diff --git a/Uebung04/Grundlagen_Matplotlib.ipynb b/Uebung04/Grundlagen_Matplotlib.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..28d68582b47226f21036f400a2e11ee216e90793 --- /dev/null +++ b/Uebung04/Grundlagen_Matplotlib.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd6b03b-7ef5-40a3-a47d-637b11ac1795", + "metadata": {}, + "source": [ + "### **Einleitendes Thema - Zeichnen mit Matplotlib**\n", + "Diagramme erstellen ist eine Aufgabe, die bei wissenschaftlicher Datenauswertung häufig vorkommt. Du hast bestimmt schon einmal Diagramme z.B. in Excel aus Tabellen erstellt. Auch in Python gibt es Module zum erstellen von Diagrammen. *Matplotlib* ist sicherlich das verbreitetste Werkzeug für diesen Zweck. Das erstellen von Diagrammen mit Python bietet dir mehrere Vorteile gegenüber anderen Ansätzen. Durch den Programmier-Ansatz kannst du Diagramme mit etwas Übung sehr schnell formatieren und die Erstellung von Diagrammen automatisieren. Dadurch kannst du z.B. ein einheitliches Aussehen erreichen. Falls du Dinge wie die Farbgebung verändern möchtest, kannst du einfach alle deine Diagramme automatisch erneut erstellen und hast mit Matplotlib generell eine riesige Auswahl an gestaltungsmöglichkeiten. Wenn du wissenschaftliche Artikel im Bereich Maschinenbau ließt, ist die Wahrscheinlichkeit sehr hoch, dass die Diagramme in diesen Artikeln mit Matplotlib erstellt wurden.\\\n", + "In diesem Grundlagen-Dokument ist eine kurze Einleitung in Matplotlib und seine Programmierschnittstellen beschrieben. Daneben gibt es noch viel mehr zu entdecken. Falls du ein Diagramm aus Daten generieren möchtest und eine Idee hast wie es aussehen soll, allerdings keine Idee wie du es umsetzen sollst, ist es immer eine gute Idee die Beispiele von Matplotlib anzuschauen *https://matplotlib.org/stable/gallery/index.html*. Hier findest du viele Beispieldiagramme und Quelltext, der diese Diagramme erstellt. \n" + ] + }, + { + "cell_type": "markdown", + "id": "0d218d73-2216-4e34-840d-b87dbecc7b2c", + "metadata": {}, + "source": [ + "### **Kompaktes Fallbeispiel - Eine Kurve zeichnen**" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "adecfb0a-1ecd-4fec-a107-6ab85d29a581", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt #pyplot ist ein Modul innerhalb von matplotlib, dass die meißtgenutzten Funktionalitäten zusammenfasst. Wir Importieren es unter dem Namen plt, da der name sonst matplotlib.pyplot wäre, was etwas lang ist\n", + "%matplotlib inline\n", + "#Diese Zeile weißt matplotlib an, die Grafiken direkt in Jupyter-Notebook auszugeben. Wird bei manchen älteren Versionen von Jupyter-Notebooks zwingend benötigt.\n", + "x=[0,1,2,3,4,5]\n", + "y=[10,2,4,5,1,2] #X und Y Daten für eine einfache Kurve.\n", + "plt.plot(x,y,\"rx-\")#Zeichnet ein X-Y-Diagramm. Das \"rx-\" bedeutet: Die Kurve ist rot. Die Punkte werden mit einem \"X\"-markiert. Die Punkte werden mit einer Line verbunden\n", + "plt.xlabel(\"X\")\n", + "plt.ylabel(\"Werte\")\n", + "plt.title(\"Ein Titel\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8d7ad515-6341-453c-8fd3-51e5eb2808a6", + "metadata": {}, + "source": [ + "### **Übersicht Matplotlib**\n", + "Matplotlib ist ein sehr umfangreiches Modul, das fast endlose Möglichkeiten bietet Daten zu visualisieren. Der Schwerpunkt liegt auf 2-Dimensionalen Darstellungen wie Kurvendiagrammen, Balkendiagrammen, Tortendiagrammen, Konturdiagrammen und vielem mehr. Es ist auch möglich einfache 3D-Diagramme zu erstellen. Darüber hinaus können statische Diagramme in diversen Dateiformaten abgespeichert werden, um sie z.B. in Artikel oder Berichte einzufügen. Es ist auch möglich interaktive Diagramme zu erstellen, was in diesem Notebook allerdings nicht gezeigt wird.\\\n", + "Prinzipiell bietet Matplotlib zwei Programmierschnitstellen. Eine sogenannte implizite Schnittstelle, die sich stark an das Programm *matlab* anlehnt. Diese Schnittstelle ist einfacher zu bedienen, bietet allerdings auch einen kleineren Funktionsumfang. Sie wird vor allem für einfache Diagramme verwendet um schnell etwas darzustellen.\\\n", + "Die zweite Schnittstelle ist eine objektorientierte Schnittstelle, die bei einfachen Diagrammen etwas mehr Schreibarbeit erfordert, allerdings mehr Kontrolle über die Darstellung bietet und für kompliziertere Diagramme oft die bessere Wahl darstellt.\\\n", + "Beide Schnittstellen bauen auf der selben Logik auf, die im folgenden kurz beschrieben wird:\n", + "#### Grundlegende Begriffe\n", + "Die Grafik beschreibt die grundlegenden Komponenten einer Grafik in Matplotlib.\n", + "<img src='anatomy.webp' width=\"400\" height=\"400\">\n", + "##### Figure\n", + "Die *Figure* ist eine komplette Grafik. Man kann eine komplette Grafik anzeigen oder als Datei speichern, Sie kann aus einem oder mehreren Diagrammen bestehen. Die Diagramme heißen bei Matplotlib *Axes*. Die Diagramme sind dabei innerhalb der *Figure* in einem Raster angeordnet. Die *Figue* bietet sozusagen die Leinwand, auf der die Diagramme gezeichnet werden.\n", + "##### Axes\n", + "Ist ein Diagramm. Ein Diagramm besteht standardmäßig aus zwei (oder im Falle einer 3D-Grafik drei) *Axis* Objekten. Also den Koordinatenaxen. Es ist auch möglich ein Diagramm mit zwei Y-Achsen etc. zu zeichnen. Das Diagramm verfügt weiterhin optional über einen Titel bzw. eine Überschrift, Achsbeschriftungen für jede Achse, ein Hintergrundgitter und eine Legende. In einem Diagramm bzw. *Axes*-Objekt können mehrere Datenkurven eingezeichnet werden. Bei mehreren Kurven ist eine Legende dann sinnvoll. \n", + "##### Axis\n", + "Eine Koordinatenachse. Jedes Diagramm kann mehrere Axen enthalten. Die *Axis*-Objekte legen fest, wie die Achsen unterteilt sind. Z.b. logarithmisch oder linear. Sie legen auch fest, an wie vielen Stellen ein zugehöriger Zahlenwert eingetragen wird (sogenannte major_ticks) und ob zwischen zwei dieser Markierung ggf. weitere kleinere Unterteilungen bestehen (minor_ticks genannt). Mit den *Axis*-Objekten wird auch gesteuert was unter den *ticks* steht. Standardmäßig sind das die zugehörigen Zahlenwerte. Es ist allerdings auch möglich die Zahlen durch Text zu ersetzen\n", + "#### Artists\n", + "Alles was in der Grafik sichtbar ist (sogar die oben genannten Objekte). Alledings sind hier meistens die sichtbaren Objekte in einem Diagramm gemeint. Z.B. eine Linie, eine Markierung, Text, die Torte eines Tortendiagramms etc." + ] + }, + { + "cell_type": "markdown", + "id": "8aaeafc3-560f-4f8f-89a2-c9591b23b179", + "metadata": {}, + "source": [ + "### **Das Objektorientierte Interface**\n", + "Das Objektorientierte Interface wird zu erst beschrieben, da das implizite Interface auf diesem Aufbaut.\n", + "Eine Einschränkung von Jupyter Notebooks ist, dass die Diagramme allerdings nur über das implizite Interface darstellbar sind. Das ist allerdings kein Problem.\\\n", + "In einem ersten Schritt wird ein *Figure*-Objekt erstellt. Es bildet den Rahmen für die Diagramme. Die erstellte *Figure* hat eine Größe von 3x2 Inches. Die Auf dem Bildschirm dargestellte Größe kann abweichen. Die Größe beeinflusst das Seitenverhältnis und die Größe der Schrift. Als nächstes müssen der Grafik Diagramme hinzugefügt werden. Die Diagramme werden als *Axes* bezeichnet und sind erst eimal generisch, soll heißen es ist zunächst egal, welche Art von Diagramm sie konkret werden. Im einfachsten Fall fügen wir ein Diagramm ein. Dazu wird die Objektmethode *subplots* ohne Parameter erwendet. Das Ergebnis ist ein Objekt der Klasse *Axes*. In dieses kann jetzt inhalt gezeichnet werden. Im Einfachsten Fall X-Y Daten. Das geht mit der Methode *plot* des *Axes* Objekts. Die erste Liste enthält die X-Werte, die zweite Liste die Y-Werte. Zum Schluss wird das Ergebnis angezeigt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0a141e67-98ef-4d4f-95b4-4fbe479098cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x2521a643ca0>]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 300x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "fig=plt.figure(figsize=(3,2))\n", + "ax=fig.subplots()\n", + "ax.plot([1,2,3],[3,2,1])\n" + ] + }, + { + "cell_type": "markdown", + "id": "615d4499-5989-4774-b3e1-c707815d800f", + "metadata": {}, + "source": [ + "Man kann auch mehrere Diagramme in einer Figure darstellen. Dafür sollte eine neue *Figure* erstellt werden, die als zweiten Parameter den Layout-Typen (\"constrained\") erhält. Dadurch wird verhindert, dass die Einzeldiagramme sich überlappen. Um mehrere Diagramme zu erstellen muss die Methode *subplots* mit zwei Parametern aufgerufen werden, die die menge an Grafiken untereinander und nebeneinander angeben. Das Ergebnis ist jetzt eine Matrix von Diagrammen (Das ist praktisch eine Liste mit zwei Indizes). Das ist im folgenden Beispiel für insgesamt 6 Grafiken in einem 3x2 Raster gezeigt. Es werden verschiedene Diagrammtypen gezeigt. Im ersten Diagramm das bekannte X-Y-Diagramm. Im zweiten Beispiel ein sogenannter Scatter-Plot, also einzelne Punkte im X-Y-Raum. Der Parameter s ist dabei optional und gibt die Fläche der dargestellten Punkte an. Das dritte Diagramm ist ein Balkendiagramm. Die erste Liste enthält die Namen der Balken, die zweite die zugehörigen Höhen. Das vierte Diagramm enthält ein Tortendiagramm. Dabei stellt der erste Parameter die relativen Anteile der Tortenstücke dar. Der Parameter *labels* gibt den Tortenstücken Namen. In den letzten beiden Diagrammen ist gezeigt, wie mehrere Linien in ein Diagramm gezeichnet werden können und wie mehrere Diagrammtypen gemischt werden." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "0382bc33-5c6b-4cc8-9c8a-59903904b06f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x2521a7f6b30>]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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G1h7OwprDGuSX/vG7HdndA1HhKkzo7Q0bqxbN30REZHRSK9akloeIiCxTc/ubVp22umDBAmzZsgVJSUktKhwBwMbGBgMHDsSFCxcaXUahUEChULQmGkmUn6s9nrmjB54aF4w9ZwsQl6JB0vlC/Hb+Kn47fxWezgrMCgvEzLBABHRyEDsuERERERH9RYuKR0EQ8NRTT2Hjxo1ISEhAly5dWrzDuro6nDhxAnfeeWeL16X2z9pKjol9fDCxjw+yiiqx+pAG645kobBMi//bcwGf7b2AsT29EBWuwtgQL1jJOTMvEREREZEUtKh4nD9/PuLj47F582Y4OzsjLy8PAKBUKmFvbw8AePjhh+Hv74+YmBgAwBtvvIGhQ4ciODgYxcXFeP/995GZmYnHHnvMyD8KtTeBbg54YXIIFk7ogZ2n8xGXkokDF69hz9kC7DlbAD+lHWaGqTAzLBA+St4blIiIiIhITC0qHlesWAEAGDNmTL32VatWYe7cuQAAjUYDufyPa9euX7+Ov/3tb8jLy0OnTp0wePBgHDhwAL17925bcrIYttZyTA31xdRQX1wqLMfqQxqsT81GTkkVPtr1Oz7dcx4TenkhSh2EkcEekHM0koiIjOz3/DL8cDATBy5cQ0V1LZztrDGmpxei1SoEuXNyNyIioA0T5pgTJwzoeKpq6rD9ZB7iUjJx+PJ1Q7vKzQGzwgNx/+BAeDrzulgiMi6p9TdSy2OJiiur8c/V6Ug6XwgruQx1uj8+FlnJZKgTBNzZzxfv3xcKR0Wb7nBGRCRZJp9t1ZzYeXZsv+eXIT5Fgw1p2SirqgUA2FjJMKmPD6LUKkR0dYdMxtFIImo7qfU3UstjaYorq3HPfw5AU1RZr2j8K7kM6OuvxNq/R8DelvcpJiLLw+KRLM6N6jr8fDwHcSkaHMsqNrR39XREVLgKMwYFoJOjrXgBiajdk1p/I7U8luaxbw9j79lC1DXjo5BcBjwwJBDLZoSaIRkRkXk1t7/hjfWo3bC3tcIDQwKxef5wbHlqBKLUKjjaWuFSYQXe2noG6pjdeGZtOo5cLkI7+E6EiCzcihUrEBoaChcXF7i4uCAiIgLbtm0TOxb9T+a1Cuw6U9CswhEAdALwY2o2iiqqTZyMiEi6WDxSu9TXX4l37umHlFcm4O17+qK3rwuqa3XYePQK7vs8GZM//g3fHriM0qoasaMSUQcVEBCAZcuWITU1FUeOHMG4ceMwffp0nDp1SuxoBCAuRdPi20HpBAHrj2SZKBERkfTxtFWyCIIg4Fh2CeIOZuLn4zmoqtEBAOxtrHB3fz9EqVUIDVDy2kgiui1T9zdubm54//33MW/evAZf12q10Gq19fIEBgay/zOByOX7kf6nSyCaQwZgYh9vfPHQEJNkIiISS3P7P04bRhZBJpNhQKArBgS64tW7emNjWjbiUjQ4X1COtUeysPZIFvr6uyAqPAjTB/hxxjwiMqu6ujqsX78eFRUViIiIaHS5mJgYLF261IzJOq4KbW2L1xEAlFe1fD0iIkvBkUeyWIIg4EjmdcQdzMQvJ/JQXacfjXRSWGP6AD9Eq4PQ24/vJyL6g7H7mxMnTiAiIgJVVVVwcnJCfHw87rzzzkaX58ij+dz/+YF6t4JqDrkMmNLXF8ujB5koFRGRODjySB2eTCZDWGc3hHV2w2vTqrEhNRvxhzTIuFqBuBQN4lI0GKhyRVS4CneF+nH6dSIyup49eyI9PR0lJSX48ccfMWfOHCQmJqJ3794NLq9QKKBQ8B625jA2xAupmddxmzt03EInAKN7eJouFBGRxHHkkToUnU7AwUvXEJeiwY5Teaj936cGFztrzBgcgGi1CsFeziKnJCKxmLq/mTBhArp164YvvvhCEnk6smvlWqjf2W3oB5rDSWGNw69M4JeNRGRxOPJI1AC5XIZhwR4YFuyBgrIqrD+SjdWHNMi+fgOr9l/Gqv2XEd7FDdFqFSb39YHCmh8QiMh4dDpdvdNSSTzuTgo8qA7Ct8mX0dzy8fFRXVk4ElGHxuKROiwvZzvMHxuMJ0Z3Q9L5QsSnaLD7TD4OZRThUEYR3Bxtcd/gAMwOV6GLh6PYcYmonVm8eDGmTJkClUqFsrIyxMfHIyEhATt27BA7Gv3PK3f1wuWiCiSeK2yygLxnoD/mjw02Sy4iIqli8UgdnpVchrE9vTC2pxdyS25g7eEsrDmUhbzSKnyZdAlfJl3CiGAPRKlVuKO3N2yseHtUImpaQUEBHn74YeTm5kKpVCI0NBQ7duzAHXfcIXY0+h8bKzm+fngIPtr1O1btv4zK6jpYyWXQ6QTI5TLU6QQo7W3w+OiueGJUN8hbeF9IIiJLw2seiRpQW6fD3nOFiEvJROLvhbj5r8TTWYEHhgRgVpgKgW4O4oYkIqOTWn8jtTyWrLK6Fv9Nz8Ghy0Wo1NbByc4aI4I9MKUfL2EgIsvX3P6GxSNRE7KKKrHmsAZrD2fjarn+WiWZDBjTwxNR6iCM7ekJa45GElkEqfU3UstDRESWicUjkZFV1+qw60w+4lIysf/CNUO7r9IOM8MCMStMBR+lnYgJiaitpNbfSC0PERFZJhaPRCaUcbUCqw9psP5IFq5X1gDQXzs5PsQLUWoVRnX35LUxRO2Q1PobqeUhIiLLxOKRyAyqauqw41Qe4g5qcOhykaE90M0es8JUeGBIIDydecNvovZCav2N1PIQEZFlYvFIZGbn88sQl6LBhrRslFXVAgBsrGSY2McH0eEqRHRzh0zG0UgiKZNafyO1PEREZJlYPBKJ5EZ1HbYcz0FcigbpWcWG9q4ejohSqzBjUAA6OdqKF5CIGiW1/kZqeYiIyDKxeCSSgFM5JYhP0WDT0SuoqK4DANhayzG1ny+i1CoMCerE0UgiCZFafyO1PEREZJlYPBJJSLlWf/+wuJRMnMopNbT38HZCVLgK9wwKgNLeRsSERARIr7+RWh4iIrJMLB6JJEgQBBzLLkF8Sib+eywHVTU6AICdjRx39/dDlDoI/QOUHI0kEonU+hup5SEiIsvE4pFI4kpu1GDT0SuIS8nE7/nlhvY+fi6IUqswfYA/nBTWIiYk6nik1t9ILQ8REVkmFo9E7YQgCEjNvI64FA22nshFda1+NNLR1grTB/ojWq1CHz+lyCmJOgap9TdSy0NERJaJxSNRO3S9ohob0rIRn6LBpasVhvYBga6IUqswLdQP9rZWIiYksmxS62+kloeIiCwTi0eidkwQBCRfuoa4FA12nMxDrU7/z9TZzhozBgUgSq1CD29nkVMSWR6p9TdSy0NERJapuf0NL6gikiCZTIZh3TwwrJsHCsu0WJ+ahfgUDbKv30DsgcuIPXAZ4Z3dEKVWYXJfH9jZcDSSiIiIiExL3pKFY2JiEBYWBmdnZ3h5eSEyMhLnzp1rcr3169cjJCQEdnZ26NevH3755ZdWBybqaDydFfjHmGAkPT8W3z4ajom9vWEll+HQ5SIsXJuOiJjdeOeXM8j402muRERERETG1qLiMTExEfPnz8fBgwexc+dO1NTUYOLEiaioaPxD64EDBzB79mzMmzcPR48eRWRkJCIjI3Hy5Mk2hyfqSORyGUb38MSXDw/B/hfH4ZkJPeCrtMP1yhp8mXQJYz9IQPTXB7H1+B+T7hARERERGUubrnksLCyEl5cXEhMTMWrUqAaXmTlzJioqKrBlyxZD29ChQzFgwAB8/vnnzdoPr/kgalhtnQ4J5woRl5KJhN8LcfNfs4eTAg8MCcDscBUC3RzEDUnUjkitv5FaHiIiskxmueaxpKQEAODm5tboMsnJyVi0aFG9tkmTJmHTpk2NrqPVaqHVag3PS0tL2xKTyGJZW8kxobc3JvT2RlZRJdYezsLaI1koLNPiPwkXsSLxIkb38ERUuArjQrxgbdWikw2IiIiIiAxaXTzqdDosXLgQw4cPR9++fRtdLi8vD97e3vXavL29kZeX1+g6MTExWLp0aWujEXVIgW4OeG5STzw9oTt2nc5HXIoG+y5cRcK5QiScK4SPix1mhQdiZlggfJX2YsclIiIionam1cMQ8+fPx8mTJ7FmzRpj5gEALF68GCUlJYZHVlaW0fdBZKlsrOSY0s8XPzymRsJzY/D4qK5wc7RFXmkVPt51HsOX7cHfvjuCvecKUKeT/J16iIiIiEgiWjXyuGDBAmzZsgVJSUkICAi47bI+Pj7Iz8+v15afnw8fH59G11EoFFAoFK2JRkR/0tnDEYvv7IVFE3tg+8k8xKVocCijCDtP52Pn6XwEdLLH7HAV7h8SAC9nO7HjEhEREZGEtWjkURAELFiwABs3bsSePXvQpUuXJteJiIjA7t2767Xt3LkTERERLUtKRK2msLbC9AH+WPd4BHY+MwqPDO8MFztrZF+/gfd3nMOwmD2YH5eG/ReuQsfRSCIiIiJqQItmW/3HP/6B+Ph4bN68GT179jS0K5VK2Nvrr6F6+OGH4e/vj5iYGAD6W3WMHj0ay5Ytw9SpU7FmzRq88847SEtLu+21kn/G2eaIjO9GdR22nshFXEomjmqKDe1dPBwxOzwQ9w0OhJujrXgBiUQgtf5GanmIiMgyNbe/aVHxKJPJGmxftWoV5s6dCwAYM2YMOnfujNjYWMPr69evx6uvvorLly+je/fueO+993DnnXc2d7fsPIlM7HROKeIPZWLT0RyUa2sBALZWctzZzwdR6iCEde7U6L9/Iksitf5GanmIiMgymaR4FAs7TyLzqNDW4r/HchCXkomTV/64RU53LydEqVW4d1AAlPY2IiYkMi2p9TdSy0NERJaJxSMRtcnx7GLEHdTgv8dycKOmDgBgZyPHtFA/RKlVGBDoytFIsjhS62+kloeIiCwTi0ciMorSqhpsOnoFcQc1OJdfZmjv7euCKLUKkQP94aRo9S1jiSRFav2N1PIQEZFlYvFIREYlCALSNNcRd1CDLSdyUV2rAwA42lph+kB/RIWr0NdfKXJKoraRWn8jtTxERGSZWDwSkclcr6jGhrRsxKdocOlqhaG9f6ArotUqTAv1g72tlYgJiVpHav2N1PIQEZFlYvFIRCYnCAIOXipCXEomdpzKQ02d/s+Js501ZgwKQJRahR7eziKnJGo+qfU3UstDRESWqbn9DS9UIqJWk8lkiOjmjohu7rharsX6I9mIP5SJrKIbiD1wGbEHLiOscydEq4Mwua8P7Gw4GklERETUXnHkkYiMSqcTsO/CVcSlZGLXmQLU6fR/Yjo52OC+wQGYHa5CV08nkVMSNUxq/Y3U8hARkWXiyCMRiUIul2FUD0+M6uGJ/NIqrD2chTWHNMgpqcJXv2Xgq98yMKybO6LUKkzs7QNba7nYkYmIiIioGVg8EpHJeLvY4Z/ju2P+2GAknCtAXIoGe88V4MDFazhw8Ro8nGzxwJBAzA5XIdDNQey4RERERHQbLB6JyOSs5DKM7+WN8b28kX29Uj8aeTgLhWVa/CfhIlYkXsSo7p6IUqswPsQL1lYcjSQiIiKSGl7zSESiqKnTYfeZfMSlaPDb+auGdh8XO8wMC8Ss8ED4Ku1FTEgdkdT6G6nlISIiy8RbdRBRu3H5agVWH9Zg/ZFsFFVUAwDkMmBciDei1SqM6uEJK7lM5JTUEUitv5FaHiIiskwsHomo3dHW1mHHqXzEHcxESkaRod3f1R5RahXuHxIAL2c7EROSpZNafyO1PEREZJlYPBJRu3ahoAzxKVnYkJaNkhs1AABruQwT+3gjWh2EiK7ukHM0koxMav2N1PIQUculZxVj28lcVFXXoV+AK+4K9eV9j0lyWDwSkUWoqqnD1uO5iEvJRJqm2NDe2d0BUWoV7hscCDdHW/ECkkUxZn8TExODn376CWfPnoW9vT2GDRuGd999Fz179hQlDxGZV1VNHf65+ih+PZ0PK7kMMgC1OgGdHGwQ+0g4+ge6ih2RyKC5/Q2nNCQiSbOzscKMwQH46R/Dse3pkXhoaBCcFNa4fK0S7/xyFkPf2Y2n1xzFoYwitIPvwqgDSUxMxPz583Hw4EHs3LkTNTU1mDhxIioqKsSORkRm8PbWM9h1Jh8AUKcTUKvT91ElN2rw0MoUlFTWiBmPqFU48khE7U6FthY/H8tBXIoGJ66UGNqDvZwQrVbh3oEBUDrYiJiQ2itT9jeFhYXw8vJCYmIiRo0aJXoeIjKd4spqhL29CzV1DX/MlgF49a7emDeii3mDETWCI49EZLEcFdaYFa7Cz0+NwH8XDMessEDY21jhQkE5lv58GuHv7MJz648hTXOdo5EkGSUl+i863NzcGl1Gq9WitLS03oOI2p8TV0oaLRxvOvynieGI2gtrsQMQEbVFaIArQgNc8fLUXth89AriUjQ4m1eGH1Oz8WNqNnr5uiBarULkQH84Kfgnj8Sh0+mwcOFCDB8+HH379m10uZiYGCxdutSMyYjIFKzltx+fkckAaytO+kbtD09bJSKLIggC0jTFiEvJxJbjuaiu1QEAHGytMH2AP6LVKvT1V4qckqTKVP3Nk08+iW3btmHfvn0ICAhodDmtVgutVlsvT2BgIPs/onamqqYOYW/tQpm2ttFlPp45AJED/c2Yiqhxze3/+DU8EVkUmUyGwUGdMDioE167qzc2pF1BXEomLhVWYPUhDVYf0qB/gBLR6iDc1d8XDrb8M0imtWDBAmzZsgVJSUm3LRwBQKFQQKFQmCkZEZmKnY0VnhofjHd+OXvLa1ZyGVRuDpjSz0eEZERtw5FHIrJ4giAgJaMIcSkabD+Za7gOxVlhjXsH+SNKHYSePs4ipyQpMGZ/IwgCnnrqKWzcuBEJCQno3r27qHmIyLwEQcBney7gs70XoP3fWTAAENa5Ez6LGgRvFzsR0xHVx/s8EhE14Gq5Fj+mZiM+RQNNUaWhfUhQJ0SpVbizH2/e3JEZs7/5xz/+gfj4eGzevLnevR2VSiXs7e3NnoeIxFFaVYPEc4W4UV2HfgFK9PLlv2WSHhaPRES3odMJ2H/xKuIOarDzTD7q/nf/LVcHG9w3KACz1Sp083QSOSWZmzH7G5ms4ckwVq1ahblz55o9DxERUWN4zSMR0W3I5TKM7O6Jkd09kV9ahXWHs7DmcBauFN/A1/sy8PW+DER0dUf0UBUm9vaBrTXvbEQt0w6+myUiImoRFo9E1OF5u9jhqfHd8Y+xwUj8vQBxBzXYe64AyZeuIfnSNXg42eL+IYGYHaaCyt1B7LhEREREomDxSET0P1ZyGcaFeGNciDeuFN/A2kMarDmchYIyLVYkXMTniRcxsrsnotUqjA/xgrUVRyOJiIio42jxJ5+kpCRMmzYNfn5+kMlk2LRp022XT0hIgEwmu+WRl5fX2sxERCbn72qPRRN7Yv9L4/D5g4MxsrsHBAFI+r0Qj3+fiuHv7sG/d/6OnOIbYkclIiIiMosWjzxWVFSgf//+ePTRR3Hvvfc2e71z587Vu/jSy8urpbsmIjI7Gys5Jvf1weS+Psi8VoHVh7Kw/kgW8ku1+HT3eXy25zzGhXghWh2EUT08YSVveJIUIiIiovauxcXjlClTMGXKlBbvyMvLC66uri1ej4hIKoLcHfHSlBA8c0d3/HoqH3EpmTh4qQi7zhRg15kC+LvaY3Z4IB4YEggv3r+LiIiILIzZLtgZMGAAfH19cccdd2D//v23XVar1aK0tLTeg4hIKhTWVpjW3w9r/h6BXYtGY96ILlDa2+BK8Q188OvvGLZsD578IRX7zl+FTscZN4mIiMgymLx49PX1xeeff44NGzZgw4YNCAwMxJgxY5CWltboOjExMVAqlYZHYGCgqWMSEbVKsJcT/nVXb6S8PB7/fqA/Bgd1Qq1OwLaTeXhwZQrGfpiALxIv4lq5VuyoRERERG0iE9pwIyqZTIaNGzciMjKyReuNHj0aKpUK33//fYOva7VaaLV/fNAqLS1FYGAgb5JMRO3C2bxSxKdosDHtCsq0tQAA2/9dOxmtViG8i1ujN5AncTX3JskdNQ8REVmm5vY3otyqIzw8HPv27Wv0dYVCAYVCYcZERETGE+Ljgjem98VLU0Lw87EcxKVocDy7BP89loP/HstBsJcTosJVmDEoAEoHG7HjEhERETWLKMVjeno6fH19xdg1EZHZONhaY2aYCjPDVDiRXYL4Q5nYnJ6DCwXleGPLaby7/SzuCvVDlFqFQSpXjkYSERGRpLW4eCwvL8eFCxcMzzMyMpCeng43NzeoVCosXrwYV65cwXfffQcA+Pjjj9GlSxf06dMHVVVV+Prrr7Fnzx78+uuvxvspiIgkrl+AEjEBoXj5zl7YlJ6DuIOZOJtXhg1p2diQlo0QH2dEDw1C5AA/ONtxNJKIiIikp8XF45EjRzB27FjD80WLFgEA5syZg9jYWOTm5kKj0Rher66uxrPPPosrV67AwcEBoaGh2LVrV71tEBF1FM52NnhoaBAeVKtwNKsYcQc12HI8B2fzyvCvTScR88sZTB/gh6jwIPQLUIodl4iIiMigTRPmmAsnDCAiS1ZSWYMNadmIP6TBhYJyQ3togBLRahWm9feDg60oVxl0OFLrb6SWh4iILFNz+xsWj0REEiEIAg5lFCEuRYPtJ/NQXacDADgrrHHPIH9EqVUI8eHfQFOSWn8jtTxERGSZJD3bKhER3Uomk0Hd1R3qru64Vq7Fj6n60cjMa5X4LjkT3yVnYnBQJ0SrVbizny/sbKzEjkxEREQdCEceiYgkTKcTcODiNcSlZGLn6XzU6vR/spX2NrhvcACi1Cp083QSOaXlkFp/I7U8RERkmTjySERkAeRyGUZ098CI7h4oKK3CuiNZWH0oC1eKb2Dlvgys3JeBoV3dEK0OwqQ+PrC1losdmYiIiCwUi0cionbCy8UOC8Z1x5NjgpH0eyHiUjKx52wBDl4qwsFLRXB3tMX9QwIRFa6Cyt1B7LhERERkYVg8EhG1M1ZyGcaGeGFsiBdyim9gzeEsrD2sQX6pFp8nXsTniRcxsrsHotVBGN/LCzZWHI0kIiKituM1j0REFqC2TofdZwsQl6LBb+cLcfMvu5ezArPCAjEzXAV/V3txQ7YDUutvpJaHiIgsE2/VQUTUQWmuVWL1YQ3WH8nC1fJqAIBcBozt6YXooSqM7uEFK7lM5JTSJLX+Rmp5iIjIMrF4JCLq4Kprdfj1dB7iDmqQfOmaod3f1V4/GhkWCC8XOxETSo/U+hup5SEiIsvE4pGIiAwuFpZjdYoGP6Zlo7iyBoD+2sk7enkjeqgKw7t5QM7RSMn1N1LLQ0RElonFIxER3aKqpg7bTuYiPkWDw5evG9qD3B0wO1yF+wcHwN1JIWJCcUmtv5FaHiIiskwsHomI6LbO5ZUhPiUTP6VdQZm2FgBgYyXD5L6+iFaroO7iBpmsY41GSq2/kVoeIiKyTCweiYioWSqra7HlWC7iUjJxLLvE0N7N0xFR6iDMGOQPVwdbEROaj9T6G6nlISIiy8TikYiIWuzklRLEpWiwOf0KKqvrAAAKazmmhvoiWh2EQSpXix6NlFp/I7U8RERkmVg8EhFRq5VV1WBzeg7iUjQ4k1tqaA/xcUa0WoXIgf5wtrMRMaFpSK2/kVoeIiKyTCweiYiozQRBQHpWMeJSNNhyPAdVNToAgIOtFe7u74dodRD6BShFTmk8UutvpJaHiIgsE4tHIiIyqpLKGvx0NBtxKRpcKCg3tPfzVyJarcK0/n5wVFiLmLDtpNbfSC0PERFZJhaPRERkEoIg4PDl64hLycS2E3mortOPRjoprHHPQH9EqVXo5ds+/1ZLrb+RWh4iIrJMLB6JiMjkiiqq8WNqFuJTNLh8rdLQPkjlimh1EKaG+sLOxkrEhC0jtf5GanmIiMgysXgkIiKz0ekEJF+6hriUTPx6Kh+1On3XorS3wYxBAYhSqxDs5SRyyqZJrb+RWh4iIrJMze1v2vfFKUREJAlyuQzDgz0wPNgDBWVVWH8kG/EpGlwpvoFv9mfgm/0ZUHdxQ/TQIEzq4w2FdfsZjSQiIiI9Fo9ERGRUXs52mD82GE+M7oak84WIO6jBnrP5SMkoQkpGEdwdbXHfkABEhasQ5O4odlwiIiJqJhaPRERkElZyGcb29MLYnl7IKb6BtYezsPZwFvJKq/BF4iV8kXgJI7t7IFqtwvhe3rCxkosdmYiIiG6D1zwSEZHZ1NbpsOdsAeJSNEg6X4ibPZCXswIzwwIxK1wFf1d70fJJrb+RWh4iIrJMnDCHiIgkLauoEqsPabDuSBaullcDAOQyYExPL0SrVRjT0wtWcplZM0mtv5FaHiIiskwsHomIqF2ortVh5+l8xKVk4sDFa4Z2P6UdZoWrMDMsEN4udmbJIrX+Rmp5iIjIMrF4JCKidudSYTlWH9Lgx9RsXK+sAaC/dnJCLy9Eq4MwItgDchOORkqtv5FaHiIiskzN7W9aPDtBUlISpk2bBj8/P8hkMmzatKnJdRISEjBo0CAoFAoEBwcjNja2pbslIqIOoKunE16Z2hvJi8fj45kDEN7ZDXU6ATtO5ePhbw5hzAcJWJFwEVfLtWJHbZbW9JlERERNybxWgZX7MvDtgcvIL60y235bXDxWVFSgf//+WL58ebOWz8jIwNSpUzF27Fikp6dj4cKFeOyxx7Bjx44WhyUioo7BzsYKkQP9se6JCPz6zCjMHdYZznbW0BRV4t3tZxERsxsL4tOQfPEapHwCTUv7TCIioqYcuVyEiR8l4e2tp/H6z6dwx0eJuFBQZpZ9t+m0VZlMho0bNyIyMrLRZV588UVs3boVJ0+eNLTNmjULxcXF2L59e4PraLVaaLV/fKtcWlqKwMBAnrZDRNSB3aiuw8/HcxCXosGxrGJDe1dPR0SFq/BQRBAU1lZt2ocpTxNtTp/J/o+IiJoy+eMk/J5fBt3/qjgruQyjenhg1dzwVm/TZKettlRycjImTJhQr23SpElITk5udJ2YmBgolUrDIzAw0NQxiYhI4uxtrfDAkEBsnj8cW54agSi1Co62VrhUWIFV+y/DWt7+7xPJ/o+IiJqSU3zDUDgCQJ1OwJXrN8yyb5P3tHl5efD29q7X5u3tjdLSUty40fAPuXjxYpSUlBgeWVlZpo5JRETtSF9/Jd65px9SXpmAt+/pi4UTupv9th6mwP6PiIia0sdfWa/Ps5LJ0M9faZZ9W5tlLy2kUCigUCjEjkFERBLnpLBGtDpI7BhGw/6PiIia8t6MUMz+6iCy/zfa2MNHP9mcOZi8ePTx8UF+fn69tvz8fLi4uMDe3t7UuyciIiIiIrIYgW4O2PnMaJy4UgIruQyhAUrYWJnn0g2TF48RERH45Zdf6rXt3LkTERERpt41ERERERGRxbG3tUJ4Fzez77fFxWN5eTkuXLhgeJ6RkYH09HS4ublBpVJh8eLFuHLlCr777jsAwBNPPIHPPvsML7zwAh599FHs2bMH69atw9atW433UxAREUlQU30mERFRe9Li4vHIkSMYO3as4fmiRYsAAHPmzEFsbCxyc3Oh0WgMr3fp0gVbt27FM888g08++QQBAQH4+uuvMWnSJCPEJyIikq6m+kwiIqL2pE33eTSXkpISuLq6Iisri/e5IiIik7l5X8Xi4mIoleaZue522P8REZE5NLf/k+Rsq39VVlYGALzfFRERmUVZWZkkikf2f0REZE5N9X/tYuRRp9MhJycHzs7OkMlafx+vmxV1e/wGl9nFwezm115zA8wuFmNmFwQBZWVl8PPzg1xunpnrbsdY/R/Qvn/H5sTj1Hw8Vs3HY9V8PFbNJ0b/1y5GHuVyOQICAoy2PRcXl3b7ZmR2cTC7+bXX3ACzi8VY2aUw4niTsfs/oH3/js2Jx6n5eKyaj8eq+Xisms+c/Z/4X6sSERERERGR5LF4JCIiIiIioiZ1qOJRoVBgyZIlUCgUYkdpMWYXB7ObX3vNDTC7WNpzdnPicWoeHqfm47FqPh6r5uOxaj4xjlW7mDCHiIiIiIiIxNWhRh6JiIiIiIiodVg8EhERERERUZNYPBIREREREVGTWDwSERERERFRk9p18ZiUlIRp06bBz88PMpkMmzZtanKdhIQEDBo0CAqFAsHBwYiNjb1lmeXLl6Nz586ws7ODWq3GoUOHRM39008/4Y477oCnpydcXFwQERGBHTt21Fvm9ddfh0wmq/cICQkxau7WZE9ISLgll0wmQ15eXr3lTH3MW5N97ty5DWbv06ePYRlzHPeYmBiEhYXB2dkZXl5eiIyMxLlz55pcb/369QgJCYGdnR369euHX375pd7rgiDgtddeg6+vL+zt7TFhwgScP39e9OxfffUVRo4ciU6dOqFTp06YMGHCLe+Hhn43kydPFj17bGzsLbns7OzqLSPV4z5mzJgG3+9Tp041LGOO475ixQqEhoYabngcERGBbdu23XYdKbzXpaIlf0ub8361RKb67GCJTNXnWxpT9dOWyFR9qyUyRX9oDO26eKyoqED//v2xfPnyZi2fkZGBqVOnYuzYsUhPT8fChQvx2GOP1SvE1q5di0WLFmHJkiVIS0tD//79MWnSJBQUFIiWOykpCXfccQd++eUXpKamYuzYsZg2bRqOHj1ab7k+ffogNzfX8Ni3b5/RMrc2+03nzp2rl83Ly8vwmjmOeWuyf/LJJ/UyZ2Vlwc3NDffff3+95Ux93BMTEzF//nwcPHgQO3fuRE1NDSZOnIiKiopG1zlw4ABmz56NefPm4ejRo4iMjERkZCROnjxpWOa9997Dp59+is8//xwpKSlwdHTEpEmTUFVVJWr2hIQEzJ49G3v37kVycjICAwMxceJEXLlypd5ykydPrnfcV69ebbTcrc0OAC4uLvVyZWZm1ntdqsf9p59+qpf75MmTsLKyuuX9burjHhAQgGXLliE1NRVHjhzBuHHjMH36dJw6darB5aXyXpeC1vwtber9aolM8dnBUpmiz7dEpuqnLZGp+lZLZIr+0CgECwFA2Lhx422XeeGFF4Q+ffrUa5s5c6YwadIkw/Pw8HBh/vz5hud1dXWCn5+fEBMTY9S8NzUnd0N69+4tLF261PB8yZIlQv/+/Y0XrBmak33v3r0CAOH69euNLmPuYy4IrTvuGzduFGQymXD58mVDmxjHvaCgQAAgJCYmNrrMAw88IEydOrVem1qtFh5//HFBEARBp9MJPj4+wvvvv294vbi4WFAoFMLq1atNE1xoXva/qq2tFZydnYVvv/3W0DZnzhxh+vTpJkjYuOZkX7VqlaBUKht9vT0d948++khwdnYWysvLDW1iHHdBEIROnToJX3/9dYOvSfW9LoaW/i1t6v3aERjrs0NHYKw+vyMwRj/dURijb+1I2tIfGku7HnlsqeTkZEyYMKFe26RJk5CcnAwAqK6uRmpqar1l5HI5JkyYYFhGCnQ6HcrKyuDm5lav/fz58/Dz80PXrl0RHR0NjUYjUsJbDRgwAL6+vrjjjjuwf/9+Q3t7OeYAsHLlSkyYMAFBQUH12s193EtKSgDglt//nzX1Xs/IyEBeXl69ZZRKJdRqtUmPe3Oy/1VlZSVqampuWSchIQFeXl7o2bMnnnzySVy7ds2oWf+qudnLy8sRFBSEwMDAW74hbE/HfeXKlZg1axYcHR3rtZvzuNfV1WHNmjWoqKhAREREg8tI9b1ubq39W3q79yvpNfUeo1s11ud3FMbopzsKY/StHYEx+kNj6VDFY15eHry9veu1eXt7o7S0FDdu3MDVq1dRV1fX4DJSOl//gw8+QHl5OR544AFDm1qtRmxsLLZv344VK1YgIyMDI0eORFlZmYhJAV9fX3z++efYsGEDNmzYgMDAQIwZMwZpaWkA0G6OeU5ODrZt24bHHnusXru5j7tOp8PChQsxfPhw9O3bt9HlGnuv3zymN/9rzuPe3Ox/9eKLL8LPz6/eH8TJkyfju+++w+7du/Huu+8iMTERU6ZMQV1dnSmiNzt7z5498c0332Dz5s344YcfoNPpMGzYMGRnZwNoP8f90KFDOHny5C3vd3Md9xMnTsDJyQkKhQJPPPEENm7ciN69eze4rBTf62Jozd/Spt6vpNfUZwf6Q1N9fkdgrH66IzBW32rJjNkfGou1UbdGJhcfH4+lS5di8+bN9a4hmDJliuH/Q0NDoVarERQUhHXr1mHevHliRAWg/wffs2dPw/Nhw4bh4sWL+Oijj/D999+Llqulvv32W7i6uiIyMrJeu7mP+/z583Hy5EmTXM9qaq3JvmzZMqxZswYJCQn1Lo6fNWuW4f/79euH0NBQdOvWDQkJCRg/frxRcwPNzx4REVHvG8Fhw4ahV69e+OKLL/Dmm28aPVdztOa4r1y5Ev369UN4eHi9dnMd9549eyI9PR0lJSX48ccfMWfOHCQmJjbaYVLrSPH9Su2bpfT5bdGe+2lza899q7lIsT/sUCOPPj4+yM/Pr9eWn58PFxcX2Nvbw8PDA1ZWVg0u4+PjY86oDVqzZg0ee+wxrFu37pZh6b9ydXVFjx49cOHCBTOla77w8HBDLqkfc0A/U+M333yDhx56CLa2trdd1pTHfcGCBdiyZQv27t2LgICA2y7b2Hv95jG9+V9zHfeWZL/pgw8+wLJly/Drr78iNDT0tst27doVHh4eoh/3v7KxscHAgQMNudrDca+oqMCaNWua9eWHqY67ra0tgoODMXjwYMTExKB///745JNPGlxWau91sRjjb+lf36+k19RnB7q9P/f5ls6Y/bSlM2bfasmM2R8aS4cqHiMiIrB79+56bTt37jR8m2Fra4vBgwfXW0an02H37t2Nnl9sLqtXr8YjjzyC1atX15s6vzHl5eW4ePEifH19zZCuZdLT0w25pHzMb0pMTMSFCxea9WHaFMddEAQsWLAAGzduxJ49e9ClS5cm12nqvd6lSxf4+PjUW6a0tBQpKSlGPe6tyQ7oZ8d88803sX37dgwZMqTJ5bOzs3Ht2jXRj/tf1dXV4cSJE4ZcUj/ugH6ab61WiwcffLDJZU1x3Bui0+mg1WobfE0q73WxGeNv6V/fr6TX1HuMbu/Pfb6lMkU/balM0bd2JG3pD43GqNPvmFlZWZlw9OhR4ejRowIA4d///rdw9OhRITMzUxAEQXjppZeEhx56yLD8pUuXBAcHB+H5558Xzpw5IyxfvlywsrIStm/fblhmzZo1gkKhEGJjY4XTp08Lf//73wVXV1chLy9PtNxxcXGCtbW1sHz5ciE3N9fwKC4uNizz7LPPCgkJCUJGRoawf/9+YcKECYKHh4dQUFBgtNytyf7RRx8JmzZtEs6fPy+cOHFCePrppwW5XC7s2rXLsIw5jnlrst/04IMPCmq1usFtmuO4P/nkk4JSqRQSEhLq/f4rKysNyzz00EPCSy+9ZHi+f/9+wdraWvjggw+EM2fOCEuWLBFsbGyEEydOGJZZtmyZ4OrqKmzevFk4fvy4MH36dKFLly7CjRs3RM2+bNkywdbWVvjxxx/rrVNWViYIgv73+NxzzwnJyclCRkaGsGvXLmHQoEFC9+7dhaqqKlGzL126VNixY4dw8eJFITU1VZg1a5ZgZ2cnnDp1qt7PJ8XjftOIESOEmTNn3tJuruP+0ksvCYmJiUJGRoZw/Phx4aWXXhJkMpnw66+/NphbKu91KWjqb2lr3q+WyBSfHSyVKfp8S2SqftoSmapvtUSm6A+NoV0XjzenhP7rY86cOYIg6KeVHz169C3rDBgwQLC1tRW6du0qrFq16pbt/t///Z+gUqkEW1tbITw8XDh48KCouUePHn3b5QVBP224r6+vYGtrK/j7+wszZ84ULly4YNTcrcn+7rvvCt26dRPs7OwENzc3YcyYMcKePXtu2a6pj3lrsguCfkp/e3t74csvv2xwm+Y47g1lBlDvvTt69Oh67wdBEIR169YJPXr0EGxtbYU+ffoIW7durfe6TqcT/vWvfwne3t6CQqEQxo8fL5w7d0707EFBQQ2us2TJEkEQBKGyslKYOHGi4OnpKdjY2AhBQUHC3/72N6N/2dCa7AsXLjS8j729vYU777xTSEtLq7ddqR53QRCEs2fPCgAMHdOfmeu4P/roo0JQUJBga2sreHp6CuPHj6+XR6rvdam43d/S1rxfLZGpPjtYIlP1+ZbGVP20JTJV32qJTNEfGoNMEAShhYOVRERERERE1MF0qGseiYiIiIiIqHVYPBIREREREVGTWDwSERERERFRk1g8EhERERERUZNYPBIREREREVGTWDwSERERERFRk1g8EhERERERUZNYPBIREREREVGTWDwSERERERFRk6zFDtAcOp0OOTk5cHZ2hkwmEzsOERFZKEEQUFZWBj8/P8jl/H6ViIjoz9pF8ZiTk4PAwECxYxARUQeRlZWFgIAAsWMQERFJSrsoHp2dnQHoO3MXFxeR0xARkaUqLS1FYGCgod8hIiKiP7SL4vHmqaouLi4sHomIyOR4iQQREdGteEEHERERERERNanNxWNMTAzCwsLg7OwMLy8vREZG4ty5c/WWqaqqwvz58+Hu7g4nJyfMmDED+fn5bd01ERERERERmUmbi8fExETMnz8fBw8exM6dO1FTU4OJEyeioqLCsMwzzzyDn3/+GevXr0diYiJycnJw7733tnXXRERERERtNnfuXMhkMsPD3d0dkydPxvHjx8WORiQpMkEQBGNusLCwEF5eXkhMTMSoUaNQUlICT09PxMfH47777gMAnD17Fr169UJycjKGDh3a5DZLS0uhVCpRUlLCax6JiMhk2N8QdUxz585Ffn4+Vq1aBQDIy8vDq6++iuPHj0Oj0Yicjkg6jH7NY0lJCQDAzc0NAJCamoqamhpMmDDBsExISAhUKhWSk5Mb3IZWq0VpaWm9BxERERGRqSgUCvj4+MDHxwcDBgzASy+9hKysLBQWFoodjUgyjDrbqk6nw8KFCzF8+HD07dsXgP6bG1tbW7i6utZb1tvbG3l5eQ1uJyYmBkuXLjVmNIPOL201yXbJ+C4vmyp2BCIiIuqAysvL8cMPPyA4OBju7u5ixyGSDKMWj/Pnz8fJkyexb9++Nm1n8eLFWLRokeH5zftuERERERGZwpYtW+Dk5AQAqKiogK+vL7Zs2QK5nDcnILrJaP8aFixYgC1btmDv3r0ICAgwtPv4+KC6uhrFxcX1ls/Pz4ePj0+D21IoFIZ7OvLejkRERERkamPHjkV6ejrS09Nx6NAhTJo0CVOmTEFmZqbY0Ygko83FoyAIWLBgATZu3Ig9e/agS5cu9V4fPHgwbGxssHv3bkPbuXPnoNFoEBER0dbdExERERG1maOjI4KDgxEcHIywsDB8/fXXqKiowFdffSV2NCLJaPNpq/Pnz0d8fDw2b94MZ2dnw3WMSqUS9vb2UCqVmDdvHhYtWgQ3Nze4uLjgqaeeQkRERLNmWiUiIiIiMjeZTAa5XI4bN26IHYVIMtpcPK5YsQIAMGbMmHrtq1atwty5cwEAH330EeRyOWbMmAGtVotJkybhP//5T1t3TURERERkFFqt1jAIcv36dXz22WcoLy/HtGnTRE5GJB1tLh6bc5tIOzs7LF++HMuXL2/r7oiIiIiIjG779u3w9fUFADg7OyMkJATr16+/ZYCEqCMz6myrRERERETtTWxsLGJjY8WOQSR5LB6JiIiIqP2pqwFKsgFtadPLyq0BZ1/Awc30uYgsGItHIiIiIpKkWl0tNGUaXCy+iAvFFzD6ai56ZyTri8byPEDQtWyDNo6AMgBwDQQ6dQY8QwCvXoBnL8DR3SQ/A5ElYfFIRERERJKgE3Q4XngcSdlJ2HdlHy4UX0CNrsbwuqNTb/TOPtT6HdRUAFfP6R9/5egJqIYC3ScBPSYBTl6t3w+RhWLxSERERESiKdGW4EDOASRlJ2H/lf24rr3e6LIaGyvTBakoBM78rH9ABvgN+F8hORHwGwTIZKbbN1E7weKRiIiIiMwqoyQDe7P2Iik7CccKjqFWqG3Wehqh2sTJbhKAnKP6R+IywNEL6H6HfkQy+A7A1sFMOYikhcUjEREREZlFck4yYk/F4kDOgVatr6kuNm6g5qooANLj9A87V2Dgg0D43/TXTRJ1ICweiYiIiMhkanW12HF5B7499S3OFJ1p07ZybxSi2koB2zqtkdK1QlUxkPwZcPA/QI/JuBbxCtw79xUvD5EZsXgkIiIiIqOrrKnEhvMb8MPpH5BTkWOUbeoEHbLdA9G14IJRttcmgg7C+V/xt8uRgLIET43rjrEhnGSHLBuLRyIiIiIymutV1/Hd6e+w7tw6lFY34x6MLaRx8ZFG8Qgg228y0i44ASXFeCT2MPr5K/HUuGBM7OMjdjQik2DxSERERERGsfnCZnxw5AMUa4tNto9MB2eTbbulYkom1nt+4koJ/v59Ksb29MRb9/SDv6u9SMmITEMudgAiIiIiat+ulF/B4zsfx6v7XzVp4QgAGmtpfHy97jMcvxR6NPja3nOFmPjvRMTuz4BOJ5g5GZHpSONfHxERERG1O3W6Onx76lvcs/meVs+g2lKZgoiT5fzJl3V33fb1iuo6vP7zadz3+QGczy8zUyoi02LxSEREREQtdq7oHB785UF8cOQD3Ki9Ybb9arTXzbavxtxw74MVWUHNWjZNU4ypn+7Dx7t+R3WtzsTJiEyLxSMRERERNVt1XTU+TfsUs7bOwslrJ82+//yqa6i2Uph9v3/2o21ki5avrtPh413ncdf//YajGvGLX6LWYvFIRERERM1SWFmIudvn4qsTX6FWVytKBp2gQ5Z780b9TKHW2R9vZfZq1bq/55fj/s+TEZ+iMXIqIvNg8UhERERETTp19RRmbZ2FE1dPiB0FmUpv0fa9RzkDWl3rP0LX6gS8vPEEYradgSBwMh1qX1g8EhEREdFtbcvYhrnb56KgskDsKAAAjb2TKPsVFEq8ohlslG19kXgJC1YfRVVNnVG2R2QOLB6JiIiIqFErT6zEC0kvoKquSuwoBpki3a7jqFckCqttjLa9rcdzEf11Cooqqo22TSJTYvFIRERERLcQBAHvHX4PH6d9LHaUW2TpzH+7DsHKFq/kjjT6dlMzr+Pe/+xHxtUKo2+byNhYPBIRERFRPTW6Gizetxjfn/5e7CgNyqw2/4yll/3uxJlyB9Ns+1ol7v3Pfhy5XGSS7RMZC4tHIiIiIjLQCTq8mPQitl7aKnaURuXfuIoqG3uz7U+ADG8VTTDpPq5X1iDq6xSkXLpm0v0QtQWLRyIiIiIy+ODIB9iZuVPsGLclQECWm8ps+7vmNxq7r7mZfD/VtTo8/kMqLhaWm3xfRK3B4pGIiIiIAACrz66W7Kmqf6Vx8TLbvj6rutNs+yqurMEjqw7jWnnbrutMTk6GlZUVpk6daqRkRCweiYiIiAhAYlYi3j30rtgxms1ct+uo9OiP2JwAs+zrJk1RJR777kibbuOxcuVKPPXUU0hKSkJOTo4R01FHxuKRiIiIqIM7fe00nk96HnVC+7nnoLlu1xFvHWmW/fzVUU0xFq5Jh04ntHjd8vJyrF27Fk8++SSmTp2K2NhY4wekDonFIxEREVEHlleRhwW7F+BG7Q2xo7SIRmf6+07WuAThXU0Pk++nMdtP5eGdX860eL1169YhJCQEPXv2xIMPPohvvvkGgtDyIpTor1g8EhEREXVQ5dXleHLXkyi8USh2lBbL1Jr+dh07XGagRicz+X5u5+t9Gfgu+XKL1lm5ciUefPBBAMDkyZNRUlKCxMREE6SjjobFIxEREVEH9eJvL+JC8QWxY7RKYdU13LA1zX0XAUBn74Z/ZQ4w2fZbYunPp3HgwtVmLXvu3DkcOnQIs2fPBgBYW1tj5syZWLlypSkjUgfR5uIxKSkJ06ZNg5+fH2QyGTZt2lTv9blz50Imk9V7TJ48ua27JSIiIqI2+Pniz0jKThI7RqsJEJDVyXS36zjsMQPXa6xNtv2WqNMJeP7H4yirqmly2ZUrV6K2thZ+fn6wtraGtbU1VqxYgQ0bNqCkpMQMacmStbl4rKioQP/+/bF8+fJGl5k8eTJyc3MNj9WrV7d1t0RERETUSkVVRXjv8Htix2gzjdLTJNsVrO3w8pWhJtl2a10pvoG3ttz++sfa2lp89913+PDDD5Genm54HDt2DH5+fvwMTm3W5q9TpkyZgilTptx2GYVCAR8fn7buioiIiIiMYNmhZSjWFosdo80y7Uxzu44LvtNw8by9SbbdFmuPZGFyXx+MDWn4HpdbtmzB9evXMW/ePCiVynqvzZgxAytXrsQTTzxhjqhkocxyzWNCQgK8vLzQs2dPPPnkk7h27dptl9dqtSgtLa33ICIiIqK2S8pOwraMbWLHMAqNCW7XIcjkWHptnNG3aywv/dT46asrV67EhAkTbikcAX3xeOTIERw/ftzUEcmCmbx4nDx5Mr777jvs3r0b7777LhITEzFlyhTU1TV+H6GYmBgolUrDIzAw0NQxiYiIiCxeRU0F3jz4ptgxjCbTBLfryPcdj31FtxZfUpFfqsW/d/7e4Gs///wztm7d2uBr4eHhEAQBoaGhpoxHFs7kVwHPmjXL8P/9+vVDaGgounXrhoSEBIwfP77BdRYvXoxFixYZnpeWlrKAJCIiImqjj1M/Rl5FntgxjCZLW2T0bX58Q/oTO36XnIn7Bweit5+L2FGogzH7rTq6du0KDw8PXLjQ+LTQCoUCLi4u9R5ERERE1HrpBelYe26t2DGMqqDqGiptHY22vTKvIViT62u07ZlKnU7Aq5tOQBAEsaNQB2P24jE7OxvXrl2Dr6/0/2ESERERWQJBEPDGwTcgwPKKjSy3IKNt61vZ3UbblqmlaYqxKf2K2DGog2lz8VheXm6YBhgAMjIykJ6eDo1Gg/Lycjz//PM4ePAgLl++jN27d2P69OkIDg7GpEmT2rprIiIiImqGpOwknL9+XuwYJpGp9DDKdqpdu+FDTTejbMtcPk+4JHYE6mDaXDweOXIEAwcOxMCBAwEAixYtwsCBA/Haa6/BysoKx48fx913340ePXpg3rx5GDx4MH777TcoFIo2hyciIiKipq06tUrsCCajsTPOaatbHO+FIMiMsi1zOZdfhr3nCsSOQR1ImyfMGTNmzG3Pt96xY0dbd0FERERErXSi8ARS81PFjmEyGqu2X4Wlc/DEvzLb5yykXyZewtieDd/3kcjYzH7NIxERERGZjyWPOgJApu5Gm7ex330GKmqtjJDG/JIvXcPJKyVix6AOgsUjERERkYXKKs3Cbs1usWOYlKaqbbfrEGwc8Up2uJHSiOOLJF77SObB4pGIiIjIQn17+lvoBJ3YMUzqqrYIFQrnVq9/xuduaG7YGTGR+f1yIhdZRZVix6AOgMUjERERkQW6XnUdmy9sFjuGWWjcAlu1niCzwpKC0UZOY351OgEr92WIHYM6ABaPRERERBZozdk1qKqrEjuGWWhcPFu1Xo7fRBwucTFyGnGsO5KFksoasWOQhWPxSERERGSBfrrwk9gRzKa1t+t4v9xy7jteWV2Hn4/niB2DLByLRyIiIiILc/LqSeRV5Ikdw2wyrVp+f8YS76HYlG9Zt7jYe5b3fCTTYvFIREREZGF2Ze4SO4JZaVpxu46vhWkmSCKuAxevoaqmTuwYZMFYPBIRERFZGEu/PcdfZVZdbdHyWree+D9NFxOlEc+NmjokX7omdgyyYCweiYiIiCzIpeJLuFx6WewYZlWkLW7R7Tp+srvXhGnEtecMT10l02HxSERERGRB9l3ZJ3YEUWS6q5q1XJ2TL97I7G3iNOLZw+seyYRYPBIRERFZkIO5B8WOIAqNc/Nu15HYaQZu1FmZOI14rhTfwO/5ZWLHIAvF4pGIiIjIQtToapCanyp2DFFk2jk0uYygcMbLmiFmSCMujj6SqbB4JCIiIrIQxwqOobK2UuwYotA0YzDxuHck8rS2pg8jMl73SKbC4pGIiIjIQhwrPCZ2BNFo6m5/uw5BboN/5Y00UxpxpWmuo7ZOJ3YMskAsHomIiIgsRGZpptgRRKPR3v4WFRq/KThe6mSmNOKq1QnIut7ye18SNYXFIxEREZGF6MjFY5G2GGV2ykZfX1YywYxpxHf5WoXYEcgCsXgkIiIishCaMo3YEUSlaeR2HUW+I7Gt0MPMacR1+SqLRzI+Fo9EREREFqCyphJXb1wVO4aoMp3dG2z/vGaqmZOIj8UjmQKLRyIiIiIL0NFHHQFAo7j1dh033Pviy+yGRyQtWca1jjnrLpkWi0ciIiIiC9CRr3e8SWMlu6Vtre09IiQRH0ceyRRYPBIRERFZAE0pRx4z6+qPttU6ByAms6dIacR1pfgGani7DjIyFo9EREREFoCnrQKaqvrXfO5SzoBW1zE/7tbpBGiKeOoqGVfH/NdEREREZGGyy7LFjiC64uoSlNi7AgB0dq54RTNI3EAiY/FIxsbikYiIiMgCVOuqxY4gCVlugQCANK97ca3aRuQ04rpRXSd2BLIwLB6JiIiIyGJkOntAsFLg1ZxhYkcRHa95JGNj8UhEREREFkNj54BLvlNxtvzW23Z0NNW1LB7JuFg8EhEREZHFyJQDbxaNFzuGJNTUCWJHIAtj3dYNJCUl4f3330dqaipyc3OxceNGREZGGl4XBAFLlizBV199heLiYgwfPhwrVqxA9+7d27prIiIiIqJ6kiuvIKBnCoaLHUQC7JUOAFRixyAL0ubisaKiAv3798ejjz6Ke++995bX33vvPXz66af49ttv0aVLF/zrX//CpEmTcPr0adjZ2bV190REREREBkXaIhRpE8SOIQn3yEeLHYEsTJuLxylTpmDKlCkNviYIAj7++GO8+uqrmD59OgDgu+++g7e3NzZt2oRZs2a1dfdERERERNQAWytbsSOQhTHpNY8ZGRnIy8vDhAkTDG1KpRJqtRrJycmNrqfValFaWlrvQUREREREzWcj79i3KiHja/PI4+3k5eUBALy9veu1e3t7G15rSExMDJYuXWrKaET1dH5pq9gRqJkuL5sqdgQiIqJ2gcUjGZskZ1tdvHgxSkpKDI+srCyxIxERERFJmrONs9gRSGJcbF3EjkAWxqTFo4+PDwAgPz+/Xnt+fr7htYYoFAq4uLjUexARERFR4wKdA8WOQBKjcuFMq2RcJi0eu3TpAh8fH+zevdvQVlpaipSUFERERJhy10REREQdSpBLkNgRSELsrOzg7eDd9IJELdDmax7Ly8tx4cIFw/OMjAykp6fDzc0NKpUKCxcuxFtvvYXu3bsbbtXh5+dX716QRERERNQ2HGWiPwt0CYRMJhM7BlmYNhePR44cwdixYw3PFy1aBACYM2cOYmNj8cILL6CiogJ///vfUVxcjBEjRmD79u28xyMRERGREXHkkf4syJnvBzK+NhePY8aMgSAIjb4uk8nwxhtv4I033mjrroiIiIioEX5OfrCWWaNWqBU7CkkAR6LJFCQ52yoRERERtYyN3Aa+Tr5ixyCJUDmzeCTjY/FIREREZCE42kQ38b1ApsDikYiIiMhC8Do3uonXwJIpsHgkIiIishBdlV3FjkAS4GzjDC8HL7FjkAVi8UhERERkIYb4DBE7AknAUL+hYkcgC8XikYiIiMhCdHPtxhEnwqiAUWJHIAvF4pGIiIjIggz15ahTRyaDDCP8R4gdgywUi0ciIiIiC8LisWPr494HHvYeYscgC8XikYiIiMiCDPcfDrmMH/E6Kp6ySqbEvyxEREREFsTNzg0DvQaKHYNEMiqQxSOZDotHIiIiIgszQTVB7AgkAg97D/R26y12DLJgLB6JiIiILMyEIBaPHdFI/5GQyWRixyALxuKRiIiIyML4OPog1CNU7BhkZmMCx4gdgSwci0ciIiIiCzQrZJbYEciM/J38MTpgtNgxyMKxeCQiIiKyQFO6TIGPo4/YMchMHur9EKzkVmLHIAvH4pGIiIjIAlnLrfFgrwfFjkFm4GLrgnuC7xE7BnUALB6JiIiILNR9Pe6Ds42z2DHIxGb2nAkHGwexY1AHwOKRiIiIyEI52jji/p73ix2DTMhWbouoXlFix6AOgsUjERERkQV7sNeDsJHbiB2DTOSubnfBw95D7BjUQbB4JCIiIrJgng6emNp1qtgxyARkkGFOnzlix6AOhMUjERERkYWb22cuZODN4y3N6IDR6KrsKnYM6kBYPBIRERFZuG6u3XBH0B1ixyAjkkGGv4f+XewY1MGweCQiIiLqAF4KfwnOtpx51VLc2/1e9PPsJ3YM6mBYPBIRERF1AJ4Onnh28LNixyAj6KTohGcGPyN2DOqAWDwSERERdRAzesxAuE+42DGojZ4Z/AyUCqXYMagDYvFIRERE1IEsiVgCOys7sWNQKw3yGoTI4EixY1AHxeKRiIiIqANRuajw5IAnxY5BrWBvbY+3RrwFmYwz55I4WDwSERERdTBzes9BL7deYsegFnp28LMIdA4UOwZ1YCweiYiIiDoYK7kVlg5bCmuZtdhRqJkifCPwQM8HxI5BHZzJi8fXX38dMpms3iMkJMTUuyUiIiKi2+jl3ounr7YT7nbueGP4GzxdlURnlq+b+vTpg127dv2xU2t+y0VEREQktr+H/h2ZpZn478X/ih2FGmFvbY/l45fDx9FH7ChE5ikera2t4ePDNzwRERGR1Lw+7HXkV+QjJS9F7Cj0F3KZHO+Neg99PPqIHYUIgJmueTx//jz8/PzQtWtXREdHQ6PR3HZ5rVaL0tLSeg8iIiIiMj4buQ0+GvsRgl2DxY5Cf/Fi2IsYEzhG7BhEBiYfeVSr1YiNjUXPnj2Rm5uLpUuXYuTIkTh58iScnZ0bXCcmJgZLly41dTQiotvq/NJWsSNQM11eNlXsCETtmrOtM5aPX47oX6Jx9cZVseMQgAd7PYioXlFixyCqx+Qjj1OmTMH999+P0NBQTJo0Cb/88guKi4uxbt26RtdZvHgxSkpKDI+srCxTxyQiIiLq0Pyc/PDZuM9gb20vdpQOb7xqPJ4Pe17sGES3MPutOlxdXdGjRw9cuHCh0WUUCgVcXFzqPYiIiIjItPp49MG7I9+FXMa7uYmln0c/LBu5jL8DkiSzvyvLy8tx8eJF+Pr6mnvXRERERNSEsaqxeEX9CmTgbSHMrauyK/5v3P/BztpO7ChEDTJ58fjcc88hMTERly9fxoEDB3DPPffAysoKs2fPNvWuiYiIiKgVHuj5AGJGxsBazturmUuYTxi+v/N7uNu7ix2FqFEm/4uQnZ2N2bNn49q1a/D09MSIESNw8OBBeHp6mnrXRERERNRKU7tOhavCFc8kPIMbtTfEjmPRpnWdhqXDlsLGykbsKES3ZfKRxzVr1iAnJwdarRbZ2dlYs2YNunXrZurdEhEREdFt5OXl4amnnkLXrl2hUCgQGBiIadOmYffu3YZlhvsPx8qJK9FJ0UnEpJbtif5P4J2R77BwpHaBV+ISERERdTCXL1/G4MGDsWfPHrz//vs4ceIEtm/fjrFjx2L+/Pn1lu3n2Q+r71qN7p26i5TWMlnLrfHW8Lcwf8D8phcmkgieyE5ERETUwfzjH/+ATCbDoUOH4OjoaGjv06cPHn300VuW93fyxw9TfsBLv72EvVl7zRnVIjnbOuOjMR9B7asWOwpRi3DkkYiIiKgDKSoqwvbt2zF//vx6heNNrq6uDa7nYOOAT8Z+gr/1+xtvI9EGAU4B+H7K9ywcqV3iv3wiIiKiDuTChQsQBAEhISEtXlcmk+Gfg/6Jbyd/i67KriZIZ7nkMjke6v0QNty9Ad1cOf8HtU8sHomIiIg6EEEQ2ryNAV4D8OO0H/FE/ydgI+dEL03p3qk7fpjyA14IewEONg5ixyFqNRaPRERERB1I9+7dIZPJcPbs2TZtx8bKBvMHzMe6u9Yh1DPUSOksi63cFgsGLMDau9ain2c/seMQtRmLRyIiIqIOxM3NDZMmTcLy5ctRUVFxy+vFxcUt2l5wp2B8P+V7vBT+EhysOap20yCvQVh/93