + "plt.plot(X_ax, Belgique[1:], label='Belgique')\n", + "plt.plot( X_ax, France[1:], label= 'France')\n", + "plt.plot( X_ax, Allemagne[1:], label= 'Allemagne')\n", + "plt.plot( X_ax, Iran[1:], label= 'Iran')\n", + "plt.plot( X_ax, Italy[1:], label= 'Italie')\n", + "plt.plot( X_ax, Japon[1:], label= 'Japon')\n", + "plt.plot( X_ax, Coree[1:], label= 'Corée du Sud')\n", + "plt.plot( X_ax, PaysBas[1:], label= 'Pays-Bas')\n", + "plt.plot( X_ax, Portugal[1:], label= 'Portugal')\n", + "plt.plot( X_ax, Espagne[1:], label= 'Espagne')\n", + "plt.plot( X_ax, EtatsUnis[1:], label= 'Etats-Unis')\n", + "plt.plot( X_ax, RoyaumeUni[1:], label= 'Royaume-Uni')\n", + "plt.plot( X_ax, Chine[1:], label= 'Chine')\n", + "plt.plot( X_ax, HongKongs[1:], label= 'Hong-Kong')\n", + "\n", + "# Plot another line on the same chart/graph\n", + "plt.yscale(\"log\")\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..2ce4238a307544434e15c19dc5df27a6de01abca 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,1017 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autour du SARS-CoV-2 (Covid-19)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## le 31 aout 2022 - blandine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le but de ce document est de créer des graphiques permettant de suivre l'évolution de l'épidémie du COVID. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Déclaration des librairies utilisées dans ce document" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import des données\n", + "Nous allons utiliser les données de Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) qui ont été mises à disposition sur GitHub. \n", + "C’est plus particulièrement sur les données time_series_covid19_confirmed_global.csv (des suites chronologiques au format csv) disponibles à l’adresse : https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "0 NaN Afghanistan 33.93911 67.709953 0 0 \n", + "1 NaN Albania 41.15330 20.168300 0 0 \n", + "2 NaN Algeria 28.03390 1.659600 0 0 \n", + "3 NaN Andorra 42.50630 1.521800 0 0 \n", + "4 NaN Angola -11.20270 17.873900 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 8/23/22 8/24/22 8/25/22 \\\n", + "0 0 0 0 0 ... 191247 191585 191967 \n", + "1 0 0 0 0 ... 326787 327232 327607 \n", + "2 0 0 0 0 ... 269971 270043 270097 \n", + "3 0 0 0 0 ... 45975 46027 46027 \n", + "4 0 0 0 0 ... 102636 102636 102636 \n", + "\n", + " 8/26/22 8/27/22 8/28/22 8/29/22 8/30/22 8/31/22 9/1/22 \n", + "0 191967 191967 192463 192906 193004 193250 193520 \n", + "1 327961 328299 328515 328571 329017 329352 329615 \n", + "2 270145 270175 270194 270235 270272 270304 270359 \n", + "3 46027 46027 46027 46027 46027 46027 46027 \n", + "4 102636 102636 102636 102636 102636 102636 102636 \n", + "\n", + "[5 rows x 958 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Raw_data = pd.read_csv ('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n", + "Raw_data.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selection des territoires économiques que l'on aimerait étudier:\n", + "à savoir les zones géographiques suivantes : \n", + "- la Belgique (Belgium), \n", + "- la Chine - toutes les provinces sauf Hong-Kong (China), \n", + "- Hong Kong (China, Hong-Kong),\n", + "- la France métropolitaine (France), \n", + "- l’Allemagne (Germany), \n", + "- l’Iran (Iran),\n", + "- l’Italie (Italy),\n", + "- le Japon (Japan),\n", + "- la Corée du Sud (Korea, South),\n", + "- la Hollande sans les colonies (Netherlands), \n", + "- le Portugal (Portugal),\n", + "- l’Espagne (Spain), \n", + "- le Royaume-Unis sans les colonies (United Kingdom),\n", + "- les États-Unis (US)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Création d'un dataframe ne contenant que les zones géographiques listées ci-dessus" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self.obj[item] = s\n" + ] + }, + { + "data": { + "text/html": [ + "
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Country/Region1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...8/23/228/24/228/25/228/26/228/27/228/28/228/29/228/30/228/31/229/1/22
0Belgium000000000...4471620447162044777704477770447777044777704482315448231544823154488054
1France002333455...33440664334619533348179033493920334939203349392033529669335533913357263133591277
2Germany000001444...31921578319215783200812232041348320413493204135032095854320958543218455332218483
3Iran000000000...7513928751659675189747520993752196975236627525628752749975289617530325
4Italy000000000...21696242217216302174506521767060217888622180650921814856218459432186775721888255
5Japan2222447711...17537636177810181800186518194170183742301853198618627815187803021894979319099600
6Korea, South112234444...22588640227019212280298522898523229838182302696023142479232463982332789723417425
7Netherlands000000000...8377269837853783797378380891838089183808918383706838499283861898386189
8Portugal000000000...5399857539985754060955409185540918554091855413173541710154201405420140
9Spain000000000...13323784133237841332378413332976133329761333297613332976133425301334253013342530
10US112255566...93776222939272769406604394186442941977939420462694306725943782879453185494649469
11United Kingdom000000000...23460787234607872349287523492875234928752349287523492875234928752352179223521792
12Hong Kong02258881010...1471509147939314879721495807150426415139721522460153130815408031551389
13China548641918140120672869550160778131...944631946165947626948009948316952082953856955555957285959314
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14 rows × 955 columns

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" + ], + "text/plain": [ + " Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", + "0 Belgium 0 0 0 0 0 0 0 \n", + "1 France 0 0 2 3 3 3 4 \n", + "2 Germany 0 0 0 0 0 1 4 \n", + "3 Iran 0 0 0 0 0 0 0 \n", + "4 Italy 0 0 0 0 0 0 0 \n", + "5 Japan 2 2 2 2 4 4 7 \n", + "6 Korea, South 1 1 2 2 3 4 4 \n", + "7 Netherlands 0 0 0 0 0 0 0 \n", + "8 Portugal 0 0 0 0 0 0 0 \n", + "9 Spain 0 0 0 0 0 0 0 \n", + "10 US 1 1 2 2 5 5 5 \n", + "11 United Kingdom 0 0 0 0 0 0 0 \n", + "12 Hong Kong 0 2 2 5 8 8 8 \n", + "13 China 548 641 918 1401 2067 2869 5501 \n", + "\n", + " 1/29/20 1/30/20 ... 8/23/22 8/24/22 8/25/22 8/26/22 \\\n", + "0 0 0 ... 4471620 4471620 4477770 4477770 \n", + "1 5 5 ... 33440664 33461953 33481790 33493920 \n", + "2 4 4 ... 31921578 31921578 32008122 32041348 \n", + "3 0 0 ... 7513928 7516596 7518974 7520993 \n", + "4 0 0 ... 21696242 21721630 21745065 21767060 \n", + "5 7 11 ... 17537636 17781018 18001865 18194170 \n", + "6 4 4 ... 22588640 22701921 22802985 22898523 \n", + "7 0 0 ... 8377269 8378537 8379737 8380891 \n", + "8 0 0 ... 5399857 5399857 5406095 5409185 \n", + "9 0 0 ... 13323784 13323784 13323784 13332976 \n", + "10 6 6 ... 93776222 93927276 94066043 94186442 \n", + "11 0 0 ... 23460787 23460787 23492875 23492875 \n", + "12 10 10 ... 1471509 1479393 1487972 1495807 \n", + "13 6077 8131 ... 944631 946165 947626 948009 \n", + "\n", + " 8/27/22 8/28/22 8/29/22 8/30/22 8/31/22 9/1/22 \n", + "0 4477770 4477770 4482315 4482315 4482315 4488054 \n", + "1 33493920 33493920 33529669 33553391 33572631 33591277 \n", + "2 32041349 32041350 32095854 32095854 32184553 32218483 \n", + "3 7521969 7523662 7525628 7527499 7528961 7530325 \n", + "4 21788862 21806509 21814856 21845943 21867757 21888255 \n", + "5 18374230 18531986 18627815 18780302 18949793 19099600 \n", + "6 22983818 23026960 23142479 23246398 23327897 23417425 \n", + "7 8380891 8380891 8383706 8384992 8386189 8386189 \n", + "8 5409185 5409185 5413173 5417101 5420140 5420140 \n", + "9 13332976 13332976 13332976 13342530 13342530 13342530 \n", + "10 94197793 94204626 94306725 94378287 94531854 94649469 \n", + "11 23492875 23492875 23492875 23492875 23521792 23521792 \n", + "12 1504264 1513972 1522460 1531308 1540803 1551389 \n", + "13 948316 952082 953856 955555 957285 959314 \n", + "\n", + "[14 rows x 955 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tout d'abord on ne selectionne que les pays qui affiche des données au niveau national\n", + "Select_data= Raw_data[Raw_data['Country/Region'].isin(['Belgium', 'France','Netherlands', 'United Kingdom', 'Germany', 'Iran','Italy','Japan', 'Korea, South', 'Portugal','Spain','US'])==True]\n", + "\n", + "# Le fichier présentent également des données pour des colonies/departements d'outre- mer ( cas de la france, holande,royaume uni) \n", + "# Pour se faire, nous ne gardons que les lignes où l'identification d'une province/state est absente (on exclut les colonies/departements d outre mer)\n", + "Select_data2= Select_data[Select_data['Province/State'].isnull()]\n", + "\n", + "\n", + "# cas de la chine \n", + "#les données au niveau national n'ont pas été aggrégées, il faut donc sommer toutes les provinces (sauf Hong Kong qu il faut traiter à part)\n", + "#pour obtenir le territoire national. Par consequent on va traiter separemment Hong kong et la Chine \n", + "\n", + "#Hongkong : selection puis renommage \n", + "HongKong =Raw_data[Raw_data['Province/State']==\"Hong Kong\"]\n", + "HongKong.loc[71,'Country/Region'] =\"Hong Kong\"\n", + "\n", + "#china : selection puis aggregation \n", + "China = Raw_data[Raw_data['Country/Region']==\"China\"]\n", + "#ChinaSum= China.loc[China['Province/State']!=\"Hong Kong\"].sum(axis=0) \n", + "ChinaSum= China.loc[China['Province/State']!=\"Hong Kong\"].agg('sum') \n", + "\n", + "#on transforme la serie obtenue en dataframe et on fait pivoter les index en colonnes (.T)\n", + "ChinaNat= pd.DataFrame(ChinaSum).T\n", + "ChinaNat.loc[0,'Country/Region'] =\"China\"\n", + "\n", + "# On met ces 3 selections de données dans un meme dataset\n", + "DatasetConcat =pd.concat([Select_data2,HongKong, ChinaNat])\n", + "DatasetConcat.reset_index(inplace=True)\n", + "# et on supprime les colonnes inutiles\n", + "Dataset= DatasetConcat.drop(columns=['index','Province/State', 'Lat','Long'])\n", + "\n", + "Dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Creation du graphique montrant l'évolution du Covid en cas cumulés au cours du temps pour cette selection de pays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### D abord mis au format date de la serie temporelle" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2020-01-22\n", + "1 2020-01-23\n", + "2 2020-01-24\n", + "3 2020-01-25\n", + "4 2020-01-26\n", + "5 2020-01-27\n", + "6 2020-01-28\n", + "7 2020-01-29\n", + "8 2020-01-30\n", + "9 2020-01-31\n", + "10 2020-02-01\n", + "11 2020-02-02\n", + "12 2020-02-03\n", + "13 2020-02-04\n", + "14 2020-02-05\n", + "15 2020-02-06\n", + "16 2020-02-07\n", + "17 2020-02-08\n", + "18 2020-02-09\n", + "19 2020-02-10\n", + "20 2020-02-11\n", + "21 2020-02-12\n", + "22 2020-02-13\n", + "23 2020-02-14\n", + "24 2020-02-15\n", + "25 2020-02-16\n", + "26 2020-02-17\n", + "27 2020-02-18\n", + "28 2020-02-19\n", + "29 2020-02-20\n", + " ... \n", + "924 2022-08-03\n", + "925 2022-08-04\n", + "926 2022-08-05\n", + "927 2022-08-06\n", + "928 2022-08-07\n", + "929 2022-08-08\n", + "930 2022-08-09\n", + "931 2022-08-10\n", + "932 2022-08-11\n", + "933 2022-08-12\n", + "934 2022-08-13\n", + "935 2022-08-14\n", + "936 2022-08-15\n", + "937 2022-08-16\n", + "938 2022-08-17\n", + "939 2022-08-18\n", + "940 2022-08-19\n", + "941 2022-08-20\n", + "942 2022-08-21\n", + "943 2022-08-22\n", + "944 2022-08-23\n", + "945 2022-08-24\n", + "946 2022-08-25\n", + "947 2022-08-26\n", + "948 2022-08-27\n", + "949 2022-08-28\n", + "950 2022-08-29\n", + "951 2022-08-30\n", + "952 2022-08-31\n", + "953 2022-09-01\n", + "Name: 0, Length: 954, dtype: datetime64[ns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "import datetime\n", + "import time\n", + "Dat = pd.DataFrame(Dataset.columns[1:])\n", + "\n", + "#D=time.strptime('1/2/20','%m/%d/%y')\n", + "\n", + "#D=[time.strptime(Dt,'%m/%d/%y')for Dt in Dataset.columns[1:]]\n", + "\n", + "#Dat[0] = Dat[0].apply(lambda x: time.strptime(x, '%m/%d/%y'))\n", + "\n", + "Calendar = pd.to_datetime(Dat[0], format=\"%m/%d/%y\")\n", + "\n", + "#D['period'] = [datetime.time(yw,'%m/%d/%Y') for yw in Dat.index]\n", + "Calendar\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Definition d une serie par pays\n", + "X_ax = Calendar\n", + "Belgique = Dataset.iloc[0].squeeze()\n", + "France= Dataset.iloc[1].squeeze()\n", + "Allemagne = Dataset.iloc[2].squeeze()\n", + "Iran = Dataset.iloc[3].squeeze()\n", + "\n", + "Italy = Dataset.iloc[4].squeeze()\n", + "Japon = Dataset.iloc[5].squeeze()\n", + "Coree = Dataset.iloc[6].squeeze()\n", + "PaysBas= Dataset.iloc[7].squeeze()\n", + "\n", + "Portugal = Dataset.iloc[8].squeeze()\n", + "Espagne = Dataset.iloc[9].squeeze()\n", + "EtatsUnis = Dataset.iloc[10].squeeze()\n", + "RoyaumeUni = Dataset.iloc[11].squeeze()\n", + "HongKongs = Dataset.iloc[12].squeeze()\n", + "Chine= Dataset.iloc[13].squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph -- Evolution du nombre cumulé de cas covid par pays" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# On indique quelles series on montre dans le graphique\n", + "plt.plot(X_ax, Belgique[1:], label='Belgique')\n", + "plt.plot( X_ax, France[1:], label= 'France')\n", + "plt.plot( X_ax, Allemagne[1:], label= 'Allemagne')\n", + "plt.plot( X_ax, Iran[1:], label= 'Iran')\n", + "plt.plot( X_ax, Italy[1:], label= 'Italie')\n", + "plt.plot( X_ax, Japon[1:], label= 'Japon')\n", + "plt.plot( X_ax, Coree[1:], label= 'Corée du Sud')\n", + "plt.plot( X_ax, PaysBas[1:], label= 'Pays-Bas')\n", + "plt.plot( X_ax, Portugal[1:], label= 'Portugal')\n", + "plt.plot( X_ax, Espagne[1:], label= 'Espagne')\n", + "plt.plot( X_ax, EtatsUnis[1:], label= 'Etats-Unis')\n", + "plt.plot( X_ax, RoyaumeUni[1:], label= 'Royaume-Uni')\n", + "plt.plot( X_ax, Chine[1:], label= 'Chine')\n", + "plt.plot( X_ax, HongKongs[1:], label= 'Hong-Kong')\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph - Evolution du nombre de cas covid cumulé - échelle logarithmique" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JHGbCwEwZ2BkL27CQho00JdK0kZaNtCRYEsx82XaSkI48q0Kg4Mysj3+CdSdJr4pS4kcr9+OpCuKrKcBTVYAa1LFMkx1r32TjC39m59pV2JbJrEQB0PiBPfpjiVtvvbVkxYoVxdlsVkmlUspTTz3Veu655zZFo1HVNE3xgx/8oPvKK68cbWlp8Zx33nnNS5cuTaxataqgoqIi99RTT7UWFBTIt99+27t8+fIpQ0NDmqqq8sEHH9wxZ86c7GQOi3qouHPoLh8IpmXzxIYe7n6tnVXtI6iK4PTpZVy+pI4zZ5bj0dztWB8JLAP+chu8fIuzwhwcc/bIDmc0PpYqZjt5QTkEy5HBMjZlB7m35yWe6H4ZW9qcUPVx/nr6X3Na3WnvWnBmZ0xSG4dIto5idCWwR7MoB9h+aEuJIcGUYONot0Rik9dunCSFQNEVFI+C6lXRfBreoI5SqOMr8uAt9KB4VNQiD2qRFzXkRSnQ37XLITk6wvrf/4n1z6wgMTJMMBRm4bkX0DB/IdWOd9PJy6NfqaN/0xENn0r57BSf/MUhhU8FqKuryz7zzDPbAdasWVOwfv36jRUVFZZhGDzxxBOtkUjE7unp0U444YSZV1xxxSjArl27fPfcc8+Ok046qf3888+fdtddd4W//OUvD19xxRVTb7zxxt6rrrpqNJVKCcuyxGQPi3qouCZ3lyOKYdk8sqaL/3yhlbahFNNKg3znvJlcurCG8iLXpH7MY2Sg7RVofQba/wJ9b++eC1e90HA6/NWPobTZMW3vgZSSdQPreK7jOZ5ZdyediU6KvcV8fs7nuXDahTSHmx0fA6M5+rqHGOlLkdoVI9gWozhjogKmlEQtSdyCpC2xfSpqUEcr0NGCHjyFOnqxB2+BB92noXtUNK/i5B4V3TuWK2heFfUw/QAMdXWw5ok/sPHFZ7FMk4YFizjruq8w9bglKKq7qPNA7M/kfsopp8QqKiosANu2xde+9rXalStXFiiKQn9/v6ezs1MDqKmpyZ500klpgIULF6ba2tq8IyMjSl9fn+eqq64aBQgEAhKQkz0s6qHiCrrLESGaNrj39Xbuea2d7miGuTVF/PeVizlndoXrcvSjQKIf3rgd3vwNpIcd8dZ8jpg3nQ1n/wuUTnfmpN95ai7B77b8jse2P0Z7rB0NnRNCJ3FN4/XM8S8gNyrpejLJ271rGO5JkkkYaMBcv0q9R2ALQbTYi1VfiG9KMSVlfhoiXgpCPlT9yFuCbClJWzbDpkXassnYNhnLJmHZZG0bS0IqEWPzqy/TsWUjaBo1F15B/YJFZENhXpPwau9I3jIgOSVcyPTgJP6xe5CR9IdNIBAYXy35y1/+MjI0NKRt2LBhs9frlTU1NfPS6bQC4PF4xp1RqKoq0+m0sr/YJZM9LOqhcsQFXQhxCvDZ/LVnSylPOtL3cJk8DMSz/OaVndyzsp1E1uTkphL+7ZJ5nD6jzHWucqxjW85ofO3dsOkxxznLzE9A3TJ49T8cc/t5/w+WXveuOW5pS9o6unn49SfYuqMdT7KAj8kruCBXjp1QkbakD+hjKwDegEa4MsjUBaWUlfkJbxiA4QzBk6opOr2OKYXOjggpJTkpydqSAcsinTJI2TYjhknCzIuvbZO2JSnLJm3ZpPKinLMlWenkOdsR7ZRtj4t2yrIZNS2S+/CJsE9q5zhpjN6Ek97Bf8ysm9yCPomJRqNqaWmp4fV65R//+MfC7u7uA26NiUQidmVlZe7uu+8Ofe5znxtNp9PCNE0x2cOiHiqHJOhCiDuAC4B+KeXcPerPBX4GqMCvpZQ3SSlfBl4WQnwSePMD6LPLJKB7NM0vX9zOfW92YFg258+r4kunNzKnuniiu+byQWFmYXgndK2CnS/Btmec0bi3GBZdBcu+5GxDe+zvoLgWPnM/lE0fPz2bNmlbN8D2dQO0bexH5hR8NDFdbyZTqqKWB0mEPMginVxQxfapmD4V0yMwVEHadmK293YMk5mhYIXDGFqW9PqtedGVGO8jeqRXEfgVBa8i8IzlQuBVFIKqQrFXx6co+FVBSNMo1BT8ikJE1/CrCj5F4EHSu+YNNj/zJEYqyfQly1h49rkUlZSiIFCFs1VNyS+mU4UYPy5w3fu+b6699trh8847r2nu3Lmz5syZk5o6dWrmYOfcc889O6+77ropP/rRj6p1XZcPPvjg9skeFvVQOaTwqUKIU4EEcNeYoAshVGArcDbQiSPen5FSbsq//gBwrZQydrDru+FTjx42dce48y87eWRtF1LCZYtq+eLpjUwtDU5011yOBLbt7AUfaoXBbc4e8aFWJ422754PD5RA01kw/a9gxvmOi9XXfg6v/zdMPRUu/x8IRABIx3Os+3MHa57voL1QYWelzc7SJPFQIYaviORBvoL8ikJAVQgoAu9ojnDGpqi2EH9Ax6s44upXBT5FwaMIPMIR5YDqnOdTFEK6SpGm4lUcAfbnxdqnKqjv05JkmSb9O7ez+dUX2PLKi6TjMernzue0z11LecOHE2zFDZ/60eOww6dKKV8SQrzzAkuBVinlDgAhxH3AxcAmIUQ9ED2QmAshlgPLAerr6w+lGy4TxGgqxx/e6uaBVR1s7I7h0xX+5vh6/va0adSGj+ziV5cPiWw8L9h54R7cmhfu7eMe2ADQg1DSCNULYd7lzmK2irlQNtPZt23mnD3lT97oRBpb8n/gvJudPdtS8vZLXTz0fBtv1GpsuqCQrOYsBAsoxZwULqbe56HW56Haqzuiq6oUaCphXSWgOmKtCIE0LAZ/u5FsW4LSq+fiaw4f8UdimSbZVJJcOu3kqSTZVCpflyKbTJIYHSE5MkRieIjBXe2YRg5V02hcfAJzzzyHhgWL3KkmlwnjcObQa4A9F0t0Aifky9cAvz3QyVLK24HbwRmhH0Y/XI4whmXT0hvn1dZBnm/pZ1XbCKYtmVNdxD9fNIeLFlS7XtyOBmzLiQg22ApDedEeE/F4z+52QnGcuZQ2w9TTHAEvbYaSJiis2nv+28w6K9dX/QY6V8HWFU640NqlcPEvxk3sscE0dz60mXvCFrtOK0DYJr70G1xSXMo/L/gE5d739kMw+qc2sjuihC+f/r7E3LZtsqkkI709DHd3MtzdRbS/j/jwEPGRIVKxKGYuB+Rt4/lc7nUs8PgD+AuL8BcVM+WUjxOuqaViWjO614tp22zbtg3btpFSIqUcLx+obtq0aZSVlR2w/y4uh8LhCPq+foZKACnlPx3SBVzHMpOC/niGNe2jrO0YYW37KOu7RskYjml1ZmUh1506jQvnVzO72nUCM+kYN5Fvh+Ht+XyHk4/sdBaqjeELOUI97QwnL22GkmaITAVtH4FEMjHHZ3rvBuhaDd1rnPLYNf0Rx9w++5OO+V3VSBpJHnntde7Z7uOtGQF0I0tx9DE+URbi68f/NXVFde/5LRoDKWKvdmEsDLLTO8DwSy2MjIyQSCRIpVJkMhlM08Q0TSzLwjJNbNvGzgvogfFCuNpJh0AKGAWwgJ4BJ61a+57f055ccMYZlJ122mFdw8UFDk/QO4E9/ztrgaPOs85HjYxhsbE7ylsdUd7qGGVN+whdo46JVVcFs6uL+czSehbWh1kyJUx1yD/BPXYBHOEebYP+zY7I9r3tRBcbbXdCgY6heqCo2hlZl89yHLUUVDhz3ormtDWzjlm9+y3YtdKpMzJOno