o83v9xjs6SxWDxSERERNTBLF++HHV1dQgPD8eGDRtw/vx5nDlzBp9++ikiIiJavD25TI7oXtHYOH0jRviPMEHi9sPJxgmvqF9B7ORYXhdKFoe36iAiIiLqYLp27Yq0tDS8/fbbePbZZ5GbmwtPT08MHjwYK1asaPV2/Zz8sGLCCqQXpGPVyVVIyE6ATtAZMbl0Ods6I7pXNB7s9SCUCqXYcYhMgsUjERERUQfk6+uLzz77DJ999pnRtz3AawA+GfcJLpdcxrenv8XPF3+Gtk5r9P1IQSdFJzzU+yHMDpkNJ1snseMQmRSLRyIiIiIyic7KzlgSsQQLBixA/Nl4rD23FiXaErFjGUWPTj0QFRKFqV2nws7aTuw4RGbB4pGIiIiITMrd3h1PDXwK8/rOw8YLGxF/Jh6aMo3YsVrMWm6NMQFjENUrCmE+YWLHITI7Fo9EREREZBYONg6I7hWN6F7RuFh8EUnZSUjMTsSxgmOoFWrFjtcgD3sPjPQfiVEBoxDhFwFHG0exIxGJhsUjEREREZldN9du6ObaDY/0fQSl1aU4cOUAkrKTsO/KPlzXXhctlwwy9PXoi5EB+oKxt1tvyGQy0fIQSQmLRyIiIiISlYutCyZ3mYzJXSZDJ+hw4uoJ7L+yH2eKzuBi8UVcKb9ikllbZZDB08ETQS5BUDmrMMBrAEb6j4S7vbvR90VkCVg8EhEREZFkyGVy9Pfsj/6e/Q1tVbVVuFRyCRklGcityEVOeQ5yK3KRW56LsuqyJrdpJbeCn5OfoUgMcgmCykWFQOdA2Fvbm/LHIbIoLB6JiIiISNLsrO3Q2703erv3FjsKUYcmFzsAERERERERSR+LRyIiIiIiImoSi0ciIiIiIiJqEotHIiIiIiIiahKLRyIiIiIiImoSi0ciIiIiIiJqEotHIiIiIiIiahKLRyIiIiIiImoSi0ciIiIiIiJqEotHIiIiIiIiahKLRyIiIiIiImoSi0ciIiIiIiJqktmKx+XLl6Nz586ws7ODWq3GoUOHzLVrIiIiIiIiaiOzFI9r167FokWLsGTJEqSlpaF///6YNGkSCgoKzLF7IiIiIiIiaiOzFI///ve/8be//Q2PPPIIevfujc8//xwODg745ptvzLF7IiIiIiIiaiNrU++guroaqampWLx4saFNLpdjwoQJSE5ObnAdrVYLrVZreF5SUgIAKC0tbXMenbayzdsg8zDG77u5+L5oP/i+oIYY631xczuCIBhle0RERJbE5MXj1atXUVdXB29v73rt3t7eOHv2bIPrxMTEYOnSpbe0BwYGmiQjSZPyY7ETkBTxfUENMfb7oqysDEql0rgbJSIiaudMXjy2xuLFi7Fo0SLDc51Oh6KiIri7u0Mmk4mYTJpKS0sRGBiIrKwsuLi4iB2HJILvC/orvieaJggCysrK4OfnJ3YUIiIiyTF58ejh4QErKyvk5+fXa8/Pz4ePj0+D6ygUCigUinptrq6upopoMVxcXPiBkG7B9wX9Fd8Tt8cRRyIiooaZfMIcW1tbDB48GLt37za06XQ67N69GxEREabePRERERERERmBWU5bXbRoEebMmYMhQ4YgPDwcH3/8MSoqKvDII4+YY/dERERERETURmYpHmfOnInCwkK89tpryMvLw4ABA7B9+/ZbJtGh1lEoFFiyZMktp/pSx8b3Bf0V3xNERETUFjKB85ETERERERFRE0x+zSMRERERERG1fyweiYiIiIiIqEksHomIiIiIiKhJLB4l4rfffoOTkxNKSkrw4osvYsqUKWJHIgm5fPkyZDIZ0tPTxY5CZrBp0yYEBwfDysoKCxcuRGxsLO91S0RERKJj8SgRQ4YMQXp6OpydnfH888/j66+/FjsSmdHcuXMhk8kMD3d3d0yePBnHjx8HAAQGBiI3Nxd9+/YVOSkZw59/37a2tggODsYbb7yB2tpaAMDjjz+O++67D1lZWXjzzTdFTktERESkx+JRIuzt7REcHAy5XA4PDw/4+/u3eBt/Ha1ojs6dO+Pjjz9u8b7I+CZPnozc3Fzk5uZi9+7dsLa2xl133QUAsLKygo+PD6ytG767jiAIhsKD2oebv+/z58/j2Wefxeuvv473338f5eXlKCgowKRJk+Dn5wdnZ2exoxIREREBYPEoKXv37oWVlRVGjBjRqvU5WtG+KRQK+Pj4wMfHBwMGDMBLL72ErKwsFBYW3nLaakJCAmQyGbZt24bBgwdDoVBg3759GDNmDP75z3/ihRdegJubG3x8fPD666/X209xcTEee+wxeHp6wsXFBePGjcOxY8cMr7/++usYMGAAvv/+e3Tu3BlKpRKzZs1CWVmZGY+G5bv5+w4KCsKTTz6JCRMm4L///a+hWBw3bhxkMhkSEhJuWffixYuYPn06vL294eTkhLCwMOzataveMjKZDJs2barX5urqitjYWAB/nAq9bt06jBw5Evb29ggLC8Pvv/+Ow4cPY8iQIXBycsKUKVNQWFhYbztff/01evXqBTs7O4SEhOA///mP4bWb2/3pp58wduxYODg4oH///khOTm77QSMiIiJRsXiUkG+++QYLFizA0aNHcf78+Raty9EKy1JeXo4ffvgBwcHBcHd3b3S5l156CcuWLcOZM2cQGhoKAPj222/h6OiIlJQUvPfee3jjjTewc+dOwzr3338/CgoKsG3bNqSmpmLQoEEYP348ioqKDMtcvHgRmzZtwpYtW7BlyxYkJiZi2bJlpvuBCfb29qiqqsK5c+cAABs2bEBubi6GDRt2y7Ll5eW48847sXv3bhw9ehSTJ0/GtGnToNFoWrzfJUuW4NVXX0VaWhqsra0RFRWFF154AZ988gl+++03XLhwAa+99pph+bi4OLz22mt4++23cebMGbzzzjv417/+hW+//bbedl955RU899xzSE9PR48ePTB79myOjhMREbV3AklCSUmJ4ODgIJw5c0aYOXOmsHjxYsNre/fuFQAIW7ZsEfr16ycoFApBrVYLJ06cqPf6nx979+4VBEEQfvvtN2HEiBGCnZ2dEBAQIDz11FNCeXm5YdtBQUHCRx99ZM4flRowZ84cwcrKSnB0dBQcHR0FAIKvr6+QmpoqCIIgZGRkCACEo0ePCoLwx+9806ZN9bYzevRoYcSIEfXawsLChBdffFEQBP37wcXFRaiqqqq3TLdu3YQvvvhCEARBWLJkieDg4CCUlpYaXn/++ecFtVpt1J+5I5szZ44wffp0QRAEQafTCTt37hQUCoXw3HPPCdevX6/3b1gQBGHVqlWCUqm87Tb79Okj/N///Z/hOQBh48aN9ZZRKpXCqlWrBEH44z319ddfG15fvXq1AEDYvXu3oS0mJkbo2bOn4Xm3bt2E+Pj4ett98803hYiIiEa3e+rUKQGAcObMmdv+DERERCRtHHmUiDVr1iAkJAQhISF48MEH8d1330Gn09Vb5vnnn8eHH36Iw4cPw9PTE9OmTUNNTQ2GDRvW4GjFxYsXMXnyZMyYMQPHjx/H2rVrsW/fPixYsECMH5GaMHbsWKSnpyM9PR2HDh3CpEmTMGXKFGRmZja6zpAhQ25puzkCeZOvry8KCgoAAMeOHUN5eTnc3d3h5ORkeGRkZODixYuGdTp37lxv9PrP2yDj2LJlC5ycnGBnZ4cpU6Zg5syZt5xi3Jjy8nI899xz6NWrF1xdXeHk5IQzZ860auTxz+8Xb29vAEC/fv3qtd383VdUVODixYuYN29evffPW2+9Ve/989ft+vr6AgDfQ0RERO1cw7NvkNmtWrUKDz74IAD9RBparRY7duyod8uOJUuW4I477gCgPzUxICAAGzduxAMPPAAvLy8AMFznBgAxMTGIjo42TJ7TvXt3fPrppxg9ejRWrFgBOzs7M/6E1BRHR0cEBwcbnn/99ddQKpX46quv8NhjjzW6zl/Z2NjUey6TyQxfRJSXl8PX17fB6+j+fCuI222DjGPs2LFYsWIFbG1t4efnZ5gMqbi4uMl1n3vuOezcuRMffPABgoODYW9vj/vuuw/V1dWGZWQyGQRBqLdeTU3NLdv68+9aJpM12Pbn9w8AfPXVV1Cr1fW2Y2Vl1eR2+R4iIiJq31g8SsCZM2dw+PBh/PTTTwAAa2trzJw5E6tWrapXPEZERBj+383NDT179sSZM2ca3e6xY8dw/PhxxMXFGdoEQYBOp0NGRgZ69eplgp+GjEUmk0Eul+PGjRtG2+agQYOQl5cHa2trdO7c2WjbpZb765cFLbF//37MnTsX99xzDwB9UXf58uV6y3h6eiI3N9fw/Pz586isrGx1XkA/Cunn54dLly4hOjq6TdsiIiKi9ofFowR88803qKurQ2BgoKFNEATY2NjUm8SkpcrLy/H444/jn//85y2vqVSqVm+XTEOr1SIvLw8AcP36dXz22WcoLy/HtGnTjLaPCRMmICIiApGRkXjvvffQo0cP5OTkYOvWrbjnnnsaPA2WpKd79+746aefMG3aNMhkMvzrX/+6ZVRv3Lhx+OyzzxAREYG6ujq8+OKLt4wot8bSpUvxz3/+E0ql0nCWxJEjR3D9+nUsWrSozdsnIiIi6WLxKLLa2lp8//33eOedd24pEiIjIxEfH2+4MfzBgwcNRd/169fx+++/33b0cNCgQTh9+nSrRzfIvLZv3264NszZ2RkhISFYv349xowZc8uoUmvJZDL88ssveOWVV/DII4+gsLAQPj4+GDVqlOF6N5K+f//733j00UcxbNgweHh44MUXX0RpaWm9ZT788EM88sgjGDlyJPz8/PDJJ58gNTW1zft+7LHH4ODggPfffx/PP/88HB0d0a9fv2bfW5aIiIjaL5nw14tiyKz++9//4t5770VeXh48PDzqvfbiiy9i165d+PDDDzF27Fj06dMHn3zyCby9vfHKK68gPT0d58+fh62tLYqLi9GpUyfs3bsXY8aMAQAcP34cQ4cOxaOPPorHHnsMjo6OOH36NHbu3InPPvsMgH5ilIULF/KDHxERERER3RZnWxXZqlWrMHr06FsKRwCYMWMG0tLScPz4cQDAsmXL8PTTT2Pw4MHIy8vDzz//DFtb20a3HRoaisTERPz+++8YOXIkBg4ciNdeew1+fn4m+3mIiIiIiMgyceSxHUhISMDYsWNx/fr1ejNiEhERERERmQtHHomIiIiIiKhJLB6JiIiIiIioSTxtlYiIiIiIiJrEkUciIiIiIiJqEotHIiIiIiIiahKLRyIiIiIiImoSi0ciIiIiIiJqEotHIiIiIiIiahKLRyIiIiIiImqStdgBmkOn0yEnJwfOzs6QyWRixyEiIgslCALKysrg5+cHuVz871fZ/xERkTk0t/9rF8VjTk4OAgMDxY5BREQdRFZWFgICAsSOwf6PiIjMqqn+r10Uj87OzgD0P4yLi4vIaYiIyFKVlpYiMDDQ0O+Ijf0fERGZQ3P7v3ZRPN48VcfFxYWdJxERmZxUThFl/0dERObUVP/Xogs6VqxYgdDQUEMnFhERgW3btt12nfXr1yMkJAR2dnbo168ffvnll5bskoiIiIiIiCSgRcVjQEAAli1bhtTUVBw5cgTjxo3D9OnTcerUqQaXP3DgAGbPno158+bh6NGjiIyMRGRkJE6ePGmU8ERERGKJiYlBWFgYnJ2d4eXlhcjISJw7d67eMlVVVZg/fz7c3d3h5OSEGTNmID8/X6TEREREbSMTBEFoywbc3Nzw/vvvY968ebe8NnPmTFRUVGDLli2GtqFDh2LAgAH4/PPPm72P0tJSKJVKlJSUtOm0nTqdALlMOqcjERGRkdTVAlZtvxKjJf3N5MmTMWvWLISFhaG2thYvv/wyTp48idOnT8PR0REA8OSTT2Lr1q2IjY2FUqnEggULIJfLsX//fqPnISIiaq3m9jet7mnr6uqwfv16VFRUICIiosFlkpOTsWjRonptkyZNwqZNm267ba1WC61Wa3heWlra2pj1bEjLxheJFxGlDsKMQf5wdbA1ynaJiEgEtdXA2Z+BI6sA1yAgcrlZd799+/Z6z2NjY+Hl5YXU1FSMGjUKJSUlWLlyJeLj4zFu3DgAwKpVq9CrVy8cPHgQQ4cONWteIiK6DUEAYmMBa2vgoYfETiNZLS4eT5w4gYiICFRVVcHJyQkbN25E7969G1w2Ly8P3t7e9dq8vb2Rl5d3233ExMRg6dKlLY3WpJ/SsnGxsAJvbjmN97afxdRQX0SrVRik6sTRSCKi9qIoA0iNBY7+AFRe1bfZHgXufB+wdRAtVklJCQD9GTkAkJqaipqaGkyYMMGwTEhICFQqFZKTkxssHk315SkREd1GcTHwxBPA2rWAgwMwYgTQpYvYqSSpxcVjz549kZ6ejpKSEvz444+YM2cOEhMTGy0gW2Px4sX1RixvTh3bVl89PASb03MQl6LBmdxS/JR2BT+lXUGIjzOi1SpMH+gPFzubNu+HiIiMrK4G+H07cOQb4OKeP9qdfYFBc4BBD4laOOp0OixcuBDDhw9H3759Aei/QLW1tYWrq2u9ZW/3Jaqpvjyl+jq/tFXsCBbt8rKpYkcgar79+4GoKECjAaysgFdfBVQqsVNJVouLR1tbWwQHBwMABg8ejMOHD+OTTz7BF198ccuyPj4+t0wMkJ+fDx8fn9vuQ6FQQKFQtDRak5ztbPDg0CBEq1VIzypGXIoGW47n4GxeGf61+RTe+eUspg/wQ5RahdAAV6Pvn4iIWqg4C0j7Tv8ov1lwyYDg8cDgR4Aek41yrWNbzZ8/HydPnsS+ffvatB1TfXlKRER/UVsLvP028MYbgE4HdO0KxMcDarXYySStzT2uTqerd4rNn0VERGD37t1YuHChoW3nzp2NXiNpLjKZDANVnTBQ1Qn/mtobPx3NRnyKBucLyrHmcBbWHM5CP38lotUqTOvvB0eF+B9MiIg6DF0dcGGXfpTx/K+AoNO3O3oCAx8CBs8BOnUWNeKfLViwAFu2bEFSUhICAgIM7T4+PqiurkZxcXG90cfbfYlqqi9PiYjoTzIzgQcfBG5+4ffQQ8BnnwGcmKxJLaqKFi9ejClTpkClUqGsrAzx8fFISEjAjh07AAAPP/ww/P39ERMTAwB4+umnMXr0aHz44YeYOnUq1qxZgyNHjuDLL780/k/SSkoHGzwyvAvmDuuMw5evIy4lE9tO5OHElRK89NMJvLX1DO4Z6I8otQq9fPmGIiIymbI8IO17IO1boCTrj/Yuo/SjjCF3AdbSmehMEAQ89dRT2LhxIxISEtDlL9fHDB48GDY2Nti9ezdmzJgBADh37hw0Go3oX6ISEXVY69YBf/87UFICODsDK1YA0dFip2o3WlQ8FhQU4OGHH0Zubi6USiVCQ0OxY8cO3HHHHQAAjUYDufyPW0cOGzYM8fHxePXVV/Hyyy+je/fu2LRpk+F6ECmRyWQI7+KG8C5uWDKtGj+mZiE+RYPL1yrx/cFMfH8wE4NUrohSB+GuUF/Y2ViJHZmIqP3T6YCMBP0o49lfAKFO327fCRgQrS8aPYJFjdiY+fPnIz4+Hps3b4azs7PhOkalUgl7e3solUrMmzcPixYtgpubG1xcXPDUU08hIiKCM60SEZlbeTnw9NPAN9/on6vV+tNUu3YVN1c70+b7PJqDWPe50ukEJF+6hvgUDXacykOtTn+olPY2mDEoAFFqFYK9nMyWh4jIYlRc1c+WmroKuH75j3ZVhL5g7D0dsLEze6yW9DeNzdK9atUqzJ07FwBQVVWFZ599FqtXr4ZWq8WkSZPwn//8p8lr/1uTh5qPE+aYFifMIclJSwNmzwZ+/x2QyYCXXwaWLAFsOFHmTc3tb1g8NlNBWRXWH9FfG3ml+IahXd3FDdFDgzCpjzcU1hyNJCJqlCAAmfv1o4yn/wvoavTtCiXQfxYw5BHAq5eoEaXQ30g5j6Vg8WhaLB5JMnQ64KOPgMWLgZoawN8f+OEHYMwYsZNJTnP7G84E00xeznaYPzYYT4zuhqTzhYhP0WD3mXykZBQhJaMIbo62uH9IAKLCVQhydxQ7LhGRdFQWAcfW6EcZr/7+R7v/YP0oY997AVv+3SQiIiPKywPmzAF+/VX//J57gK++Atzdxc3VzrF4bCEruQxje3phbE8v5JbcwJpDWVh7OAt5pVX4IvESvki8hJHdPRCtVmF8L2/YWMmb3igRkaURBCD7sH6U8dRGoLZK327jCIQ+oB9l9O0vbkYiIrJMW7cCjzwCFBYC9vbAxx8Df/ub/pRVahMWj23gq7THM3f0wFPjgrHnbAHiUjRIOl+I385fxW/nr8LTWYFZYYGYGRaIgE7i3byaiMhsqkqA4+uAI6uAglN/tHv30xeM/e4H7Hj6JRERmUBVFfDCC8D//Z/+eWgosHo10Lu3uLksCItHI7C2kmNiHx9M7OODrKJKrD6kwboj2Sgs0+L/9lzAZ3svYGxPL0SrVRjT0wtWcn7rQUQWJueofpTxxI9ATaW+zdoe6DtDXzT6D+Y3vkREZDqnTwOzZgEnTuifP/00sGwZYGf+ydcsGYtHIwt0c8ALk0OwcEIP7Dydj/hDmdh/4Rr2nC3AnrMF8FPaYVa4CjPDAuHtwjczEbVj2nLg5AZ90Zib/ke7Zwgw5FEgdCZg7ypWOiIi6ggEAfjiC+CZZ/Qjj56eQGwscOedYiezSCweTcTWWo6pob6YGuqLS4XlWH1Igx9Ts5FTUoV/7/wdn+w+jwm9vBCtDsKIYA/IORpJRO1F3kn95DfH1gLVZfo2K1ugd6R+lFEVwVFGIiIyvWvXgMceAzZt0j+fOBH49lugmbdDopZj8WgGXT2d8MrU3nh2Yk9sP5mH+BQNDl0uwo5T+dhxKh8qNwfMDlfh/iEB8HBSiB2XiOhWNTeAU5v0o4zZh/5od+umLxj7RwGOnMGOiIjMZO9e4KGHgCtX9PdrXLYMWLgQkHOySlNi8WhGdjZWiBzoj8iB/vg9vwzxKRpsSMuGpqgS724/i3/vPIdJfXwQpVYhoqt7ozegJiIym8Lf9aOM6fFAVbG+TW4NhNylPzW180h21EREZD41NcCSJfpiURCAnj31k+IMHCh2sg6BxaNIeng74/W7++DFySH4+XgO4lM0SM8qxpbjudhyPBddPR0RFa7CfYMD4OpgK3ZcIupIarXAmZ/1M6Zm7vuj3VUFDJ4LDHgQcPYWLR4REXVQFy8CUVHAof+dATNvHvDJJ4Aj7xVsLiweRWZva4UHhgTigSGBOHmlBPGHNNh89AouFVbgra1n8N6Oc7irny+i1CoMDurE0UgiMp2iS0BqLHD0B6Dymr5NJgd6TNGfmtptHCC3EjUiERF1UD/8APzjH0BZGeDqCnz5JXD//WKn6nBYPEpIX38l3rmnH16+sxc2p19B3EENTueW4qejV/DT0Svo6e2MKLUK9wzyh4udjdhxicgS1NUA57bpr2W8tPePdmc/YNDD+ofSX7x8RETUsZWW6ovGuDj985Ej9YWkSiVurg6KxaMEOSmsEa0OQlS4CseySxCfkon/HsvBufwyLPnvKSzbdhZ39/dD9FAVQgNcxY5LRO1RcRaQ9i2Q9h1Qnv+/RhkQPEE/yth9EmDFLoKIiER08KD+NNWMDMDKSn+t48sv6/+fRMFPBhImk8kwINAVAwJd8crU3tiYlo34Qxr8nl+OtUeysPZIFvr6uyBaHYS7+/vBUcFfJxHdhq4OOL9TP8p4YScg6PTtjl7AoIeAQXOATkHiZiQiIqqr00+Is2SJ/v+DgoD4eGDYMLGTdXisNtoJpb0N5g7vgjnDOuNI5nXEHczELyfzcPJKKRb/dAJvbz2DyIF+iAoPQm8/F7HjEpGUlOYCR78HUr8FSrP/aO8yWj/K2HMqYM2JuYiISAKys/W34EhI0D+fNQtYsUJ/nSOJjsVjOyOTyRDW2Q1hnd3wWkU1NqTqRyMzrlbgh4Ma/HBQg4EqV0Srg3BXqC/sbDisT9Qh6XT6axiPfKO/plGo07fbuwEDo4HBjwDu3cTNSERE9GcbN+pnUL1+XT+D6vLlwMMPA5wwUjJYPLZjbo62+NuornhsZBckX7yGuEMa7DiZh6OaYhzVFOONn09hxuAARKtVCPZyFjsuEZlDeSGQ/oN+1tTrl/9oVw3T35ex1zTAxk6sdERERLeqrAQWLQK++EL/fMgQ/Wmq3buLm4tuweLRAshkMgwL9sCwYA8UlFVh/ZFsrD6kQfb1G1i1/zJW7b+M8C5uiFarMLmvDxTWHI0ksiiCAFz+TX9fxjM/A7oafbtCCQyYrb83o1cvUSMSERE16Ngx/ampZ8/qn7/wAvDmm4AtL6eQIhaPFsbL2Q7zxwbjydHdkHS+EPEpGuw6k49DGUU4lFEEN0db3D84ALPDVejswRuqErVrlUXAsdX6ovHa+T/a/YfoRxn73APYOoiXj4iIqDGCAHz6qb5YrK4GfH2B774DJkwQOxndBotHCyWXyzCmpxfG9PRCbskNrD2chbWHs5BbUoUvki7hi6RLGBHsgWi1ChN6e8PGSi52ZCJqDkEAslL0BeOpjUCdVt9u6wSEPqC/ltE3VNyMREREt1NQAMydC2zbpn8+bRqwciXg6SlqLGoai8cOwFdpj4UTemDB2GDsPVeI+JRMJPxeiH0XrmLfhavwdFZg5pBAzAoPREAnjlIQSVJVCXB8nX4CnILTf7T7hOpHGfvdByh4bTMREUncjh3AnDlAfj6gUAAffgj84x+cFKedYPHYgVhbyXFHb2/c0dsbWUWVWHNYg7WHs1FYpsVney9gecIFjOnhiSh1EMb29IQ1RyOJxHclTV8wntwA1FTq26ztgX4zgMGPAv6D2OESEZH0abXAyy8D//63/nmfPsCaNUDfvuLmohZh8dhBBbo54PlJIVg4oQd2ns5HfIoG+y5cxd5zhdh7rhC+SjvMClNhZlggfJScmZHIrLTlwMkf9UVj7rE/2j176e/LGDoTsHcVLR4REVGLnDsHzJ4NHD2qfz5/PvD++4C9vbi5qMVYPHZwNlZy3NnPF3f280XG1QqsOaTBuiP6ayM/2vU7Pt1zHuNDvBClVmFUd0/I5RzhIDKZvBP6axmPrwOqy/RtVgqgT6T+WkbVUI4yEhFR+yEI+msZn35afzsOd3fgm2+Au+8WOxm1EotHMuji4YjFd/bCook9sP1kHuJSNDiUUYRfT+fj19P5CHSzx+xwFe4fHAhPZ4XYcYksQ80N/cQ3R74Bsg//0e7WTX8t44AowMFNvHxEREStcf068Pe/Az/+qH8+frx+NlU/P3FzUZuweKRbKKytMH2AP6YP8Mf5/DLEpWjwU1o2sopu4L3t5/DRzt8xsY8PotUqRHR1h4wjIUQtV3hOP8p4LF4/GQ4AyK2BXtP0o4xdRnGUkYiI2qfffgOio4GsLMDaGnj7beC55wA559No71g80m1193bG63f3wYuTQ7DleA7iD2lwVFOMrcdzsfV4Lrp6OCJKrcKMQQHo5MibuRLdVq0WOPOzfpQxc/8f7a4qfcE48EHAyUu8fERERG1RWwu88Ya+WNTpgOBgID4eCAsTOxkZSYvK/5iYGISFhcHZ2RleXl6IjIzEuXPnbrtObGwsZDJZvYedHSdgaW/sba1w/5BAbPzHcGz95whEq1VwtLXCpasVeGvrGahjduOZtek4crkIgiCIHZdIWq5dBH79F/DvXsCGefrCUWYFhNwFRG8A/nkMGLmIhSMREbVfly8Do0YBb76pLxznzAHS0lg4WpgWjTwmJiZi/vz5CAsLQ21tLV5++WVMnDgRp0+fhqOjY6Prubi41CsyeZpj+9bHT4m37+mHxXf2wn/TcxCXkolTOaXYePQKNh69gh7eTohWByFyoD+U9jZixyUSR10NcO4X/SjjpYQ/2l38gUFzgEEPAS687oOIiCzAmjXA448DpaWAiwvw+ef62VXJ4rSoeNy+fXu957GxsfDy8kJqaipGjRrV6HoymQw+Pj6tS0iS5aSwRpRahdnhgTieXYL4FA02H7uC3/PLseS/pxCz7Qzu7u+HKHUQ+gco+aUBdQzFGiD1W+Do90B5/v8aZUD3O/QT4ATfAVjxigFLkJSUhPfffx+pqanIzc3Fxo0bERkZaXh97ty5+Pbbb+utM2nSpFv6UiKidqusDPjnP4HYWP3ziAj9aaqdO4uZikyoTZ9gSkr0kzy4ud1+JsDy8nIEBQVBp9Nh0KBBeOedd9CnT59Gl9dqtdBqtYbnpaWlbYlJJiaTydA/0BX9A13x8tRe2HT0CuJTNDiXX4Z1R7Kx7kg2+vi5IFodhLsH+MFJwQ/OZGHqaoHzvwKpq4DzOwH879RtRy9g0MP6R6cgUSOS8VVUVKB///549NFHce+99za4zOTJk7Fq1SrDc4WCM1UTkYU4fBiIigIuXNBPhPPKK8Brr+knyCGL1erfrk6nw8KFCzF8+HD07du30eV69uyJb775BqGhoSgpKcEHH3yAYcOG4dSpUwgICGhwnZiYGCxdurS10UhESnsbzBnWGQ9HBCE18zriUzTYciIXp3JK8fLGE3h762lEDvRHlFqFPn5KseMStU1pDpD2PZD2LVB65Y/2rmP0o4w97wSseOq2pZoyZQqmTJly22UUCgXPvCEiy6LTAR98oC8Wa2uBwEDghx/01zuSxWt18Th//nycPHkS+/btu+1yERERiIiIMDwfNmwYevXqhS+++AJvvvlmg+ssXrwYixYtMjwvLS1FYGBga6OSCGQyGYZ0dsOQzm741129sSEtG/EpGly6WoG4FA3iUjQYEOiKaLUKd4X6wd7WSuzIRM2j0wEX9+hHGc9tA4Q6fbu9m3621MFzAfduokYk6UhISICXlxc6deqEcePG4a233oK7u3ujy/PMGyKStJwc4OGHgd279c/vuw/48kugUydxc5HZtKp4XLBgAbZs2YKkpKRGRw8bY2Njg4EDB+LChQuNLqNQKHhqjwXp5GiLx0Z2xbwRXZB86RriUzTYcSoP6VnFSM8qxptbTuPeQQGIVqvQ3dtZ7LhEDSsvAI7+AKTGAsWZf7QHDdffZqP33YA1/27RHyZPnox7770XXbp0wcWLF/Hyyy9jypQpSE5OhpVVw1+Y8cwbIpKsn38GHnkEuHYNcHAAPv0UePRR3pO4g2lR8SgIAp566ils3LgRCQkJ6NKlS4t3WFdXhxMnTuDOO+9s8brUvslkMgzr5oFh3TxQWKbF+tQsrD6kQVbRDcQeuIzYA5cR3tkNUWoVJvf1gZ0NRyNJZIIAXP5NP2PqmS2ArkbfbqcE+kfpRxm9QkSNSNI1a9Ysw//369cPoaGh6NatGxISEjB+/PgG1+GZN0QkOTduAM8/Dyxfrn8+YACwejUQwv6vI2pR8Th//nzEx8dj8+bNcHZ2Rl5eHgBAqVTC3t4eAPDwww/D398fMTExAIA33ngDQ4cORXBwMIqLi/H+++8jMzMTjz32mJF/FGpPPJ0V+MeYYDwxqht+u3AV8SmZ2HWmAIcuF+HQ5SJ0+tkG9w8JxOxwFbp4NH4bGCKTqCwC0uP1p6Ze+9NZEgFh+lHGPvcAtg7i5aN2qWvXrvDw8MCFCxcaLR555g0RScrJk/pbbpw8qX++aBHwzjsA/051WC0qHlesWAEAGDNmTL32VatWYe7cuQAAjUYDuVxueO369ev429/+hry8PHTq1AmDBw/GgQMH0Lt377YlJ4sgl8swuocnRvfwRF5JFdYezsKawxrkllThy6RL+DLpEoYHuyNaHYQ7envDxkre9Ebp/9u787iq6vzx46/LjuzILlxENldQUVDUFDcgNU1LEypbv9WvphynqXGmNKvJpmWqmZpqptIWUTOXSsNd1FxANnfZRC47uACCigLn98fJSyjmEnhZ3s/H4z70fj7n3Pvm3Avnvu/7fD4fcSsUBfIT1Srj4TVQ/8u4MzMbCJoOgx4Gt34GDVG0bwUFBZw6dQp3d3dDhyKEEL9NUeA//4E//Qlqa8HFBb78EqKiDB2ZMDCNoiiKoYO4nqqqKuzs7KisrMTW1tbQ4YhWVlffQEJGOXFJOrZllHH5Hepkbc6MwZ7cN1iLl6NUfUQLOV8BB75Vk8byo43tbkEw+FHoew+YWxssPHF73cz5prq6Wj9+f8CAAfzzn/8kIiICR0dHHB0dWbBgAdOmTcPNzY2cnBxeeOEFzp49y8GDB2+4uijnv9bR/S/rDB1Ch3bizQmGDkH8HidPqmMZf/xRvR8dDYsWgaurYeMSrepGzzeyEItoc0yMjRjb25WxvV0pOHOOZUn5LE/Op/xsLR9ty+E/CTmMDHAmNsybiEBnTKQaKW6WokBhKqR8AQdXQt15td20C/SdplYZPQbKJADiNyUnJxMREaG/f3ms4qxZs/j44485cOAAX375JRUVFXh4eDB+/Hhee+01uSxVCNF2bdkCDzwAxcVgZgZvvQXPPivnQ6EnyaNo0zwduvB8ZCDPjfVn85FS4pJ07Mw6SUJGOQkZ5bjZWnBfqBczBnvhbmdp6HBFW1d7Fg5+p1YZSw40trv0VtdlDJquToYjxA0YNWoUv3XxzoYNG25jNEII8TtcvAjz5qnJoqKok+EsWwbBwYaOTLQxkjyKdsHU2Ijofu5E93PnxMkalu7TsSK5gJKqC7y/OYt/bcliTC9XYsO03OHvjJGRfEMmfqX4gDr5zYFv4WK12mZsrk58M+gR8AqVb1WFEEJ0TllZEBMDycnq/f/7P3jvPXU5DiGuIMmjaHe6O1kxN7oXc8YFsP5QCXGJOhJzT7PpSCmbjpTi6WDJzFAt0wd54Wwjl4d1WhfPweFVkLwICpMb27v6qQlj8Ezo4mi4+IQQQghDUhT46it4+mmoqQEHB/jsM5g61dCRiTZMkkfRbpmbGDO5fzcm9+9GdtlZ4hLz+S4ln4Iz53l7Qwbvbcokso8bsWFahvp2RSOVpc6h7JhaZUxfCrWVapuRKfSapCaN3YdLlVEIIUTnVlkJTz6pXpoKMHIkfPMNeHoaNi7R5knyKDoEPxcb5k3qzQtRgaw9UExcYh6pugrWHSxm3cFifJysiAnVMi3EE0crM0OHK1rapQtw9Ae1yqjb3dhu761OftP/frB2Nlx8QgghRFuxZ496meqJE2BsDK++Ci++qP5fiOuQ5FF0KBamxtwT4sk9IZ4cLa4iLlHH6rRCck/W8PefjvL2hgzu7OdGTJg3g7s7SDWyvTuVo1YZ05bA+dNqm8YYAqPVpLHHaDCS2XiFEEII6uvhjTdgwQL1/z4+EBcHQ4YYOjLRjkjyKDqsXu62vDalL3+J7skP+4tYkpjHocIq1qQXsSa9CH8Xa2LDtNw90BM7S1NDhytuVP0lOLZOnTE1d3tju203CHkIBjwAtrIIuxBCCKGn08H998POner9mBj4z3/ATmYYFzdHkkfR4VmZmzAzVMvMUC0HCiqIS9TxfXoRWWXVvPLjEd5cf4xJQR7EhGnp72Uv1ci26kwepH4JqV9DTdkvjRrwH69WGf3GgbH8SRNCCCGa+O47ePxxqKgAa2v4+GM1kRTiFsgnLdGpBHnaE+Rpz18n9GJNWiFxiTqOlZxlRUoBK1IK6O1uS+wQLZP7d8PaXH49DK6+DrI2qlXG7M3AL2vqWbvCwAfVm73WoCEKIYQQbVJNDcyerc6gChAaql6m6utr0LBE+yafjkWnZGthyoNDu/PAEG9SdWdYkqhj7YFijhRX8bfVh3hj3VEmD+hGTKiWvt3kko7brrIQ0r6GlC/hbFFje48IdcbUwGgwlkuNhRBCiGalpcHMmZCRoc4w/pe/qGMdTeXcKX4fSR5Fp6bRaAjxdiTE25F5E3vzXUoBcUk6jpfXEJeoIy5RR7CXPbFhWiYFeWBpJjORtZqGesjZqs6YmhkPSoPa3qUrDLgfBs6CrvJtqRBCCHFNDQ3wwQdqsnjxInh4wNdfw+jRho5MdBCSPArxC/suZjw2ogePDvdh7/HTxCXpWH+omP35FezPr+C1tUeYNtCTmDAtAa42hg6346gu+6XKuBgqdI3t3sPVsYy9JoGJucHCE0IIIdqFkhJ46CHYsEG9P2WKeslq166GjEp0MJI8CnEFjUbDUN+uDPXtysnq3qxILmBpkg7d6XMs3n2CxbtPMLi7A7Fh3kT1dcPCVKqRN62hAU7sUKuMx9ZCQ53abmEH/WPVWVOdAw0aohBCCNFuxMeriWNZGVhYwHvvwRNPqJesCtGCJHkU4jc4WZvz1ChfnrijBz9nnyQuUcemo6XsO3GGfSfO4PCjKfeEeDIzVEsPZ2tDh9v21ZyC/XFq0ng6p7HdM1Qdy9hnCphaGiw8IYQQol2prYUXX1QvVQXo1w+WLYPevQ0bl+iwJHkU4gYYGWm4I8CZOwKcKa26wLf78lmapKOo8gL/25nL/3bmEu7bldgwb8b1dsXMRBam11MU0O1RE8Yja6D+otpuZgPBMyDkYXDra9AQhRBCiHbn6FF1Upz9+9X7zz4L//iHWnkUopVI8ijETXK1teAPY/z5fxF+JGSUEZeoY2tGGbtzTrE75xRO1mZMH+TFzFAtXo5dDB2u4ZyvgP3LIGURlB9rbHfvr1YZ+04Dc6nWCiGEEDdFUeB//1OX4Th/HpycYPFimDDB0JGJTkCSRyFukbGRhjG9XBnTy5XCivMsT9KxbF8+ZWdr+U9CDh9vz+EOf2diw7SM7umCiXEnqEYqChSmqFXGQyuh7rzabtoF+t2jVhm7DTRsjEIIIUR7dfo0PP44rFql3h83Dr78EtzdDRuX6DQkeRSiBXSzt2TO+ED+MMafLUfLWJKYx86sk2zPLGd7ZjluthbMGOzFfaFeuNt1wDF9tWfh4ApI/gJKDja2u/RRZ0wNmq5OhiOEEEKIW5OQAPffD4WF6nqNCxfCH/8IRp3gy2nRZkjyKEQLMjU2IqqvG1F93cg7VcPSpHxWJOdTUnWBD7Zk8e+tWYzu6UpsmJY7ApwxNmrns6AV71erjAdXwMVqtc3YHPpOVauMXqEy05sQQgjxe1y6BAsWwBtvqFf4+PvD0qUQEmLoyEQnJMmjEK3Eu6sVf4nuyR/H+bPhcClxiXnsPX6azUdL2Xy0lG72lsSEabl3kCcuNu1ocPvFc3B4lVplLExpbO/qr45lDL4PujgaLj4hhBCiozh+HGJjYe9e9f4jj6gzq1rLnAHCMCR5FKKVmZsYc1ewB3cFe5BdVs3SJB3fpRRQWHGetzdk8N6mTMb3cSU2zJuhPbpi1FarkWVH1Srj/mVQW6m2GZlC77vUKmP34VJlFEIIIVrKkiXw1FNw9izY2cF//wvTpxs6KtHJSfIoxG3k52LNyxN78+fIQNYdKCYuSUdK3hl+OljCTwdL6N61CzFhWu4J8cLRyszQ4cKlC3D0B7XKqNvT2O7QXU0Y+8eCtbPBwhNCCCE6nKoqeOYZ+Ppr9f6wYWoi6e1t2LiEQJJHIQzCwtSYaSGeTAvx5FhJFXGJOlalFnLi1Dne+OkY72zIJLqfG7Fh3gzu7oDmdlf0TmarS2ykx8H502qbxhh63qlemuozSgboCyGEEC0tKUldu/H4cfU8O28e/O1vYCIf2UXbIO9EIQysp5str07uy4tRPflxfxFxSToOFFTyfXoR36cX4ediTWyYlqkDPLHrYtp6gdRdhGNr1aQxd0dju60nhDwEA+4HW5kKXAghhGhx9fXw1ltqslhXB1otxMWpVUch2hBJHoVoI6zMTbgvVMt9oVoOFlQSl5TH9+lFZJdVs+DHI7wZf4xJwR7Ehmnp72XfctXIMycg5UtI+xpqytU2jRH4j1erjH5jwci4ZZ5LCCGEEE0VFsIDD8C2ber96dPh00/B3t6gYQnRHEkehWiD+nnasdAziL/e2Ys16UUs2ZvHsZKzfJdSwHcpBfRytyU2TMuUAd2wNr+FX+P6Oshcr1YZs7cAitpu7QYDH1Rv9l4t+jMJIYQQ4gpr1sCjj8Lp02BlBf/+Nzz0kExAJ9qsmxq0tHDhQgYPHoyNjQ0uLi5MmTKFjIyM6+63YsUKevbsiYWFBf369eOnn3665YCF6ExsLEx5YIg38c+NYOVT4Uwb6Im5iRFHi6t4ac0hQv++mbmrDnKosPLGHrCyALa9Ae/3g+WxkL0ZUMB3NMz4Bv54CEb/TRJHIYQQojWdO6fOpHr33WriGBICqanw8MOSOIo27aZKFtu3b+fpp59m8ODB1NXV8de//pXx48dz5MgRrKysmt1n9+7dzJw5k4ULFzJx4kTi4uKYMmUKqamp9O3bt0V+CCE6Oo1GQ4i3AyHeDrw8sRerUgtZkphHTnkNS5N0LE3SEexpR2yYNxOD3eli9qtf7YZ6tbqYskitNioNansXJ3UcY8gscOxhmB9MCCGE6GwOHFAnxTlyRL3/5z/D66+DWRuYZV2I69AoiqLc6s7l5eW4uLiwfft27rjjjma3mTFjBjU1Naxdu1bfNmTIEPr3788nn3xyQ89TVVWFnZ0dlZWV2Nra3mq4QnQoiqKQmHuauEQd8YeKuVSv/irbmJswdWA3HuhniV/BanU8Y6WuccfuI2DQw9BzIpiYGyh6Idqmtna+aWvxdBTd/7LO0CF0aCfenGDoENomRYEPP1STxdpacHODr76CceMMHZkQN3y++V1jHisr1UvlHB0dr7nNnj17mDNnTpO2yMhI1qxZc819amtrqa2t1d+vqqr6PWEK0SFpNBqG9OjKkB5dOVXdm+9SCliaeAKPimRCk9/BOzUFNPUAKBb2aPrHqrOmOgcYNnAhhBCisykvVy9JXffLFxcTJ8IXX4CzrJUs2pdbXqitoaGB2bNnM2zYsN+8/LSkpARXV9cmba6urpSUlFxzn4ULF2JnZ6e/eXnJ+CshfktXTTVPmKxjm8XzxJm9wQTjJEw19aQ0+PPHi08xpPZDXq+7nxw8DB2qEB3Gjh07mDRpEh4eHmg0mqu+FFUUhXnz5uHu7o6lpSVjx44lKyvLMMEKIQxn40YIClITR3NzdVKcH36QxFG0S7ecPD799NMcOnSIZcuWtWQ8AMydO5fKykr9LT8/v8WfQ4h2T1HgxC5Y+Rj8sydsehnN6eNgbguDH+fUA9vYM2opSbbjKT1vxGc/5zLm3e3M/O9e1h4o4mJdg6F/AiHatZqaGoKDg/noo4+a7X/rrbf417/+xSeffEJiYiJWVlZERkZy4cKF2xypEMIgLl5UL1GNjISSEujdG5KS4JlnZFIc0W7d0mWrzzzzDGvXrmXHjh14enr+5rZubm6UlpY2aSstLcXNze2a+5ibm2NuLmOxhGjW+TOwfzkkfwEnfzXbsccAdV3GvtPAzIquwDO+8NQoP7ZnlhGXqGPrsTL2HD/FnuOncLI2495BXswcrEXbtYvBfhwh2qvo6Giio6Ob7VMUhffff5+XXnqJyZMnA/DVV1/h6urKmjVruO+++25nqEKI2y0zU50UJzVVvf/UU/Duu2Bpadi4hPidbip5VBSFP/zhD6xevZqEhAR8fHyuu8/QoUPZsmULs2fP1rdt2rSJoUOH3nSwQnRaigIFyeqMqYdWQt0vlQtTK+h3jzoBjseAZnc1NtIwuqcro3u6UlRxnmX78lmWpKPsbC0fJ+TwyfYcRvg7ExumZUxPF0yMb/mCBCHEL3JzcykpKWHs2LH6Njs7O8LCwtizZ881k0cZ8y9EO3bqFHz/PaxcCZs2waVL4Oiojm385UskIdq7m0oen376aeLi4vj++++xsbHRj1u0s7PD8pdvUh588EG6devGwoULAXjuuecYOXIk7777LhMmTGDZsmUkJyfz3//+t4V/FCE6oAtVcPBbSF4MpQcb2137qgljv+lgceMzMHrYWzJnXAB/GO3HlqNlxCXp2JFZrr+52pozY7CW+wZ74WEv344Kcasunx9vZcz/ggULWjU2IdqrtjhLrnP1GSKz9hCVsYshuoOYKI1DQnZ69+f5CbMp3WMCe9pe7FeSWXLFjbip5PHjjz8GYNSoUU3aFy1axEMPPQSATqfDyKixchEeHk5cXBwvvfQSf/3rX/H392fNmjWyxqMQv6UoXa0yHlgBl2rUNhML6DNVvTTVc9DvGi9hamxEVF83ovq6oTt1jqX7dHy7L5/Sqlr+tSWLD7dmMbqnC7Fh3twR4IyxkYzNEOJ2mDt3bpMZyquqqmTSOCHaGI+qMqIy9hCVuYtBBUcxonHVu8MuPYgPDCc+YBg5TvK7Kzqem75s9XoSEhKuarv33nu59957b+aphOh8Ltaol6QmL4Ki1MZ2pwA1YQyaAV2uvSzOrdJ27cKLUT3549gANhwuIS5Rx57jp9h8tIzNR8voZm/JzFAvpg/ywsXWosWfX4iO6PK4/tLSUtzd3fXtpaWl9O/f/5r7yZh/Idom7zNFRGfsJipzF/2Lm86anOYeSHxgOOsDwtE5uF/jEYToGH7XOo9CiBZQekStMu5fBrW/jG8yNoNed6lJo3f4bZmVzczEiEnBHkwK9iCnvJqliTq+Sy2gsOI872zM5P3NWYzr7UpsmDfhvl0xkmqkENfk4+ODm5sbW7Zs0SeLVVVVJCYm8tRTTxk2OCHE9SkK/id1RGfuJjpjF73KT+i7GtCwz7M3639JGIttZckN0XlI8iiEIVy6AEfWqFXG/L2N7Q4+6ljG/rFg5WSw8HydrXlpYm+ejwwk/lAxS/bqSM47Q/yhEuIPleDdtQsxoVruCfGkq7VUSUTnVF1dTXZ2tv5+bm4u6enpODo6otVqmT17Nq+//jr+/v74+Pjw8ssv4+HhwZQpUwwXtBDi2hSFPqU5vySMu/E9XaDvqtMYsUcbxPrAcDb6D6Xc2sGAgQphOJI8CnE7ncyClMWQvkRdcgNAYww9J6hVRp+RYNR2Zju1MDXm7gGe3D3Ak4ySs8Ql5rEqtZC8U+dYGH+MdzdmEtXXjZgwLWE+jmhk3SrRiSQnJxMREaG/f3ms4qxZs1i8eDEvvPACNTU1/N///R8VFRUMHz6c9evXY2Ehl38L0VZolAb6F2USnbGLqMzdaCsbl5erNTbh5+4DWB8Qzib/MCosb3yCOiE6Ko1yIwMZDayqqgo7OzsqKyuxtZVfXNHO1F2EYz+qVcYTOxvb7bwgZBYMeABsrr3uaVtz7mIda/cXsyQxj/0Flfp2X2crYsO8mTbQE7supgaMUIhb19bON20tno6iLc7a2ZG01qydLfW6GTXUE1pwmMjMPURl7Ma9+pS+77yJOQk9QogPDGerbyjV5p1nHWSZbbVzu9HzjVQehWgtp3Mh9UtI+wZqytU2jRH4R6qXpvqNBSNjw8Z4C7qYmTB9sBfTB3txqLCSJYk6vk8vJKe8hlfXHuEf648xMciDmDAtA7X2Uo0UQghhcCb1dQzVHSA6Yzfjs/bgdK7xy8+zZpZs9Q0lPjCc7T4hnDeTqwOEuBZJHoVoSfV1kBmvVhlztjS227jDwAfVm52n4eJrYX272bFwaj/+emdPvk8vYkmijqPFVaxMLWBlagE93WyIHeLNlP4e2FhINVIIIcTtY153keEn0ojO2M3Y7ETsL1Tr+yosrNnkN4T4wHB2de9PrYmZASMVov2Q5FGIllBZAKlfqbezxY3tvmPUsYwBUWDccX/dbCxMuX+IN7FhWtLyK4hL1PHj/iKOlZzl5TWHWPjTUSb39yAm1Jt+nnaGDlcIIUQHZXnxAqOOJxOduZvROfuwvnhe31fexZ6NAUOIDxjGXm0/6jrweVmI1iK/NULcqoZ6yN6sVhmzNoDSoLZ3cYKBD8DAWeDoY9gYbzONRsNArQMDtQ68PKE3K1MLiEvSkV1WzdKkfJYm5RPkaUdsmJZJwR50MZM/QUIIIX4fm9oaRmcnEZ25m5HHU7Gsq9X3Fdk4sSFgKPGBw0ju1ouGdjhcRIi2RD65CXGzzpZA2teQ8iVU5je2dx+hVhl7TgS5/AW7LqY8MtyHh4d1Jyn3NHFJOuIPlnCgoJIDBQd5fe1R7h7YjZgwLT3dZCIQIYQQN87+fBXjsvYSnbGbYXnpmNfX6fvy7N2IDwhnfeAw9rv7o2jazizmQrR3kjwKcSMaGiA3Qa0yZvwEDb+cpCwd1DUZQx4CJ39DRthmaTQawnp0JaxHV+ZPush3KfnEJeo4ceocX+3J46s9eYR4OxAbpuXOfu5YmMq3wkIIIa7mXH2GyKw9RGXsYojuICaXr/gBsrp66RPGIy4+IJO1CdEqJHkU4rfUnFRnS01ZDGdyG9u9hqhVxt6TwVRmZbtRjlZm/N8dvjw2vAe7c04Rl5THxsOlpOSdISXvDAt+PMI9IZ7MDNXi52Jt6HCFEEIYmk4Hq1bBypUk/rwLIxpXmDvs0oP4wHDiA4aR4+RlwCCF6DwkeRTiSooCebvUKuPRH6D+otpubgvB90HIw+Da27AxtnNGRhqG+zsx3N+JsrMXWJFcQFyijsKK83z+cy6f/5zLkB6OxIZ5E9nHDTMTueRICCE6jexsWLlSve3bp282AtLcA4kPDGd9QDg6B3fDxShEJyXJoxCXnTsN+5dByiI4mdnY7jFQrTL2nQpmVoaLr4NysbHg6Qg/nhzpy46scpbs1bH1WCl7j59m7/HTdLUy495BXsSEatF27TyLNQshRKehKHDkSGPCeOBAY59GA8OHw7RpDM2wo9jW2XBxCiEkeRSdnKJAwT61ynh4FdRdUNtNrSDoXrXK6NHfoCF2FsZGGiICXYgIdKGo4jzL9+WzbJ+O0qpaPtmewyfbcxjh70RsmJYxvVwxNZZqpBBCtFuKAmlpjQljRkZjn7ExRETAtGkwZQq4uQFQ/Jd1holVCKEnyaPonC5UwYHl6ljG0kON7a79YNDD0O9esJAZQA3Fw96SP44L4A+j/dh6rIwliTp2ZJWzM+skO7NO4mJjzn2DvZgRqqWbvaWhwxVCCHEjGhogMVFNFletgtxfzSVgZgbjxqkJ4113QdeuhotTCHFNkjyKzqUoTa0yHvwOLtWobSYW0HeaWmX0HCQztLUhJsZGjO/jxvg+buSfPsfSJB3fJudTdraWf23N5sNt2UQEuhATpmVUoAvGRvLaCSFEm1JfDzt3qgnj6tVQWNjYZ2kJ0dFqwjhxItjKl7ZCtHWSPIqO72INHFoJyV+oyeNlToHqWMbgGeqSG6JN83LswgtRPZk9NoBNR0pZkpjH7pxTbDlWxpZjZXSzt1SrkYO9cLGVGXCFEMJgLl2CrVvVhHHNGigvb+yzsVETxWnTICoKrGQuASHaE0keRcdVelitMh5YDrVVapuxmbq8RsjD4B0uVcZ2yMzEiAlB7kwIcud4eTVLk3SsSCmgsOI8727K5P0tWYzr5UrsEC3DfJ0wkmqkEEK0OvO6i4zITSM6cxd8ej9UVDR2OjjA5Mlqwjh2LFjIF3xCtFeSPIqO5dJ5OLxGnTE1P7Gx3bGHmjD2jwUrGUfRUfRwtuZvE3rzp/GBxB8qJi5Rx74TZ1h/uIT1h0vQOnYhJkzLPSGeOFmbGzpcIYToUCwvXmDU8WSiM3czOmcf1hfPN3a6uMDdd6sJ46hRYGpqsDiFEC1HkkfRMZRnqpPfpC+BCxVqm5EJ9JygXpra/Q4wktk5OyoLU2PuHuDJ3QM8ySw9S1yijpWpBehOn+PN+GO8uzGDqL7uxIZpCfNxRCMVZyGEuCU2tTWMzk4iOnM3I4+nYllXq+8rsnFiQ8BQHv7n8zBsmDprqhCiQ5HkUbRfdbVw9Ec1aTyxs7HdTgshs2DAA2DjarDwhGEEuNrwyl19eDGqJz8eKGJJoo79+RX8uL+IH/cX4etsRUyYN9MGdsO+i5mhwxVCiDbP/nwV47L2Ep2xm+En0jFrqNP35dm7ER8QzvrAYex390fRGPHwHXcYMFohRGuS5FG0P6ePqwlj2hI4d1Jt0xhBQJRaZfQdDUbybWdnZ2lmzPRBXkwf5MWhwkriknR8n1ZITnkNr609wlvrjzEhSK1GDtQ6SDVSCCF+xbn6DJFZe4jK2MUQ3UFMlAZ9X1ZXL33CeMTFR+YPEKITkeRRtA/1lyAjXh3LmLO1sd3GHQbOgoEPgJ2n4eITbVrfbna8cXc//npnL75PL+SbvTqOFlexKrWQVamF9HSzITZMy+QB3bC1kHE5QojOyaOqjKiMPURl7mJQwVGMUPR9R1x8iA8IJz5wGNlOWgNGKYQwJEkeRdtWkQ+pX0Lq11Bd8kujBvzGqFVG/0gwlrexuDHW5ibEhnkTE6olPb+CuEQdPx4o4ljJWV7+/jBv/HSMyf09iAnTEuRpb+hwhRCi1XmfKSI6YzdRmbvoX5zVpC/dPYD4wHDiA4ahc3A3UIRCiLZEPnWLtqehHrI2qVXGrI1w+VIZK2d1HGPILHDobtAQRfum0WgYoHVggNaBlyb2ZnVqAUsSdWSVVbNsXz7L9uXTr5sdsWFaJgV7YGUufyqFEB2EouB/Ukd05m6iM3bRq/yEvqsBDfs8e7M+MJz1AeEU2zobLk4hRJskn4hE21FVDGnfqJXGyvzGdp871Cpj4AQwkQlORMuyszTloWE+zArvTnLeGZbszeOngyUcLKzkL6sO8vq6o9w9oBsxYVp6udsaOlwhhLh5ikKf0pxfEsbd+J4u0HfVaYzYow1ifWA4G/2HUm7tYMBAhRBtnSSPwrAaGuD4NrXKeOwnUOrVdksHdU3GkIfByc+wMYpOQaPRMLi7I4O7OzJv0kVWphQQl6Qj92QNX+/N4+u9eQzU2hMT5s3EIHcsTGVSJiFE26VRGhhQlEFUxm6iMnejrSzV99Uam/Bz9wGsDwhnk38YFZbyxZgQ4sbcdPK4Y8cO3n77bVJSUiguLmb16tVMmTLlmtsnJCQQERFxVXtxcTFubm43+/Sio6guh/Rv1FlTz5xobNcOVRPG3pPB1MJQ0YlOztHKjMfv6MGjw33Ye/wUSxJ1bDhcQqquglRdBa+tPcK0gZ7EhGnxc7E2dLhCCAGAUUM9oQWHicrYTWTmHtyrT+n7zpuYk9AjhPjAcLb6hlJt3sWAkQoh2qubTh5ramoIDg7mkUceYerUqTe8X0ZGBra2jd9subi43OxTi/ZOUeDEz2qV8cgP0HBJbTe3g+D7YNDD4NLLsDEK8StGRhrC/ZwI93Oi7OwFViQXsDRJR8GZ83yxK5cvduUS5uNI7BBvIvu4Ym4i1UghxO1lUl9HeN5+ojJ3Mz5rL07nKvV9Z80s2eobSnxgONt9QjhvJl/KCiF+n5tOHqOjo4mOjr7pJ3JxccHe3v6m9xMdwLnTsH8pJC+CU7+aya1biDqWsc9UMJNvQEXb5mJjwdMRfjw50pcdWeXEJerYcrSUxNzTJOaextHKjHsHeRITqsW7q5WhwxVCdGDmdRcZkZtGdOYuxmYlYldbo++rsLBmk98Q4gPD2dW9P7UyV4AQogXdtjGP/fv3p7a2lr59+/LKK68wbNiwa25bW1tLbW2t/n5VVdXtCFG0JEWB/CS1ynhoFdT/8nqaWUO/e9Uqo3uwYWMU4hYYG2mICHQhItCF4srzLN+Xz7KkfEqqLvDp9uN8uv04I/ydiA3TMqaXK6bGRoYOWQjRAVhevEDE8WSiM3YRcTwZ64vn9X3lXezZGDCE+IBh7NX2o06WsBJCtJJW/+vi7u7OJ598wqBBg6itreWzzz5j1KhRJCYmMnDgwGb3WbhwIQsWLGjt0ERruFAJB75Vq4xlhxvb3fqpVcZ+94K5jeHiE6IFudtZMntsAM9E+LH1WBlxSTq2Z5azM+skO7NO4mxjzn2DvZgx2AtPB6muCyFujk1tDaOzk7gzYxcjc1OxqLuo7yuycWJDwFDiA4eR3K0XDUZy2bwQovW1evIYGBhIYGCg/n54eDg5OTm89957fP31183uM3fuXObMmaO/X1VVhZeXV2uHKn6PwlS1ynjwO7h0Tm0zsYS+09SksdtA0GgMG6MQrcTE2IjxfdwY38eN/NPnWLZPx/J9BZSfreXfW7P5cFs2EYEuxIZpGRXogrGR/C4IIZrncK6ScVmJRGXuZviJdMwa6vR9efZuxAeEsz5wGPvd/VE0cmWDEOL2Msh1DaGhofz888/X7Dc3N8fc3Pw2RiRuSW01HPpOrTIWpze2O/dUE8agGWBpb6johDAIL8cu/DmyJ8+NCWDz0VKWJOaxK/sUW4+VsfVYGR52FtwXqmXGYC9cbWXyio7slVdeueoqmsDAQI4dO2agiERb5Vx9hsisPURn/EyY7hAmSoO+L6urlz5hPOLiI1/ECiEMyiDJY3p6Ou7u7oZ4atESSg5B8hfq5akXz6ptxmbQe4qaNGqHyMlNdHpmJkbc2c+dO/u5k3uyhqVJOlYk51NUeYF/bsrkgy1ZjO3lQkyYNyP8nDCSamSH1KdPHzZv3qy/b2IiY9GEyqOqjKiMPURl7mJQwVGMUPR9R1x8iA8IJz5wGNlOWgNGKYQQTd30Way6uprs7Gz9/dzcXNLT03F0dESr1TJ37lwKCwv56quvAHj//ffx8fGhT58+XLhwgc8++4ytW7eycePGlvspROu7dB4Or1arjAVJje2OvurkN8ExYNXVcPEJ0Yb5OFnx1zt7MWdcABsOl7Bkr46kE6fZcLiUDYdL0Tp2YWaolnsHeeJkLVdddCQmJiayprHQ8z5TRHTGbqIyd9G/OKtJX7p7APGB4cQHDEPnIF+wCyHapptOHpOTk4mIiNDfvzw2cdasWSxevJji4mJ0Op2+/+LFi/zpT3+isLCQLl26EBQUxObNm5s8hmjDyjPVsYzpcXChQm0zMoGeE9UqY/cRYCRjLoS4ERamxkzu343J/buRVXqWJYk6VqYWoDt9jn+sP8Y/N2UQ2ceNmDAtQ3t0RSMV/HYvKysLDw8PLCwsGDp0KAsXLkSrvXYlSWYb72AUBf+TOqIzdxOdsYte5Sf0XQ1o2OfZm/WB4awPCKfY1tlwcQohxA3SKIqiXH8zw6qqqsLOzo7KykpsbW0NHU7HV1cLR39Uq4x5vxqbaq+FkIeg//1g42qw8IToSM5frGftgSKWJOpIz6/Qt/dwtiImVMu0gZ44WMk6bbdLS55v4uPjqa6uJjAwkOLiYhYsWEBhYSGHDh3Cxqb5WaebGycJyPmvhXX/y7rWe3BFoU9pzi8J4258Txfou+o0RuzRBrE+MJyN/kMpt3ZovTgM6MSbE1rlcVv1dRPyurVTLfW63ej5TwZfiEanciBlMaQvgXOn1DaNEQREq5em+o4GmQpciBZlaWbMvYO8uHeQF4eLKolL1LEmrZDj5TW8vu4ob23IYGI/d2LCtIR4O0g1sh2Jjo7W/z8oKIiwsDC8vb359ttvefTRR5vdR2Ybb580SgMDijKIythNVOZutJWl+r5aYxN+7j6A9QHhbPIPo8JSvgQQQrRfkjx2dvWXIOMntcp4fFtju40HhMyCAQ+AXTfDxSdEJ9LHw46/392PuXf24of0IpYk5nG4qIpVaYWsSisk0NWGmDAtdw/shq2FqaHDFTfJ3t6egICAJvMGXElmG28/jBrqCS04TFTGbiIz9+BefUrfd97EnIQeIcQHhrPVN5Rqc1nnVQjRMUjy2FlV6CD1K/VWffkbUg34jVWrjP6RYCxvDyEMwdrchJgwLTNDvdhfUElcYh4/7C8io/Qs8384zJvxx7gr2IPYIVqCPO0NHa64QdXV1eTk5PDAAw8YOhRxi0zq6wjP209U5m7GZ+3F6Vylvu+smSVbfUOJDwxnu08I581kKR4hRMcj2UFn0lAPWRvVKmPWRrg8LbiVCwx8AAbOAgdvg4YohGik0Wjo72VPfy97/jahN2vSClmSmEdmaTXLk/NZnpxP3262xIZ5c1ewB1bm8ie9LXn++eeZNGkS3t7eFBUVMX/+fIyNjZk5c6ahQxM3wbzuIiNy04jO3MXYrETsamv0fRUW1mzyG0J8YDi7uven1kTGJwshOjb5pNEZVBVB6tdqlbGqceA+PiPVGVMD7wQ54QnRptlZmjIrvDsPDvUmOe8McYk61h0s5lBhFXNXHeTv644yZYAHMaHe9PaQMVVtQUFBATNnzuTUqVM4OzszfPhw9u7di7OzzKrZ1llevEDE8WSiM3YRcTwZ64vn9X3lXezZGDCE+IBh7NX2o06u0hFCdCLyF6+jamiA41vVKmNGPCj1arulIwyIhZCHoauvYWMUQtw0jUbD4O6ODO7uyLyJvVmZWkBcoo7jJ2v4Zq+Ob/bqGKC1JzbMm4lB7liYyiRXhrJs2TJDhyBugk1tDWOyk4jO2MXI3FQs6i7q+4psnNgQMJT4wGEkd+tFg0weJ4TopCR57GiqyyDtG0j9Es6caGzXhqtVxl6TwFTGYQjREThYmfHYiB48OtyHPcdPsSRRx4ZDJaTpKkjTVfDqj4eZFuJJbJgWP5fml4YQojNzOFfJuKxEojN3MezEfswa6vR9efZuxAeEsz5wGPvd/VE0sqaxEEJI8tgRKAqc2AnJX8DRtdBwSW03t4P+M9W1GV16GTREIUTr0Wg0hPs6Ee7rRPnZWlak5BOXqKPgzHkW7TrBol0nCPVxJDZMS1RfN8xNpGoiOrGSEu5P+4nojJ8J0x3CRGnQd2V19dInjEdcfECWxhFCiCYkeWzPzp2G9DhIWQSnfjX1e7dBapWxz91gJtODC9GZONuY8/9G+fHkHb7szD7Jkr15bDlWRlLuaZJyT+NoZca9IZ7MDNXS3cnK0OEKcXvodLBqFaxcCbt28bqi6LuOuPgQHxBOfOAwsp20BgxSCCHaPkke2xtFgfxEtcp4eA3U16rtZtYQNF0dy+geZNAQhRCGZ2SkYWSAMyMDnCmpvMDyffks26ejuPICn+44zqc7jjPcz4mYMC3jertiaiyX5IkOJjtbTRZXroR9+5p0pbsHEB8YTnzAMHQO7gYKUAgh2h9JHtuLC5Wwf7laZSw70tjuFqRWGfvdA+YypkkIcTU3OwueG+vP0xG+JGSUsyQxj4TMcn7OPsnP2SdxtjFnxiAv7gv1wtNBrlYQ7ZSiwJEjjQnjgQONfRoNDB8O06bB1KlM+ejAtR9HCCHENUny2JYpChSlqjOmHloJl86p7SaW0G8ahDwC3QbKmAwhxA0xMTZibG9XxvZ2Jf/0uV+qkfmUn63lw23ZfJSQzagAZ2LCvIkIdMZEqpGirVMUSEtrTBgzMhr7jI0hIkJNGKdMATe3X+0oyaMQQtwKSR7botpqOLhCrTIW729sd+6lVhmDpoOlvcHCE0K0f16OXXg+MpDnxvqz+UgpSxJ1/Jx9km0Z5WzLKMfdzoIZg724b7AWNzuZoVm0IQ0NkJjYmDCeONHYZ2YG48apCeNdd0HXrgYLUwghOiJJHtuSkoPqWMYDK+DiWbXN2Bz6TFHHMmqHSJVRCNGiTI2NiO7nTnQ/d06crGFpko4VKQUUV17g/c1Z/HtrNmN6uhATpuUOf2eMjORvkDCA+nrYuVNNFlevhsLCxj5LS4iOVhPGiRPB1tZwcQohRAcnyaOhXTwHh1erVcaCXw3od/RVq4z9Y6CLo+HiE0J0Gt2drJh7Zy/mjA9g/aESliTqSMo9zcYjpWw8UoqXoyUzQ7XcG+KFs425ocMVHd2lS7B1q5owrlkD5eWNfTY2aqI4bRpERYGVzBwshBC3gySPhlJ2TE0Y9y9VJ8MBMDKBXpPUKqPPHVJlFEIYhLmJMZP7d2Ny/25klZ4lLknHypQC8k+f5631Gby3KZPxfdyIDdUy1LcrGvlbJVrKhQuwcaOaMP7wA1RUNPY5OMDkyWrCOHYsWMjl1EIIcbtJ8ng71dXCkR/UpDFvV2O7vVZNGAfcD9YuhotPCCGu4O9qw/xJfXghsifrDhazJDGPNF0F6w4Us+5AMT2crIgJ0zJtoCcOVmaGDle0R9XVEB+vJozr1qn3L3NxgbvvVhPGUaPA1NRgYQohhJDk8fY4laMmjOlxcO6U2qYxhsBoGPQw9BgNRjKroRCi7bI0M+aeEE/uCfHkSFEVcUl5rEkr4vjJGl5fd5S3NmQwoZ87MWFaBnk7SDVS/LbKSvjxRzVhXL9erThe5ukJU6eqCeOwYeqsqUIIIdoESR5bS/0lOLZOTRqPJzS223aDgbNg4ANg62Gw8IQQ4lb19rDl9Sn9mBvdix/2F7EkMY9DhVWsTitkdVohAa7WxIZ5M2VAN+wspVIkfnHyJHz/vZowbt6sjmm8rEcPNVmcNg0GD5YvVIUQoo2S5LGlncmD1C8h7RuoLv2lUQP+49QJcPzGgbEcdiFE+2dlbsLMUC0zQ7UcKKhgyV4dP+wvIrO0mvk/HGZh/FHuCvYgJsybYE87qUZ2RsXF6uyoK1fC9u3qrKmX9erVmDAGB8s4fyGEaAcki2kJ9XWQtVFdZiN7M6Co7VYuMPBB9ebgbdAQhRCiNQV52hN0jz1/m9iLNWmFLNmrI6P0LN8mF/BtcgF9PGyJCdMyuX83rM3l1NOh6XSNazDu3g2K0tgXHNyYMPbubbgYhRBC3BI5g/8eVUWQ+pV6q/rVmlM9RqlVxsA7wVgu2RJCdB62FqY8OLQ7DwzxJlV3hiV7daw9WMzhoir+tvoQb6w7ypQB3YgJ09LHw87Q4YqWkpXVmDAmJzftCw1tTBh9fQ0TnxBCiBYhyePNamiAnK3qWMaMeFB+uQTH0lGdLTXkIegqJ0chROem0WgI8XYkxNuRlyf2ZmVqAXGJOo6frGFJoo4liTr6e9kTE6ZlUpAHlmYyKUq7oihw5Ah8952aMB482Nin0cDw4WqyOHUqeHkZLk4hhBAtSpLHG1VdBmlfQ8qXUJHX2O49TF1mo/ddYCKLZgshxJUcrMx4bEQPHh3uw97jp1mSmMeGwyWk51eQnl/Ba2uPMG2gJzFhWgJcbQwdrrgWRYG0tMYKY0ZGY5+xMUREqAnjlCng5mawMIUQQrQeSR5/i6JA7g51LOOxtdBQp7Zb2EFwjFpldOlp0BCFEKK90Gg0DPXtylDfrpysrmVFcgFxSXnknz7P4t0nWLz7BKHdHYkJ0xLV1w0LU6lGGlxDAyQmNiaMJ0409pmZwbhxasJ4113QtavBwhRCCHF7SPLYnHOnIX0JpCyGU9mN7Z6D1Spjn7vBrIvBwhNCiPbOydqcp0b58sQdPdiZfZK4xDw2Hy0j6cRpkk6cxuFHU+4d5MXMUC0+TlaGDrdzqa+HnTvVZHHVKigqauyztIToaDVhnDgRbG0NF6cQQojbTpLHyxQFdHvVsYyH10B9rdpuZgNB02HQw+DWz6AhCiFER2NkpGFkgDMjA5wprbrA8n35LEvSUVR5gf/uOM5/dxxnmF9XYsO8GdfbFVNjWf+vVVy6BFu3qgnjmjVQXt7YZ2OjJorTpkFUFFhJMi+EEJ3VTSePO3bs4O233yYlJYXi4mJWr17NlClTfnOfhIQE5syZw+HDh/Hy8uKll17ioYceusWQW9j5CjiwHJIXQfnRxna3IBj8KPS9B8ytDRaeEEJ0Fq62Fjw7xp+nI/xIyChjSaKObRll7Mo+xa7sUzhZmzNjsCf3Ddbi5ShXf/xuFy7Axo1qwvjDD1BR0djn4ACTJ6sJ49ixYGFhsDCFEEK0HTedPNbU1BAcHMwjjzzC1KlTr7t9bm4uEyZM4Mknn2TJkiVs2bKFxx57DHd3dyIjI28p6N9NUaAwVR3LeGgl1J1X2027QN9papXRY6AsWCyEEAZgbKRhTC9XxvRypeDMObUauS+f8rO1fLQth/8k5DAywJmYUC2je7pgItXIG1ddDfHxasK4bp16/zIXF7j7bjVhHDUKTGWpKSGEEE3ddPIYHR1NdHT0DW//ySef4OPjw7vvvgtAr169+Pnnn3nvvfduf/JYexYOrlCrjCUHGttdeqvrMgZNVyfDEUII0SZ4OnThT+MDeXaMP1uOlrIkUcfOrJMkZJSTkFGOm60F94V6MWOwF+52loYOt+1avx4+/VT998KFxnZPT3U5jWnTYNgwddZUIYQQ4hpafczjnj17GDt2bJO2yMhIZs+efc19amtrqa2t1d+vqqpqmWCWzoQTO9X/G5urE98MegS8QqXKKIQQbZipsRFRfd2J6uvOiZM1LN2nY0VyASVVF3h/cxb/23Gc5JfGyXqR15Kaqo5lBOjRQ00Wp02DwYPBSCq3QgghbkyrJ48lJSW4uro2aXN1daWqqorz589jaXn1N8ULFy5kwYIFLR9M0Aw4W6wmjMEzoYtjyz+HEEKIVtXdyYq50b2YMy6ADYdLWbI3Dy/HLpI4/pbp0+HcObjnHggOli9MhRBC3JI2Odvq3LlzmTNnjv5+VVUVXl5ev/+Bg2fCgPvlpCmEEB2AuYkxdwV7cFewB5fqGwwdTtvm5wevv27oKIQQQrRzrZ48urm5UVpa2qSttLQUW1vbZquOAObm5pibm7d8MMZtMlcWQgjxO8kSHkIIIUTra/Wz7dChQ9myZUuTtk2bNjF06NDWfmohhBDC4D766CO6d++OhYUFYWFhJCUlGTokIYQQ4pbcdPJYXV1Neno66enpgLoUR3p6OjqdDlAvOX3wwQf12z/55JMcP36cF154gWPHjvGf//yHb7/9lj/+8Y8t8xMIIYQQbdTy5cuZM2cO8+fPJzU1leDgYCIjIykrKzN0aEIIIcRNu+nkMTk5mQEDBjBgwAAA5syZw4ABA5g3bx4AxcXF+kQSwMfHh3Xr1rFp0yaCg4N59913+eyzzwy3xqMQQghxm/zzn//k8ccf5+GHH6Z379588skndOnShS+++MLQoQkhhBA37aYHAY4aNQpFUa7Zv3jx4mb3SUtLu9mn0rv8fC22ZIcQQgjRjMvnmd86z92oixcvkpKSwty5c/VtRkZGjB07lj179jS7z5VLVVVWVjaJS7SMhtpzhg6hQ2ut96u8bq1LXrf2qaVetxs9/7WLGWTOnj0L0DIzrgohhBDXcfbsWezs7H7XY5w8eZL6+vpml6s6duxYs/tca6kqOf+J9sTufUNHIG6FvG7tU0u/btc7/7WL5NHDw4P8/HxsbGzQ/I5lNi4v+ZGfn4+trW0LRtj6JHbDkNhvv/YaN0jshtKSsSuKwtmzZ/Hw8Gih6G7OlUtVNTQ0cPr0abp27fq7zn+i/WrPv5udmbxu7VNnft1u9PzXLpJHIyMjPD09W+zxbG1t2+0bQmI3DIn99muvcYPEbigtFfvvrThe5uTkhLGxcbPLVbm5uTW7T3NLVdnb27dIPKJ9a8+/m52ZvG7tU2d93W7k/CcLYwkhhBCtwMzMjJCQkCbLVTU0NLBlyxZZrkoIIUS71C4qj0IIIUR7NGfOHGbNmsWgQYMIDQ3l/fffp6amhocfftjQoQkhhBA3rVMlj+bm5syfP/+qS4LaA4ndMCT226+9xg0Su6G05dhnzJhBeXk58+bNo6SkhP79+7N+/fqrJtER4lra8vtbXJu8bu2TvG7Xp1FaYj5yIYQQQgghhBAdmox5FEIIIYQQQghxXZI8CiGEEEIIIYS4LkkehRBCCCGEEEJclySPQgghhBBCCCGuS5JHIYQQQohWtHPnTqytramsrOTFF18kOjra0CGJm3TixAk0Gg3p6emGDkUAa9aswc/PD2NjY2bPns3ixYuxt7c3dFidQrtOHnfs2MGkSZPw8PBAo9GwZs2a6+6TkJDAwIEDMTc3x8/Pj8WLF1+1zUcffUT37t2xsLAgLCyMpKQkg8a9atUqxo0bh7OzM7a2tgwdOpQNGzY02eaVV15Bo9E0ufXs2bNF476V2BMSEq6KS6PRUFJS0mS71j7mtxL7Qw891Gzsffr00W9zO477woULGTx4MDY2Nri4uDBlyhQyMjKuu9+KFSvo2bMnFhYW9OvXj59++qlJv6IozJs3D3d3dywtLRk7dixZWVkGj/1///sfI0aMwMHBAQcHB8aOHXvV+6G51yYqKsrgsS9evPiquCwsLJps01aP+6hRo5p9v0+YMEG/ze047h9//DFBQUHY2trq/97Fx8f/5j5t4b0uxG8ZNGgQ6enp2NjY8Oc//5nPPvvM0CGJK1z5961r165ERUVx4MABALy8vCguLqZv374GjrRz+PXrYWZmhp+fH6+++ip1dXUAPPHEE9xzzz3k5+fz2muvGTjazqVdJ481NTUEBwfz0Ucf3dD2ubm5TJgwgYiICNLT05k9ezaPPfZYk0Rs+fLlzJkzh/nz55OamkpwcDCRkZGUlZUZLO4dO3Ywbtw4fvrpJ1JSUoiIiGDSpEmkpaU12a5Pnz4UFxfrbz///HOLxXyrsV+WkZHRJDYXFxd93+045rcS+wcffNAk5vz8fBwdHbn33nubbNfax3379u08/fTT7N27l02bNnHp0iXGjx9PTU3NNffZvXs3M2fO5NFHHyUtLY0pU6YwZcoUDh06pN/mrbfe4l//+heffPIJiYmJWFlZERkZyYULFwwae0JCAjNnzmTbtm3s2bMHLy8vxo8fT2FhYZPtoqKimhz3pUuXtljctxo7gK2tbZO48vLymvS31eO+atWqJnEfOnQIY2Pjq97vrX3cPT09efPNN0lJSSE5OZnRo0czefJkDh8+3Oz2beW9LsRvsbS0xM/PDyMjI5ycnOjWrdtNP8aVlZYb0b17d95///2bfq7O6td/37Zs2YKJiQkTJ04EwNjYGDc3N0xMml8iXVEUfWIjWsbl1yMrK4s//elPvPLKK7z99ttUV1dTVlZGZGQkHh4e2NjYGDrUzkXpIABl9erVv7nNCy+8oPTp06dJ24wZM5TIyEj9/dDQUOXpp5/W36+vr1c8PDyUhQsXtmi8l91I3M3p3bu3smDBAv39+fPnK8HBwS0X2A24kdi3bdumAMqZM2euuc3tPuaKcmvHffXq1YpGo1FOnDihbzPEcS8rK1MAZfv27dfcZvr06cqECROatIWFhSlPPPGEoiiK0tDQoLi5uSlvv/22vr+iokIxNzdXli5d2jqBKzcW+5Xq6uoUGxsb5csvv9S3zZo1S5k8eXIrRHhtNxL7okWLFDs7u2v2t6fj/t577yk2NjZKdXW1vs0Qx11RFMXBwUH57LPPmu1rq+91Ia60detWxcjISBk2bNgt7e/i4qK8+OKLSmFhoVJVVXVD+3h7eyvvvffeLT1fZ9Pc37edO3cqgFJWVqbk5uYqgJKWlqYoSuPnm59++kkZOHCgYmpqqmzbtk0ZOXKk8oc//EH585//rDg4OCiurq7K/PnzmzzumTNnlEcffVRxcnJSbGxslIiICCU9PV3ff/mzxVdffaV4e3srtra2yowZM274de8Imns9xo0bpwwZMkQBmty2bdt21fk3OztbueuuuxQXFxfFyspKGTRokLJp06Ymj9fcZ0E7Oztl0aJFiqIo+td8+fLlyvDhwxULCwtl0KBBSkZGhpKUlKSEhIQoVlZWSlRUlFJWVtbkcf73v/8pPXv2VMzNzZXAwEDlo48+0vddftyVK1cqo0aNUiwtLZWgoCBl9+7dv/u43Q7tuvJ4s/bs2cPYsWObtEVGRrJnzx4ALl68SEpKSpNtjIyMGDt2rH6btqChoYGzZ8/i6OjYpD0rKwsPDw969OhBbGwsOp3OQBFerX///ri7uzNu3Dh27dqlb28vxxzg888/Z+zYsXh7ezdpv93HvbKyEuCq1//Xrvdez83NpaSkpMk2dnZ2hIWFtepxv5HYr3Tu3DkuXbp01T4JCQm4uLgQGBjIU089xalTp1o01ivdaOzV1dV4e3vj5eV1VcWsPR33zz//nPvuuw8rK6sm7bfzuNfX17Ns2TJqamoYOnRos9u01fe6EFf64osveOaZZ0hLS7vpy6al0nL7VVdX88033+Dn50fXrl2vud1f/vIX3nzzTY4ePUpQUBAAX375JVZWViQmJvLWW2/x6quvsmnTJv0+9957L2VlZcTHx5OSksLAgQMZM2YMp0+f1m+Tk5PDmjVrWLt2LWvXrmX79u28+eabrfcDtwOWlpZcuHBBPwxj5cqVFBcXEx4eftW21dXV3HnnnWzZsoW0tDSioqKYNGnSLX1Gmz9/Pi+99BKpqamYmJgQExPDCy+8wAcffMDOnTvJzs5m3rx5+u2XLFnCvHnz+Pvf/87Ro0d54403ePnll/nyyy+bPO7f/vY3nn/+edLT0wkICGDmzJntonrdqZLHkpISXF1dm7S5urpSVVXF+fPnOXnyJPX19c1uc+UYPUN65513qK6uZvr06fq2sLAwFi9ezPr16/n444/Jzc1lxIgRnD171oCRgru7O5988gkrV65k5cqVeHl5MWrUKFJTUwHazTEvKioiPj6exx57rEn77T7uDQ0NzJ49m2HDhv3muItrvdcvH9PL/97O436jsV/pxRdfxMPDo8mH/6ioKL766iu2bNnCP/7xD7Zv3050dDT19fWtEfoNxx4YGMgXX3zB999/zzfffENDQwPh4eEUFBQA7ee4JyUlcejQoave77fruB88eBBra2vMzc158sknWb16Nb17925227b4XhfiSlVVVaxatYqnnnqKSZMmsWjRIn3f5bkB1q1bR1BQEBYWFgwZMkR/6XVCQoI+WRw9ejQajYaEhAQAfv75Z0aMGIGlpSVeXl48++yz1720Xlzb2rVrsba2xtraGhsbG3744QeWL1+OkdG1Py6/+uqrjBs3Dl9fX/0XdEFBQcyfPx9/f38efPBBBg0axJYtWwD1NUtKSmLFihUMGjQIf39/3nnnHezt7fnuu+/0j9vQ0MDixYvp27cvI0aM4IEHHtA/RmejKAqbN29mw4YNjB07Vj/0ydHRETc3N8zMzK7aJzg4mCeeeIK+ffvi7+/Pa6+9hq+vLz/88MNNP//zzz9PZGQkvXr14rnnniMlJYWXX36ZYcOGMWDAAB599FG2bdum337+/Pm8++67TJ06FR8fH6ZOncof//hHPv3006sed8KECQQEBLBgwQLy8vLIzs6+6fhut+Yv3BZtVlxcHAsWLOD7779vMm7w1zO3BQUFERYWhre3N99++y2PPvqoIUIF1A/TgYGB+vvh4eHk5OTw3nvv8fXXXxssrpv15ZdfYm9vz5QpU5q03+7j/vTTT3Po0KFWGc/a2m4l9jfffJNly5aRkJDQZOKZ++67T///fv36ERQUhK+vLwkJCYwZM6ZF44Ybj33o0KFNKmTh4eH06tWLTz/91GAD+m/luH/++ef069eP0NDQJu2367gHBgaSnp5OZWUl3333HbNmzWL79u3XTCCFaOuWLVtGz5496dmzJ/fffz9PPvkkr7/+epOk5M9//jMffPABbm5u/PWvf2XSpElkZmYSHh5ORkYGgYGBrFy5kvDwcBwdHcnJySEqKorXX3+dL774gvLycp555hmeeeaZJsmpuHERERF8/PHHAJw5c4b//Oc/REdH/+YkfoMGDbqq7XIF8jJ3d3f9PA779++nurr6qmrm+fPnycnJ0d/v3r17kwrzrx+js7iczF+6dImGhgZiYmJ45ZVXuHTp0nX3ra6u5pVXXmHdunUUFxdTV1fH+fPnb6ny+OvX8/IXkf369WvSdvm1qampIScnh0cffZTHH39cv01dXR12dnbXfFx3d3cAysrKWmXCy5bUqZJHNzc3SktLm7SVlpZia2uLpaUlxsbGGBsbN7uNm5vb7Qy1WcuWLeOxxx5jxYoVV12mdSV7e3sCAgLa5DcYoaGh+g+yTk5ObfqYg/qN1xdffMEDDzzQ7Ldbv9aax/2ZZ55h7dq17NixA09Pz9/c9lrv9cvH9PK/paWl+j9Yl+/379+/ZQPn5mK/7J133uHNN99k8+bNV52Ir9SjRw+cnJzIzs5u8eTxVmK/zNTUlAEDBujfD+3huNfU1LBs2TJeffXV627bWsf98sx6ACEhIezbt48PPvjgqm9toe2914VozqJFi7j//vsBtYJfW1vLhg0bmnwBOX/+fMaNGweoX1h6enqyevVqpk+fflWlBdRZlWNjY/WT5/j7+/Ovf/2LkSNH8vHHH18107O4PisrK/3fHoDPPvsMOzs7/ve//111Jcav97mSqalpk/sajYaGhgZATWrc3d311eNf+/VSE7/1GJ3F5WTezMwMDw8P/WRFFRUV1933+eefZ9OmTbzzzjv4+flhaWnJPffcw8WLF/XbaDQaFEVpsl9ziemvXwuNRtNs269fX1Bnjg8LC2vyOMbGxtd93PbwGneqy1aHDh16Vcl/06ZN+kqBmZkZISEhTbZpaGhgy5Yt1xxvc7ssXbqUhx9+mKVLlzaZOv9aqqurycnJafJhqa1IT0/Xx9WWj/ll27dvJzs7+4Yqia1x3BVF4ZlnnmH16tVs3boVHx+f6+5zvfe6j48Pbm5uTbapqqoiMTGxRY/7rcQO6uyYr732GuvXr2/2W90rFRQUcOrUKYMf9yvV19dz8OBBfVxt/biDuuxFbW2t/oPub2mN496choYGamtrm+1rK+91Ia7l6NGj7Nu3T1+5NzExYcaMGVdVB3/9fnR0dCQwMJCjR49e83H379/P4sWL9ZdZWltbExkZSUNDA7m5ua3zw3QyGo0GIyMjzp8/32KPOXDgQEpKSjAxMcHPz6/JzcnJqcWepyO4nMxrtdprznJ7Lbt27eKhhx7i7rvvpl+/fri5uXHixIkm2zg7O1NcXKy/n5WVxblz535XzK6urnh4eHD8+PGrXt9b+RzRFrXrymN1dXWTCk9ubi7p6ek4Ojqi1WqZO3cuhYWFfPXVVwA8+eSTfPjhh7zwwgs88sgjbN26lW+//ZZ169bpH2POnDnMmjWLQYMGERoayvvvv09NTQ0PP/ywweKOi4tj1qxZfPDBB4SFhenH6VhaWupL4M8//zyTJk3C29uboqIi5s+fj7GxMTNnzmyxuG8l9vfffx8fHx/69OnDhQsX+Oyzz9i6dSsbN27UP8btOOa3Evtln3/+OWFhYc2OF7sdx/3pp58mLi6O77//HhsbG/3rb2dnh6WlJQAPPvgg3bp1Y+HChQA899xzjBw5knfffZcJEyawbNkykpOT+e9//wuoJ8TZs2fz+uuv4+/vj4+PDy+//DIeHh5XXZp7u2P/xz/+wbx584iLi6N79+76fS5/OKqurmbBggVMmzYNNzc3cnJyeOGFF/Dz8yMyMtKgsb/66qsMGTIEPz8/KioqePvtt8nLy9N/Y92Wj/tln3/+OVOmTLnqkqrbddznzp1LdHQ0Wq2Ws2fPEhcXR0JCgn5Jpbb6XhfiWr744gvq6+vx8vLStymKgqmpaZMJUm5WdXU1TzzxBM8+++xVfVqt9pYftzOrra3V/708c+YMH374IdXV1UyaNKnFnmPs2LEMHTqUKVOm8NZbbxEQEEBRURHr1q3j7rvvvqEvTMX1+fv7s2rVKiZNmoRGo+Hll1++qqo3evRoPvzwQ4YOHUp9fT0vvvjiVRXfW7FgwQKeffZZ7Ozs9FcaJCcnc+bMGebMmfO7H9/gDDfR6+93eZrkK2+zZs1SFEWd5nfkyJFX7dO/f3/FzMxM6dGjh3463l/797//rWi1WsXMzEwJDQ1V9u7da9C4R44c+ZvbK4q65Ii7u7tiZmamdOvWTZkxY4aSnZ3donHfSuz/+Mc/FF9fX8XCwkJxdHRURo0apWzduvWqx23tY34rsSuKOqW/paWl8t///rfZx7wdx725mIEm792RI0c2eT8oiqJ8++23SkBAgGJmZqb06dNHWbduXZP+hoYG5eWXX1ZcXV0Vc3NzZcyYMUpGRobBY/f29m52n8tTnZ87d04ZP3684uzsrJiamire3t7K448/rpSUlBg89tmzZ+vfx66ursqdd96ppKamNnnctnrcFUVRjh07pgDKxo0br3rM23XcH3nkEcXb21sxMzNTnJ2dlTFjxjSJp62+14VozqVLlxRXV1fljTfeUA4ePNjk5uvrq/z73//Wn5uWL1+u3+/06dNKly5d9G1nzpzRL0lwWUxMjDJmzJjffH5ZquPGzZo1q8nfSxsbG2Xw4MHKd999pyiKcs2lOq5cimzkyJHKc88916Rt8uTJTf5uVVVVKX/4wx8UDw8PxdTUVPHy8lJiY2MVnU6nKErzy4C99957ire3dwv+xG3bby0N1dzvw5VLdeTm5ioRERGKpaWl4uXlpXz44YdXvTaFhYXK+PHjFSsrK8Xf31/56aefml2q4/JrrijNv+7NLdO1ZMkSfb7h4OCg3HHHHcqqVauu+bjN/UxtlUZRrrjYVwghhBBC/G4//PADU6dOpaSk5KpLEl988UU2b97Mu+++S0REBH369OGDDz7A1dWVv/3tb6Snp5OVlYWZmRkVFRU4ODiwbds2Ro0aBcCBAwcYMmQIjzzyCI899hhWVlYcOXKETZs28eGHHwLqpCuzZ8/Wj4sUQojfq1ONeRRCCCGEuF0WLVrEyJEjmx3LNm3aNFJTUzlw4ACgzi793HPPERISQklJCT/++ONvTtIWFBTE9u3byczMZMSIEQwYMIB58+bh4eHRaj+PEEJI5VEIIYQQwkASEhKIiIjgzJkzTWbbFEKItkgqj0IIIYQQQgghrkuSRyGEEEIIIYQQ1yWXrQohhBBCCCGEuC6pPAohhBBCCCGEuC5JHoUQQgghhBBCXJckj0IIIYQQQgghrkuSRyGEEEIIIYQQ1yXJoxBCCCGEEEKI65LkUQghhBBCCCHEdUnyKIQQQgghhBDiuiR5FEIIIYQQQghxXf8fEo13nbk6UFEAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 900x600 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "fig=plt.figure(figsize=(9,6),layout='constrained')\n", + "diagramme=fig.subplots(3,2)\n", + "diagramme[0,0].plot([1,2,3],[3,2,1])\n", + "diagramme[0,1].scatter([1,2,3,0.5],[3,2,1,4],s=[90,30,10,30])\n", + "diagramme[1,0].bar([\"Äpfel\",\"Birnen\",\"Pflaumen\"],[20,10,5])\n", + "diagramme[1,1].pie([20,10,80],labels=[\"A\",\"B\",\"C\"])\n", + "diagramme[2,0].plot([1,2,3],[3,2,1])\n", + "diagramme[2,0].plot([1,2,3],[1,2,3])\n", + "diagramme[2,1].bar([\"Äpfel\",\"Birnen\",\"Pflaumen\"],[20,10,5])\n", + "diagramme[2,1].plot([-1,1,2],[1,10,20],\"r\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c603cfb6-9efa-4653-9f3d-8f21030509d9", + "metadata": {}, + "source": [ + "Soll ein Diagramm erneut gezeichnet werden (z.B. weil es nachträglich verändert wurde), reicht es einfach den Diagrammnamen an das Zellenende zu schreiben. Hier wird einem Diagramm ein Titel hinzugefügt und alles erneut gezeichnet" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "f1025510-9a62-4561-8db2-d09c597437e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 900x600 with 6 Axes>" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diagramme[0,0].set_title(\"erstes Diagramm\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "93737992-91ab-49d9-86e6-5a058560ac10", + "metadata": {}, + "source": [ + "Nach diesem Beispiel sollen die Möglichkeiten der Diagramme genauer beleuchtet werden. Das Beispiel erstellt ein Diagramm, mit mehreren, benannten Linien und stellt verschiedene Möglichkeiten zur Formatierung dar. Den Linien können Farben, eine Linienart, die Liniendicke, Punktmarkierungen und die Markierungsgröße zugewiesen werden. Alle Parameter sind optional." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "43fff7bf-d044-4c42-a8e0-a1eee4c01fef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(1.5, 15, 'Eine Anmerkung')" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 500x270 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig=plt.figure(figsize=(5, 2.7), layout='constrained')\n", + "ax = fig.subplots()\n", + "ax.plot([1,2,3], [1,2,3], label='linear',color=\"blue\",linestyle=\"-\") \n", + "ax.plot([1,2,3], [1,3,9], label='quadratisch',color=\"red\",linestyle=\"--\",marker=\"x\",markersize=20) \n", + "ax.plot([1,2,3], [1,8,27], label='kubisch',color=\"green\",linestyle=\":\",linewidth=4) \n", + "ax.set_xlabel('X-Werte') # eine X-Beschriftung\n", + "ax.set_ylabel('Y-Werte') # eine Y-Beschriftung\n", + "ax.set_title(\"Ein Titel\") # ein Titel\n", + "ax.legend(); # Eine Farblegende hinzufügen\n", + "ax.grid(True)# aktivirt ein Hintergrundgitter\n", + "ax.set_xlim(0,4)#setzt manuell den Bereich der X-Achse\n", + "ax.set_ylim(0,30)\n", + "ax.minorticks_on()#aktiviert die minorticks\n", + "ax.annotate('Eine Anmerkung', xy=(1.5, 5), xytext=(1.5, 15),arrowprops=dict(facecolor='black'))# So kann man einen Punkt mit einem Pfeil und Text markieren" + ] + }, + { + "cell_type": "markdown", + "id": "a685d605-7a95-4686-978a-de96c629f716", + "metadata": {}, + "source": [ + "Man kann Grafiken auch speichern. Das funktioniert mit der Methode *imsave*. Viele Dateiformte sind möglich. Hier ist das Speichern als Bild (png Format) gezeigt. Dabei kann in einem Parameter angegeben werden, wie viele pixel pro inch Grafik verwendet werden sollen, also die Auflösung. Es ist auch möglich Diagramme als Vektorgrafik, z.B. pdf zu speichern. Die Auflösung ist hier irrelevant, da die Dateien verlustfrei skalierbar sind." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "ede243fb-4d84-442f-8821-5b546f463e9d", + "metadata": {}, + "outputs": [], + "source": [ + "fig.savefig(\"diagramm.png\",dpi=400)\n", + "fig.savefig(\"diagramm.pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "2597c9d2-d024-4a88-829d-8b72f4ae3795", + "metadata": {}, + "source": [ + "Das sind die wichtigsten Funktionen von Matplotlib. Für weitere Möglichkeiten an Farben, Linestyles etc. ist der offizielle Quick-Start-Guide von Matplotlib eine gute Quelle\n", + "https://matplotlib.org/stable/tutorials/introductory/quick_start.html" + ] + }, + { + "cell_type": "markdown", + "id": "0acab7f1-2b94-4779-aa3c-45d102ab6beb", + "metadata": {}, + "source": [ + "### **Das implizite Interface**\n", + "Das implizite Interface verwendet im Endeffekt das Objektorientierte Interface. Das implizite Interface wird über Funktionen aus dem Paket *matplotlib.pyplot* gesteuert. Es wird also nicht angegeben, welches *Axes* Objekt verwendet werden soll. Viel mehr wird erst ausgewählt, welches Diagramm bearbeitet werden soll, anschließend kann das Diagramm bearbeitet werden. Die meißten Funktionen haben den selben Namen wie im objektorientierten Interface. Als Beispiel das letzte Diagramm im impliziten Interface" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "aa9e7e45-3c5a-4696-bf88-3f8c9c4555f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 500x270 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5, 2.7), layout='constrained')#Erstellt eine figure mit standardmäßig einem Diagramm, das im folgenden das aktuelle Diagramm darstellt\n", + "plt.plot([1,2,3], [1,2,3], label='linear',color=\"blue\",linestyle=\"-\") \n", + "plt.plot([1,2,3], [1,3,9], label='quadratisch',color=\"red\",linestyle=\"--\",marker=\"x\",markersize=20) \n", + "plt.plot([1,2,3], [1,8,27], label='kubisch',color=\"green\",linestyle=\":\",linewidth=4) \n", + "plt.xlabel('X-Werte') # eine X-Beschriftung Die Funktion heißt xlabel und nicht set_xlabel\n", + "plt.ylabel('Y-Werte') # eine Y-Beschriftung\n", + "plt.title(\"Ein Titel\") # ein Titel. Die Funtion heißt hier nur title und nicht set_title\n", + "plt.legend(); # Eine Farblegende hinzufügen\n", + "plt.grid(True)# aktivirt ein Hintergrundgitter\n", + "plt.xlim(0,4)#setzt manuell den Bereich der X-Achse. Die Funktion heißt nur xlim und nicht set_xlim\n", + "plt.ylim(0,30)\n", + "plt.minorticks_on()#aktiviert die minorticks\n", + "plt.show()#Zeigt die aktuelle *Figure* an. Nach dem Anzeigen wird eine neue *Figure* angefangen!, das erstellte Datenobjekt ist verloren" + ] + }, + { + "cell_type": "markdown", + "id": "4701dd39-6b33-42f9-969d-9ce4711127d7", + "metadata": {}, + "source": [ + "So lange nur eine *Figure* erstellt wird bzw. eine *Figure* nach der anderen, funktioniert das ganze bis auf kleine Unterschiede in der Namensgebung identisch. Es ist auch möglich mehrere Diagramme in einer *Figure* zu erstellen. Das ist hier gezeigt:" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "65b70027-b716-42e2-a2aa-e395d5065e8f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 640x480 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(1,2,1)#Erstellt ein Raster von 1x2 Diagrammen. Wählt das erste Diagramm aus\n", + "plt.plot([1,2,3],[3,2,1])#Zeichnet eine gerade in das gewählte Diagramm\n", + "plt.title(\"Diagramm 1\")#Gibt dem gewählten Diagramm einen Titel\n", + "plt.subplot(1,2,2)#Wählt das zweite Diagramm im 1x2 Raster aus\n", + "plt.title(\"Diagramm 2\")#Gibt dem Diagramm einen Titel\n", + "plt.scatter([1,2,3,0.5],[3,2,1,4],s=[90,30,10,30])#Erzeugt einen Scatterplot\n", + "plt.subplot(1,2,1)#Wählt wieder das erste Diagramm als das aktive Diagramm\n", + "plt.plot([1,2,3],[1,2,3])#Fügt eine zweite Linie hinzu\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5a006675-3008-4cc2-b9df-10e9ca3cde0a", + "metadata": {}, + "source": [ + "Die Logik dabei ist, dass immer ein Diagramm als das aktuell zu bearbeitende Diagramm ausgewählt wird. Die Diagramme im Raster sind dazu durchnummeriert, beginnend mit 1. Anschließend kann das ausgewählte Diagramm bearbeitet werden." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Uebung04/Uebung04.ipynb b/Uebung04/Uebung04.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..06dec386d471d75e20b96e6a6d35088e78c335c0 --- /dev/null +++ b/Uebung04/Uebung04.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e990c2fb-c4c7-470f-8090-86de9651ec50", + "metadata": {}, + "source": [ + "# <font color='blue'>**Übung 4 - Module und Zeichnen mit Matplotlib**</font>" + ] + }, + { + "cell_type": "markdown", + "id": "9caa439c-ae2c-4871-9b81-950a32f458a2", + "metadata": {}, + "source": [ + "## <font color='blue'>**Einleitung**</font>\n", + "\n", + "In den vorherigen Übungen haben wir uns mit den Grundlagen der Programmiersprache Python befasst. Inhalt dieser Übung ist die Verwendung von Modulen. In der letzten Übung haben wir Klassen und die Objektorientierte Programmierung kennen gelernt. Als Beispiel diente eine einfache Klasse um grundlegende Vektorrechnung zu ermöglichen. Vektorrechnung ist etwas, das in vielen Programmen benötigt wird und sicherlich bereits von vielen Programmierern implementiert wurde. Um das Rad nicht bei jedem Programm neu zu erfinden bietet Python ein sogenanntes Modul-System an, durch das Programmcode wiederverwendet werden kann. Ein Modul bietet Klassen oder auch Funktionen an, die von anderen Programmierern wiederverwendet werden können Dabei beschränkt sich ein Modul meistens auf ein spezielles Themengebiet. Ein solches Modul kann selbst in Python geschrieben sein, es gibt allerdings auch viele Module, die in anderen Programmiersprachen programmiert sind, sich allerdings trotzdem mit Python verwenden lassen.\\\n", + "Module können in Paketen zusammengefasst sein, die sich über einen zentralen Paketmanager namens *pip* automatisch installieren lassen. Viele davon sind im Internet bereitgestellt und lassen sich darüber hinaus sogar automatisch herunterladen. In dieser Übung geht es darum, wie solche Module genutzt werden können. Außerdem geht es um die Verwendung eines konkreten Moduls namens Matplotlib, das im wissenschaftlichen Kontext oft zur Erstellung von Grafiken genutzt wird\n", + "\n", + "### **Weitere Notebooks, die dir helfen könnten**\n", + "* Python Grundlagen Teil 1\n", + "* Python Grundlagen Teil 2\n", + "* OOP Grundlagen\n", + "\n", + "### **Vorkenntnisse**\n", + "* Übung 1\n", + "* Übung 2\n", + "* Übung 3\n", + "\n", + "### **Lernziele**\n", + "* Module\n", + "* Grundlegende Verwendung von Matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "fdde8bab-000b-452f-aa39-98cbcd11b8f4", + "metadata": {}, + "source": [ + "# <font color='blue'>**Abschnitt 1 - Importieren von Modulen**</font>" + ] + }, + { + "cell_type": "markdown", + "id": "e082132f-33ef-4956-955f-d7b462d20a14", + "metadata": {}, + "source": [ + "Ein Modul kann man sich prinzipiell als eine Sammlung von Klassen, Funktionen und ggf. Variablen vorstellen. Am einfachsten ist es, diese Module mit *import* zu importieren, was immer das komplette Modul lädt. Python selbst bringt automatisch einige Module mit. Diese werden als die Standardbibliothek bezeichnet. Eine vollständige Liste findet sich unter *https://docs.python.org/3/library/*. Ein Beispiel ist die Bibliothek *time*, die verschiedene Funktionen rund um das Thema Zeit beinhaltet" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eee8bebd-4394-4149-b66c-3bad8de9658e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time.struct_time(tm_year=2022, tm_mon=11, tm_mday=21, tm_hour=16, tm_min=50, tm_sec=41, tm_wday=0, tm_yday=325, tm_isdst=0)\n" + ] + } + ], + "source": [ + "import time\n", + "print(time.localtime())" + ] + }, + { + "cell_type": "markdown", + "id": "9765a216-c831-4f97-8f51-de6a908a0cff", + "metadata": {}, + "source": [ + "Das Beispiel importiert ein Modul mit dem Namen *time*. Das ist möglich, da dieses Modul automatisch mit Python installiert wird. Um Funktionalität im Module *time* (z.B. die Funktion *localtime*, aber auch Klassen oder Variablen) zu nutzen, muss dem Funktionsnamen ein *time.* vorangestellt werden. Ähnlich wie bei der Objektorientierten Programmierung. Der Sinn ist, dass verschiedene Module unter Umständen Funktionen oder Klassen mit identischen Namen beinhalten könnten, was ohne diese Regel zu Problemen führen würde. Jedes Modul kann nur einmal geladen werden. Mehrmaliges Importieren eines Moduls führt zwar nicht zu Fehlern, das Modul wird aber nicht neu geladen. Das kann allerdings nur dann zum Problem werden, wenn man selber Module programmieren möchte. Falls der Modulname zu lang ist, kann man dem Modul in einem Programm auch einen Ersatznamen geben. Es sind auch mehrere Ersatznamen möglich" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4f2c174a-09e9-4044-b7eb-96ce07c6481b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time.struct_time(tm_year=2022, tm_mon=11, tm_mday=21, tm_hour=17, tm_min=0, tm_sec=25, tm_wday=0, tm_yday=325, tm_isdst=0)\n" + ] + } + ], + "source": [ + "import time as ti\n", + "print (ti.localtime())" + ] + }, + { + "cell_type": "markdown", + "id": "7d565fc0-2a58-48bf-9c01-1464b9ecf522", + "metadata": {}, + "source": [ + "Das Beispiel macht das selbe, wie die erste Zelle. Das Modul *time* wird lediglich unter dem Alias *ti* in das Programm geladen. Es ist weiterhin möglich, nur einzelne Funktionen aus einem Modul zu laden. Diesen kann optional ein neuer Name gegeben werden" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d142c8f4-5fd4-4299-b914-abbc9b5913fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time.struct_time(tm_year=2022, tm_mon=11, tm_mday=21, tm_hour=17, tm_min=2, tm_sec=16, tm_wday=0, tm_yday=325, tm_isdst=0)\n" + ] + } + ], + "source": [ + "from time import localtime as Lokalzeit\n", + "print (Lokalzeit())" + ] + }, + { + "cell_type": "markdown", + "id": "482c83c0-cbcd-4d05-a9d9-e1d05e4a4d53", + "metadata": {}, + "source": [ + "Das Beispiel importiert nur die Funktion *localtime* aus dem Modul *time*. Im Programm ist sie unter dem Namen *Lokalzeit* verfügbar. Alle drei Beispiele machen das selbe. Welche Methode ein Modul zu importieren die beste ist, hängt von der jeweiligen Situation ab. Mit der möglichkeit *import as* funktioniert es meistens am Einfachsten.\\\n", + "Module können in Python verschachtelt sein. Das bedeutet ein Modul kann aus mehreren Modulen bestehen, die ihrerseits dann Klassen, Funktionen und Variablen anbieten. Ein Beispiel dafür ist das Modul matplotlib, das ein Modul pyplot enthält. Schaue dir das Grundlagen-Notebook an" + ] + }, + { + "cell_type": "markdown", + "id": "33039b46-4f2b-4eea-8254-79061a7504ca", + "metadata": {}, + "source": [ + "# <font color='blue'>**Abschnitt 2 - Zeichne eine Sinus und eine Kosinusfunktion**</font>" + ] + }, + { + "cell_type": "markdown", + "id": "25e7cf2e-5eea-4cd2-a0b7-85959683097a", + "metadata": {}, + "source": [ + "## <font color='blue'>*Aufgabe*</font>\n", + "Erstelle eine Grafik, die eine Sinus- und eine Kosinuskurve zeigt. Die Grafik soll auf der x-Achse von -Pi bis Pi gehen. Die entsprechenden Sinus- und Kosinuswerte sind selbst zu berechnen. Die Grafik soll aus 100 Stützstellen bestehen, die gleichmäßig über die x-Achse verteilt sind. Alle Achsen sollen sinnvoll beschriftet sein und die Grafik soll über eine Legende verfügen. Um an diesem Punkt die Aufgabe etwas besser zu gliedern, besteht sie aus mehreren Teilaufgaben, in denen du auch einige Techniken der vorangegangenen Übungen wiederholen kannst." + ] + }, + { + "cell_type": "markdown", + "id": "348e7767-baf7-42b4-935c-d7496bb4a34c", + "metadata": {}, + "source": [ + "## <font color='blue'>*Hinweise*</font>\n", + "Der Sinus und der Kosinus können über die Funktionen *sin(x)* bzw. *cos(x)* aus dem Modul *math* berechnet werden. Weiterhin enthält das Modul eine Variable namens *pi*" + ] + }, + { + "cell_type": "markdown", + "id": "79aa95c6-adc0-40ee-9566-49439896681f", + "metadata": {}, + "source": [ + "### <font color='blue'>*Teilaufgabe 1*</font>\n", + "Um etwas zeichnen zu können, brauchen wir zunächst die Werte der Sinus und der Kosinus Funktion an 100 gleichmäßig verteilten Stellen zwischen -Pi und Pi. Dazu sollen 3 Listen erstellt werden. Eine mit den x-Werten, eine mit den Sinus-Werten und eine mit den Kosinus-Werten. Dafür benötigst du eine Schleife, bei der in jedem Durchgang ein weiterer Wert angehängt wird\n", + "### <font color='blue'>*Lösung Teilaufgabe-1*</font>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f361c08d-ade7-446a-a519-d953c4ea267f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e45669c5-f500-494a-bac4-eb49846204ba", + "metadata": {}, + "source": [ + "### <font color='blue'>*Teilaufgabe 2*</font>\n", + "Um etwas mit Matplotlib warm zu werden, solltest du zu erst versuchen die Sinusfunktion zu zeichnen. Da du nur eine einfache Grafik zeichnen möchtest, kannst du hierfür das *pyplot* interface verwenden. Versuche die Grafik zu beschriften. Die Kurve soll im rot und gestrichelt sein\n", + "### <font color='blue'>*Lösung Teilaufgabe-2*</font>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b77a06d-a486-45f4-a0e3-ad2fd087ddfd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "704cd96d-5767-4be4-9761-9bec88e62cfd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a9d7fe62-9ffd-418d-819e-ee72a8617bd3", + "metadata": {}, + "source": [ + "### <font color='blue'>*Teilaufgabe 3*</font>\n", + "Für diese Teilaufgabe soll das Objektorientierte Interface von Matplotlib genutzt werden. Erstelle zunächst ein *Figure*-Objekt für die Zeichnung. Es soll die Größe 5 zu 2.5 besitzen. Die Zeichnung soll einen Graphen enthalten, der sowohl die Sinus und die Kosinus Funktion enthält. Die Sinus-Funktion soll in blau, die Kosinusfunktion in rot dargestellt sein. Außerdem soll die Grafik einen sinnvollen Titel und eine Farblegende besitzen. Die X-Achse soll mit \"X\", die Y-Achse mit \"Y\" beschriftet sein" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb035a92-6fb1-4306-bf47-b585bfa5a43d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3c798465-575f-4d0a-b803-aeb93d023fc0", + "metadata": {}, + "source": [ + "# <font color='blue'>**Aufgabe zum selbst probieren**</font>" + ] + }, + { + "cell_type": "markdown", + "id": "91ebfd38-aabb-4538-a547-f0fdae15c22d", + "metadata": {}, + "source": [ + "Probiere ein neue Grafik zu erstellen, in der zwei Graphen untereinander positioniert sind. Einer soll die Sinus-Funktion enthalten, einer die Kosinus-Funktion. Probiere die beschriebenen Möglichkeiten im Grundlagen-Notebook selbst aus. Markiere z.B. das Maximum der Sinus-Funktion mit einem Pfeil. Hinterlege die Grafiken mit einem Hintergrundraster, damit du Werte besser ablesen kannst. Die x- und y-Achse sind momentan noch recht grob in Einerschritten beschriftet. Füge kleinere Markierungen für 0.1-er Schritte in der Beschriftung hinzu" + ] + }, + { + "cell_type": "markdown", + "id": "cc3b1290-6063-4985-9ef4-1c035c96582c", + "metadata": {}, + "source": [ + "# <font color='blue'>**Lösung**</font>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2845551-20ea-4d56-be1c-e6dfce2ddadb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Uebung04/anatomy.webp b/Uebung04/anatomy.webp new file mode 100644 index 0000000000000000000000000000000000000000..cb6521ea0a57bf807eee321f001e3d8c479e3a62 Binary files /dev/null and b/Uebung04/anatomy.webp differ