3lY313QnaPwCaeAsfsfsIXoWYxgyXT2Gol6Ep20ZdspePV59kZG6Q9diad5bOR9TDD2MEXpni5ZMrfE/KF3vWWpGVhdHZi9PRiDg5ijYxgJ+JY8QR2OkXGNNllS3aJMJ3eNNnNJmx2zvVbFgHLwmOZ6KYJtoVtWxj5ACgKoEqJJsEjJR5bOrkl8do2XtN2fLpbNkLaCNtJ5HNhOQFVhGUhbJnPLZAgpHz/iXxu766rnTvvA/3ouHx0OBxBfxNoFkJMBbqAvwGuOCK9cjki5EzHdL6+a5QNnVHWd0bZ2hcfjxVeVexjUX2Yq09uYGF9iDnVxW4Es4lCSkiPwEibI7S9651RdqzLieudje87WMk7sXLONUbaDt5WKKD5ndG57nf2jXuCEJ4CDScji2ogWIUsnUGvHuAvXa+xuudNtq36Bf3xHhQbFAmaVNFCn2Bj9ZfIVmnUJHL8NCRZrNcg+w1k91pihoGdTGF0dpJrayO7fTu5HTuQOWe0L4FYURFDpSUMlZczVFJCtKAAVIFPZqhLSsoHdhLI5TCNLKOKzaCwieI8EwGEhUaJqhPWPIQ0D0WaF1VVEaoCmgKqghAKqCpCEXmf7QKhqKAoTjvxHtspCijvaKeO+YYXCE1HaKpTp2oIXdtd1lSEpuFpaHh/nxkXl3dwqNvW/hc4HSgVQnQC/ySl/I0Q4nrgKZxta3dIKd/tiPkAuK5fjyyJrMmqtmFW7hhm5Y4hNnXHxuOIhwK6E81s5jQW1IZYUBeiwvXc9m7MnCOisW4nfGdywPEzbmbhY193RqpGErIJZ5RrZnePeq1cPhnvyHPOdfd8PZd05rKHt0OizzFv7yvCl1AcsS2sdkbaoTpnS5g/5CxY8wQcIVZ1UD1IRcfOWo4f87SBncphpXPYySxWOoeVymIn01jJNHY8gRWPY8fj2NkMMptDZrPI7E7sXAsyk3F+aOSZk09j9IcjvDF7IQ+c/Uk6KkpRLMk3//dZznvpDgSSXft6vkKgV1XhaWrEc+KJtEci7LJM2oeHyeTF3efzUVtby6K6OsLrLIqHVFqmrqM9oxIbiIMKmtdLdfMcZs+cQ+2sOVQ1zUD3uZ9nl482h7Rt7QO7+e459Ou2bds2Yf04mukYTrHi7R7+9HYv6zqjWLZEUwTH1YVYNCXM/NpiFtSGqA373dW3eyKlM8fc8YZjwh7Y4pizo53kl4J8uPiKHdGOTIOKOVCzBOqXgn/vBWAyl8Po6cHo6sLo7nZSVxdGVze57i7Mvn6wDhz6UwQCqIWFqEWFKEXFKAVBFJ8f4fUQFzm2G4NsMwbpJkXKH0QPNeALz8EsqGdUL2JA1enXdXYW6UghCGRsLmtNcEVNhqYiBaHroGkIXXdGqLpTVnw+tOpqFI+H119/neeff55MJkNhYSFNTU1MmTKF2tpaQsVFtL65kl3PrGZ2eglrhp6lw2qhbs58amfPo6p5OuUNjajah+Po0rYNDDOKaUQxzRimGceyUlhWGstOY9tZbCvj5HYWKS1saSClhbTzubSQ0nRy9jy2qa+7hpKSU95X3471bWt33XVX6POf/3zjmjVrNi5cuDDT0tLiueCCC5q3bdu28fHHHy+85ZZbKp5//vnWie7nh8lhb1v7oHBH6O8Py5b8eXMf//NaG6+2DgEwt6aIL53WyLJpJSyaEiLgcb367oVtOauye9bBrtdgxwuOUxRw5p1LZ0D9MicKWHEtFNc4I+JgmbP4a+1dzmpxbwF4C52Rup43V4+PkL2OWTwXh/SoM7of28fdt9GZnwbnHk1nQe3xTlCSkqbxRWmmLenJGQzkDAaTJkPdnQy27yLW3U2it5/k6ChZTSOre8jpHrKeAFbjApi7DNvnx/Z6sVQdS1WxVA1LUbDEWHLcpJoSLCSmHEs2ppRYgKUo+4xcNobHkBSmbYpNOK8PTi0IsHRahFnnlhzSD0bLsnjrrbdYsWIFDQ0NnHnmmdTV1SGEIJtKsf7ZFax58g8kRoY5p/4LmF6TRV/7NBfMmoGifHDTQaYZJxpdQzbbRzS6FtNKkErtxDTjZLO9SHlo/kWE8KAoOkKoCKEhhIYiNBAqiqIBKkr+NYSCENohX/ujyH333RdZtGhR4u67744sXLjQXaN1ENzwqUcRI8kc96/q4O7X2ukaTVNV7OPGc6Zz8XE11EU+4vvBpXTM3kbKieplZiDRC+0rHa9mPW9BZtRpq3odUZ16KhTVgK/IibttZp0tWMl+Z7GYlQMru7e53Mwi8/luU/rusjBTe3dL9WKFZ2BM+QS5yhPJlp+A5askZ9vszOZY155lx7Zd7LQMdtkm/dLGfqcuigDUNCGqG9EtiW6DboNmgW5LFBuE5YQIU9ISRYKQoEiJIi2EtPDYzmIuRbI72Xsc24Cw8QhJoa5TpOsEdZWQrlHi1agr8jHp2R2kAAAgAElEQVSlJEBFWZCCiPd9BS3p6+vj4Ycfpq+vj/Lycj71qU9RUFCAbVmsevIPvP7w/WRTSernzueci76M/pJB6MImCuZUvb/PxD6Q0iKVaicWW0ci2UIquZ3R6GpMc/cCQE0rwuMpw+utoKBgBl5POR5vFVIUYlOASZCs5SWR1UmZKtG0QtbUyVkKKUOSzJoYlsS0bExbYtkS07YxLadsSenUW05+daiB0tIj9haPGaLRqLJq1aqCZ599tuXiiy9u+vd///f9CnosFlOuueaa+s2bN/styxLf+973uq+88srRW2+9teSxxx4L2bYtWlpa/F/5yld6c7mccv/995d4PB776aef3lZRUWHdcsstpb/97W/LDMMQDQ0N2YceemhnYWGhvXHjRu8VV1wx1bIscdZZZ0Vvv/32ilQqtfbxxx8v/Jd/+ZfqSCRitLS0+OfNm5d69NFHdyqKwssvvxz4xje+UZdKpZRwOGzee++9bVOmTDE+jGfmjtCPAtI5i18838qvXt5B1rRZNi3CP35iFmfPrkA71t1G5lL5RV47HbejsW5nTjs9DKnh3XkmyiGbyq0sDGxyUh6JQCoepNCxhY6Fjo2OJTUsqWPaGqatYloapqVhUYQl8+1kvh06GbuQpBUmaUdIWKVErSpSAxp9xSr9PSp9oVGGC+J0lmjYqqPcmikJJy0qRiyakjahlE0wbVKYMSm0ICDAqysIVSIVE0sxMJQcOZEhJ7KkSZEmSVImSMkEpmJgKjksxciXDSzFQNUFAV+AoN9PJBiipriaKeF6ppc3M7N0OrqmH/m/HzA4OMhLL73Ehg0b8Pv9XH755cyaNQtFUehpbeGZ23/OQPtOpi06nhM/dQUV05ro//lb2BGV4OKK/f8ZbUk0bTCUyDIQz5JIRxlN9pPNDmGaI1jmCNjDCHsYjX4K1G4KtH40xfluNW2NmBGhIz6HgUwlHfEGhjMRhjJhDFvFsiW2LclaNjlzbEFiLp/2jyJAVxU0RaAqAk1VnHzsWBEoikBXnPrUAcK8Tga+/+r361pHWo/oiKEp3JT60ck/OmDQl3vvvTd0+umnR+fPn58NhULWK6+8EigrK9unOeO73/1u1RlnnBF78MEH2wYHB9UlS5bMuuiii2IAW7du9a9bt25TOp1WZsyYMff73/9+1+bNmzddc801db/85S9LfvCDH/R/9rOfHfn7v//7QYAbbrih+tZbby393ve+13/99dfXffnLX+7/27/92+Gbb755L2cBmzdv9r/11ls7GhoajMWLF8985plnCk4//fTkDTfcUP/EE0+0VldXm7/61a/CN954Y82DDz7YdoQe3QFxR+iTnOe29PGDP2ykcyTNxcdV86XTG5lZeQzvB8/GHXP41qeg43XHEcqeQq0HIRB2VmcL4Zi4FRVnnXO+neZ3zNjlM6B8DlbVYqJRheEByfCgRXTYJjZsEh81yGYVbKlho2IJQdoryHoUZIGKFVQxfJDzCrJem5wmyeo2GdUmI2yyQpIRkpwQGIrAEAqmouRzFUOomIqCLXabir1WhoJclAXdXUxv62Dh29uZtWMXupllpFiytUHwdoPJqtocMc/+f9R7VS8hb4iwL+zk3jB1vhBhbyUhn3M8lhd7iwl5Q/i0D2/RmGmabNu2jXXr1tHS0oKqqpx44omcfPLJBINBpJS8/uiDvHLfXRSEI1z4je9SNncxsYzJ1pc7CHYl6D65gtfWdTGSGCSR7iaR7MG2+sEaQGOIgDpEsTdGoSdBgZ5EUyxKxjoggPzvk4zlI2FEGMpU0mYuIGbWEjWbSNl1aKqKqihoXkGlX1AjHLEdE19FCHRN4NdVfLqKT1Pw6SoBr0Yk4MHvUYkEPXg1BY+m4NdVAh7VXa9yBHjggQciX/3qV/sBLrvssuG777478o1vfKN/X21feOGFoqeeeip06623VgJks1nR2trqATjppJPi4XDYDofDdkFBgXX55ZePAsybNy+1fv36AMDq1av9P/jBD2ri8biaTCbV0047LQqwdu3agqeffroV4Nprrx364Q9/WDt2z3nz5iUbGxsNgDlz5qS2b9/uiUQi5rZt2/xnnnnmdHAcGpWVlX0oo3NwR+iTlq7RNP/82Eae3tRHU3kB9y1fxrJpJQc/8WhESmh7Gf7yc9j+HNgGeIug4WRoPgcQjtAn+5y91yNtjIm3FSjDrlqIXjnXWUxWOY+4NoW2tjhtbVF2vRWn888pEipkPArZgI4V9mDWqmQCKgmPIKZKhm2T5D59Je2rvzZCZhEyi4qJKnOoGCjSQMVEw8Bnm/hyOYKZLOF4nGk9ncxr2c70rb0UjzojvKxfpXNGhDUXltAzu5xshSO6NaqXRtWHV3NEe0yw9xRovzb5FjmmUina2tpoaWmhpaWFTCZDMBjkpJNO4sQTT3TM67ZkXdsQL/32F+S2vEF/2Uyeql3Moy+uouyNJ6gIDHCeL444foQ+MUxoOEqxaoIHJwFSCnIyhCVKQW1A1yP4vBG8njBFwXKCvlIKA6X4faV4PKWoqrv6/XA42Ej6g6C3t1dduXJl0datW/3XX389lmUJIYT8+te/vk9Bl1Ly0EMPtS5YsCC7Z/0rr7wS3DOMqqIo+Hw+OVY2TVMALF++fOpDDz3UeuKJJ6ZvvfXWkhdffLHwYH30er17hmfFNE0hpRRNTU3pt956a8v7fe+Hg7tyapJhWDZ3vLKT/3h2GxLJt8+dyTUfm4pHO0ZN60Pb4Q/Xw66/QLAU5lzi7IXOjEL7a9CygqhawJqiWbxdtpTtzRfQ7a9kQCtmAA/DpkQFSlFI9dmk+jMYqhNykzAQVoG9/zdVIKhYqDKGZQyTS/eDFSVgxYjoGlX+QmoCIaoDESIefz4FKPEGiXiDRDyFBAwF+voxevswB+KYAwMYPd0YnV35Vedd43usAcdtaEMDvo+dg2/2bPzHHYd//jyO+5BWah8ppJTEYjEGBwcZGhpicHCQgYEBBgYGSCSc0KA+n4/p06czf/58pk6diqqqZE2Lu15r47+e28bC1seYG9hEwV95mF77HOdoj+xxAx0tHUb6KqktPh6fr5LigmoKA9X4fZV4vRV4PGUoygczPeAyObj77rvDl1566dDvfve79rG6448/fkZbW9s+fU6fccYZsVtuuaXizjvv3KUoCq+++qr/5JNPTu+r7b5IpVJKfX29kc1mxX333RepqqoyAI477rjEnXfeGb7uuutG7rjjjsjBrjN//vzM8PCw9uyzzwbPOuusZDabFRs2bPAuWbLkoFHgjgSuyX0S8cbOYf7x0Q1s7Utw1qwKfnjR7GM3+Iltwyu3wIs3O8ehKc6WsQ0PADAQmc0jM7/CHwuOY7UsxM6PnkuEQpklCMYkM+IGynCOUb+CBOoUhUiBh9JiH2UlfsIhlZQ5wEiynf54K53RTXSMbsa04gig0FPI/LL5zK6azZKKk5lXNo9Cz27xN/r7yW7bRq6tDaOre1yoB7q6sEZG3vWW1OJi9NpavNOnU3DGGei1NXhqatBra9Grq1H8R5/XPcuy6OjooLW1lY6ODnp7e8lmdw+CPB4PZWVlNDU1UVZWRk1NDXV1daiqM81g25LH13fz/z25ha6RFJ+xn2LRsleINMdQ1SAlJadTXLyQYGAaft80ov/Vg1BVKr66yHHW4vKR5MEHHyz51re+1bNn3cUXXzzy4x//eJ8rJG+66abu5cuX18+cOXO2lFLU1tZm38t2tu985zvdS5cunVVTU5ObNWtWKpFIqAC33XZbx2c/+9mpt956a+U555wzWlBQcMAFDz6fT953333bb7jhhvp4PK5aliW+9KUv9X1Ygj6h+9DH+KiHT+2PZ7hpxRYeXtNFTcjPDy+aw9mz978Y6KgjMeC4Ku3bCIMt0LPB8Qk+tl1H80HlfJhyIv2RRdxszeCBZJYcUGLAom6Tso40tX0G3vwp/iIPkaoABdUaarlBriTKiD5AV7KTtmgbbbE22mPtyLxpPuwNM7tkNs3hZqYVT2Ne6TymhaahiN2Wj9yuXSReeYX0mrWk16zB6N69qFZ4POg1NXun6mr0qkq0sjK00lKUwLHx4ysajdLa2sq2bdvYsWMHuVwORVGorq6msrKSiooKSktLKSkpobCwcL+m/55omn94eAMvtAxQFlT5h4bfUlixGkVVaWhYzpQpy9G03T+gkqv6GHloKyWfm4V/jrvs+1A41vehTzTxeFwJBoO2oijcfvvt4fvvvz/y5z//eftE9mnS7kP/qNMfz/Drl3dyz8p2DMvmy6c3cv2ZTUfvHvJswhHs/i3Qvynvc3yjsw1sDD3obCkTAjn7UoYav0T3aCXrd0V5pDfLG6gYWpbmrhxnbEwzRVfwhi2M6iSJ5iF6PZ10qTvpMnbRn+rHzJiwC8bcknkUD/VF9TSHmzl/6vnMKpnFzMhMKgIV+xQeo7ub2Io/EVuxgszbbwOglZXhX7SI8FWfwzdzFp5pU9FKSx03n8cYYyb0rq4u2tvb2bFjBwMDAwAUFRUxb948mpqamDp1Kr5D8MQ2msrxausQD63u4PmWAXy6wo1n19Ic+we04haU7FyWnfKf+P01e/fDtIn9uR29pgDf7GN0rYjLUcerr74a+OpXv1ovpaSoqMi688472ya6TwfiKFWOoxfblqzcOcTvV3fxx/XdmJbNhQuq+erHm5lWVjDR3XtvZOOw7j5ofRb6NkF0D2efqtcJDtJ8jrNYzTaRa+5CDG0jXnwCq9Svs/21IG1r0qxq6mF9gwdb0ZiXSjNjZAPDrOSJmevJsdu8q+ZUKvVKqrxVLIosojJYSUWggvJAORWBCkr9pZQFyvYade8Lc2CA2J+eIrZiBek1awDwzZ1L+be+ReFZH0fPOzo5mrAsi1wut1cyDGOfdZlMhtHRUYaGhhgaGsIwnEW4mqZRV1fHwoULx03o+3sOli0ZiGeJZwzah1Ks74rS0hvjuS39GJakJOjhsyfU85kFNj1b/g+iaBQ1fianXXT7Pq+ZXNWHNZIl9Mmmo+7Zuxy7nHvuuYmWlpZNB285OXAF/UOifSjJ79d08fvVnXSNpinwanxqcS3LT5lGQ2lworv33kgOwl9ug1V3ON7PIo1QtxQWXQXlM6FsJoSnIoH4W8/Dy/9O0ehfiNoVPGF9l6etk4mW+dg1U6dbd0zikcyr2CN/osdoI+eLML9qPleGPktDUQO1hbXUFNRQHihHUw79IyulxBwYILd9O9nW7WRbW8m2tJBevx5sG++MGZR97WsUnX8envr6D+hh7RvbtkmlUuPpUMX4nXVj9dZB3L3uiaIohEIhSkpKaGhoIBKJjJvTtX0s0pNSsrE7xur2ETb3xNjcE6OlLz4eYhecvdelBV4+t6yBkxpL+FhzCdvX/g9dLT9BYlGQvJaTLvrOPsVaGjbx53bhmVKEb/p7j3Xu4uLi4C6K+wCxbcnTm3q545U23mgbRgj4WFMp3zp3BufMrsTvOQojm629B5640TGbz74YTvyKI+Z5LMtkx8rnyK25mdqRZym2hxlVivi/9V/iP6dcQlZxXJz6hIHH7CA48iKFuc2cWj6N04+/muPKjmNq8dT3NEqTUmL2D5Bt3eaI97ZWstu3k92+HTu62wOYUlyMt7GR0i9+kaLzz8N7BD53UkosyxqPx72/lM1mGRgYoK+vj76+PkZGRg4aq1sIgcfjeVcKBAKEQiE8Hg+6rqPkQ3IqUqIIgSoEisAJITrm2EQozn5rVRnfnCelREobGR9heMswQ5s3IG0bKW0s26Z9MMGmriit/XHiaQOBxK8rNBZ4WFboIRzR8aqCAo9CaYEHVYA92k786U5WvPY0RVN7sXI+mup+QvOiT+z3fSZe78GK5Qh/eoY7OndxOQzcfegfADnT5tG3uvjvF7ezYyBJfSTAt86dwSULa6gqPvpWOgNOhLAHvwBbHndcpp5/C5RNpzdr8EZ3L30tz1Pd+hTH979IszFMRnh4LrKUP5afSXvNGdSGwlyQ28mG7kcZiL6JzxriuPLjOH3G6Vza/IO9VpfvDyklZl9ffrS9t3jb8fh4OzUUwtvURNF55+JtbMLb3IS3sRG1tPSggiGlJJFIMDo6uldKJpOk02kymQzpdJpsNjsu5O+FSCRCRUUFc+bMoaCggGAwSCAQ2Kdwq+reDkqklPRt30bbujV0b9vEYFcHscEBpH0IYVXfJwFg/jsr80sikkBSCEaEoFMoCEVQWJug9tRO/N4cQXEup553M7pn/xYoO2cRf6EDb2MxvsZ3x0w/lrHicTKbNuOZ2oBeXj7R3XE5BnBN7keQZNbkf9/Yxa9f3klvLMPsqiJ+fsVCzptbhXq0b8FZe7cj5sFyYpfdza8GMryy9XVObbmbL3Q/StiMkxI+1vqX8pfGs/Evu5jp5RXc4vWwqucVbnrjH+mIdzArMovrlyznnCnnEPLt+wtcSonZ0+OMssfEuzU/4s7vdQZQIxG8jY0UX3gBnsbGcfFWI5FDGunlcjl6e3vHU39/PwMDA3ttywLw+/0UFhbi8/kIhUJUVVWNj441Tdsrqar6rjpN09B1nZKSEjyefW6jPSDSttn25musfPh+Btp2AFBSW09l0wxmnnwawVAYb7AAbyCApntRdQ1V11E1HUXJx+xGIBSBEMKJ9S0EEmgfSrOxN8YbO0fY2p+gbTiNRDC/NsTfnDCFRVMiVIcD4+c511Cc5yvE+HPOZvvo6Lyb9vZfUlAwnVkzb6KoaN5B31v8uQ7shEHR5xre83OZ7EgpsaNRjO5ucl1dmPnc6O4mt62VXLuzxbryR/9C+PLLJ7i3LscCR1zQhRAK8COgCFglpfyfI32PyYZtSx5a08lNK7YwnMyxbFqE//up+ZzafPAR4VFB7wZ45ocMl87lP8++h1+vauPKjoe4p+03+K00m+xlrMydQ83HL+KE06ai5acS+lP9/OPLN/FM+zM0Fjfy09N/ypn1Z44vWpO27Qh3a6sj3Nudee5cayt2aneQE7WkBG9TE8UXXYSnqRFvUxPepia0yEH9PDj3kZJoNEpPT8+4ybuvr4/h4eHxNn6/n4qKCubNm0dZWRnhcJhQKDRu2p4odr61mpfv/S0Du9oIV9dy1rVfYcaJp+Ar2P8CSiklacMimjYYSZtE0wbRtEEsbRBN58bnwDtH0gwnHec3hT6N4+rK+PSp5Vx0XA3hgH5In91Uqp3+/idpa/9vLCtBRcWFzJr5Y1T14Fv4cp1x4i91EFhcgXfK0eXO2M5kMAeHsIYGMQcHMQeHMAcHnHJP73h4WzuZ3Os8JRBAr6nGO72Z4ks+iW/OHPzz32UDcQECgcDCVCq1dqL7cTRxSIIuhLgDuADol1LO3aP+XOBnOM63fi2lvAm4GKgBhoHOI97jSUbHcIpvPPAWb7aNsHhKmF9dtYTFU46hhT2DrRj3XM7P66/k1tq/JrRzGw9s/ylLB/7CYOAkHtv1aUoWHs9ZV85E9zpCHs/Fub/lfn694deYtslXF32VK6suwty8lZFn7tgt3tu3I/cU7rJSvI1NFF9yybiZ3NPUhBY+9OeZy+XGPZj19PTQ09NDb28v6fRup1FjZu958+ZRVVVFVVUVRUVFk+bHl2HZ9HR08urv7qB73Sp8JRU0XHYdWvNiNuZsVq7uI5ruJJYxiOYFOzYu2AaxjIFh7X9+PhTQmVtdzMlNQZY2hFlQF2JOdfE+rUi2nSWXGyKXGyKb7SWd6SST6SaT7iCZ2kEq5WzJjYQ/xowZPyQQmHpI71EaNiMPbUUJeghdMO09PyMpJWZvL0ZPD+bgINboKDKbQ+ZySGMsN5GmiTQMpGkgTROMseP8a6aBNAyn3jzIa3u8zn6mWtTiYrSqKvS6OgLLljm+Cmqqx/0WqKHQpPmcHY2YprnPhZsuDof6ZO4Efg7cNVYhhFCBXwBn4wj3m0KIx4AZwGtSyl8KIR4C/nxEezyJWLljiC/fuwbDsrn5svl8anEtytFuWt+T1DAvPPoD/mHOv7PTW8V3Bp/i71puQ5EWLSXf4NmNH2PJ+VNZeqGziM2yLZ5uf5qb37wZu2+AqwYbOau/HHHH79jZfcv4ZbWyMrzNTYQuuwxvYyPe5iY806YdsnCbpkk0GmV4eHhcvMe2YMVisfF2qqpSXl7OrFmzqKyspKqqioqKivc94rZsSSJjksyZpHImqZxFKmeRzlmkDYucaZPLR+bKmTbGWNnad30ia5HMmiTyKZ4xkYlhZg+tZW58EzaCVeETWFu4AHuNCmvWOe9LmAQ9OSIBSdgvKPZJ6ouguFxS6JUUeKHAaxPUbQIeG79m4dctfJqFRzXxqBZS5pB2DtvOYcezbNmcw5Y5bDuLacTIGYPkckOYZuxdz0FR/Ph8NQQCU6mu/mvKy87F7699V7sDMfrkDozeFCVfmIPiP7SvIWkYxJ9/ntgfHyf11lqsgYP4PdF1hKYhxvKxpOugawht79eVgB+0QoTu2bu9Rwdt7/ZKwI9WWopaWopWUopWVooWiSAm0JpzrPL4448X/uhHP6oqLy83Nm3aFNi+ffvGs846q7Gnp8eTzWaVL37xi3033njjIDij+muuuab/6aefLvb5fPbjjz/eWldX95EJOH9I/0lSypeEEA3vqF4KtEopdwAIIe7DGZ13sDu+4OSOC3gYPLq2ixsfXMeUkgC/umrJ0beH/CBsiCb57spXebPxW8yWUdZ23kTVjj9Bwym8Hf4OL67IseT8Bk64yBldbRzayPdf+h6Fq7fxzbV+pm63gK0oNUn8xy3E99nP4pszB9/MmaihAy9+sm2beDzO6OgoIyMj78r3FG1wfIePbcEqKSkZ92JWWlp60F/zti0ZSGTpHEnTH8vQm0/9sSxDyRyjqRwjqRyjKYN45v19LwgBHtWJxuXVFPR8OeDRCPkMpoVilNJNyeAq/OY2tGkm3poyArVlLPG0oPIGQsbBTiLtJFJmD35TG8g6yQIS+bS7TxqK4kFRvCjCyYXiQVE8aFohBQWz8Hic4CYevQSPpxSvtxyfrxZdDx/WKDO9cZDkaz0UfKwG/8xDmzaJP/c8ff/6rxjd3WgVFRScdDK+BfPx1NWjlZaghsMIrxehe1A8uiPmk3wkbNgG8Vwcn+ojoE9eL4Pd3/1eXXbbtiPaQW9zc6r6x/92yEFf1q9fH1y7du3GmTNn5gDuvffetoqKCiuRSIiFCxfOvvLKK0cqKyutdDqtnHjiiYnbbrut64tf/GLtbbfdVnbzzTf3HOz6xwqHY7uowRHvMTqBE3BM8LcJIU4BXtrfyUKI5cBygPoPeQ/w4fLMpj7+/sF1LG2I8MurFlPkO3YCRbwwHOP2Xb08N5KilCD/bKzmmq23ow1tg3P+lY7wZ3jp1vU0Lipn6YVTiWaj3PLGTxj8/9k77zgrqvP/v2fm9rq977LsLrv0FcRFxYYVFUtsXyWa2DWWxN/XaIpJTDQmJsYkkthiNAa/tkiMBWvEAqKIIG0pW4DtvdxeZ+b8/ri7S4dFOt63r3nNmZlzzpy5svOZ55Tnmfcfbl8kkdOjY8hxknrHDTjPOB1TSckuX6xCCLq6umhtbaWtrY22tjY6Ozu3W1ftdDpJTU2luLiY1NTUoS0jIwObzbbbe3T6oqzv8FHfFaChN0hTX5iW/hAt/eEtYl0nMCoSWU4LGQ4TqTYTJRl2Umwm3FYjLqsRh1nBajJgMyrYzAo2kwGrUcFkkDHIYFQ0jFIUhAc0D7rWTzTWQzTaObB1EI12EIl0oGmbZ+ezhfM0WfZjMnkwGlMxGtMxGksxGJwYFAeKwYGi2FBkC7JsQpKMCWEeEGRJMiIrm4V68PzWeQ7OkslYe5C+uXUY8x24ZxQPq0z/y/+i45e/xFxeTsFjj+E4+SQk5eu3P67FCakhgvEgoXgITWhoQktMYhM6OvpQWhPa0F7TtaG8g2lVV4eux7QYYTVMSA0Rioe22ofjm88H4gH8MT8hNTHc9MAJD3B+6flf+3m+CUycODE4KOYAv/vd77LfeuutFICOjg7jmjVrLDk5OUGj0Sguv/xyL8DRRx8d/OCDDw6vyRl7yd4I+o7eoEIIEQKu211hIcTfJElqB84zmUxH70U7Diifb+jl1he+Yny+m6e+OwWH+fAfzwlrOn9v6ebplh46YnHStSCXd33CvZY2UmvfAEmGq17Fn3Is7//mS1Jy7Jz6ndHU9dfx+N9u5Mx3uijuFBjKy8j60U24ZpyV6NbcCZqmbRVm0zuwVtxsNpObm0tVVRVpaWlbTUzb0tIWQqDrEVQtiKb2EAgEUdUg/nCAvmCI/mCI3kCIDm+ADm+ALl+IqBpDkTQMsobNCKPsMseky7itMm4LOMwSNqPAahSYFIFARegqulARQkWIOELX0EU8cRxXEVEV3R8npEXw62E0LYyuhxFiJx1TQgLdgRYxE/XJhPpMxPyZiLiNwjHTGDvtHNzpIzAaU1GUw3R5404QQhBa0Y3ntXpks0L6rNFIw4gg6J8/n45778V+8kkUPPII8jDcz25Ja6CV+Y3zWdG9ggZfA82+ZiLa/o+TYTPYsBltW+1dZhc59hxsBhtOkxOX2YXL5GJ8xvjdV3gQ2RNLen9hs9mGvrrnzZvn/OSTT5xLly5d73Q69aqqqopwOCwDGAwGIQ+4aDYYDEPhUb8p7I0atQCFWxwXAG07ybtDDrd16NWtXm6Ys5QRaTaevfqYw17MmyMxHtjQxlvdXuJCcKLLxI29H3Pd8l9jrvwfWPliwn3r5c+j2gt59+Gv0FWds28az/vz/0pk9t+4sVEgcrPIe+guXOees0t/5+3t7Xz22WfU1tYSjUYxGAyUlpZy0kknMWLECNLS0pC3KR8M1tPQ+Dy9nmrC4VbUeAcIHxK7dsqSCqRaYYwVyNl5PklSkHQDUsRILGpAlQ1I0uZNlo1bHCvoGmgxDTWmDuw11KiCGrERj3Ies7gAACAASURBVJiJhx3EwhpClVEjCmpYGdgbiIcSdaTk5JGeX0BeXgFZU0opmXwMRvORGbNbCEG8NYD/o2bCa3oxjXCRNms0Brd5t2WjGzfSdvePsEyYQMHs2cjm3ZcZJBQP8cAXDzBv4zx0oZPvyKcspYxjc4/FbXJjN9qxG+1YDVaMcqJ7XpbkzRvyVucMsgFFUhKbrGyXNsgGDLIBm8GGxWDZrfvhJF8fj8ejuN1uzel06suXL7esXLnyMHO1uf/YG0X6EhglSdJIoBW4HJi1JxUcTp7i/JE4Nz23DLfVyHPXTSXVfvhOfqkLRvj1xjbe60mMRV+ek8a37HFO/s/FiRCmpSfDiv+Dwqlw5b/B7OSjf6yhq8nPqTOcVN99OaVL6gk7jDjuvpWCK6/Z5WSg3t5ePvroI6qrqzGbzYwdO5aKigpKSkqI6BKd3gjr+iN0NLTS4U2MYcfDazjK/TeyrY3oQqIjmEVPOJ3eyHgCMQeqsGAyOrCaHNitLlxWJyl2F2l2G+lOG5lOO1kuO8pWgrytQBt221Xf29xIw8qvaFqzio76WsL+zeP3smLC5k7BnpKK1eXG6nCS4nJiyXNgcbiwOhxYHM6BLZE22+3I8mHoIXCYCFUn3hUi3hogutFLdIMHzRdDMsq4zhqB8+TCYYVFFULQ/tN7kMxmCmY/skdi3h/p5+YPbmZ933quGnMVV4y5gnxH/u4LJjksuPjii71/+9vfMsvLy8eWlpZGKisrg7sv9c1guMvWXgROATIkSWoB7hVCPC1J0m3AeySWrT0jhFiz31p6kLl/3lravWHmfu94ctyHpzXlUzUebujgyeZunIrMd/PSuSovnfEdi+CV70MsAGPPh+p/w7iL4MLHwGhl08puar/oZFR8BfzoKaxmWDGznIt+/ixm985npnf29PPfDz9iw7rVICuYCsbRYy/mpU6Nttp2On2btvIHDmBWonx7zJucnLuIiOZmfegGdPMpZOTkcZTbQo4rsaUMc530nqLrGh31dWxYupi6JZ/R357odErNK6B0ylSyRpaSUVBESk4ejtS0IzIC254g4jrRTV4iNX1EN3qJd4VgYMmc7DBiLnFjLk3BNjFz2LPZAQIffUx4xQpy7vsVxtwdhsDeIZ6Ih2vfu5ZmfzOzp8/m5MKT9/iZkhwaDK5Bnzlzpn/mzJlDk02sVqtYsGBB3a7KAFxzzTX911xzTf/+b+mhw3BnuV+xk/NvA29/3ZsfLl3uH9d08a+lLdxySimTiw7PNeb/7fHyg/VN9Mc1jk9x8LvyAkbFu2D+XQlrPKMcMisSYj71Zjjrt8S7e+h+7WXmf5mKPeonbcNLzDlVpvjKG7jxuB8gSRLeUJw17V42dgfZ2B2ktT9AsLsVm7+FHNGHAGq0TFareUQ3GMl2+chLsTIh380ZY7LJcpnJHhDpNFML3c0/JBxuID//CkpL7sRodO/33ybk89K48is2Ll9Kw6rlRPw+JFmmcNxEjj73QkomV+FMT8bnHkSPaoRWdhFZ20d0gwcR18EgYS524zwxH2OuA2OeHUOG9Wt/dHleeQVDbi4p3/rW8NsldH7y6U9o9DXy+OmPMzV36te6d5IkhyvJ4Cy7IRLXuPeNNZRk2vnB6aMOdnP2mOZIjO+va+RzT5BUg8Kbk0cxxW2HVf+C128DPQ5TvwetS2HjR8TGfQ/PumwCf7uEyLp1VI+7gVhGAcYxn3L99BD/M+5Kjs+/mt+9W8Oi+h6q27wIAS4pwmhjL2VKD7kihjCYseWMpnjMRGbmZpGfaiXbZcGobG/R6nqMxsYn2VT3GEajm0mT5pCWetx++00iwQAta6tpXrOKpjWr6GlqAMDqclMyaQojjzqaEZWTsTp271/+m0S8M0hgcTuhr7oQUQ0l3YJtSjaWijTMJW7kfRRsSAhBaNkyXGeducvJldvy/Lrn+bT1U3429WdJMU/yjSQZnGU3vLa8lcbeEP+8tgqz4fAZ+wyoGo81d/HHhk5MksSvR+VzeU4aDjT48New4CEoPhF16o/gze+jBBtpW56H76XXQVGwTZpE77fvo7s1Hfm4bv7Au5xZcBkffXYcT3YuwiBLHF3g5ObxMiZPE97udiRJYtSoUUyePJlRo0ahDGNpUXf3+9TV/5ZwuInsrJmUl/8ck2nvreFYJEzI4yHo9eDv6aKnuYme5kZ6mxvxdHWAEBhMZvJHj2X08ScxYuIkskeWfuO70LdFqDrhNT0EPm8n1uADRcI2MRP7cbmYCp37ZdhD7exE9/kwjxkz7DK+mI8nVj7BtLxpXFZx2T5vU5IkhwNJC303vLCkifJsByeNOny6XJd4AnxvbSOt0ThnZ7i5fUQWk112RH8zkaeuJby6mqg0HnN7A67a85CNgvbqIqTx55N/y0nYjz+OjbVhVj+9Brk0wGPi15TZTubNj6aQ7dT4xSlZ2APN1NcsJ9wVx5aezumnn05lZSVO5/CsWr9/DY1Nf6ez8w0cjtFUTvw7GRnTd1suGgrh6WzH19VJ0NNP0Osh5O0n5PUQ9AzsvR7UbQKsSLJMam4+WSNLGXvyqRSOmUDOqAoMe2ABflPQ/DEiNf1EavuI1HkQYRUlzYL77JHYpmSj2PfvbxatSwyP7kl42+fWPocv5uOOo+845B3KJEmyv0ha6LtgdYuXVS1efnne2MPiJdEciXFPbQvv9/pIMyr8OS+Fc5rqiLz/Ko2fzSdcswGzU8VdLJFVtgZJFsSsE+HEO8m99/wh67R6QSufvFgD2SGeSv8VE1NOZdHi0zh/pMIYUU/T4nZMJhPjx49n0qRJFBYWDiMsqYbH+xWe/sX09S3C4/0SWTZTXHwrI4tvQ5a3niWvxuM0Va+gc0M9no42+jvb8XZ2EPJ6tq5YkrA6XdjdKdjcKeSVj8E2kLanpGJzp+BISyc1Nz8p3jtAj2nE2wLEmgPEWvzEW/yovYl12rLTiHVsOrbKTMxlKcOanb4viNbVA2AeNbwhrlA8xIvrX2R64XRGp43en01LkuSQJmmh74K5y5oxG2S+NXnP/FQfaIQQ/KG+hb+09BBD4rINa7n+5X9ibG6i3ahjy4qTOiJIwQUqilFFyAYYdynSyXdjztj80vT1hFk0t56NK7rx57bzcuHDzBg1gwULT+bclGbS2luIuN3MnDmTCRMmYN7NUiIhNPr7F9Pd/V+6ut8lFusGJOz2MsrKfkJe7mUYjVs7cuptbWbZvP9Q8/lCYgMBVZzpmaTk5FI6ZSop2bmk5uThysrGkZqG1elC3guvYd8UhBBo3ihqV5h4Z5B4R4hYix+1K8Tgkn7FbcJY4MR2TA6W8lSMufaD8iEbra9P+Egfpm///9T/B2/Uy7Xjr93PLUuS5NAmaaHvBF0XvFPdwfSKLNzWQ8+yU/v66Ph0Ec92eXg3K4UMrY+71y/i1LrPyTeFMU81YTwxhKIPWLSyAcpOh4pzkMacB7Y04jGN3k1eepoDtG/wUL+0C03SWD7ifdYWfsodk35AT0sVFeH3yFQ8HH/88Zx66qm79Y8eDjfR1v5vOjpeJxJpRpYtpKefQlbWDDLST8Fg2L5b3tvVycdznqL+y8UYjCZGn3Ay5VOnUTBuAkbT8NcgJwHVGyXeGiDeFULtCg3sw4jYZg92ssOIKd+BdXwGpgIHpgInivPQ8K0Qra8fdne7qqvMWTOHSVmTOCrrqP3csiQHksHwqTU1NaaPPvrIcfPNN/ftKn9NTY1p5syZo+rq6tYsWLDA9swzz6Q/++yzB93L3YHk8HZ1th/5qqmfLn+Usyfsws3YvkYIUKMQC0K4H4LdQ5sIdKM116C21hLxdeGzStjlCD9VffysZQuvaUWAPROIQdADllQ4+W7CFd+mu0OlpzlAz4vt9DTX4ukMIQaKxg1RajO+5Kv8/3JCxVT+M+VVvH4bv3rpOcoVD2effTZTp+565nB//xJa216kq+tthNBJTamirOxuMtKn79SVqa5rrHz/bT59KRHI79iLr2DSjJnYXPt/udqRghaIEd3kI7rRQ7Teg9q9OVSs7DJhzLJhn5KNIcuGMcuKIdu+38fBvy5CCGL19bgvumhY+T9o/IC2YBs/qvrRfm5ZkoNFXV2d+eWXX07bnaBvyUknnRQ66aSTQrvPeWSR7HLfCR/VdGGQJaaPzto3FQoBgU7obxjYGhN7b3NCtEO9CRHXdxzRSwIiWOgyp9OZlYPX4CbFlk5eTjFFOSPBXQjOPNSOGgLzn6KnS6cn+yf0KJPoeS1E0Ltsc1McMXzObjYVrqPD2oDH3kFZ4QimFUzjJyNeoNCV8Oj7+7+/QLnSzTHHHr9LMY9EO6ir/TVd3e+gKA4KCq6iqOh6LOZdfwzFImHm/elBNq1YxoiJkzjjhttwZ2Xv4Q/7zULoArUrRLTRR6zRR6zJj9qTEHDJKGMa6cZelYNphAtjlg3Zcnh9s6ttbeih0LAsdCEE/1zzT4qcRZxSeMr+b1ySg8I999yTv3HjRsvo0aPHXnHFFT2XX365Z9asWSMH/bc/8sgjTWecccZW3uLmzZvnfPjhh7M/+uijep/PJ1933XVF69ats2qaJt1zzz1tV155pWfHdzu8SXa574SVzV4qcpxfP5JasAc2fIRe/1/01uXI3iZkdXNQCIFEyJaF156Pz1yA1zEer8GJV1jwRAU9qpFmQyp16YV0WtLpM7oBhaqYTLlqZHJAQgnrVDdpLF8fJtzVTcBfT1R3AXcmbuIVxN0t9DnaaEyrocPaQK+tDc0co8RdwqSsSczKu4Gq3Crsxq3dIb85/1Nc/bWYsko456wzdvqYvb0LqV5zB7oeorTkTgoLr0VRdu9Jr2VtNR/NeYruhk2cdt0tVJ5x9mEx8fBAI1SdWJM/YX0PCLiIJrrOZbsR0wgX9mNyMI10YcpzDCvgyaFMrLERANPIkbvN+0rtK1T3VvPL436Z9J2+H5k/Z11hX2tgn4ZPTct3hE77zphhdYc/8MADrYPiDOD3++WFCxfW2mw2sXr1avMVV1xRUl1dvW5n5X/605/mTp8+3ffKK6809PT0KFOmTBlz/vnn+1wul76zMocrh9fn+wFC1wUrWzycV5m327wRTac2FGFlb4Dulk2U1r/C2J6FlAZqkBF4DC6+cE+kIWcSjZZcmiy5NFrzaLFkE5UHxoaFwKSCSRWbt7jAGhNktGqUeTUyvUEy/BoGAQazQkDRicpRhOYlpnmIGP14MmN0WIO0K+302drpt3XgtrkoTy1nQmoFl6SdSnlqOSXuEozKzj9UamprWbrwAzpFCr+56rKdCm1T8z+oq3sAh72cCRMexWbb/Uu4Zf0aPn/leZqqV2Fzp3D+D++hbErSCcggQhfE24NE6vqJ1vUTbfSDqoMExmw7tqMyMRW5MI1wYUi3HHEfQfG2hKtdY/6u//Y2ejby0JcPMS1vGt8aNXxvckkOf2KxmHTdddeNWLt2rVWWZRobG3c5yebjjz92vffeeymzZ8/OAYhGo1J9fb1p8uTJ+z/s3gEmKeg7oKE3iD+iUlmw/Tju/F4fv9nYhqKDLxSnSWhkxnq4q+EZbu98F0XorLSN5ZnM77LOVkW/qZxUYcSNRIYsM0KTmRFRcMRlrOEQxnXVsHIFkrcPg1nBMb4U68QitEyZqOYh7OjBk9VLX7yTnmg7rfFeukSEbkWmU1GIDCw1UySZke4SylPLOSd1GhVpFVSkVpBhzdijl35/fz8v/Wsu/bqV6edeQJpzx9Z2Y9Pfqa//LZmZZzFu7B9QlF1/wLfVruOzV16gcdVybO4UTvnO9Uw8fcYRG2VsT1D7IkTrPUQ2JMbA9WAcAGOOHcexuQl/6MUuZNuhOe69L4m3tYEsY8ze+dBLIBbgrgV3YTVYuX/a/UnrfD8zXEv6QPHAAw9kZ2Vlxf/9739v0nUdq9W6y/DbQgjmzp1bX1lZGd1VviOBpKDvgJUtieGViQUpQ+eius4taxt5qzsRu9uoCio6/DzSM5cLfC+joOEvmYVy/O1U5mQyKeqBeBitbxOenvX096ynX4/SG/EQX9dDypd+0jdoCEmwqVjwxTHwRZlMt/lzVCTo3r5dJgmyLCayjGmMsWVzclo5ZdlHUZE+htKUUszK3s0Gj8fjPP/iS0TjGpHC47msqni7PELobNz4RxoaHycr6xzGjf0Tsrzzf0Zttev4fO6LNKz8CqvLzUlXXstRZ5yDcQ/jWh9J6DGN6AbPgPOWfrS+wXXfJizlqZjLU7GUpRwys84PJPHWNgxZWTt1+eqL+bh9/u1s9GzkL6f9hUxb5gFuYZIDjdvt1gKBwNDaVK/XqxQUFMQUReGvf/1ruqZpuyrO9OnTfQ8//HD2s88+2yTLMosWLbJOmzYtvMtChynJSXE7YGWzF6tRYVSWA4DemMptaxr4yBNgam2EU9ZHqZyocKLnTozeWsKuPJZazKz3zafpnbdpMSj0KDJ9ioJHlhGShKIJpq8SnL1Up6wHvDaYN01h5RQrcoqdVKOD40wuUs2puK1puKzpOG2ZOO3ZpDsLyXbk4DK59lsXqxCCt956i56uTj7Xy/nnRVXb3UsIQU3tL2ltfZ683MuoqLhvp2Lesq6az//9Ek2rV2B1ujhx1tVMOmvmN1LIhS6ItwUGutETY+FoAskoYy5NwTktD3NZCoYs2xHXhb6nxNvaMObtuLu9PdDOLfNvocHXwIMnPcgJ+Scc4NYlORhUVVWFDQaDqKioGDtr1qyeO+64o+viiy8ufe2111JPOOEEv9Vq3eVY+IMPPth24403Fo0ePXqsEEIqKCiIDo7HH2lIQojd59rPTJkyRSxduvRgN2OICx9dhFGReOXm4+mMxrngy1qaozHOWh7iGpuXybYXoektzLrGLzPSeN3pGCqbJpspMKWQZU4hbUCgR7TbKPrnJ5iau6G8BOe3Lyfr/IswWe27aMWBZcmSJbz99tusUPOYfsr0HQaiaWh4jA0bH2ZE0Y2Ult69Q8FvXrOaxf9+kea1q7G5UzjmvIuo/AZY5EII9GAcrT+K2h9J7HvDidjgnSFEOLF6wZhrxzwqFcuoFMzFbiRjsrt4SzacNQPLuLHk//GPW52v6avhlg9uIaSGeGT6I1TlVh2kFh5aSJK0TAgxZV/Xu3LlyobKysqefV1vkr1n5cqVGZWVlcU7upbsct+GSFxjTZuXa6eNJKhqXLi4hrZYnBuWhbhe+SMfRRZT0hfGLXR+mFeAvfQ0fp57LBWZ4xjpHonLtNnzWXTjJrp+/3sCH7+NsaCA7McewzH9lEPOCtuwYQPvvPMOLbqbzFGTuHV66VbXhRBsavgLmzY9QnbWTEpL79ruGdpq1/Hpi3NoXrsae0oqp3znBiaeftYRNUauRzW0/ghqX2LT+iKoA8dafwQR29pQkG0GDFk2bBMyhuKCfxO70fcELRhE3ibK3edtn/P/Pv5/OIwO/nn2PylPLT9IrUuS5NBmnwu6JEmnAPcDa4CXhBAf7+t77E/WtHmJa4LJI1K57fN6Nmlxbq3TOa/kZe7wrOZ/+1SyNZ0vZ/yS30353g7HrTWvl+5HH6X/hReRzWayfngnqVddhbwbV6kHgw0bNvB/z7+AV1iptY7jzUuPwrBFiFMhBPX1v6Wp+Wmys89ndMX9SFtMQmpeu5pPX5xDW+06bO4Upl99IxNOO+uw9e4mhED3x4h3hLb2tNYdHpqsNohkUjCkmTGkWRJj3qkWDGmWxD7VfNitAT9YxLU4ITVEKB5CC/jpk0M0ti+mxd/Cyu6VzNswj5EpI3nstMfIsR9AR09JkhxmDOuNI0nSM8BMoEsIMX6L8zOARwAF+LsQ4kESnqEDgAVo2ect3s+saklMeuuOxnlHDXN2B4yZVss1yz/kZ4Ewx0cicMGjHDfpyu3KClWl/1//omf2X9B8PlIuuYTMH3wfQ3r6gX6MYTH/ixUseOcN+nUz7VnH8Mxlx+DeYia1rsdYX/Nz2tvnUpB/FeXl9w5Z5i1rq/ls7gs0r1mFIzWN6VffyPjpZ2Cy7Ngj3KGIHtNQO0PEO4LE24OJfUcQPbTZuc+glW0dm46SbsEwKNppFmSb4ZDrbdnfDIpvRI0QVsNDW0SNEFJD+GN+fDEf/pifYDw4JNQhNURYDQ+lh87Fw6gi8XtLuuDliMbclreZ+/67ADiMDi4ou4A7p9yJ05SMT58kya4YrgnxLPBXYM7gCUmSFOBR4AwSwv2lJElvAAuFEJ9IkpQN/BH49j5t8X5mU08Qh9nAoxs7cdllbjgWbv30Qb7t83GxzwdVN8E2Yq7HYvjeepueJx4n3tiEbepUsn/6EywVFQfpKXZNmyfMI6/Mx9yyDK9kY+zJ5/Kn6WNQtoimFYv1sLr6+3g8XzCy+HaKi79PT1MDm1Yso37pYtpr12NPSWX6d29gwukzDmmLXKg6as/moCTxzhBqZxC1LzIUmEQyyhhz7FjHZWDMsWHIsWPMtiHbjftUtDVdQxUqqp7Y4np8aB/X4kS1KFEtSkyLDe3jepy4HkcT2lA5VVfRhDZUfvB4y3r3JL+qq1u1S9O1oXNxPY6ma8S02JD47g4JCbvRjs1ow2awYTVYsRltpFnSKDAWbHXOZrBhM9pwRGXgPs4ZfxHnnHU+ufZc8hx5yWVpSZIMk2EJuhBigSRJxducrgLqhRAbASRJegm4QAixduB6P3DovuV3wqaeIC4hsTHdwPVOG79YdgsjdIX/7fNBajGc/suhvPGODvpfegnPK3PRensxjxlDwWOP4pg+/ZC03Fa3eHnhiwY2rfyc0XIHkiuTe679DlmpW1s+Xu8KVq++lVi8D4f+HWrfFfx39TUE+noByCwuSYyRn7G9kAsh0ISW2AbES9O1IfHY9rwu9CEhiagRQvHQ1pafFkEXOrrQE93hbJEWeuJYH9hrGmo4jhaKoYXjqOEYWiiOFomjIxL/STpYZES2DCMUMMtgkcEooTNQb1hH36SjbUi0TxNb7PWtj4fS+vZ5B/NvKZSC/TcJ1SAZMMibN0VShtJG2bjV8eB1k2zCZrBtl39oG6hTkRXMihmrwYpFsWA1WrEaNm+DAu00OXGanNiN9j0W4nhHB/XcR1neBFJzjtlPv1KSJEcuezPIlw9s6XCgBZgqSdJFwFlACgmrfodIknQjcCNAUVHRXjRj31Lf5ic+8B5q9z9HX6SPV/pCyAi48HF0gwX/F5/T/cLz9C/8GFUWWE44DueFF2CcVEm/BL2+xiERGnzBDwqPJjQEAk0f2A+89AeFStO1ofSWdQwKpS70rSy7IZEcEMpBgdR0jb5QhJ5AhJ5gmA5vCF8oSI7iw5QdY21KCrm5/dz/1U+Ja3HC4QDhkI+o2o1u8KFpEPZZ0LTXwSCjHGNCNpuQjQZ01qAGVqK9+tB2Yq2L/e9NUUZGkqTEXkjIA5skJCQG0kjIkoxsUZBtMrKiICsyskFJnB/chIwUkZCjiePBchISiqygSIn8JtmUyC/LQ+cMkgFZGjje4rwibS6nyAoGKSGIW4rrtuJrkA1YFAsmxYRZMW+1N8rG7UR68NzgsSIph+RH5J6geRPDXYrDsZucSZIk2RF7I+g7ensIIcSrwKu7KyyE+JskSe3AeSaTaZeefvYFQgh6I700+5tp8bcQioeGujIHuxajapw+Wx2KSyevu4sl4fWkK1YucUuEUwuJfnIrcV1FSMBkYLJE4mf4Ahq/gMb9/RS7R0IBIaELmURDZRAyZgUcDkEQCcxWfHTjaWpDj8bRY1EMJg2LPY7BKFDiZmwil6wsNza7G4vVNiRKWwrIlseDQrbltV2V2VEei8GSsPSMCYvPrBqRezRETxStK4LWHUHrCqN5Njt8kkwKhkwrhkwrxozE3pBhw5BpRTYl46QfToSXLwfAMnbsQW5JkkOBwfCpB7sdhxN7I+gtQOEWxwVA2941Z98zb+M83tr4Fl92fElU27XnPwkJY5oMsoJBTyMOTIlp2CIxRK8TvS2G2WrHdcxUnGMmYDJbt7KUJEkassy23XZ0XpGUIUtQlmRkEhagzPZltqzfIBsIxQSLN3hYvMHDskYvTb0RQMIgy4zJdTEp10qBwYfU30xnayOqpuHUotC4AaIhFLNKerlG5hgJY4oXJIHdOo6y8h+SnnbiAbf29JhGvC1ArDlArNVPvKWPYM9mZ06SUcaQYU1EETvWjinPgTHHhuw0HfaWaZIEwS++wJCdjXHEiIPdlCRJDkv2RtC/BEZJkjQSaAUuB2btSQX7O9raoyse5YmVT5Bnz+OC0gsoSSmhyFlEgbMAp8mJUTZu1Z356DOreLiulejUTHLEH5hpdPGL1R/QU5NC9/IoaddeS+bttyFbD/xM7nZvmKWb+lnW2MdXTf2safOh6YIUm5Gq4jSuOCaFUnscydfOhtpVdFR30QZIahwl4MXl76OoLJeMb+ViSG0gqtcCGlZrEVlZN5GVeRZO54QDJo5CCOItAULLu4hs8KB2hYYmqCluE8Z8J7ajszDmOjBm2VBSzEhyUriPVIQQhL5Ygv2EackPtCRDeL1eecaMGWVer1dRVVX6xS9+0XbllVd6ampqTDNmzBg1adKkYHV1ta2kpCTyyiuvNDidTv311193/vjHPy7UNI3KysrQnDlzGq1Wq8jPz59w2WWX9b733ntuVVWll19+eeOkSZOOqAAtw1229iJwCpAhSVILcK8Q4mlJkm4D3iOxbO0ZIcSaPbn5/nT9uq53HU+sfIILSi/gV8f/CkXedferrguqa3pAhoIUmc6mdVzTEULXZPprLBRePQ7H3Xd97faomo4/ouKLxPGG4/jCibQvHB/Ybz72huP4IupW18LxhL9ii1GmsiCFm08uYXpFJimqh+rq1dR9+RGfBRMhgZVwAFPAR36Ok7LJGbjyHcRlHx7Pm2hCw2QuYkTWDWRlnY3TMe6AvkCFEETW9eH7sIl4SwAMMuYSN9bxGZgKHJgKEA6H7QAAIABJREFUnEnnK99AonV1aH192Kcee7CbkmQb3nv8z4U9zY37NHxqRuGI0Fnfu2O3QV9sNpv+1ltv1aelpent7e2GqVOnjp41a5YHoKGhwfLkk082nHnmmcFLL720+KGHHsr88Y9/3HXTTTeNfP/992smTpwY/da3vlX80EMPZf7iF7/oAsjIyFDXrl277sEHH8x88MEHs19++eVDYKB03zHcWe5X7OT828Db+7RF+4glHUsAuOPoO3Yr5gA9zX564ipYJZxSA+5YnPxAD75mJwXTo1jHF+62jmBUpa4rQG2Hnw3dAVr6w7T0h2jpD9MbjO2yrCyBy2rEbTXishhxWQ1kOR1D6bwUK5MK3WQoEbo6O2hqWs/8l18nHA5jsSikmr04qcHp7CfnGCcml59YvJ0IEPGC1VpEUdHBEfFB4h1BPG9sILrRiyHdQsoFpdgmZSUdsCQh9EXi79U2NRlKN8lmdF2X7rjjjoLFixc7ZFmmq6vL1NLSYgDIycmJnXnmmUGAq666qnf27NlZK1eu9BUUFEQnTpwYBbj66qt7H3300SygC2DWrFn9AFVVVaE33ngj9SA91n7joL5J92eXe3uwHYfRQYY1Y1j5W2s89JhAtyp09X7CX6uDyKmguKxYnV0w8uQdlqvv8vPBui4+XNfFl419DLrGNxlkClKs5KdaOTPPTbbLPCTWTosBq6RillVM6BglDUmLE41GiUQiRCIRwmHf5rQ3RLy1iwXLWjCavFgsAez2MEdNUlHkPiTZv0WLJGw2Cw7HZJyOsTidY3A4xmI2H7yoVEIT+D9qwvdhM7JFIeWCUuxVOUhKcn1xkgShJV9gzM/HVJB/sJuSZBuGY0nvL5588sm03t5ew+rVq9eZzWaRn58/IRwOy8B2RokkSewuNonFYhEABoNBqKp6xI3tHLHR1jqDnXsUWrG1th+/SUJYFORgI5XeOLpDwpHaBaPOhHEXDuVVNZ3nv2hi7rIWVrcmltqMznFy2/Qyxue7Kc+y41JU/D4vXq8Xr7cfj8eDt91Lu9fLeq+XeDy+s6ZgMETJyuohJaUbl7uPzKxuZHlLC1/BYskl4lHo3SRjMlYwbtpFZBUcjd1ejsFw6AR9UT1R+l5cT6zRh7Uyk5TzS1HsR35c7yTDR+g6wSVf4jz9tIPdlCSHGF6vV8nIyIibzWbx5ptvOtva2obG49rb200ffPCB/fTTTw++8MILaccff3zgqKOOirS2tpqqq6vN48ePj86ZMyf9xBNP9O/qHkcSR6SF3hZo44OmDzg2d3jjcbqm01bvIeDQEVYTp69qwZEZQ9fBIAEn/3gob18wxm0vfMVnG3oZn+/inhmjmJyhE+jtpL19OfV1vSzp62PbGL12ux23201mZiZlZWW43W7sdjtmsxmTSUHTa4iElxEMfUUgUA0IFMWBwzEap/NUHPbRWK1FWK1FmEzZfPby86x4fS5HnXUp06++EXkYwwoHEqELgl924H2nAXRB2uUV2I7KOtjNSnIIEtuwAd3rxTYl6UwmSYJ4PI7JZBLXX39939lnn102fvz4MePGjQuNHDlyaBJbSUlJ5Jlnnkm/5ZZbRowcOTL6wx/+sNtms4knnnii4dJLLy0dnBT3wx/+sPtgPsuB5IgcvHym+hmMspHvT/r+sPJ3NwcIRVRiFh3FrHL5Kh/G4zXEzD/DlGuG8nX6IlzyxGf0+kL85Bgz9nAjmxZ+zJsD4p2enk5GRgajRo0iLS2N1NRU3G43brcbo3FrqzQa7aS3dwG9vZ/Q3rEQTQsgSQZcromMHPkD0tNOxOWaQMLD7tYseX0uS16fy8TTZ3DqNTcfcrOCY60B+l+rJ97sx1ziJvWiURgyDh8f70kOLOFVqwGwVlYe5JYkOVRYunSptbCwMJqbm6uuWLFi/bbXa2pqTLIs88ILLzRte+2CCy7wX3DBBWu3Pd/a2rp6MH3SSSeFlixZUrPvW35wOeK63EPxEG9seINzS85lQuaEYZVprenHJyfGXkyimyJzYr26VDp9c70xlev/uRQR6OXq1BbaV3tJTU2lqqqKkpISCgoKsO5iOZsQAr+/mp6e+XT3zCcQSPx7M5uyyc46h4yM6aSlnYCi7Hoy6aYVy1j44j+pOO5ETr/ulkNKzNXeMP4FLQSXdCDbjaT9TwXWozIPqTYmOfQIr16F7HRiKk6uP08Cv//97zOffPLJrIceeuigjd0frhxxXe6ft31OWA1zXsl5wy7TWushmmmCaJTi3kbsuVFEykik1OKhPA+8tQ6to5bTTc0YZRff/e53KS4u3qVYqaofr/crunvm09Mzn2i0A5BxuydRWnIX6ekn43CMHrbg9bW18Nbs35NZVMxZ3/sBknxoTCqL94QJLGghuLQDJAn7sbm4zyxGth6RHUBJ9jGRVauxThh/yPx7TnJwufvuu7vvvvvuXXaTV1RUxOrq6vZomfQ3gSPujftxy8c4TU4mZU8aVn6hC9o3ePCWm6EJjt+4EXtODGnU6UN5/ru2k8XLVnKisYmK8gouvPDCHVrjmhbF51tJX99C+vo+xeevBnQUxUZa2olkZvwv6emnYDLteTjVttp1vPb7+5EVAxf88B6MZsse17GvEJpOvDNErMlHaEU3sQYfKBKOY/NwnlKA4jrsYvIkOUjokQiR2lrSr7vuYDclSZLDniOqy10XOgtaFnBi/okY5eHNpPZ0hYhHNDpNCUdlM7pXIecLKD0VgEhc46FXP2OaYSO5+QVccsklW42HC6HT0/MBDQ2P4w+sRQgVSVJwuY5iZPGtuN1Hk5JShaJ8fZGr++Iz3v7LH3Ckp3PRT36FOyvna9c1XIQu0AMxVE8UrS+C2htB7Q2jdoeJtQdBTQRhMWRacc0oxj45KynkSfaYyNp1oKpYJw5veCxJkiQ754jqcm8NtNIX6eOYPQi92NWYWNHQFo8jmSVKLC0ISUEqPgGAOZ/WUxlfi93p4MpZV2A0GhFC0NPzX9raXqG3byFCxLHZSigquh636yhSUqZiNLr2xSPx1duv89Gcv5NbVs6Fd/8Cm8u9T+rVwyrx7hCaN4rmjaH5YgPpKJoniuaLgb71mk7FbcaQbsFxbO5mr27pluQYeZKvjf+998BgwHrUUQe7KUmSHPYcUV3udf11AJSnlg+7THejH4NRpj8QwyLFMRVEkfIng8VFKKby8YJPKZfizPqf72C324nHvaxddzc9PR9gNKaSl3sJKSlVZGXNQJb3nctSX083H/7jSTYsXUzZMcdxzu13fq1udqHqxLtCxDtDxDuCqB1B4h1BNO82nusUCcVtRnGbMY90J9IppoSIp1kwpFmQjIfW0rgkhzdaIIBn7lxcZ52FIX3Ph6GSJBku77//vl3XdWnGjBmBg92W/ckRJei1/bUAlKUMvwu/q8lHRqGDcLCb3Lgfp0OF/CkAPLdgPSP1VnJHllFYWEg43Mry5VcSibYxquweCgq+gyzv258w0N/Hmo8/4IvXXkHoOid9+xqOnnnhHq0zF0IQqekn9FUnkXV9iPhAjHJFwphpwzzSjSHHPhT0RHGbkW2GpKWd5IDiffVV9GCQtKu/e7CbkuQQpKmpyXDLLbcUrVy50mYymURBQUH0L3/5S/OgW9fhsmjRIuvTTz+dMWfOnO2WuA2Xmpoa08yZM0ftyUS8F1980X3ffffl67qOqqrSzTff3HnXXXf1DLf8xRdfXDxz5kzvNddc0z/cMkfUGHpdfx2FzkJsxuHFEdB1QXdzgPyjU9DX6ZQq9RgQkHcUUVXji88+pVASXHLe2Xi9y1m1+nvoepTJk18gxb13Idx1TSPo6cff20Ogv5fe5iZaa9bStHolQuiUTD6GU6+5GXdW9h7VG+8OJfyl13mQ7UZsk7Mwl6RgzLFhyLAm3a0mOSQQmkbfnOewTp6MdUJy/DzJ1ui6zvnnn182a9as3nnz5m0E+Oyzz6xtbW3G4Qi6qqoYDAl5mzZtWnjatGkHNAhLNBqVfvCDH4z4/PPP15WWlsbD4bBUW1u736NOHVFj6LX9tYxKGTXs/J6OEGpUo6N/E5Iwkm+rhRiQM5G3l9RSoHVQUDEBs6WPZcuuw2BwUDnxKVyuHb+AhBBEAn78vT2EfF7Cfh9hn49IwEfY7yPo6SfQ24u/r4dgfz9C6JsLSxLp+YVUXXgJY086lbS8gj16dj2q4vuwmcCnrUhGGfd5JTiOzU0KeJJDEv+HHxJvaSHrrq8fwTDJkcu8efOcBoNBbLl87fjjjw9DQuy/973vFXz44YduSZLEXXfd1X7DDTf0z5s3z3n//ffnZmVlxdeuXWvbsGHDmsceeyzt8ccfz47H49LkyZODc+bMaTQYDLz66quu++67Ly8Wi0kjRoyIvvTSSw1ut1vfsg0LFy60XX/99cVWq1WfOnXqUFf97Nmz05cuXWoftPinT59eduedd3bOnDlzyMWsx+ORVVWVsrOzVQCr1SoqKyujsL3lbbPZJoVCoeW6rnP11VcXLVq0yFlYWBjdnV/6HXHEdLlrukazv5kzRpwx7DJdTT4ANjXVgHE8FXIDyApkjOLL555BkhQumlHFihVXIMtGJk/6P6zWoq3qaFlXzbpPP6a9rgZPRzvx6I7D61rsDqzuFJzpGYyYMAlnejqOtAyc6Rk40tJxZ+Vgtu1ZhEKh6miBGNENXrzvNaD7YtiOzsY9ozgZgjTJIUu8tZXO3/4W44iipP/2w4C+ubWF8Y7gPg2fasyxh9IuKd+p45hVq1ZZKysrQzu6NmfOnJTVq1db161bt6a9vd1QVVU15swzzwwMlLMvX758zejRo2NfffWVZe7cuWlLly5dbzabxZVXXln0xBNPpF988cXe3/zmN7kLFiyodblc+j333JNz//33Z//hD39o3/I+1113XfGf/vSnpnPPPTdw00037ZGFlZ2drZ1xxhmeoqKiidOmTfOdc8453htvvLFPUXY+dPrcc8+l1NfXm2tqata0tLQYJ0yYMO7qq6/u3ZP7HjGC7ol60IQ27OhqAD0tARRZ0DJgxU5QOyF9FJ39Poz+drTs0YRDC4hG25ly9CtbiXlPUwMLX/wnG7/6EpPVRu6oCgrHTcSVkYkzPQObOwWr043V5cJidyDv4n/ktgghEGEV1Tsw89w3MBPdG91iNnoMEVGHyhjzHaRfOQZz0b6ZXZ8kyb5CCEF03Tr88z8k+OmnhFevRrZaKXr2H0h78HeRJAnAwoULnZdddlmfwWCgsLBQnTp1auDTTz+1ud1ufeLEicHRo0fHAN59911ndXW1rbKycgxAJBKRs7Ky1I8//ti+YcMGS1VV1WiAeDwuHX300VtNluvt7VX8fr9y7rnnBgCuvfba3g8//HCPlhi9/PLLjUuWLOl65513nLNnz8754IMPXP/+978bdpb/k08+GXqu4uLi+HHHHbfHQWX2i6BLkmQHFgD3CiHm7Y97bEtfpA+ANGvasMt4O0PYhY/2zDzohWIRgswK3vhgEQCnHDeFltbbsdnKcLk2O6pZOu8/fPJ/z2C22jhx1tVMOvs8jKY9W4Oth1XU/ghafwS1P5pY490bSaz59kSH1nkPIYHsMKG4TRjSrZhL3ChOE4rDhCHDiqnYhSQnJ7UdiQghQNdB1xNpTdsqLXQdhtIChJ5ICwGqiojHEZqG0LTEUkRdQ2h6Yj9QL7qeKDt0bkfpRN1C0xCRKHo4jIiE0UNh9EhkczoYRAv40QNB9FAI3etF83pBlrFOmED6jTeQcvHFmAoLD/ZPm2QY7MqS3l9MmDAh/Nprr+0wXvmuuqJtNpu+RT7p0ksv7X300Udbt8zzwgsvuE844QTfm2++uWln9QghdjpJ2GAwCF3f/H6ORqM7HdesqqoKV1VVhW+88ca+srKyCUCDwWAQg8G7dF0nHo8P3WhvJyYPS9AlSXoGmAl0CSHGb3F+BvAIoAB/F0I8OHDpR8C/9qple0hvJNEzkW4Z/vIXT2cIS18T3ZPGYO4NkqaF0dPLafpsDT1SKmOyq1m/vpZxY/809EOvmv8unzz3NKOqjueMm27H6nBuVafQdPSIhh5WEWEVzRdLiHV/BK0/iuZJCLiIbh2NTbIoGNKtGHPtWMakDSwhM6G4ErPQFacxOR6+C4SmJQQmHEYf2LZMbz6ODByHENukhaoOCOWgiG2dRojtxXWH6R2X32l6K1HdQfprjKUdUIxGZKsV2WJBslpQ7A5khwNjbi6y3Y5ss2GtnIjjlFOSy9OSDIvzzjvP//Of/1x6+OGHM+68884egE8++cQWCATkk08+2f/UU09l3nbbbb1dXV2GJUuWOGbPnt28atWqrdx3zpgxw3fRRReV/fSnP+3Mz89XOzs7Fa/Xq5xyyinBO++8s2gwxKrf75c3bdq01WS7jIwMzeFwaO+9957jrLPOCjz77LNDlmJpaWnsqaeesmmaxqZNm4yrVq3aLl611+uVFy5caB8cV//iiy+seXl5MYARI0bEli1bZrv++uv7n3/++ZTBuOyDz3Xrrbf2tra2GhcvXuy84oor+vbkdxuuhf4s8FdgzuAJKREG7FHgDKAF+FKSpDeAPGAtcEB9k7b4WwDItecOK7+m6Xh7whT42uiTj2KsVI+MoCnuQtH6SC+eyMb6+7AbR2PfOAnv8k30N7bir27kzFHXku0oxfdsPd6ohojp6DENEdNA2/HLVzIrGFItKKkWzCUpieViqRYMqYl9ctnYZoSuE2tsJLp+PdFNm9B9fjS/D90fGNrrgQB6JDIk1CIW233FWyJJSFZrQoisVmSrBYxGJEkGWQZZSqQlCWQ58f9GlpFkOdETsm2+PUnLMiCBkqgPSU6kB/JIijxU/+a0lOieHkzLykB7pMS8j8F6t0lLsoRkMoGiJMoPPAOykqhbHrxPom5JlmC79GB98ua9xbJZxI3J+PZJ9i2yLPPGG29suOWWWwr//Oc/55jN5qFla2effXbgs88+c4wZM2acJEniV7/6VUtRUZG6atWqreo4+uijIz/72c9aTzvttHJd1zEajWL27NlNp512WvDJJ59suPzyy0tisZgEcO+997ZuO3v+6aefbhicFHfqqaf6Bs+fccYZgUcffTRaUVExrqKiIjx27Njtxvp1Xeehhx7Kvu2220ZYLBbdZrPpTz/99CaA22+/vXvmzJllEyZMGHPSSSf5rFarDnDVVVd55s+f76qoqBg3cuTISFVV1R53uUvDnUknSVIxMG/QQpck6Tjgl0KIswaOfzKQ1QHYgbFAGPiW2Go69/ZMmTJFLF26dE/bvhUPffkQL9e8zJJvL0GWdm/JejpDPH/vYjLbXuN/z76YS1rf4s+GJ5lXfD9fbvJwQX4K/WWPkP/V/8PRUwmKRCQeRJd1XDlZyBYDsllBMg1u8uZjs4JsNSBbDYku8XQLsi350tsRan8/0ZpaonV1RGtridbWEqmrQ4Q2/41INhuK04nsdKA4XYm9w5EQZIsV2WYdEBjbdumE1WhDtiYEaFDEJbM5+QGV5LBHkqRlQogp+7relStXNlRWVg57zXSSA8fKlSszKisri3d0bW/G0POBLcdWWoCpQojbACRJuhro2ZmYS5J0I3AjQFFR0Y6y7BGbvJsodhUPS8wh4cMdwFeWjRTXmCRvRMdC08YYubiIZSzCSDrFl87CmOZg9aL/Mv8fj3Ppz39DzviJe93ebyIiHie8ahXBRYsIr1hJpK4WrXvzO0NxuzGXl5Ny8cVYRo/GPLoCc1kZsjnpIz5JkiRJdsfeCPqOzJshc18I8eyuCgsh/iZJUjtwnslk2jsvLSQEfXzG+N1nHKC/xYtVgv7Rx4BPp5JWVGUk3QRpJ4Xy1BUUFnwXy4g0NDXOkjfmklc+hsJxSScYe4IeDOKd9xaBBQsILV6MHgyCLGMeXYFj2gmYy8sHtlEYMpOx05MkSZLk67I3gt4CbDlNtQBo27vmfD10odMebGfGyBnDLhNf1sxpLgN/NDlQov0UyU1ES85A1Aksmf0IoZKRkQih+tXbb+Dv7eaMG29LCs4wUbu76fu/5+l/6SV0rxdjfj6uc8/FPm0a9mOnorj3TZCZJEmSJEmSYG8E/UtglCRJI4FW4HJg1p5UsK88xfVF+tCERpYta1j5490hcv0yvliU9lFuUjb1kSYF2CQl1rAXZ3cjSSZcrkqErvPV269TXDmZ4srJe9PMbwTRjZvo+8czeF97HaGqOE8/jbRrr8V61FHJj6EkSZIk2Y8Md9nai/z/9u48vKrifvz4e+65S3KzkwUSSEwgCVmNGHZRBAVFBBHcQFuwFhR+6pfWqq1bq9VWpbhQl+KCSt1ohVJUCgVXLHUD2ZcEEAghISF7bnK3c+b3x01owAQCySWL83qePEnOnXPO52bQuTNnZj5wMRAlhDiEb335q0KI24HV+JatLZJStnrj+obrtste7qV1vt0BYwJb16A7vi5GAvvrCtnjDSfVWwAWOFhvx4GVAWG7CQ3NRtNslOzfR21FOSOmTlcN0knUbdxI2auLqP3oI4TVStjkyfSYMR1bUlJHh6YoivKj0KoGXUo5tYXjK4GV7RrRGdhRtgOAc0LPOWVZqRs4vimm2COxJYRR5DFxRcOTgh1lUGsxCNa+JybmRgD2b97ou3b2jytfs/R6MRwOjNpadIfDt0mIw4HhqP3B8bqvv8a5bRtaWBhRc2YTceONar2xoijKWdYtkrPsLN9JiDWEfuH9TlnWU+RAOnUKPQaWvpFItyRVHMKl2TlSJzCF+XYADAvNAeDAlu+Iij+H4B5dq4GSUmJUV+MpLsZ79ChGTQ16TY1vTXet77tRW4PeuKa7thbdUXus4ZbO5vekP5Gw2bD27UvPBx4gfPLVmE5zP3pFUZTmaJqWm5KSUq/rukhOTq7/29/+tj8kJOSkS6DPxO7du605OTlZiYmJTikldrvdeP31179vTKbSlXSL9Kl1njpCraGtGhJ3H/St1S/3SmL7RCDK6kk1HaIy8ByoFQTaffsHBAWl4K6vo3DXds677Mo2xedvek0NNf/+N/WbNuMpKsJTXIT3cBFGXbO5DcBkwhQSghbs29HLFBKMOToaa2Ki7/fgYExBdt/rQUENvwdhCvJ914KDGn4PUpuKKIriFzabzdi1a9cOgIkTJybNnz8/+ne/+90Rf9wrPj7e1XivefPmRT388MOxy5Yt2++Pe/lTt+ih13nrCDQHnrog4D5YjVfoOCVY46MRRftJFoWUmgciEfQKK8dmi8VsDiF/43p0r5d+uYPbEp7feMvKOLpwIVVLl2E4HGhhYVji47ElJRE0fDiW2Dgssb0wR0VhCglFCw3BFBKCyW5v2K1MURSl8xsxYkRt49aul156ab+ioiKry+Uy3XbbbUd+9atfHX366aejtm3bFvjqq68WAMyfPz9q586dAU899dThiRMn9i0qKrIahiHuueeewzNnzqw42b2qq6u18PBwHXy992nTpiXV19ebAJ599tmDY8aMcRw4cMAyZcqUvrW1tZqu6+LPf/7zgcsvv7z2ZNc9G7pFtrV6bz12c+uGel0FNdR63djcDgwb9KgvJ0pUs11G4DAFkR1eQnBwKgDbPl1LQEgocf0z/Bn+Gan59FOK7n8Avbqa0LFj6TH9pwRkZ6uJe4qitKvly5fHl5SUtOuztJiYmLpJkya1KumLx+Nh9erVoWPHjq0GeOutt/b37NlTr62tFQMGDMi46aabKm655ZbyzMzMDJfLdchms8k333wzauHChQeWLVsW2qtXL8+nn366B3xZ1Jq7R0FBgS0tLS3D4XCYnE6naf369bsA4uLivOvWrcuz2+1y69attqlTp/bdtm3bzkWLFvW45JJLqp544olir9dLTU1Np+ghdZsh90DLqXvohtOLXuakwu0lSFZTX3OE9Dpfwp1CZyAlXgth1sMEBY2lvraG77/7loETJqOZO9fnniPz5lH+6iJs/fuT8NoiAlJTOzokRVGUduVyuUxpaWkZAEOGDKn5v//7v6MATzzxRM8PP/wwHKC4uNiyffv2gEsuucRxwQUX1CxZsiQsOzvb6fF4xODBg+sDAgKM+++/P3727Nm9r7rqqqqWetFNh9xffvnliJ/97GfnrFu3Lt/tdotbbrnlnB07dgSaTCYOHDhgAxg6dKjj1ltvTfR4PKZrrrmmYvjw4fVn569yct1iyL3eW09EQLOZ9o6jV/rmOFR6NUIDvRx1VJBRvw/wNeiG1YUJD0H2fhzcuhlpGPTLHdKW0Npd5dJllL+6iPDrr6fn/fdhslo7OiRFUbqx1vak21vTZ+iNPvjgg5DPPvss5Ntvv90VEhJiDB48uH/jcPisWbOOPvbYY71SU1OdN91001GAc88917Vx48YdS5cuDbv//vt7r127tvqKK66onjNnzjkADz74YOHAgQOPa4ynTp1aeeeddyYCPPbYYz1jYmI8S5cu/d4wDAIDA3MBxo0bV/v555/vXrp0adiMGTOS7rzzziO333572Vn4s5xU5+p6nqHWPkP3NjToNViJj7Sxvb6CjNq9lBOCQwRhsTsAsNuT2L7tS6yBgcQmd57erys/n+Lf/x77sKH0euhBX4YsRVGUH4nKykotLCxMDwkJMb777ruAzZs3H0tdOnr0aMftt99u3b59e9DWrVu3A+zfv98SExPjnTNnTnlISIjxxhtvRP7pT38qavpBYffu3cf1itasWRMSHx/vAqiqqtL69Onj1jSN5557LrIxj3leXp41KSnJfddddx11OBymjRs32oEfd4PeXkPuDo+DYEvwKcvplb6lWPWGJDw2mBJnNeP0/RRa48Ej6BnumxUeaE/k4LaF9EnPwtRJGk0pJUUP/RZTYCC9581TjbmiKD86U6ZMqXrppZeiU1NTM/r16+fMyclxNH190qRJFVu2bLFHR0frABs2bAj8zW9+08dkMmE2m+ULL7xwoLnrNj5Dl1IOaZeDAAAgAElEQVRisVjkX/7ylwMAc+fOLZkyZUq/5cuXR4wYMaKmMdXp6tWrQxYsWNDLbDZLu92uv/XWW9/7+723RpcfcjekQaWrkvCA8FOW1StdSAFOCeE97ZTWHSaVQ3xhuQTDY6J3RDWaFoyzSqeyuIjzxnae5WrVK1ZQ/913xD72KOaoqI4OR1EUxa/q6uq+O/FYYGCg/Pzzz/NbOue///1v8Ny5c48tbZsyZUr1lClTdrRUHqB///5up9O5sbnXsrOzXXl5ecfOf/755wsB7rjjjrI77rijw3vkJ+oUM/PaotpVjSENegT0OGVZb4ULr0kipE5YXBiHHKHYhIcjpliqCSQ2qIgge18Ktm0BICE7x9/ht4peWcmReX8iIOdcwq6+uqPDURRF6VSOHj2qJSYmZgUEBBhXXXVVTUfH01G6/DP0CpdvSWG4rXU9dKc0CKgvw9rjHGJ3lgBw1G2jXLcSZt5DaOi17PnvJuxh4UTFn3or2bOh+PePoldWkvDyS2r9uKIoygmioqL0/fv3b+voODpah7YOQogJQoiXqqqqzvgala5KACJsrZvlXuc1CHCW4Q2ACLfv8Uu1EzxmHRNOLFpv8r78gpTBwzrFmu7qVauo/vBDov/fHALS0zs6HEVRFKWT6tAGXUr5vpRyVlgbcmOXO8sBTvkMXeoSvdqFwws2dxXlnhpiXL5zXcKGKcC3cqGqsA5D18kefdkZx9RePCUlFP/uYQKys4mc2aaVfYqiKEo31+XHbyudvh76qZ6hGw43SKj1CgIMBxX1ZcR4yqmTNgQCa4BvhntxXikBIaHEJPb1e+wn4zlyhANTp2HU1xP3xOOITra5jaIoitK5dPkGvfEZepjt5L18vdoNQL0BAZqbqqpSoj0VlOFL6tIr3Lekbe9XO+g7YGCHPqt25uWx/9rr0CsqiH/pJWx9O/bDhaIoitL5tXurJYRIF0L8RQjxnhBidntf/0SVzkoCzYGn3FimsUF3SrDbDA5WHyHaW0EZYbhMduLDahAE46pxkz16rL/DPo7hcOA+eJCKd5dwcNYs9l9zLQDnvP0WQUM6Z2IYRVEUf9I0LTctLS0jJSUlc9y4cX1Pd7/0X//61738Edfu3butKSkpmf64dlu1ahxXCLEIuBIokVJmNTl+OfAsoAGvSCkfl1LuBG4TQpiAl/0Q83EqXBWtm+Fe09CgG5KgIBMH6isYZFRwmGjKdRu5gWU4KwURcX3onda+dWW4XOiVleiVVeiVlRj1dUi3G++REhxffkntxx+DlABYEhIIv+F6Im++GUtsbLvGoSiK0lWcafpUwzCQUrJgwYLYxx9/vNj/kXYerX0w+zrwHLC48YAQQgOeB8YAh4BvhBArpJQ7hBATgV83nONXFc6K1u3jXu1GAi4JgSEah7weomQlO2UiZYaNIO8B6sth9PSZP5jd7jlSgnvfXrwlJXiPHkWvrMJwOpFeD3i9SI8X6fEg3W4Mp9PXYNc7MZxO9PJy9IqWs/WJwEB63HwzttQULHFx2AcN6hSz6xVFUTqLpulTf/e73/V86623ogB+8pOflD700EMlu3fvto4bNy5l+PDhNRs2bAjOzMysa0zukpqaWj9v3rzCK6+8MiU/P387wEMPPdSztrZWe+qppw5/9tln9pkzZyba7XZjyJAhtR9//HFYfn7+9pZSp3bcX+HUWtWgSyk/F0IknnB4MLBHSrkPQAjxLnAVsENKuQJYIYT4EHi7/cL9oUpXZauWrBnVbgyLCSkNgkKtlEo3kdRQI+0YbjcWSyWW8PNIPC/3f+e4XBx5/HEql/wNDON/FzObfTnFNQ3MGiaLFWGxgMWMKSAQU2AgpqhILAGBaKGhWHrHoYVHoIWHoYWH+861WDBHRaFFRakGXFGUTmvHznvjHbV57Zo+NSg4tS4j/YnTTp+6bt06+9tvvx25YcOGnVJKcnNz0y+55JKahnXoAS+//PL+N9988yCA3W6PaOzhn7hfe1M///nPk1544YX9Y8aMccyZM6d34/GWUqe29b37U1umTvcGmlbIIWCIEOJiYDJgA1a2dLIQYhYwCyAhIeGMgyh3lpMQeurzPSV1uMwmrO5qzHYNr/S99RqCyHJsxGSWJA3437Nzw+Xi0P+7HccXXxBx442EjB2LpWcMWlQ0piC7aoQVRVH8qLn0qfPmzYu+4oorKkNDQw2A8ePHV3zyySch1157bWVsbKz7kksuOa0e9NGjRzWHw2Fq7HlPnz69fM2aNeEALaVO7cza0qA316JJKeWnwKenOllK+ZIQogiYYLVac09VviWt7aF7ih3UCrC5KjEHmtG8HgAc2InV8gAICf3fbPLSZ57F8Z//EPvo7wm/5pozDU9RFKVLa21Pur01lz5VNsw1ao7dbjdaes1sNkujySir0+k0nep6LaVO7czaMsv9EBDf5Pc+wOG2hXN63Lobh8dxymfohltHunQcbh2bqxJTAIS4vQA4DRuVsb4tXm22GAC8FRVUvPMOYRMnqMZcURSlkxg9enTtypUrw2tqakzV1dWmlStXRowaNarZvdvNZrN0uVwCoE+fPt7y8nJzcXGxVl9fL1avXh0GEB0drQcFBRkfffRREMBf//rXYxuaVFVVabGxsR5N03jhhReOpU7tzNrSoH8DpAghkoQQVuAGYEX7hNU6jdu+nmqWu9Eww73GqWNzV+HQHES5fOvOPYYJc5KvZ242hwBQ/a9/IZ1OesyY4afIFUVRlNM1YsSIumnTppWdf/756bm5uek/+clPSi+44IL65sreeOONpenp6RkTJ05Mstls8q677ioaPHhw+iWXXJKcnJzsbCy3cOHC/bNnzz7nvPPOS5NSEhISooMvdeo777wTmZOTk5aXlxfQmDq1MxMnG3I4VkiId4CLgSjgCPBbKeWrQogrgGfwLVtbJKV87EyCGDhwoPz2229P+7wtpVu4ceWNPDvqWUYnjG6xnOtANaUvbua/tV6C8leQdJ2X96td3FO1hLvr7yDz0h7Ea88z4oIvsdmi2X/DVAyHg6QV/1TPyhVF6bSEEBuklAPb+7qbN2/en5OTc7S9r9sZVVVVmcLCwgyA++67r1dRUZHltdde65DHDK2xefPmqJycnMTmXmvtLPepLRxfyUkmvp2KEGICMCE5OfmMzt9VvguA1IjUk5Zr7KG7pCTCXUOZZtDXVUahjKSGQKKDvVAPZnMQ7oMHqd+0iZhf3aUac0VRlG7ub3/7W9j8+fNjdV0XvXv3dr399tv7OzqmM9WhG4RLKd8H3h84cOAZZR7RhEZmZCa9g3uftJxe65sA5zLA6qnle2sg57oKOCB6UWSKJMhyFL3ehMkUSPn774MQhI4ffyYhKYqiKF3IzJkzK2bOnNnyZiFdSJdOnzoldQrvXvnuKXvSRm1jDx0sXgf7TGb6GYUcoiflIhicn6Fpvq1jq1e8j33wYLVLm6IoitKldPn0qa2h13qQFhMSCLBBdZUgWDg5QiTV0obuPggInFu24D5wgLCJE/waj6IoiqK0ty7dQ28t6dYxNF8vPsCuEVjrmwhYLsPxmgC8JCXdTtWK9xE2GyFjz25yFkVRFEVpqx9FD126dQwhENLAFmylp9uX+7zSFEOwzbeE0apFUb1yJcGjR6GFhPg1HkVRFEVpbx06Ke5skR4DHbBKJ257IL2No1TLQEoIJzzANzpgOuRGr6ggZNSojg1WURRFQdO03JSUlGNrzCdPnlz+hz/84UeVPe10/SgadMOt4zUkFr2Ow5pOvFHKXtkbh6ERH+b79yK3FwFgz+30u/spiqJ0e81t/aqc3I/kGbqB15CYXdXku2pJkEfYK+Oo0TWuzPIl4XGt34I5NhZzXJxfY1EURVHO3Jw5c3r369cvMzU1NWPWrFl9AKZMmZI4bdq0hNzc3P6JiYlZ77zzThj4sqzl5ub2z8jISM/IyEhfs2ZNEICu69x0000JycnJmaNGjUoeOXJk8muvvRYB0Lt37+xf/OIXcRkZGempqakZ3333XQBAdXW16dprr03MyspKT09Pz3jzzTdPvkVpB+jS69BbfR+Pjtsrcbt24jI76WlUss+IxSnNxATV4Kq3Urf+K3rcPENtJqMoitLE3J0H43c5nO2aPjUtKKDumfSEk+7G1jTbGsBdd91VNHHixOqVK1dG7Nu3b5vJZOLo0aNa4+sFBQW2r7/+eveOHTtsl156af+rrrpqa0spUBcvXhxRUFBg3b179/bCwkJzVlZW1owZM8oarxUVFeXdsWPHzscffzz68ccf77lkyZID9913X+yoUaOq//73v+8/evSoNnDgwPSJEydWN2Z+6wx+FEPu0m3g8RrUcpCoEN+gxCEZhS7MhFgcUB0ORiUBqSffcU5RFEU5O5obcvd4PNhsNuOGG244Z/z48VXXX3/9seHdKVOmlGuaRnZ2tis+Pt61adOmgP79+7ubS4G6bt264MmTJ1domkZCQoJ36NChxyV4mTZtWgXA4MGD61asWBEB8Omnn4auXr06fMGCBb0AXC6X2LNnj/X888930kn8SBp0HZfHhdtUT1CEr0EvN4XSKyIMXa8koCQQqMSamNihcSqKonQ2p+pJn00Wi4VNmzbtXLFiRei7774b8eKLL8Z8+eWXecAPRleFEC2mQD1VDpOAgAAJvoxtXq9XNJ7z3nvv7cnJyXH54721hx/FM3TDbeDVffcwB/qWrFWLEPrFRuLxVGL5XoLZjK1/f7/GoSiKopy5qqoqU3l5uXb99ddX/eUvfynYuXPnsUcBy5Yti9B1ne3bt9sKCgpsOTk5zpZSoF544YW1y5cvj9B1nYKCAvNXX311yrXKo0aNqp4/f37Pxrzq//nPfwL99T7PVLd/hi4NCV4Dj7cUq1eA1QNuqMFOWp9IPK5ygr6sxD5wIKaAAH+FoSiKopyGE5+hjx49uuree+8tufLKK5Mb85w/+uijx0YPkpOTXYMHD+5fVlZmeeaZZw7Y7XY5d+7ckilTpvRbvnx5xIgRI2oaU6BOnz69Yu3atSGpqamZSUlJzpycHEd4ePhJE54//vjjh2fNmpWQlpaWIaUUffr0cX3yySd7/PX+z0S3H3KXXt+nKY9eSnStRJh16tw23NJCXLgd8UUZosRNxK+v7eBIFUVRlEa6rm9o7vjWrVt3Nnd8xIgRta+++upxjweys7NdeXl5x57DP//884UAmqbx4osvHgoLCzOKi4u1QYMGpefm5tYBFBYWbm0sf9FFF9V9/fXXuwGCg4Pl22+/faDt78x/un+D7vZ96NINDzE1XhAeSmQ4FsMgzHAStMQFPYIIvvTSDo5UURRFOVvGjBmTUl1drXk8HnH33XcXJSQkeDs6prbyS4MuhJgEjAdigOellP/2x31aQ7p9PXSvNAh2eZDSw1EZhkkahP/lCUwlEDhvEiartaNCVBRFUdpg6dKl+0/3nMaed3fS6klxQohFQogSIcS2E45fLoTYLYTYI4T4NYCUcrmUciYwA7i+XSM+TdLT0EMnEJPhJUS6qDCCuXbzGrRPP6Nmgk7AwJyODFFRFEVR2ux0Zrm/Dlze9IAQQgOeB8YBGcBUIURGkyIPNLzeYXSXBwDDFIIJndBSJ9Gryrh430bMo4dSO9bAbFbJWBRFUZSurdUNupTyc6D8hMODgT1Syn1SSjfwLnCV8HkC+JeUcmNz1xNCzBJCfCuE+La0tPRM4z+l6uKShviDsOCkdpUVW62Hf2RdTMiDM8AEmjnYb/dXFEVRlLOhrevQewNNZxUeajh2B3ApcI0Q4rbmTpRSvgQ8DGy0+vH5dcnevQBEFedhrndgj3bz8aW5fJQ6FJPm2x/ArLXrroaKoiiKcta1tUFvbuNzKaVcIKXMlVLeJqX8S0snn4186IXbdyClQVThl+h2MwkjyzkY1gvNJDAMX6Y1TTXoiqIonYqmablpaWkZjV/33XdfL4BHHnkkpqam5pRtV2vLNdq9e7c1JSUls+mxX/7yl3EPPfRQz5Od9/nnn9tnzJgR39r7+FNbZ7kfApq+kT7A4daeLISYAExITk5uYxjN071eqoqL8FR/TkB9KXWpvmfllTIYq1XDqzsA0LQgv9xfURRFOTMtpU9duHBhz5kzZ5aHhIScNClKa8u11UUXXVR30UUX1fnzHq3V1h76N0CKECJJCGEFbgBWtD2s9lFeWEBoaTGuLW/jskXgOse3E1y5DMaqaei6rw5UD11RFKXze/TRR2NKSkosI0eOTB0yZEgqwI033piQlZWVnpycnPmLX/wirrlyXq+XKVOmJKakpGSmpqZmPPzwwzGne+/Bgwf3nz17du/s7Oz0xMTErFWrVgUDfPDBByGjRo1KBvjwww+DG0cU0tPTMyoqKs7q9uqt7qELId4BLgaihBCHgN9KKV8VQtwOrAY0YJGUcntrr+nvrV8L1qym994toNnYcf5c+tifAaBGBBFmMVNcvBwATet0W/IqiqJ0Cne/tzk+r7imXXs9qb1C6uZdk3Pa6VMfeOCBkhdffLHnZ599lhcbG+sFeOqppwp79uype71ehg8f3v+rr74KPLHcunXr7EVFRZb8/PztwHFpV0+H1+sVW7du3blkyZKwRx55JO7yyy/Pa/r6/Pnzey1YsODA2LFjHVVVVSa73X5WU6u2ukGXUk5t4fhKYOWZ3NyfQ+7127cTMH8B0qQRdPH9eF0BRNrKKRHhHDbF0MNsxuOpwGSy4Vt9pyiKonQWLQ25n+iNN97o8frrr0d5vV5RWlpq2bx5c8CQIUPqm5ZJS0tzFRQU2KZPnx4/YcKEqquvvrr6xOucmK2tuePXXnttBcDw4cMdd9999w9mcw8dOrT2V7/6Vfx1111XPnXq1Ip+/fp1zgbdH/zVQ5deL8W//R0SqL9yBmGiFzhLCdHq2SnOwSwMLBYzbnc5CfEz2vPWiqIo3cqpetIdadeuXdbnnnuu54YNG3ZGR0frU6ZMSXQ6nT8Y5o6Ojta3bdu24x//+EfoCy+8ELNkyZIef/zjHwuvvPLKFICf/exnpbfeemtZVVXVcb278vJyLSkp6Vi61CZpVdF1/QefAP7whz8UT5o0qeqf//xn2PDhw9NXrVqVN2DAgLOWL71bpk91HziAc9s28ntFYI1P8R3UvYRTzyGisaJjD9CR0o3FGtmu91YURVH8JygoSK+qqjIBVFRUaIGBgUaPHj30goIC86effhrWXLmioiKzruvMmDGj8tFHHy3cunWrPTk52bNr164du3bt2nHPPfeUhoWFGTExMZ5//vOfIQBHjhzRPv3007DRo0fXtja27du32wYPHlz/2GOPFWdnZzu2bdt2VlN4dsseuqeoGIByewAZdt+mMQGinFDp4nvZCyteQmy+CXFWa1R73lpRFEVpB82lT33hhRcKp0+ffnTcuHEpMTExnq+++iovKyurLiUlJTMhIcGVm5t7rPFtWu6ZZ54puOWWWxINwxAAjzzyyKHm7vnGG298P2fOnIR77703HuDee+89nJmZ6WqubHOefPLJmPXr14eaTCaZmppaf80117Rvb/UUhJTybN7v+Jv/7xn6zPz8/Ha7buV771H0wIN8kp7Aldc+gmmLmz3ly7g4bhGT9YepNcIYca7kgujHOC/ndSIjL2y3eyuKopwtQogNUsqB7X3dzZs378/JyTna3tdV2m7z5s1ROTk5ic291qFD7v7aWMZTVIwUAqfFjC0gCIkkJmAvLqGx1ZuIDZ1gm2/OhOqhK4qiKN1Bhzbo/uIpLkIGBSE0M94t1SAhJnAfWwPj8UgLFqETE+BbXacadEVRFKU76JaT4jwHDyGtoYyNn4F06hw1iugRcIj1Qf0BsOIl3LwLAIslol3vrSiKoigdodsNuXvLyqj7bhMB4WkE2kMJG5dErb4Jk5B8aB8MgBUdTdTTs+cETKYOnReoKIqiKO2iW7VmUkp2z/o/TIakpk8K5z4wEs1sJmX1xwDsk75t581CxyxqsVrUkjVFURSle+hWz9B3rfsEsScPEZtN5CxfY87h7+hl3UWZMx6n7tu9UGBgog6zxX9Z3hRFURTlbOo2DXrxnjzWPDsP4aoh6NzzSBg4ALxuWPswhtRYVzoNvBoIiWb2AGAxh3Zw1IqiKEpzGtOnpqSkZI4ePTr5TPdf72jNpWDt3bt3dlFR0UlHyOfOnRu3fPnykNO5V7eZFFd6cD/hHl9925KToLoInkiEfZ+wtfZy6k0WRKVA08DU0KCbzaqHriiK0hk17uWen5+/PTw83Dtv3rzojo7pbHrmmWcOT5o0qeZ0zuk2k+JqK8qI8lgAsKb15ctlN4HHwT/7DuJp9428HBKHqcrAbJaYzF4ALGrIXVEUpdMbOnSoo7Cw0ApgGAa33nprn8ZUqC+//HIEwKRJk5LefPPN8MZzJk6cmPTWW2+F7d6925qbm9s/IyMjPSMjI33NmjVBcHzaU4Cf/vSnCQsWLIgEXw/69ttv733eeeelZWVlpX/xxRf2ESNGpMTHx2c9+eSTxz5YPPjggz2zsrLSU1NTMxpTt56O3bt3W/v27Zt5ww03nJOcnJx5wQUXpNTW1gqAKVOmJL722muntQyr20yKc5SVkfT9brDY2RO6k0H7v2V1UDx/JpW9drAbdoxQC32iKzFVuwEwqyF3RVGUk1v+/+Ip2dGu6VOJyahj0vOtSvri9Xr55JNPQm655ZajAIsXLw7funVr4M6dO7cXFRWZBw8enD527NjamTNnlj799NM9b7rppsqysjJtw4YNwUuXLv3e6XSa1q1bl2e32+XWrVttU6dO7btt27adp7pvfHy8e9OmTbtuueWW+J/97GeJX3311a76+npTVlZW5j333FO6bNmy0D179gRs2bJlp5SSSy+9NPlf//pX8Lhx41q99zvAwYMHA9588819w4cPP3DFFVf0Xbx4ccScOXPKT+cajdq9hy6E6CuEeFUI8V57X/tkjIJiQGJNH4x9zVw0YH757ezZMR4pYDKluIfFEO2tRbM0DLmrHrqiKEqn1LiXe0RExHmVlZXmSZMmVQOsW7cu5Lrrris3m83Ex8d7hwwZUvvFF1/Yx48fX3vgwIGAwsJC86uvvtpj/PjxFRaLBbfbLaZNm5aYmpqace211/bbu3dvqxKmXHfddZUA2dnZdeeff74jIiLCiIuL89psNuPo0aPaqlWrQj///PPQjIyMjMzMzIy9e/cG7Nq16wfXFkI0u7964/HevXu7hg8fXg8wYMCAuv3799vO9G/Wqh66EGIRcCVQIqXManL8cuBZQANekVI+LqXcB9xythv0wL2+vfaXnBvEfW43b3ovwWHvw5tJ3/Gv9YMI6uHb6jXSUYU50LfXvkU9Q1cURTm5Vvak21vjM/SysjJt7NixyY8//njMAw88UHKy/CPXXXdd2SuvvNJj6dKlPRYtWrQf4LHHHusZExPjWbp06feGYRAYGJgLYLFYpGH8L125y+U6Lh1qY6pUk8mE1Wo9dlOTyYTH4xFSSubOnVt09913H7fn/R//+MfoN954Ixpg1apV+ZGRkd6ioqLjcqc7HA4tKipKr6qq0ppeW9M0WV9ff8Yd7dae+DpwedMDQggNeB4YB2QAU4UQGT889ewIOlyCOyiajxypAOw3J7JK3Mnwvc8RZ1jQLb66sug6FkvjkLtq0BVFUTqzyMhIfcGCBQeff/75ni6XS4wcObLmvffe6+H1ejl8+LD566+/Dr7wwgsdALfddtvRhQsX9gQYOHCgE6CqqkqLjY31aJrGCy+8EKnrOgD9+vVz7dmzJ7C+vl6UlZVpX3zxxWk9gx03blz1X//616jGFK3ff/+9pbCw0Pyb3/ymtDEta2JioueSSy6pXb16dVhFRYUJ4I033ghPS0urM5vb/4l3q64opfxcCJF4wuHBwJ6GHjlCiHeBq4Ad7Rlga+i1DgKLv2dnbBY98E0KfMD2NwiJw3vZMzDfiaeXb9tXzdAJtLoxmWxo2hmPbCiKoihnyQUXXFCfnp5e/8orr0TMnj27fP369cHp6emZQgj58MMPH0pISPACxMfHe/v16+ecMGFCZeO5c+fOLZkyZUq/5cuXR4wYMaImMDDQAEhOTvZMmDChIj09PTMpKcmZmZlZdzoxTZ48uXr79u0BgwYNSgOw2+3GW2+99X3v3r29TcsNGTKkfubMmSVDhw5NE0IQGRnpaRw9aG+tTp/a0KB/0DjkLoS4BrhcSvnzht9/AgwBfgs8BozBNwz/xxauNwuYBZCQkJB74MCBM34T1WvXUnj7HbyffhkDc7cw2rsBLv4NXPxrXGWVvHL/RooGGbzSN4rp61cS3Xc7Q+L3ceGI/57xPRVFUTqaSp96vJqaGlNGRkbGpk2bdkZGRuodHY8/+Ct9qmjmmJRSlkkpb5NS9mupMW8o+BLwMLDRarW2VKxVqj/2be36Sd9kX2MOEJ4AgOHyDa97zL63qhkGdotLLVlTFEXpRpYvXx6SmpqaOXPmzJLu2pifSlsG8Q8B8U1+7wMcPp0LSCnfB94fOHDgzDMNwrVnDzXL/oGBYE7giv+9EBwDgO7yzWj3ao0Nuo7d6lRL1hRFUbqRSZMm1UyaNGlrR8fRkdrSQ/8GSBFCJAkhrMANwIpTnHOc9tgpzlN8BIBlOZOxCAMpGnYHtPp2zDPcvgbdYzYhpAQpCDDXqRnuiqIoSrfSqgZdCPEO8F+gvxDikBDiFimlF7gdWA3sBP4mpdzuv1CbZ9RUA7Ax4hx6CDd6SMOWubZgAPSGIXevZsKMxDCZCdDqMFtUD11RFEXpPlo7y31qC8dXAivP9ObtMeSuV/tmtR+0BWHFg6m6YdQ/wLcDoOH2TTj0aCYsUqILCzaTQy1ZUxRFUbqVLp+cpbGHXmsJoIfJiSkiCaYugbDeAOiNDbrZhFkaGEismhpyVxRFUbqXLp+cpexIObow4dKsBOKBsD7Q/3974OiPq90AABBgSURBVBjuhlnuJhNmKTFpjdu+qiF3RVGUzurgwYPmK6+8sm98fHxWv379MkeOHJn8pz/9KappQpWmrr/++nM2bNjQqm1du6sOTc4ihJgATEhObrZ+WqW+xoFHs/JzCZpJgnb8Ejjd2TApTjNhkV6EybeaQTMFnvE9FUVRFP8xDIOJEycmT5s2reyDDz7YB7B+/frAf/zjH+EtnbNkyZIz38ykm+jyPXSPw4FbM5NrCkIzAZrluNcNl2/fdrfmG3JvbNBNJrVLnKIoSmf0wQcfhJjNZnnPPfeUNh4bPnx4/ciRI2sdDod2+eWX901KSsqcOHFiUuN+7IMHD+7/+eef2wHsdvuAO+64o3f//v0zcnJy0goKCswAhw8fNl922WX9srKy0rOystL//e9/B3XIG/STLt9Dd1VV4TGZsSKwBmg/aNB9s9zNviF3j4EQDQ262vZVURTllB78z4Pxeyr2tGv61OSI5LrfX/D7FpO+bNmyJTAnJ6fZrVh37twZuGnTpn2JiYme3NzctDVr1gRfdtllx6Usra+vNw0bNqz2z3/+c+Ftt93W589//nP0k08+WXTrrbfG//KXvzxy2WWX1ebn51svu+yylH379p311Vn+0qENenvMcjecLtyahW8CP+cSmwVMJ/bQvfgadIEmDTD5Ps1pph/1oxZFUZQuKTs729GvXz8PQGZmZt3evXt/sNWoxWKRN9xwQxVAbm6uY+3ataEA//nPf0Lz8/OPPW+tra3VKioqTBEREcaJ1+iKOrRBbw/SUYPbZMFpdoDuxqNrfPbaDnTdVz+1+3wbzbiFwGwYIHyz3tWQu6IoyqmdrCftL9nZ2fXLly+PaO41m83WNN0oXq/3B9uQm81maTKZGn8+VkZKybfffrszODi4dUlMupguv2yNulo8mhl3gBN0LzWVOru/Kqb0QA1HC2px1AtCag6gW0xohoFJNeiKoiid2oQJE2rcbreYP39+VOOxzz77zP7JJ58Et+W6I0aMqH7iiSdiGn9fv359t5od3eUnxQmvjkuz4AyWoLvxGr4e+dTfDuHGh4cyPruQQRuexGMSmA392JC7atAVRVE6J5PJxIoVK/Z+9NFHofHx8VnJycmZv/3tb+Pi4uI8bbnuSy+9VLBx48ag1NTUjH79+mU+99xz0e0Vc2fQ5YfchVfHbTHjDKyDCg9erwmLTUNryK4m3b5Z7i4pCTN0hOqhK4qidHqJiYmelStX7jvx+F133XUsrevixYsPNv789ddf7278ua6u7rvGn2+++eaKm2++uQIgNjbW++GHH/7gmt1Fl27QK4oKCasqw9MzBrPZBc4q6qTEHWFhzVHfMH6VxU519vnU6gaxhkFQgG+rWNWgK4qiKN1Jl162lvfBPwk1dGy6m5v2+hrq0qNWll8YyLat3/sK9T0X5pwLukGgx0WfKN+HM4ulxf0JFEVRFKXL6dLP0FMHDAJgT3w2AdI3lN73/z2CFmdnYKidf+Wm8taOr/jL04+wemAqgw/vx+O14iUai0Xt5a4oiqJ0Hx3aoLeVPczXy9ZsXkDgkFH07BtJvZT0tFkYEGony1FNWsF+ckLsoPsafUyRHRe0oiiKovhBl27QDcO3lFAIHdFkJWKdoRNo+uFbMwwDiUCIHyxbVBRFUZQurd2foQshgoAXADfwqZTyrfa+RyOPp2EbV+HroTeq0w3sWvMNOkAzLymKoihKl9aqpk0IsUgIUSKE2HbC8cuFELuFEHuEEL9uODwZeE9KOROY2M7xHsdZ3zCELryIJg16/UkadOmL259hKYqiKG1kt9sHNP19wYIFkT/96U8T/HnP3bt3W1NSUjIbf58/f35URkZGemlpqebP+7aX1vZVXwcub3pACKEBzwPjgAxgqhAiA+gDNG4VqLdPmM2ra2jQpclzbMjdkJJ6Q560h25SDbqiKIpyEs8//3yPhQsXxnz00Uf50dHRfm3L2kurGnQp5edA+QmHBwN7pJT7pJRu4F3gKuAQvka91dc/U8s/8y1Bk8KDx6RRYxX0XbURgLf/s58xT33G378twOnRGfPUZ+iGjkSoIXdFUZQuLC8vzzps2LDU1NTUjGHDhqXm5+dbAaZMmZI4Y8aM+AEDBqT16dMn+7XXXosA0HWdm266KSE5OTlz1KhRySNHjkxufK05r7zySsTTTz8du3bt2rzY2Fgv+LaJzcnJSUtNTc0YM2ZMv8Ze++DBg/vPnj27d3Z2dnpiYmLWqlWrggFqampMV1xxRd/U1NSM8ePH9z333HPTGtO7+ktbnqH35n89cfA15EOABcBzQojxwPstnSyEmAXMAkhIOLNRlJS4UADqgvbgNIei6+B2eOjlhWyLlZCeViKCLJiEIKVnMKIaQgLM2MxdYvREURSlwx2+7/54V35+uzZEtpSUurg/PHbSpC8ul8uUlpaW0fh7VVWVNmbMmCqA2267LWHatGlld9xxR9kzzzwTOXv27Pi1a9fuBThy5Ijl22+/3bVp06aAq6++Ovnmm2+uWLx4cURBQYF19+7d2wsLC81ZWVlZM2bMKGv2/R4+bL3nnnsSNmzYsCMhIcHbeHzGjBlJTz/99MHx48fXzp07N+7ee++NW7RoUQGA1+sVW7du3blkyZKwRx55JO7yyy/PmzdvXnR4eLiel5e345tvvgkYNmxYZnP3a09t6as2N24tpZQOKeXNUsrZJ5sQJ6V8CXgY2Gi1/iD7XatcelEiAG5r5bFj5rxq/j44lb9en8sLN+ZyaXovrGYTL9yYi0kIIoOtzQauKIqidB42m83YtWvXjsav3/zmN4cbX/vuu++CZs2aVQ4we/bs8g0bNhxL2jJx4sRKTdPIzc11lpWVWQDWrVsXPHny5ApN00hISPAOHTq0pqX7RkREeOPi4tyLFy8+1oMvKyvTampqtPHjx9cCzJw5s+zLL788ds9rr722AmD48OGOQ4cOWQHWr18fPHXq1HKAQYMGOVNTU5vN796e2tJDPwTEN/m9D3C4hbKKoihKF3SqnnRnExAQcCw1qpTyuO8n+vjjj4PmzJlzDsCDDz5YOHDgwPrAwEDj3//+d/7w4cPTYmJivLNnzz7xcXOL9zSbzei6fixV69nWlh76N0CKECJJCGEFbgBWnM4F2iPbmqIoivLjMmDAAMcrr7wSAbBw4cIeAwcOrD1Z+QsvvLB2+fLlEbquU1BQYP7qq69CAEaPHu1oHAG48cYbj+XxjouL865atSrv97//fe+lS5eGRkZG6qGhoXrj8/FXX301ctiwYSe95/Dhw2vffffdCIANGzYE5OXl+T1Va6t66EKId4CLgSghxCHgt1LKV4UQtwOrAQ1YJKXcfjo3b+te7oqiKMqPz4svvnhw+vTpic8++2yvyMhI7+LFi/efrPz06dMr1q5dG5KampqZlJTkzMnJcYSHh5905npaWpp76dKle6666qqUiIiIva+99tr3s2fPPufOO+80JSQkuN55552T3vPuu+8uve666xJTU1MzsrKy6vr3718fERHh19nyrWrQpZRTWzi+EljZrhEpiqIoP3pNU6AC3HnnnWVAGUD//v3dX375Zd6J5yxdunR/c9fQNI0XX3zxUFhYmFFcXKwNGjQoPTc39wfPtPv37+/Oz88/1jEdNmxYfUlJyZbG3zdv3rzrxHOapm2NjY31FhYWbgWw2+3GsmXLvrfb7XL79u22sWPHpqakpLhb/xc4fR2abU1K+T7w/sCBA2d2ZByKoihK9zZmzJiU6upqzePxiLvvvruo6Qx2f6ipqTFdeOGF/T0ej5BS8vTTTx9o+nzfH7p0+lRFURRFaY2mPemzISIiwti2bdvOs3nPLp0+VVEURfELwzAMtcK3k2moE6Ol19WeaYqiKMqJtpWWloapRr3zMAxDlJaWhgHbWiojOmKt3LGbNwy5A9cD+acoHgUc9XtQp6+zxgUqtjOlYjsznTW2zhpXo7bEd46UMro9gwHYsGFDjNlsfgXIQnX8OgsD2Ob1en+em5tb0lyBDm3QT4cQ4lsp5cCOjuNEnTUuULGdKRXbmemssXXWuBp19viUrkN98lIURVGUbkA16IqiKIrSDXSlBv2ljg6gBZ01LlCxnSkV25nprLF11rgadfb4lC6iyzxDVxRFURSlZV2ph64oiqIoSkuklO3+hS+t6ifATmA78H8Nx3sAa/AtUVsDRDQcHwNsALY2fB/d5Fq5Dcf3AAtoGFVo5p7NlgMuAjYCXuCaThbbDKAU2NTwdXcniu0c4CNgC/ApMKgdY3sMKABqT/HvqCPqtK2x+bNO2xqbX+oUsAMfArsarvN4G+v01k4Ul1/qs51iO7E++/jj/+fqq+t8+eeiEAuc3/BzCJAHZABPAr9uOP5r4ImGnwcAcQ0/ZwGFTa71NTAMEMC/gHEt3LPZckAicC6wGN///DtTbDOA5zrp3+3vwPSGn0cD77VjbEMb3uupGqaOqNO2xubPOm1rbH6pU3yN06iGn63AOtr23+nPO1FcfqnPdortxPr868n+Xaiv7v91dm4C/8T3SXU3ENtwLBbY3UxZgS+jjq2hzK4mr00FFjZzzinLAa8D13Sm2E78n0Uni207DZ/4G65d3R6xnXC8xYapI+q0PWLzV522U2x+r9OG154FZrZXnXZkXGejPtsQ20nrU339+L78/gxdCJGI71PqV0BPKWURQMP3mGZOmQJ8J6V0Ab2BQ01eO9Rw7EStLdcZY5sihNgihHhPCBHfiWLb3HBNgKuBECFEZDvE1lodUaftFZs/6rQ9YvN7nQohwvHt/vjRacbWrE4Sl1/rsw2xtVifyo+TXxt0IUQwsBSYK6WsbkX5TOAJfM/QwPep80SyuVNbWa6zxfY+kCilPBdYC7zRiWL7FTBSCPEdMBIoBLztEFtrdUSdtkds/qrT9ojNr3UqhDAD7wALpJT7TjO25u7TGeLya322MbZm6/NUsSjdl9/SpwohLPj+wb8lpVzWcPiIECJWSlkkhIgFSpqU7wP8A/iplHJvw+FDQJ8ml+0DHBZCaPgmmACsAF5srlxnj01KWdbk+MvAE50otsPA5IZ7BOPrCdQBH7QxtmZ1kjptc2x+rNP2iM3fdfoSkC+lfOZ0Y2vmfbXn3+yM4zoL9dmW2H5Qn1LKKpQfL3+M4+P7VLkYeOaE4/M4fuLIkw0/h9MwfNTMtb7BNxmocULIFS3c86TlaHg215lio+GZW8PPVwNfdqLYogBTw8+PAY+0V2xNzj3V5K6zXqdtjc2fddoOsfmtToFH8TVypnaq004Rlz/rsx1i+0F9nuw66qv7f/nnojAC37DQFv633OMKIBLfc6L8hu89Gso/ADialN0ExDS8NhBfuri9wHO0vPyq2XL4luYcarh+GfB9J4rtj/gmtmzGtxRmWieK7ZqG++UBrwCj2jG2JxvqxGj4/rtOVKdtjc2fddrW2PxSp/h6jRLfUq7G4z9vQ51WdaK4/FKf7RTbifX5g8l26uvH9aV2ilMURVGUbkDtFKcoiqIo3YBq0BVFURSlG1ANuqIoiqJ0A6pBVxRFUZRuQDXoiqIoitINqAZdURRFUboB1aAriqIoSjegGnRFURRF6Qb+P9dDhB1KW5pqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# On indique quelles series on montre dans le graphique\n", + "plt.plot(X_ax, Belgique[1:], label='Belgique')\n", + "plt.plot( X_ax, France[1:], label= 'France')\n", + "plt.plot( X_ax, Allemagne[1:], label= 'Allemagne')\n", + "plt.plot( X_ax, Iran[1:], label= 'Iran')\n", + "plt.plot( X_ax, Italy[1:], label= 'Italie')\n", + "plt.plot( X_ax, Japon[1:], label= 'Japon')\n", + "plt.plot( X_ax, Coree[1:], label= 'Corée du Sud')\n", + "plt.plot( X_ax, PaysBas[1:], label= 'Pays-Bas')\n", + "plt.plot( X_ax, Portugal[1:], label= 'Portugal')\n", + "plt.plot( X_ax, Espagne[1:], label= 'Espagne')\n", + "plt.plot( X_ax, EtatsUnis[1:], label= 'Etats-Unis')\n", + "plt.plot( X_ax, RoyaumeUni[1:], label= 'Royaume-Uni')\n", + "plt.plot( X_ax, Chine[1:], label= 'Chine')\n", + "plt.plot( X_ax, HongKongs[1:], label= 'Hong-Kong')\n", + "\n", + "# Plot another line on the same chart/graph\n", + "plt.yscale(\"log\")\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.show()" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1028,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -