{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# STP 行銷策略之 Python 商業應用實戰專書\n", "# 章節 3 市場區隔程式實作\n", "\n", "- 作者: [鍾皓軒](https://tmrmds.co/about/)\n", "- 建議讀者可以配合[STP 行銷策略之 Python 商業應用實戰](https://www.tenlong.com.tw/products/9789865025878?list_name=b-r7-zh_tw)專書的章節 3 市場區隔程式實作一同參考，效果會更好\n", "\n", "\n", "## 資料\n", "以下是某一賣場的匿名資料，我們挑選出其中三件商品，切入R 及F 的模型分\n", "析。本次資料共4,384 筆資料，可在我們所提供的QR code 下載，或點擊[本鏈接](https://bit.ly/Python-RFM-RF-basics-data)下載。而該賣場的內部資料有以下\n", "欄位：\n", "\n", "* 產品（product）：本資料集含咖啡（coffee）、燕麥麵包（oatmeal bread）\n", "與瓶裝水（bottled water）共三種產品為例。產品部份礙於賣場揭露之限\n", "制，所以僅擷取產品大分類。\n", "* 交易代號（orderId）：每一筆交易的代號\n", "* 顧客編號（clientId）：每一位顧客的專屬編號\n", "* 性別（gender）：分為男（male）與女（female）\n", "* 交易日期（orderdate）為顧客購買產品時的時間\n", "\n", "## 完整資料與內容可以查看[github](https://github.com/HowardNTUST/Marketing-Data-Science-Application/tree/master/Python-RFM-RF-basics)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 載入必要套件\n", "import pandas as pd\n", "import seaborn as sns\n", "import RFM\n", "\n", "# 動態繪圖套件\n", "import plotly.graph_objs as go\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "import plotly.offline as pyo\n", "pyo.init_notebook_mode()\n", "import numpy as np\n", "import plotly.express as px\n", "import tools\n", "import seaborn as sns\n", "\n", "theOS, ecode = tools.checkPlatform()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 載入資料" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0coffee582230female1/8/201845
1coffee2276male2/18/201845
2coffee725277female2/3/201845
3coffee597279male3/5/201845
4coffee76192female2/8/201845
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4379bottled water501215male4/9/201825
4380bottled water1000273female1/6/201825
4381bottled water50213male3/13/201825
4382bottled water50213male3/13/201825
4383bottled water50213male3/13/201825
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" ], "text/plain": [ " product orderId clientId gender orderdate grossmarg\n", "0 coffee 582 230 female 1/8/2018 45\n", "1 coffee 2 276 male 2/18/2018 45\n", "2 coffee 725 277 female 2/3/2018 45\n", "3 coffee 597 279 male 3/5/2018 45\n", "4 coffee 761 92 female 2/8/2018 45\n", "... ... ... ... ... ... ...\n", "4379 bottled water 501 215 male 4/9/2018 25\n", "4380 bottled water 1000 273 female 1/6/2018 25\n", "4381 bottled water 502 13 male 3/13/2018 25\n", "4382 bottled water 502 13 male 3/13/2018 25\n", "4383 bottled water 502 13 male 3/13/2018 25\n", "\n", "[4384 rows x 6 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "''' \n", "資料讀取 \n", " 注意編碼問題，尤其是windows作業系統出來的檔案，都有獨特的編碼格式。\n", "'''\n", "orders= pd.read_csv( 'orders2.csv', encoding=ecode)\n", "\n", "\n", "#空值該列全部刪除\n", "orders.dropna(inplace = True)\n", "orders\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 資料處理" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "\n", "# 設定今天的日期： 2018/4/11\n", "'從今天來看過往的銷售狀況'\n", "from dateutil import parser\n", "assess_date = parser.parse('2018-04-11')\n", "start_date= parser.parse('2018-01-01')\n", "\n", "type(assess_date)\n", "type('2018-04-11')\n", "\n", "# 計算每個人在三個產品的消費數量與時間\n", "df2, recent_recency, frequency = RFM.RFM_cal(orders,start_date,assess_date)\n", "\n", "# merge recency\n", "df2 = recent_recency.merge(df2, on = ['clientId', 'orderdate'])\n", "\n", "# frequency merge\n", "df2 =df2.merge(frequency, on = ['clientId'])\n", "\n", "# ！！！！特別注意！！！！ 下面的product所代表的是「index」哦~ 並非產品哦~！" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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012018-04-038male2.00.02.05
122018-03-3111male4.00.01.01
232018-04-101female0.01.00.06
342018-02-0169female2.01.01.01
452018-03-2616male0.00.01.04
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2842962018-03-1329male1.00.02.04
2852972018-04-083female4.01.01.02
2862982018-03-2121female1.00.02.03
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" ], "text/plain": [ "product clientId orderdate recency gender bottled water coffee \\\n", "0 1 2018-04-03 8 male 2.0 0.0 \n", "1 2 2018-03-31 11 male 4.0 0.0 \n", "2 3 2018-04-10 1 female 0.0 1.0 \n", "3 4 2018-02-01 69 female 2.0 1.0 \n", "4 5 2018-03-26 16 male 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "284 296 2018-03-13 29 male 1.0 0.0 \n", "285 297 2018-04-08 3 female 4.0 1.0 \n", "286 298 2018-03-21 21 female 1.0 0.0 \n", "287 299 2018-01-27 74 female 2.0 0.0 \n", "288 300 2018-02-03 67 male 1.0 0.0 \n", "\n", "product oatmeal bread frequency \n", "0 2.0 5 \n", "1 1.0 1 \n", "2 0.0 6 \n", "3 1.0 1 \n", "4 1.0 4 \n", ".. ... ... \n", "284 2.0 4 \n", "285 1.0 2 \n", "286 2.0 3 \n", "287 0.0 1 \n", "288 0.0 2 \n", "\n", "[289 rows x 8 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 成果將每一個人的product進行攤平，並計算出Recency與Frequency\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 最近一次（天）的消費與顧客數量分佈圖" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 注意！ 以下開始會有動態圖的呈現，建議可以到[本鏈接](https://nbviewer.jupyter.org/github/HowardNTUST/Marketing-Data-Science-Application/blob/master/Python-RFM-RF-basics/01%20-%20RFM%E6%A8%A1%E5%9E%8BPython%E5%AF%A6%E6%88%B0nb.ipynb)完整查看" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "alignmentgroup": "True", "hovertemplate": "recency=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# 最近一次（天）的消費與顧客數量分佈圖\n", "df2['顧客數量'] = 1\n", "recency_table = df2.groupby('recency', as_index = False)['顧客數量'].sum()\n", "\n", "fig = px.bar(recency_table, y='顧客數量', x='recency', text ='顧客數量' )\n", "fig.update_traces(texttemplate='%{text:}人', textposition='outside')\n", "fig.update_layout(\n", " title=\"最近一次（天）的消費與顧客數量分佈圖\",\n", " xaxis_title=\"距離上次購買的天數\",\n", " yaxis_title=\"顧客數量\",\n", " font=dict(\n", " size=18,\n", " )\n", ")\n", "\n", "pyo.iplot(fig, filename='最近一次（天）的消費與顧客數量分佈圖.html')\n", "\n", "# 如果是在離線python script，請使用下述方法，會將html一同存到您的工作目錄\n", "# plot(fig, filename='最近一次（天）的消費與顧客數量分佈圖.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 消費頻率與顧客數量分佈圖" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "alignmentgroup": "True", "hovertemplate": "frequency=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# 消費頻率與顧客數量分佈圖\n", "df2['顧客數量'] = 1\n", "frequency_table = df2.groupby('frequency', as_index = False)['顧客數量'].sum()\n", "\n", "fig = px.bar(frequency_table, y='顧客數量', x='frequency', text ='顧客數量' )\n", "fig.update_traces(texttemplate='%{text:}人', textposition='outside')\n", "fig.update_layout(\n", " title=\"消費頻率與顧客數量分佈圖\",\n", " xaxis_title=\"消費頻率\",\n", " yaxis_title=\"顧客數量\",\n", " font=dict(\n", " size=18,\n", " )\n", ")\n", "pyo.iplot(fig, filename='消費頻率與顧客數量分佈圖.html')\n", "# plot(fig, filename='消費頻率與顧客數量分佈圖.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 進行階層式分群演算法（Hierarchical Clustering）" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "\n", "# clustering \n", "from sklearn.cluster import AgglomerativeClustering\n", "cluster = AgglomerativeClustering(n_clusters = 6, affinity='euclidean', linkage='ward')\n", "recency_table['cluster'] = cluster.fit_predict(recency_table)\n", "frequency_table['cluster'] = cluster.fit_predict(frequency_table)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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"bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "【分群後】消費頻率與顧客數量分佈圖" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "消費頻率" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "顧客數量" } } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# 【分群後】消費頻率與顧客數量分佈圖\n", "frequency_table['cluster'] = frequency_table['cluster'].astype(str)\n", "\n", "fig = px.bar(frequency_table, y='顧客數量', x='frequency', text ='顧客數量' ,\n", " color=frequency_table['cluster'])\n", "# ['red','blue','blue','blue','blue','blue','blue','blue','blue']\n", "fig.update_traces(texttemplate='%{text:}人', textposition='outside')\n", "fig.update_layout(\n", " title=\"【分群後】消費頻率與顧客數量分佈圖\",\n", " xaxis_title=\"消費頻率\",\n", " yaxis_title=\"顧客數量\",\n", " font=dict(\n", " size=18,\n", " )\n", ")\n", "\n", "pyo.iplot(fig, filename='【分群後】消費頻率與顧客數量分佈圖.html')\n", "# plot(fig, filename='【分群後】消費頻率與顧客數量分佈圖.html')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 要開始將Recency與Frequency進行顧客分羣\n", "我們會根據上述分羣的結果作爲切割準則,分成\n", "* 頻率分類\n", "* 近因分類\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "\n", "# 切割 recency\n", "recency_label = ['0-17 day', '18-27 day', '28-40 day', '41-56 day', '57-68 day', '>69 day']\n", "recency_cut = [-1, 17, 27, 40, 56, 68, df2['recency'].max()]\n", "df2['近因'] = pd.cut( \n", " df2['recency'] , #目標欄位\n", " recency_cut, #切割條件\n", " labels =recency_label) #切割後的分類內容\n", "\n", "# 切割 frequency\n", "frequency_label = ['1 freq', '2-4 freq', '5 freq', '6 freq', '7 freq', '>8 freq']\n", "frequency_cut = [-1, 2, 4, 5, 6, 7, df2['frequency'].max()]\n", "df2['頻率'] = pd.cut( \n", " df2['frequency'] , #目標欄位\n", " frequency_cut, #切割條件\n", " labels =frequency_label) #切割後的分類內容\n", "\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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productclientIdorderdaterecencygenderbottled watercoffeeoatmeal breadfrequency顧客數量近因頻率
012018-04-038male2.00.02.0510-17 day5 freq
122018-03-3111male4.00.01.0110-17 day1 freq
232018-04-101female0.01.00.0610-17 day6 freq
342018-02-0169female2.01.01.011>69 day1 freq
452018-03-2616male0.00.01.0410-17 day2-4 freq
....................................
2842962018-03-1329male1.00.02.04128-40 day2-4 freq
2852972018-04-083female4.01.01.0210-17 day1 freq
2862982018-03-2121female1.00.02.03118-27 day2-4 freq
2872992018-01-2774female2.00.00.011>69 day1 freq
2883002018-02-0367male1.00.00.02157-68 day1 freq
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289 rows × 11 columns

\n", "
" ], "text/plain": [ "product clientId orderdate recency gender bottled water coffee \\\n", "0 1 2018-04-03 8 male 2.0 0.0 \n", "1 2 2018-03-31 11 male 4.0 0.0 \n", "2 3 2018-04-10 1 female 0.0 1.0 \n", "3 4 2018-02-01 69 female 2.0 1.0 \n", "4 5 2018-03-26 16 male 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "284 296 2018-03-13 29 male 1.0 0.0 \n", "285 297 2018-04-08 3 female 4.0 1.0 \n", "286 298 2018-03-21 21 female 1.0 0.0 \n", "287 299 2018-01-27 74 female 2.0 0.0 \n", "288 300 2018-02-03 67 male 1.0 0.0 \n", "\n", "product oatmeal bread frequency 顧客數量 近因 頻率 \n", "0 2.0 5 1 0-17 day 5 freq \n", "1 1.0 1 1 0-17 day 1 freq \n", "2 0.0 6 1 0-17 day 6 freq \n", "3 1.0 1 1 >69 day 1 freq \n", "4 1.0 4 1 0-17 day 2-4 freq \n", ".. ... ... ... ... ... \n", "284 2.0 4 1 28-40 day 2-4 freq \n", "285 1.0 2 1 0-17 day 1 freq \n", "286 2.0 3 1 18-27 day 2-4 freq \n", "287 0.0 1 1 >69 day 1 freq \n", "288 0.0 2 1 57-68 day 1 freq \n", "\n", "[289 rows x 11 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 從下表中，我們可以看到不同等級的recency與Frequency被歸類到「頻率」與「近因」的分類欄位裏面\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# RFM 分析開始！\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RFM顧客區隔面（RF交叉分析）" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# RF交叉分析\n", "RF_table = pd.crosstab(df2['頻率'].astype(str),\n", " df2['近因'].astype(str))\n", "\n", "# 重新排序\n", "RF_table['freq'] = RF_table.index\n", "# RF_table = RF_table.sort_values('freq',ascending = False)\n", "\n", "collist = ['freq'] + recency_label\n", "RF_table = RF_table[collist]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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近因freq0-17 day18-27 day28-40 day41-56 day57-68 day>69 day
頻率
1 freq1 freq281716131117
2-4 freq2-4 freq6418191342
5 freq5 freq2249000
6 freq6 freq1130200
7 freq7 freq812000
>8 freq>8 freq410000
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" ], "text/plain": [ "近因 freq 0-17 day 18-27 day 28-40 day 41-56 day 57-68 day \\\n", "頻率 \n", "1 freq 1 freq 28 17 16 13 11 \n", "2-4 freq 2-4 freq 64 18 19 13 4 \n", "5 freq 5 freq 22 4 9 0 0 \n", "6 freq 6 freq 11 3 0 2 0 \n", "7 freq 7 freq 8 1 2 0 0 \n", ">8 freq >8 freq 4 1 0 0 0 \n", "\n", "近因 >69 day \n", "頻率 \n", "1 freq 17 \n", "2-4 freq 2 \n", "5 freq 0 \n", "6 freq 0 \n", "7 freq 0 \n", ">8 freq 0 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RF_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 定義顧客" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "\n", "# 以個別消費者來說，這四種顧客分別是個體的誰？\n", "# 分類並標示出新客戶、常貴客、先前客\n", "df2['customer'] = np.where( (df2['frequency'] >=frequency_cut[4]) & (df2['recency']<=recency_cut[3]), '常貴客',\n", " \n", " np.where( (df2['frequency'] >=frequency_cut[4]) & ( df2['recency']>recency_cut[3]), '沉睡客',\n", " \n", " np.where( (df2['frequency'] < frequency_cut[4]) & ( df2['recency']>recency_cut[3]), '流失客',\n", " \n", " '新顧客' )))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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productclientIdorderdaterecencygenderbottled watercoffeeoatmeal breadfrequency顧客數量近因頻率customer
012018-04-038male2.00.02.0510-17 day5 freq新顧客
122018-03-3111male4.00.01.0110-17 day1 freq新顧客
232018-04-101female0.01.00.0610-17 day6 freq常貴客
342018-02-0169female2.01.01.011>69 day1 freq流失客
452018-03-2616male0.00.01.0410-17 day2-4 freq新顧客
.......................................
2842962018-03-1329male1.00.02.04128-40 day2-4 freq新顧客
2852972018-04-083female4.01.01.0210-17 day1 freq新顧客
2862982018-03-2121female1.00.02.03118-27 day2-4 freq新顧客
2872992018-01-2774female2.00.00.011>69 day1 freq流失客
2883002018-02-0367male1.00.00.02157-68 day1 freq流失客
\n", "

289 rows × 12 columns

\n", "
" ], "text/plain": [ "product clientId orderdate recency gender bottled water coffee \\\n", "0 1 2018-04-03 8 male 2.0 0.0 \n", "1 2 2018-03-31 11 male 4.0 0.0 \n", "2 3 2018-04-10 1 female 0.0 1.0 \n", "3 4 2018-02-01 69 female 2.0 1.0 \n", "4 5 2018-03-26 16 male 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "284 296 2018-03-13 29 male 1.0 0.0 \n", "285 297 2018-04-08 3 female 4.0 1.0 \n", "286 298 2018-03-21 21 female 1.0 0.0 \n", "287 299 2018-01-27 74 female 2.0 0.0 \n", "288 300 2018-02-03 67 male 1.0 0.0 \n", "\n", "product oatmeal bread frequency 顧客數量 近因 頻率 customer \n", "0 2.0 5 1 0-17 day 5 freq 新顧客 \n", "1 1.0 1 1 0-17 day 1 freq 新顧客 \n", "2 0.0 6 1 0-17 day 6 freq 常貴客 \n", "3 1.0 1 1 >69 day 1 freq 流失客 \n", "4 1.0 4 1 0-17 day 2-4 freq 新顧客 \n", ".. ... ... ... ... ... ... \n", "284 2.0 4 1 28-40 day 2-4 freq 新顧客 \n", "285 1.0 2 1 0-17 day 1 freq 新顧客 \n", "286 2.0 3 1 18-27 day 2-4 freq 新顧客 \n", "287 0.0 1 1 >69 day 1 freq 流失客 \n", "288 0.0 2 1 57-68 day 1 freq 流失客 \n", "\n", "[289 rows x 12 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# customer即爲本次定義的顧客部分\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 以視覺化呈現 RFM顧客區隔面（RF交叉分析）" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "\n", "# 以頻率與近因爲羣組，統計他們的顧客數量\n", "df_seg = df2.groupby(['頻率', '近因'], as_index = False)['顧客數量'].sum()\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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product頻率近因顧客數量
01 freq0-17 day28.0
11 freq18-27 day17.0
21 freq28-40 day16.0
31 freq41-56 day13.0
41 freq57-68 day11.0
51 freq>69 day17.0
62-4 freq0-17 day64.0
72-4 freq18-27 day18.0
82-4 freq28-40 day19.0
92-4 freq41-56 day13.0
102-4 freq57-68 day4.0
112-4 freq>69 day2.0
125 freq0-17 day22.0
135 freq18-27 day4.0
145 freq28-40 day9.0
155 freq41-56 dayNaN
165 freq57-68 dayNaN
175 freq>69 dayNaN
186 freq0-17 day11.0
196 freq18-27 day3.0
206 freq28-40 dayNaN
216 freq41-56 day2.0
226 freq57-68 dayNaN
236 freq>69 dayNaN
247 freq0-17 day8.0
257 freq18-27 day1.0
267 freq28-40 day2.0
277 freq41-56 dayNaN
287 freq57-68 dayNaN
297 freq>69 dayNaN
30>8 freq0-17 day4.0
31>8 freq18-27 day1.0
32>8 freq28-40 dayNaN
33>8 freq41-56 dayNaN
34>8 freq57-68 dayNaN
35>8 freq>69 dayNaN
\n", "
" ], "text/plain": [ "product 頻率 近因 顧客數量\n", "0 1 freq 0-17 day 28.0\n", "1 1 freq 18-27 day 17.0\n", "2 1 freq 28-40 day 16.0\n", "3 1 freq 41-56 day 13.0\n", "4 1 freq 57-68 day 11.0\n", "5 1 freq >69 day 17.0\n", "6 2-4 freq 0-17 day 64.0\n", "7 2-4 freq 18-27 day 18.0\n", "8 2-4 freq 28-40 day 19.0\n", "9 2-4 freq 41-56 day 13.0\n", "10 2-4 freq 57-68 day 4.0\n", "11 2-4 freq >69 day 2.0\n", "12 5 freq 0-17 day 22.0\n", "13 5 freq 18-27 day 4.0\n", "14 5 freq 28-40 day 9.0\n", "15 5 freq 41-56 day NaN\n", "16 5 freq 57-68 day NaN\n", "17 5 freq >69 day NaN\n", "18 6 freq 0-17 day 11.0\n", "19 6 freq 18-27 day 3.0\n", "20 6 freq 28-40 day NaN\n", "21 6 freq 41-56 day 2.0\n", "22 6 freq 57-68 day NaN\n", "23 6 freq >69 day NaN\n", "24 7 freq 0-17 day 8.0\n", "25 7 freq 18-27 day 1.0\n", "26 7 freq 28-40 day 2.0\n", "27 7 freq 41-56 day NaN\n", "28 7 freq 57-68 day NaN\n", "29 7 freq >69 day NaN\n", "30 >8 freq 0-17 day 4.0\n", "31 >8 freq 18-27 day 1.0\n", "32 >8 freq 28-40 day NaN\n", "33 >8 freq 41-56 day NaN\n", "34 >8 freq 57-68 day NaN\n", "35 >8 freq >69 day NaN" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_seg" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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product頻率近因顧客數量customer
01 freq0-17 day28.0新顧客
281 freq18-27 day17.0新顧客
451 freq28-40 day16.0新顧客
611 freq41-56 day13.0流失客
741 freq57-68 day11.0流失客
851 freq>69 day17.0流失客
1022-4 freq0-17 day64.0新顧客
1662-4 freq18-27 day18.0新顧客
1842-4 freq28-40 day19.0新顧客
2032-4 freq41-56 day13.0流失客
2162-4 freq57-68 day4.0流失客
2202-4 freq>69 day2.0流失客
2225 freq0-17 day22.0新顧客
2445 freq18-27 day4.0新顧客
2485 freq28-40 day9.0新顧客
2575 freq41-56 dayNaNNaN
2585 freq57-68 dayNaNNaN
2595 freq>69 dayNaNNaN
2606 freq0-17 day11.0常貴客
2716 freq18-27 day3.0常貴客
2746 freq28-40 dayNaNNaN
2756 freq41-56 day2.0沉睡客
2776 freq57-68 dayNaNNaN
2786 freq>69 dayNaNNaN
2797 freq0-17 day8.0常貴客
2877 freq18-27 day1.0常貴客
2887 freq28-40 day2.0常貴客
2907 freq41-56 dayNaNNaN
2917 freq57-68 dayNaNNaN
2927 freq>69 dayNaNNaN
293>8 freq0-17 day4.0常貴客
297>8 freq18-27 day1.0常貴客
298>8 freq28-40 dayNaNNaN
299>8 freq41-56 dayNaNNaN
300>8 freq57-68 dayNaNNaN
301>8 freq>69 dayNaNNaN
\n", "
" ], "text/plain": [ "product 頻率 近因 顧客數量 customer\n", "0 1 freq 0-17 day 28.0 新顧客\n", "28 1 freq 18-27 day 17.0 新顧客\n", "45 1 freq 28-40 day 16.0 新顧客\n", "61 1 freq 41-56 day 13.0 流失客\n", "74 1 freq 57-68 day 11.0 流失客\n", "85 1 freq >69 day 17.0 流失客\n", "102 2-4 freq 0-17 day 64.0 新顧客\n", "166 2-4 freq 18-27 day 18.0 新顧客\n", "184 2-4 freq 28-40 day 19.0 新顧客\n", "203 2-4 freq 41-56 day 13.0 流失客\n", "216 2-4 freq 57-68 day 4.0 流失客\n", "220 2-4 freq >69 day 2.0 流失客\n", "222 5 freq 0-17 day 22.0 新顧客\n", "244 5 freq 18-27 day 4.0 新顧客\n", "248 5 freq 28-40 day 9.0 新顧客\n", "257 5 freq 41-56 day NaN NaN\n", "258 5 freq 57-68 day NaN NaN\n", "259 5 freq >69 day NaN NaN\n", "260 6 freq 0-17 day 11.0 常貴客\n", "271 6 freq 18-27 day 3.0 常貴客\n", "274 6 freq 28-40 day NaN NaN\n", "275 6 freq 41-56 day 2.0 沉睡客\n", "277 6 freq 57-68 day NaN NaN\n", "278 6 freq >69 day NaN NaN\n", "279 7 freq 0-17 day 8.0 常貴客\n", "287 7 freq 18-27 day 1.0 常貴客\n", "288 7 freq 28-40 day 2.0 常貴客\n", "290 7 freq 41-56 day NaN NaN\n", "291 7 freq 57-68 day NaN NaN\n", "292 7 freq >69 day NaN NaN\n", "293 >8 freq 0-17 day 4.0 常貴客\n", "297 >8 freq 18-27 day 1.0 常貴客\n", "298 >8 freq 28-40 day NaN NaN\n", "299 >8 freq 41-56 day NaN NaN\n", "300 >8 freq 57-68 day NaN NaN\n", "301 >8 freq >69 day NaN NaN" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 要將customer分類融合到df_seg裏面，這樣才可以繪製不同區隔顏色的圖\n", "df_seg = df_seg.merge(df2[['頻率', '近因','customer']],\n", " on =['頻率', '近因'], how = 'left')\n", "\n", "df_seg = df_seg.drop_duplicates()\n", "\n", "df_seg" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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00Bx99NH5zne+s9HnwOanuczLqaeemt133z0jR45Mkuy6667Zfvvt88tf/jJJMn369AwfPjxXXXVVg28WxowZk5qamtx1111J3vyN0t///vfzi1/8osEP9973vvflz3/+c4Nztm3btsHtt2Zm5MiR2XvvvXPllVemR48eOeOMM5IkN910U4499thsvfXWhX8Mluar2jNz0kknrfc1/9nPfna9j3nPPfes92tMkixbtiwzZ87MbrvtluXLl+f+++/PSy+9lCSZMWNG9thjj0r4uqGvMT/+8Y8za9asjBs3LiNGjEipVMqaNWvy0EMPZa+99spRRx2V4cOHb/C/E5unas/LP3v55Zdz8803Z6+99qqEr//6nupf56Vt27b54x//WJmD2267LSeddFKGDx+ee++9N4888kgeeeSRHH744dljjz3yu9/9Lol54d3RXOanbdu2GT16dI488sicfPLJeeSRR5Iky5cvz8KFC1Mul/OXv/wlS5cuTU1NTZ588skcc8wxSd5c1qNNm4aR0tvNx2677ZYrr7wy55xzTp544onKD+N/8YtfZOLEiXnyyScbLEEAmxvhazM0ZMiQ1NTU5JOf/GRuu+22De47ZsyY/PCHP8yTTz6ZJDnvvPMyYsSI9e7bmJ+ArV27Nn/961+zzz775OGHH87w4cMzderUTX4OZ5xxxtuGPXV1dTnxxBNz4403Zrfddqv8Ft9+/frlnnvuyeOPP95grcr58+fn0ksvzfbbb5+zzjprk3th81PNGXk79fX16du3b+X2Tjvt1GBt13/tqU2bNhk7dmyD7aVSKcccc8w6a48lb75B69KlS97//vfn6aefzkknnVRZ+2+vvfbKl770pTzzzDO5+OKL8/TTTyd5M0y68MILG6zNxJan2vNy4oknZsiQITn11FMr21avXp2ddtoprVu3zpo1a/L+978/ixcvXufY8ePHZ/z48ZXbixcvzmc/+9m0a9cukyZNyrbbbpvXXnstH/3oR3PUUUc1OHbp0qX58Ic/nLlz5+aQQw5J165dk7w5L3fccUf++Mc/Nnhud9xxR372s59V1ttky1XNmXm71/ym6Ny5c1588cXKJx8mTpyYF198MePGjUv79u1TX1+f/fffP7/4xS8qxzzxxBMZNGhQunTpkpUrV+bEE0+s3LfHHntk1KhR+cc//pHDDjsskydPTvLm15hTTjklr776al555ZVN6pHNR7W/xrz1em/dunWOOOKIShi1ofdUb1m9enWWLVuWnXbaKX/605/ykY98JA8//HDK5XJ22223JG9+sql3794NXuPmhXdLteenS5cuufnmm3PdddflyCOPbHDfzJkzc/LJJ+eOO+7Idtttl+222y4rVqyovHfbeuutc+655+akk05qcNz8+fPzyU9+Mt/85jfTvXv3DBkyJJdffnl23333LFiwII888kgOPvjgbLXVVknefL/WoUOHnHzyybn66qs32jO0VMLXZmbPPffMqaeeml//+tcZMWJEhg0bljZt2qR169YZMmRIDjzwwFxzzTW56aabMmTIkLz66qsNFuJ+y3bbbZevfOUr76iHz3/+8/nxj3+c9u3b5ze/+U3uvvvu9e73iU98IqNGjcree+9d+ZjPZz7zmWy33XbZcccdGywn8M8uuuii/PSnP83o0aPz+9//PvPnz0+S/PKXv8wvf/nLnHDCCZWrB9/y4IMPZqeddsr//u//vqPnxOajOczIZZddln333Tc77bRTamtrc9NNN+Waa67JMccck7q6uvz973/Pfffd1+BjNm/ZZZddcumll2bevHmpra1NknznO9/JnXfemSOOOCL33Xdfg58W/7PRo0fnl7/8ZVatWpWpU6dWriD//ve/n5tuuinLli3LY489Vtn/T3/6Uz7wgQ/k1ltvfUfPk5av2vOy9dZb5yc/+UkWLFiQWbNmJfl/H42+9dZbU1dXl1WrVmXJkiW58cYbG/249fX1+drXvpaZM2emVatWmThxYuWKvrd87nOfy/XXX5/Vq1fngQceqPzyxx//+Me57rrrMmbMmNTV1VX2f/XVV/PSSy9l+fLl6yxhwJaj2jOzITNmzEj37t3TrVu37LnnnvnKV75SeQ/1z66//vp07do1pVIpV155ZeUTTFdccUVmzZqVtWvXZsqUKZVv4JM3l+P46le/mlmzZmXFihWVK2+TN5fImTt3bp5//vn88Y9/rGy/7777cv311+fss89+V58nLUdzmJdLL700H/zgB5O8+Yt7FyxYkCQbfU/1lnPOOSfXXXddkuSpp57KZZddllatWuX666/PvHnzsmbNmvz0pz9t8EuHzAvvhuYwPy+//HKOOOKI9V51O2fOnBx66KGVq1G/8IUvJHkzBD7mmGOy1VZb5Zvf/OY6SwJOnDgxP/nJTzJ37tw888wzle/r3/rldPfdd19mz56dV199tXLMr371q5x66qmb/MNGaElKeXPxV5qJt36SREPnnHNOli5dao0kzMgmuu2223LmmWfmueeeq3YrVIF52TTf+973cuONN1a+eWfLY2Y2zZw5cyrryLLlMS+bxrzwz8zP//Oxj30sO++8s08esVlrVe0GaMj/gNfVunXrfOxjH8vtt99e7VZoBsxI4+24444pl8uC1y2YeWm8jh07Zq+99hK8buHMTOMdcMABefTRRwVJWzDz0njmhX9lfv6fM844o/L7XmBzVlaquVa/fv3KdXV15Y997GNV70WpllSf+tSnyvPmzSv37du36r0o1dzr0EMPLS9cuLB8wAEHVL0XpVpCnXfeeeXa2tpyTU1N1XtRqrmXeVFq/VVTU1NesGBB+ayzzqp6L0oVXZYdAAAAAAAogGUHAAAAAAAKIHwFAAAAAChAiwlf77rrrmq3AC2GeYHGMy/QeOYFGs+8QOOZF2Bz1mLC15qammq3AC2GeYHGMy/QeOYFGs+8QOOZF2Bz1mLCVwAAAACAlkT4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABmiR87dq1a6ZOnZr58+dnxowZSZLx48dnwYIFmTNnTvr27dsUbQAAAAAANJk2TXGSq6++Oj/4wQ8qwevQoUPTsWPHDBo0KP3798+ECRNy9NFHN0UrAAAAAABNovArX3v06JFtt922ErwmybBhwzJ58uQkyaJFi9KnT5+USqWiWwEAAAAAaDKFX/m655575rnnnsvNN9+c7t2754YbbkivXr3yzDPPVPZZtmxZunbtmhUrVhTdDgAAAABAkyg8fK2pqUm/fv0ydOjQ1NfXZ+bMmVm9enXWrFlT2Wft2rVZu3btOseOHDkyo0aNqjwO8PbMCzSeeYHGMy/QeOYFGs+8AFuScpF1yCGHlK+44orK7a997Wvlp59+uvyhD32osu3BBx/c6OPU1dUV2qdSm1OZF6UaX+ZFqcaXeVGq8WVelGp8mRel1OZcha/5On/+/Oy7775p3759SqVS9t9//1xzzTUZPnx4kqR///5ZsmRJ0W0AAAAAADSpwpcdePXVV3PFFVdk1qxZWbt2baZMmZJrr702V111VebOnZv6+vqMGDGi6DYAAAAAAJpU4eFrkkydOjVTp05tsG306NFNcWoAAAAAgKoofNkBAAAAAIAtkfAVAAAAAKAAwlcAAAAAgAIIXwEAAAAACiB8BQAAAAAogPAVAAAAAKAAwlcAAAAAgAIIXwEAAAAACiB8BQAAAAAogPAVAAAAAKAAwlcAAAAAgAIIXwEAAAAACiB8BQAAAAAogPAVAAAAAKAAwlcAAAAAgAIIXwEAAAAACiB8BQAAAAAogPAVAAAAAKAAwlcAAAAAgAIIXwEAAAAACtAk4esf/vCH1NbWpra2NuPHj0+SjB8/PgsWLMicOXPSt2/fpmgDAAAAAKDJtGmKk7z22ms56KCDKreHDh2ajh07ZtCgQenfv38mTJiQo48+uilaAQAAAABoElVZdmDYsGGZPHlykmTRokXp06dPSqVSNVoBAAAAAChEk4SvK1euzAMPPJBp06Zl9913T69evfLMM89U7l+2bFm6du26znEjR45MXV1d6urqUlNT0xStQotlXqDxzAs0nnmBxjMv0HjmBdiSlJuqBg4cWH7kkUfKd955Z7lnz56V7TNnzix36dJlg8fW1dU1WZ9KtfQyL0o1vsyLUo0v86JU48u8KNX4Mi9Kqc25mnTZgbq6utTX1+f5559Pz549K9s7d+6clStXNmUrAAAAAACFKjx8bdeuXTp06JAkef/7359SqZTp06dn+PDhSZL+/ftnyZIlRbcBAAAAANCk2hR9go4dO2bGjBl5+eWXs3r16px++ul57LHHctBBB2Xu3Lmpr6/PiBEjim4DAAAAAKBJFR6+rlixIgMGDFhn++jRo4s+NQAAAABA1TTpmq8AAAAAAFsK4SsAAAAAQAGErwAAAAAABRC+AgAAAAAUQPgKAAAAAFAA4SsAAAAAQAGErwAAAAAABRC+AgAAAAAUQPgKAAAAAFAA4SsAAAAAQAGErwAAAAAABRC+AgAAAAAUQPgKAAAAAFAA4SsAAAAANBOlUilJUlNTk969eydJunXrlk6dOlWzLd6hNtVuAAAAAAB403nnnZcf/vCHSZLRo0enVatWmT9/fo466qg8++yzWbhwYaZPn17lLmksV74CAAAAQDNxzTXX5JJLLskLL7yQ+fPn5/e//30GDx6c3/72t3nggQdSX19f7RbZBMJXAAAAAGgmVq1alcsuuywTJ05M586dM3/+/DzxxBMpl8u54oorqt0em8iyAwAAAADQTJx++umZMmVKPve5z+Xoo4/OoEGD8uijj2bVqlVZtmxZli5dWu0W2QTCVwAAAABoJu68885cdNFF2XHHHfPcc8+lS5cuOfPMM7NkyZKcddZZueOOO3LwwQdXu00aybIDAAAAANBMPPfcc7ntttvSr1+/XHXVVZk2bVr+/ve/5/bbb88ZZ5yRTp06VbtFNkGThK/t27fP7373u5x77rlJkvHjx2fBggWZM2dO+vbt2xQtAAAAAECLsMsuu2T58uWV26VSKaVSKXPmzEmbNj7I3pI0Sfh64YUXpq6uLkkydOjQdOzYMYMGDcrZZ5+dCRMmNEULAAAAANDsHXfccXnhhRfywgsv5Jxzzsnxxx+fTp065fjjj8+TTz6ZFStWVLtFNkHhUXm/fv3So0eP1NbWpqamJsOGDcvkyZOTJIsWLUqfPn1SKpVSLpeLbgUAAAAAmrXFixfnqaeeyl133bXe+y+99NIm7oh/R6FXvpZKpXz729/O+eefX9nWq1evPPPMM5Xby5YtS9euXdd7/MiRI1NXV5e6urrU1NQU2Sq0eOYFGs+8QOOZF2g88wKNZ17g7T311FNJksGDB6/3/lmzZjVlO7wLykXVF77whfL/+T//p5ykPGLEiPK5555bvvPOO8s9e/as7DNz5sxyly5dNvpYdXV1hfWp1OZW5kWpxpd5UarxZV6UanyZF6UaX+ZFqfXXrFmzyknKO++8czlJeaeddiofddRR5enTp1e9N9X4KnTZgRNPPDGrVq3KJz7xibzvfe9L27Zt0759+/Ts2TNLly5NknTu3DkrV64ssg0AAAAAaFHeWqLzjTfeyJAhQ7L33ntn0qRJlV9oT8tQaPh6wAEHVP4+YsSI1NTU5C9/+UuGDx+eBx98MP3798+SJUuKbAEAAAAAWpxSqZQkGTNmTMaMGZMhQ4Zk7dq1Ve6KTVXomq/rc/PNN6d9+/aZO3durrjiinzlK19p6hYAAAAAoNkaOHBgOnbsmH333TcHHXRQzjnnnOy66675xje+kc6dO+e///u/q90ijVTola//bPLkyZW/jx49uqlOCwAAAAAtSo8ePdKuXbtsv/32efbZZ/Pggw+mZ8+emThxYoYNG5Z777232i3SSE1+5SsAAAAA8PbuuOOOLF++PHfccUd+//vfp1OnTrn66qvzj3/8I8uXL8+LL75Y7RZpJOErAAAAADQzb635+s1vfmpzyYEAACAASURBVDPt27fPcccdl1deeaWynZahyZYdAAAAAAAap1wuJ0lOO+20XHXVVenUqVMOOeSQynZaBuErAAAAADQz3/72t5Mk48ePz+rVq/PSSy/lL3/5Sx588MEqd8amsOwAAAAAADQzM2fOTJKsXr26wfZVq1ZVox3eoY2Gr506dWpwu23btkmSQw89tJiOAAAAAAA2AxsNX2+99dYccsgh6d27dzp27JgxY8YkSb785S8X3hwAAAAAQEu10fC1VCrl3nvvzSmnnJJjjz02P/7xjyvbAQAAAIBi7LPPPg3+pOXZaPhaLpezzTbb5NZbb83rr7+er371q5XtAAAAAEAxrrjiigZ/0vJsMHzt3LlzunXrlq222ipnnnlmVq1ald133z3/9V//lW7duuW//uu/mqpPAAAAANiivHXxo4sgW64Nhq+HHHJI3njjjXz+859P586d86EPfSi9evXK3/72t9TX1+f5559vqj4BAAAAYIti2c+Wb4Ph680335wXX3wxF110UZ544ol897vfTW1tbRYuXJiXXnopdXV1TdUnAAAAAGxRXPHa8m10zdckadWqVVatWpWBAwdm/PjxSfzjAwAAAECRZs+e3eBPWp6Nhq+rV6/OEUcckWnTpmX27Nlp06ZNEpc9AwAAAECRvvGNbzT4k5anzcZ2uPzyyzNr1qzK7SeffDJJMnbs2CRJ375989RTTxXUHgAAAABAy7TR8PWCCy7IggULcu2112bXXXfN448/nhdffDE9evTINttskw4dOghfAQAAAAD+xUaXHaipqcnOO++cW265JatWrcott9yS7373u+natWu6du2aW2+9tSn6BAAAAABoUTYavr7xxhv5yEc+knK53OCXbHXr1i1LliwptDkAAAAA2NJ8+9vfXu/2ffbZJ+eee24Td8O/Y6Ph66pVq/I///M/OfbYY/Oe97wnH/3oR3PWWWdl8eLF+djHPpb3vve9TdEnAAAAAGwR9t133/VuX7x4cU455ZSmbYZ/y0bXfD3zzDNz5JFH5tvf/nY6deqUnXfeOStXrsyvf/3rbLXVVjn55JNz3XXXNUWvAAAAALDZ+9CHPpRHH320wafQ37J27drstttuefzxx6vQGZtqo+Frz549c8EFF+RHP/pRVq9endmzZ2f//ffPpEmTcvnll280eO3QoUN+8YtfZOutt067du0yevToLF68OOPHj8/gwYPz+uuv5/TTT/dLuwAAAAAgSV1dXQ455JBqt8G7YKPLDiTJueeem1KplF133TX77bdfXnzxxXz+85/P+eefn06dOm3w2Pr6+nz84x/PwQcfnK985Ss5//zzM3To0HTs2DGDBg3K2WefnQkTJrwrTwYAAAAAWrqbbrqp2i3wLtlo+NquXbv853/+Zz74wQ/m/vvvz+6775799tsvXbt2zde+9rV85jOf2eDxa9asyT/+8Y8kya677pqHH344w4YNy+TJk5MkixYtSp8+fVIqld6FpwMAAAAALdu1115b7RZ4l2x02YHp06dn+vTp6dSpU9q1a5epU6emd+/eSZL//d//zeWXX77Rk4wZMyaf/vSns3Tp0hx11FGZMmVKnnnmmcr9y5YtS9euXbNixYoGx40cOTKjRo1KktTU1GzSE4MtjXmBxjMv0HjmBRrPvEDjmRdgS1FKsu7KvQU5/vjjc/LJJ2errbbKGWeckaVLlyZJZs6cmRNPPDErV65822Pr6uoycODApmoVWjTzAo1nXqDxzAs0nnmBxjMv0Djt27fP66+/Xu022ESNWvP13XLLLbekb9++ef7559OzZ8/K9s6dO28weAUAAACALUWfPn3Wqdra2uywww7Zeuutq90em2Cjyw78u3r16pXly5fn9ddfz957752nn34606dPz/Dhw/Pggw+mf//+WbJkSdFtAAAAAECLMGnSpJRKpZTL/+8D6x/4wAcyceLEdO/ePXfffXfGjh1bxQ5prMLD1z59+uT222/PqlWrsmrVqpx55pl57rnnctBBB2Xu3Lmpr6/PiBEjim4DAAAAAFqEY489dp1td911V2X7/fffL3xtIQoPX+fNm5cBAwass3306NFFnxoAAAAAWpw+ffqss+3Tn/505e+nn356U7bDv6Hw8LUp1Q2wQDctx8CH6qrdAgAAANAM/fOyA1tvvXU++MEP5qGHHsoRRxyRJHnyySer3CGNtVmFrwAAAADQ0v3rsgNdunTJpEmTqtQN/45W1W4AAAAAAHh7K1euzPPPP1/tNngHXPkKAAAAAM1Ily5dMnbs2BxwwAFZs2ZN7r777lxwwQXVbot3wJWvAAAAANCMXH/99Xn88cdzwAEH5NVXX80999yT73//+9Vui3dA+AoAAAAAzUiXLl1yzTXXZPXq1SmVSpkzZ0569+5d7bZ4B4SvAAAAANCMrF27Nq1bt67c3mOPPVIul6vYEe+U8BUAAAAAmpGLLroo//Ef/5EkeeWVV/LlL385n/70p6vcFe+EX7gFAAAAAM3I7Nmz8573vCf77LNPzj///Dz55JNZs2ZNtdviHRC+AgAAAEAzctxxx+XrX/966urq0rZt2+yxxx4ZPXp0FixYUO3W2ETCVwAAAABoRr72ta/lgAMOyMsvv5wk6d69e2655ZYMHjy4yp2xqaz5CgAAAADNyIoVKyrBa5IsW7Ysr7zyShU74p1y5SsAAAAAVFmfPn0qf7/nnnsyduzY/PznP8/q1avz0Y9+NI8++mgVu+OdEr4CAAAAQJVNmjQppVIp5XK5sq1///6Vv5dKpWq0xb9J+AoAAAAAVXbsscdWuwUKYM1XAAAAAIACCF8BAAAAAAogfAUAAAAAKIDwFQAAAACgAMJXAAAAAIACFB6+tmrVKldeeWVqa2vz4IMP5uyzz06SnHvuuamrq8tvfvOb7L///kW3AQAAAADQpNoUfoI2bXLXXXfli1/8Ylq1apWFCxfmoYceyqGHHpqBAwemV69eue222zJw4MCiWwEAAAAAaDKFX/laX1+fmTNnJknWrl2bp59+OoMGDcoNN9yQJHn22WfzwgsvpFevXkW3AgAAAADQZJp0zdftt98+3bp1S69evfLMM89Utv/1r39Njx491tl/5MiRqaurS11dXWpqapqyVWhxzAs0nnmBxjMv0HjmBRrPvABbilKSclOcqEOHDrn99tszZsyYfOYzn8lPf/rTzJs3L0nyox/9KD/4wQ+yaNGitz2+rq5uo0sT1A2wdAEtx8CH6gp77MbMC/Am8wKNZ16g8cwLNJ55ATZnTXLla7t27fLzn/88l19+eR599NE8//zz6dmzZ+X+973vfXn22WebohUAAAAAgCZRePjaunXr/PSnP821116be+65J0kyffr0nHzyyUmSXr16pW3btlm2bFnRrQAAAAAANJk2RZ/g9NNPz5AhQ9KtW7eMGTMmSXLyySfnt7/9bWXZgTPPPLPoNgAAAAAAmlTh4eu1116ba6+9dp3t48aNy7hx44o+PQAAAABAVTTJmq8AAAAAAFsa4SsAAAAAQAEKX3YAaJ7qBgysdgvQaAMfqqt2CwAAVNHhX/t5tVuATTLjkpOq3QLNhCtfAQAAAAAKIHwFAAAAACiAZQcAYCMs00FLY6kOAABoHlz5CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFKBNtRsAAGDzcfjXfl7tFmCTzLjkpGq3AABsxlz5CgAAAABQAOErAAAAAEABhK8AAAAAAAVosvB1m222Se/evZvqdAAAAAAAVVV4+Lrddtvl1ltvzR/+8IeccMIJle3jx4/PggULMmfOnPTt27foNgAAAAAAmlSbok/wxhtvZNy4cdlnn31SU1OTJBk6dGg6duyYQYMGpX///pkwYUKOPvroolsBAABoNg7/2s+r3QJskhmXnFTtFgBanMKvfH3llVfy6KOPNtg2bNiwTJ48OUmyaNGi9OnTJ6VSqehWAAAAAACaTFV+4VavXr3yzDPPVG4vW7YsXbt2rUYrAAAAAACFKHzZgfVp165d1qxZU7m9du3arF27dp39Ro4cmVGjRiVJZckCYP3MCzSeeYHGMy/QeOYFGs+8AFuKqlz5+vzzz6dnz56V2507d87KlSvX2e9HP/pRBg4cmIEDB2bFihVN2SK0OOYFGs+8QOOZF2g88wKNZ16ALUVVwtfp06dn+PDhSZL+/ftnyZIl1WgDAAAAAKAwhS870Llz59x6663p0aNH2rZtm2OOOSann356DjrooMydOzf19fUZMWJE0W0AAAAAADSpwsPXF198MQcddNA620ePHl30qQEAAAAAqqYqyw4AAAAAAGzuhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQgKqGr5/85Cfz4IMPZv78+Rk2bFg1WwEAAAAAeFe1qdaJt91223zhC1/IAQcckPbt22fevHm58847U19fX62WAAAAAADeNVW78vXwww/PtGnTUl9fn5dffjlz587NvvvuW612AAAAAADeVaUk5Wqc+Oyzz84LL7yQG264IUly4YUX5vHHH8/NN99c2WfkyJEZNWpUkmSXXXbJkiVLqtHqFq+mpiYrVqyodhtbpBUrVuTII49s1L7mpXkwL9VjXloe81I95qXlMS/VY15aHvNSPeal5TEv1bMp80LLVrXw9bzzzsuzzz6bG2+8MUlywQUX5PHHH8+tt95ajXbYgLq6ugwcOLDabUCLYF6g8cwLNJ55gcYzL9B45gWKV7VlB55//vn07Nmzcvt973tfnn322Wq1AwAAAADwrqpa+Dpz5sz893//d9q0aZOOHTtmn332SV1dXbXaAQAAAAB4V7VOMq4aJ37llVfSqlWrTJo0KSNGjMiFF16YP/7xj9VohUZYtGhRtVuAFsO8QOOZF2g88wKNZ16g8cwLFKtqa74CAAAAAGzOqrbsAAAAAADA5kz4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFED4CgAAAABQAOErAAAAAEABhK8AAAAAAAUQvgIAAAAAFKDFhK933XVXtVuAFsO8QOOZF2g88wKNZ16g8cwLsDlrMeFrTU1NtVuAFsO8QOOZF2g88wKNZ16g8cwLsDlrMeErAAAAAEBLInwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoABtqt3Au6luwMBqtwCNNvChumq3AAAAAECBXPkKAAAAAFAA4SsAAAAAQAGErwAAAAAABRC+AgAAAAAUQPgKAAAAAFAA4SsAAAAAQAGErwAAAAAABWiS8LVr166ZOnVq5s+fnxkzZiRJxo8fnwULFmTOnDnp27dvU7QBAAAAANBk2jTFSa6++ur84Ac/qASvQ4cOTceOHTNo0KD0798/EyZMyNFHH90UrQAAAAAANInCr3zt0aNHtt1220rwmiTDhg3L5MmTkySLFi1Knz59UiqVim4FAAAAAKDJFH7l65577pnnnnsuN998c7p3754bbrghvXr1yjPPPFPZZ9myZenatWtWrFjR4NiRI0dm1KhRSZKampqiW4UWzbxA45kXaDzzAo1nXqDxzAuwpSglKRd5gpNOOilf/OIXM3To0NTX12fmzJlZvXp1hg8fnqVLlyZJZs6cmRNPPDErV65828epq6vLwIEDN3iuugEbvh+ak4EP1RX22I2ZF+BN5gUaz7xA45kXaDzzAmzOCl92YPny5bn//vvz0ksv5R//+EdmzJiRHXfcMT179qzs07lz5w0GrwAAAAAALU3h4ev8+fOz7777pn379imVStl///1zzTXXZPjw4UmS/v37Z8mSJUW3AQAAAADQpApf8/XVV1/NFVdckVmzZmXt2rWZMmVKrr322lx11VWZO3du6uvrM2LEiKLbAAAAAABoUoWHr0kyderUTJ06tcG20aNHN8WpAQAAAACqovBlBwAAAAAAtkTCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAAChAk4Svf/jDH1JbW5va2tqMHz8+STJ+/PgsWLAgc+bMSd++fZuiDQAAAACAJtOmKU7y2muv5aCDDqrcHjp0aDp27JhBgwalf//+mTBhQo4++uimaAUAAAAAoElUZdmBYcOGZfLkyUmSRYsWpU+fPimVStVoBQAAAACgEE0Svq5cuTIPPPBApk2blt133z29evXKM888U7l/2bJl6dq16zrHjRw5MnV1damrq0tNTU1TtAotlnmBxjMv0HjmBRrPvEDjmRdgS1FKUm6qkw0cODA/+tGPsnTp0pxxxhlZunRpkmTmzJk58cQTs3Llyrc9tq6uLgMHDtzg49cN2PD90JwMfKiusMduzLwAbzIv0HjmBRrPvEDjmRdgc9akyw7U1dWlvr4+zz//fHr27FnZ3rlz5w0GrwAAAAAALU3h4Wu7du3SoUOHJMn73//+lEqlTJ8+PcOHD0+S9O/fP0uWLCm6DQAAAACAJtWm6BN07NgxM2bMyMsvv5zVq1fn9NNPz2OPPZaDDjooc+fOTX19fUaMGFF0GwAAAAAATarw8HXFihUZMGDAOttHjx5d9KkBAAAAAKqmSdd8BQAAAADYUghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAACAAghfAQAAAAAKIHwFAAAAACiA8BUAAAAAoADCVwAAAABoJkqlUpKkpqYmvXv3TpJ069YtnTp1qmZbvENtqt0AAAAAAPCm8847Lz/84Q+TJKNHj06rVq0yf/78HHXUUXn22WezcOHCTJ8+vcpd0liufAUAAACAZuKaa67JJZdckhdeeCHz58/P73//+wwePDi//e1v88ADD6S+vr7aLbIJhK8AAAAA0EysWrUql112WSZOnJjOnTtn/vz5eeKJJ1Iul3PFFVdUuz02kWUHAAAAAKCZOP300zNlypR87nOfy9FHH51Bgwbl0UcfzapVq7Js2bIsXbq02i2yCYSvAAAAANBM3Hnnnbnooouy44475rnnnkuXLl1y5plnZsmSJTnrrLNyxx135OCDD652mzSSZQcAAAAAoJl47rnnctttt6Vfv3656qqrMm3atPz973/P7bffnjPOOCOdOnWqdotsAuErAAAAADQju+yyS5YvX165XSqVUiqVMmfOnLRp44PsLYl/LQAAAABoJo477ri88MILeeGFF3LOOeekc+fO6dSpU44//viMGjUqK1asqHaLbALhKwAAAAA0E4sXL85TTz2Vu+66a733X3rppU3cEf8Oyw4AAAAAQDPx1FNPJUkGDx683vtnzZrVlO3wbxK+AgAAAEAz8/Wvfz1JsvPOOydJdtpppxx11FGZPn16NdtiEzVJ+Nq+ffv87ne/y7nnnpskGT9+fBYsWJA5c+akb9++TdECAAAAALQY5XI5SfLGG29kyJAh+ehHP5rp06enbdu2Ve6MTdEk4euFF16Yurq6JMnQoUPTsWPHDBo0KGeffXYmTJjQFC0AAAAAQItRKpWSJGPGjEldXV06deqUtWvXVrkrNlXh4Wu/fv3So0eP1NbW/v/s3X+UlXW9L/D3jDCAKYgMSjhwMkPNc9TARpKrEYL5Wzlq6QlcnBuigScz5WhKKpp2zYVXjmR6qE4R5vG2EH8lYiNwNeGIG8kfHRUt6yIpIj8cxZQBZu4frWYdDipb9JnNwOu11rNm72c/ez/vWfD5572++ztJkuHDh2fatGlJksWLF6dv376t/5kAAAAAYEdXX1+frl275tBDD82QIUPyzW9+M/vvv3+uuuqqdO/ePaeddlqlI1KmQsvXqqqqXHvttbn44otbz9XV1WXp0qWtz1esWJEePXq86/vHjBmTUqmUUqmU2traIqNCu2deoHzmBcpnXqB85gXKZ17g/fXq1Ss1NTXZc889s2zZsixatChr1qzJjTfemA4dOmTOnDmVjkiZqpK0FPXh3/jGN7JmzZr87Gc/y6hRo1JbW5uhQ4fmrLPOyssvv5wkaWhoyOmnn57Vq1e/72eVSqXU19e//zWHvP/rsC2pf7xU2GeXMy/AX5gXKJ95gfKZFyifeYF3N2fOnAwdOjT/8i//kvnz5+c///M/8//+3//L3XffnaFDh1Y6HmXqUOSHn3766WlsbMw//MM/ZK+99krHjh3TqVOn9O7du7V87d69+xaLVwAAAADYkfx1m87vfve7+eIXv5hTTjkl3/nOd2zf2c4UWr4OGjSo9fFfV76+9NJLGTlyZBYtWpQBAwZkyZIlRUYAAAAAgHanpeUvX1b/6le/mptuuindunXL0KFDW8/TPhRavr6bGTNmZMiQIZk/f36ampoyatSoto4AAAAAANu0a6+9NkkyadKkrF+/Pm+88UZeeumlLFq0qMLJ+CDarHydNm1a6+OxY8e21W0BAAAAoN1paGhIkqxfv36T842NjZWIw1aqrnQAAAAAAIDtkfIVAAAAAKAAylcAAAAA2Ab1799/k5+0P8pXAAAAANgGXX/99Zv8pP1RvgIAAADANqilpWWTn7Q/Wyxfu3Xrtsnzjh07JkmOOuqoYhIBAAAAAKmqqqp0BD6kLZavM2fOzNChQ9OnT5907do148ePT5J861vfKjwcAAAAAOyorHht/zps6YKqqqrMmTMnl112Wf74xz/m3/7t31rPAwAAAADFeOihhzb5SfuzxZWvLS0t2WWXXTJz5sysW7cul156aet5AAAAAKAYV1111SY/aX/et3zt3r17evbsmc6dO2fcuHFpbGzMAQcckBNPPDE9e/bMiSee2FY5AQAAAADalfctX4cOHZoNGzbkvPPOS/fu3fPZz342dXV1efXVV9PU1JTly5e3VU4AAAAAgHblfcvXGTNmZM2aNbn88svz3HPP5YYbbsi8efPy2GOP5Y033kipVGqrnAAAAACwQ9h5550rHYGPyBb3fE2S6urqNDY2pr6+PpMmTUpiz1cAAAAAKMLq1atz+umnb3Z+2LBhOe644yqQiK21xfJ1/fr1OeaYY3LPPffkoYceSocOHZIkVVVVhYcDAAAAgB3No48+mmOOOSb/+I//uMn5uXPn5p//+Z8rE4qtssXy9brrrsusWbPyhz/8IUny/PPPJ0muuOKKJEm/fv0KjAcAAAAAO5bm5ub8z//5P3PYYYfl/PPP3+S8b6O3Lx22dMGECROycOHCTJ06Nfvvv3+effbZrFmzJr169couu+ySLl265IUXXmiLrAAAAACw3ftrwXrOOedk4sSJmT59em677bbst99+efPNNyucjg9iiytfa2trs+++++aOO+5IY2Nj7rjjjtxwww3p0aNHevTokZkzZ7ZFTgAAAADYIUyfPr318cSJE3P99dfnsMMOy0477ZSvfOUrFUzGB7XFla8bNmzI5z//+SxdunSTZc09e/bMkiVLCg0HAAAAADuan/70p5s8f+KJJ/LEE09UJgwfyhZXvjY2NuZHP/pRTj755Oy888456aSTcu655+bpp5/O3//93+fjH/94W+QEAAAAgB1OQ0NDpSPwIWxx5eu4ceNy7LHH5tprr023bt2y7777ZvXq1bnvvvvSuXPnjBgxIj/+8Y/f8/1dunTJL37xi3zsYx9LTU1Nxo4dm6effjqTJk3KEUcckXXr1mX06NH2jQUAAACA/6a6eotrJ9mGbfFfr3fv3pkwYUKOPPLIHHTQQVm4cGFqa2szZcqU9OzZ832L1yRpamrKl770pRx55JG55JJLcvHFF2fYsGHp2rVrBg4cmPPPPz+TJ0/+yH4hAAAAANhevPbaa5WOwIdQVnV+4YUXpqqqKvvvv38+97nPZc2aNTnvvPNy8cUXp1u3bu/73o0bN+add95Jkuy///75zW9+k+HDh2fatGlJksWLF6dv376pqqr6kL8KAAAAAGw/Dj/88PzDP/xDpWPwIWyxfK2pqcnhhx+egw8+OL/+9a9zwAEH5HOf+1x69OiRb3/72/na1762xZuMHz8+L7zwQkaOHJlbbrkldXV1Wbp0aevrK1asSI8ePT7cbwIAAAAA25Ebbrgh9913Xw4++OBKR2ErbXHP19mzZ2f27Nnp1q1bampqctddd6VPnz5Jktdffz3XXXfdFm8yadKkTJo0KaeeemqmT5+empqabNy4sfX15ubmNDc3b/a+MWPG5Oyzz06S1NbWlv1LwY7IvED5zAuUz7xA+cwLlM+8QHlWrFiRESNG5Hvf+15WrVqVK6+8MuvWrat0LD6AqiQtbXnDp59+OqVSKT/4wQ+yaNGiJMmiRYvy2c9+9n3fVyqVUl9f//7XHPL+r8O2pP7xUmGfXc68AH9hXqB85gXKZ16gfOYF3tusWbNy3HHHJUm+8IUv5J//+Z8zadKkzJs3r8LJKNcWV75+WHV1dXnttdeybt26fOYzn8mLL76Y2bNnZ+TIkVm0aFEGDBiQJUuWFB0DAAAAANqFOXPmpKqqKgcddFDmzp2bJGlpaUnHjh0ze/bsdOrUqcIJKVfh5Wvfvn1z9913p7GxMY2NjRk3blxeeeWVDBkyJPPnloJg9AAAIABJREFUz09TU1NGjRpVdAwAAAAAaBdOO+20tLS05Be/+EVOP/30SsfhQyi8fF2wYEEOOeSQzc6PHTu26FsDAAAAQLuzZs2aJMlJJ52Ud955p8Jp+DCqKx0AAAAAANic4rX9U74CAAAAABRA+QoAAAAA27ibb7650hHYCoXv+QoAAAAAlO/JJ59MVVVVWlpaWn9+8pOfzP/4H/8jb731Vq6++urcd999lY5JGZSvAAAAALANOfjggzc7N2fOnAwdOjQ777xz5s6dq3xtJ2w7AAAAAADbuHPPPTdJ8uc//zmTJ0+ucBrKZeUrAAAAAGxD/uu2A507d051dXXefvvtHHTQQUmS22+/vcIJKZfyFQAAAAC2If9924Fhw4Zl0KBBFUrDh2HbAQAAAADYhj344IP5/Oc/X+kYbAUrXwEAAABgG3PKKafk8MMPz8aNG3P//fdn2LBhlY7EVrDyFQAAAAC2Iddff31OO+20zJo1K4MHD84RRxyRiy66qNKx2ArKVwAAAADYhhxxxBH5yle+kgcffDBvvvlmrrzyyhx11FGVjsVWUL4CAAAAwDbk7bffbn1cVVWVTp06paampoKJ2FrKVwAAAADYhsycOTN/93d/lyTZY4898tBDD+Xqq6+ucCq2hj+4BQAAAADbkH/5l39pfTxw4MC89dZbFUzDh2HlKwAAAABsQ/bff//Mnj07TzzxRObPn59bb701e+yxR6VjsRWsfAUAAACAbciPf/zjnH/++SmVSkmSo446Kj/60Y9y0kknVTgZH5SVrwAAAACwDWlqamotXpOkoaEhXbp0qWAitpbyFQAAAAC2IfPnz8+RRx7Z+rx///5ZsmRJBROxtWw7AAAAAAAV9uSTT6aqqiotLS1JknHjxmXVqlVpampK79698/LLL1c4IVuj8PK1uro6kyZNSv/+/bPrrrvm1ltvzeTJk3PhhRfmjDPOyIYNG3LBBRfkP/7jP4qOAgAAAADbpIMPPrjSEShA4eVrhw4dcv/99+eCCy5IdXV1HnvssTz++OM56qijUl9fn7q6utx5552pr68vOgoAAAAAQJspfM/XpqamNDQ0JEmam5vz4osvZuDAgZk+fXqSZNmyZVm1alXq6uqKjgIAAAAA0Gba9A9u7bnnnunZs2fq6uqydOnS1vN/+tOf0qtXr82uHzNmTEqlUkqlUmpra9syKrQ75gXKZ16gfOYFymdeoHzmBdhRVCVpaYsbdenSJXfffXfGjx+fr33ta7n11luzYMGCJMkPf/jD3HzzzVm8ePF7vr9UKm1xa4LSIbYuoP2of7xU2GeXMy/AX5gXKJ95gfKZFyifeQG2Z22y8rWmpia33357rrvuujz11FNZvnx5evfu3fr6XnvtlWXLlrVFFAAAAACANlH4H9zaaaedcuutt2bq1Kl58MEHkySzZ8/OJZdckhkzZqSuri4dO3bMihUrio4CAFvFNytob4r8dgUAAFC+wsvX0aNHZ/DgwenZs2fGjx+fJBkxYkSefPLJ1m0Hxo0bV3QMAAAAAIA2VXj5OnXq1EydOnWz8xMnTszEiROLvj0AAAAAQEUUXr4C2yZfo6Y98RVqAAAA2qM2+YNbAAAAAAA7GuUrAAAAAEABlK8AAAAAAAVQvgIAAAAAFED5CgAAAABQAOUrAAAAAEABlK8AAAAAAAVQvgIAAAAAFED5CgAAAABQAOUrAAAAAEABlK8AAAAAAAVQvgIAAAAAFED5CgAAAABQAOUrAAAAAEABOlQ6AAAA24+jv317pSPAB/LA1WdUOgIAsB2z8hUAAAAAoADKVwAAAACAAihfAQAAAAAK0Gbl6y677JI+ffq01e0AAAAAACqq8PJ1t912y8yZM/O73/0uX/7yl1vPT5o0KQsXLszDDz+cfv36FR0DAAAAAKBNdSj6Bhs2bMjEiRPTv3//1NbWJkmGDRuWrl27ZuDAgRkwYEAmT56c448/vugoAAAAAABtpvCVr2vXrs1TTz21ybnhw4dn2rRpSZLFixenb9++qaqqKjoKAAAAAECbKXzl67upq6vL0qVLW5+vWLEiPXr0yMqVKze5bsyYMTn77LOTpHXVLPDuzAuUz7xA+cwLlM+8QPnMC7CjaLM/uPVf1dTUZOPGja3Pm5ub09zcvNl1P/zhD1NfX5/6+vrNillgU+YFymdeoHzmBcpnXqB85gXYUVSkfF2+fHl69+7d+rx79+5ZvXp1JaIAAAAAABSiIuXr7NmzM3LkyCTJgAEDsmTJkkrEAAAAAAAoTOF7vnbv3j0zZ85Mr1690rFjx5xwwgkZPXp0hgwZkvnz56epqSmjRo0qOgYAAAAAQJsqvHxds2ZNhgwZstn5sWPHFn1rAAAAAICKqci2AwAAAAAA2zvlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAF6FDpAAAAADuio799e6UjwAfywNVnVDoCQLtj5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAE6VDoAAAAAwPs5+tu3VzoCfCAPXH1GpSOwjbDyFQAAAACgAMpXAAAAAIACVLR8/cpXvpJFixbl0UcfzfDhwysZBQAAAADgI1WxPV933XXXfOMb38igQYPSqVOnLFiwILNmzUpTU1OlIgEAAAAAfGQqtvL16KOPzj333JOmpqa8+eabmT9/fg499NBKxQEAAAAA+EhVJWmpxI3PP//8rFq1KtOnT0+SXHbZZXn22WczY8aM1mvGjBmTs88+O0my3377ZcmSJZWIusOrra3NypUrKx1jh7Ry5coce+yxZV1rXrYN5qVyzEv7Y14qx7y0P+alcsxL+2NeKse8tD/mpXI+yLzQvlWsfL3ooouybNmy3HbbbUmSCRMm5Nlnn83MmTMrEYf3USqVUl9fX+kY0C6YFyifeYHymRcon3mB8pkXKF7Fth1Yvnx5evfu3fp8r732yrJlyyoVBwAAAADgI1Wx8rWhoSGnnXZaOnTokK5du6Z///4plUqVigMAAAAA8JHaKcnEStx47dq1qa6uzpQpUzJq1Khcdtll+f3vf1+JKJRh8eLFlY4A7YZ5gfKZFyifeYHymRcon3mBYlVsz1cAAAAAgO1ZxbYdAAAAAADYnilfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADtpny9//77Kx0B2g3zAuUzL1A+8wLlMy9QPvMCbM/aTflaW1tb6QjQbpgXKJ95gfKZFyifeYHymRdge9ZuylcAAAAAgPZE+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFaJPytUePHrnrrrvy6KOP5oEHHkiSTJo0KQsXLszDDz+cfv36tUUMAAAAAIA206EtbvKDH/wgN998c2vxOmzYsHTt2jUDBw7MgAEDMnny5Bx//PFtEQUAAAAAoE0UvvK1V69e2XXXXVuL1yQZPnx4pk2bliRZvHhx+vbtm6qqqqKjAAAAAAC0mcJXvv7d3/1dXnnllcyYMSN77LFHpk+fnrq6uixdurT1mhUrVqRHjx5ZuXLlJu8dM2ZMzj777CRJbW1t0VGhXTMvUD7zAuUzL1A+8wLlMy/AjqSlyOOMM85oeeyxx1q6du3a0rlz55Zf//rXLXPnzm3p3bt36zUNDQ0tu++++/t+TqlUKjSnw7E9HebF4Sj/MC8OR/mHeXE4yj/Mi8NR/mFeHA7H9nwUvu3Aa6+9ll//+td544038s477+SBBx7IJz7xifTu3bv1mu7du2f16tVFRwEAAAAAaDOFl6+PPvpoDj300HTq1ClVVVU57LDDcsstt2TkyJFJkgEDBmTJkiVFxwAAAAAAaFOF7/n61ltv5frrr8/cuXPT3Nycn//855k6dWpuuummzJ8/P01NTRk1alTRMQAAAAAA2lTh5WuS3HXXXbnrrrs2OTd27Ni2uDUAAAAAQEUUvu0AAAAAAMCOSPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABWiT8vV3v/td5s2bl3nz5mXSpElJkkmTJmXhwoV5+OGH069fv7aIAQAAAADQZjq0xU3efvvtDBkypPX5sGHD0rVr1wwcODADBgzI5MmTc/zxx7dFFAAAAACANlGRbQeGDx+eadOmJUkWL16cvn37pqqqqhJRAAAAAAAK0Sbl6+rVq/PII4/knnvuyQEHHJC6urosXbq09fUVK1akR48em71vzJgxKZVKKZVKqa2tbYuo0G6ZFyifeYHymRcon3mB8pkXYEfS0lZHfX19yxNPPNEya9aslt69e7eeb2hoaNl9993f972lUqnNcjoc7f0wLw5H+Yd5cTjKP8yLw1H+YV4cjvIP8+JwOLbno023HSiVSmlqasry5cvTu3fv1vPdu3fP6tWr2zIKAAAAAEChCi9fa2pq0qVLlyTJJz/5yVRVVWX27NkZOXJkkmTAgAFZsmRJ0TEAAAAAANpUh6Jv0LVr1zzwwAN58803s379+owePTq//e1vM2TIkMyfPz9NTU0ZNWpU0TEAAAAAANpU4eXrypUrc8ghh2x2fuzYsUXfGgAAAACgYtp0z1cAAAAAgB2F8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAgG1EVVVVkqS2tjZ9+vRJkvTs2TPdunWrZCy2UodKBwAAAAAA/uKiiy7Kv/7rvyZJxo4dm+rq6jz66KM57rjjsmzZsjz22GOZPXt2hVNSLitfAQAAAGAbccstt+Tqq6/OqlWr8uijj+aZZ57JEUcckSeffDKPPPJImpqaKh2RD0D5CgAAAADbiMbGxnzve9/LjTfemO7du+fRRx/Nc889l5aWllx//fWVjscHZNsBAAAAANhGjB49Oj//+c/z9a9/Pccff3wGDhyYp556Ko2NjVmxYkVefvnlSkfkA1C+AgAAAMA2YtasWbn88svziU98Iq+88kp23333jBs3LkuWLMm5556be++9N0ceeWSlY1Im2w4AAAAAwDbilVdeyZ133pkDDzwwN910U+655578+c9/zt13352zzjor3bp1q3REPoA2KV87deqU//zP/8yFF16YJJk0aVIWLlyYhx9+OP369WuLCAAAAADQLuy333557bXXWp9XVVWlqqoqDz/8cDp08EX29qRNytfLLrsspVIpSTJs2LB07do1AwcOzPnnn5/Jkye3RQQAAAAA2OadcsopWbVqVVatWpVvfvObOfXUU9OtW7eceuqpef7557Ny5cpKR+QDKLwqP/DAA9OrV6/MmzcvtbW1GT58eKZNm5YkWbx4cfr27Zuqqqq0tLQUHQUAAAAAtmlPP/10Xnjhhdx///3v+vo111zTxon4MApd+VpVVZVrr702F198ceu5urq6LF26tPX5ihUr0qNHjyJjAAAAAEC78MILLyRJjjjiiHd9fe7cuW0Zhw+p0PL1vPPOy//5P/8nq1ataj1XU1OTjRs3tj5vbm5Oc3Pzu75/zJgxKZVKKZVKqa2tLTIqtHvmBcpnXqB85gXKZ16gfOYFtuzKK69Mkuy7775Jkr333jvHHXdcZs+eXclYfEBVSQr7vv+CBQvS2NiYJNlrr73SsWPHdOrUKV/+8pezaNGiJMmiRYvy2c9+doufVSqVUl9fX1RU2K6YFyifeYHymRcon3mB8pkXeHdz5szJ0KFD88lPfjJ9+vTJZz7zmUyZMiUNDQ0ZOnRopeNRpkL3fB00aFDr41GjRqW2tjYvvfRSRo4cmUWLFmXAgAFZsmRJkREAAAAAoN2pqqpKkowfPz7jx4/P4MGD3/Pb42y7Ct124N3MmDEjnTp1yvz583P99dfnkksuaesIAAAAALDNqq+vT9euXXPooYdmyJAh+eY3v5n9998/V111Vbp3757TTjut0hEpU6ErX/+radOmtT4eO3ZsW90WAAAAANqVXr16paamJnvuuWeWLVuWRYsWpXfv3rnxxhszfPjwzJkzp9IRKVObr3wFAAAAAN7bvffem9deey333ntvnnnmmXTr1i0/+MEP8s477+S1117LmjVrKh2RMilfAQAAAGAb89c9X7/73e+mU6dOOeWUU7J27drW87QPbbbtAAAAAABQnpaWliTJV7/61dx0003p1q1bhg4d2nqe9kH5CgAAAADbmGuvvTZJMmnSpKxfvz5vvPFGXnrppSxatKjCyfggbDsAAAAAANuYhoaGJMn69es3Od/Y2FiJOGylLZav3bp12+R5x44dkyRHHXVUMYkAAAAAALYDWyxfZ86cmaFDh6ZPnz7p2rVrxo8fnyT51re+VXg4AAAAAID2aovla1VVVebMmZN//Md/zMknn5x/+7d/az0PAAAAABSjf//+m/yk/dli+drS0pJddtklM2fOzLp163LppZe2ngcAAAAAinH99ddv8pP2533L1+7du6dnz57p3Llzxo0bl8bGxhxwwAE58cQT07Nnz5x44oltlRMAAAAAdih/XfxoEWT79b7l69ChQ7Nhw4acd9556d69ez772c+mrq4ur776apqamrJ8+fK2ygkAAAAAOxTbfrZ/71u+zpgxI2vWrMnll1+e5557LjfccEPmzZuXxx57LG+88UZKpVJb5QQAAACAHYoVr+3fFvd8TZLq6uo0Njamvr4+kyZNSuIfHwAAAACK9NBDD23yk/Zni+Xr+vXrc8wxx+See+7JQw89lA4dOiSx7BkAAAAAinTVVVdt8pP2p8OWLrjuuusyd+7c1ufPP/98kuSKK65IkvTr1y8vvPBCQfEAAAAAANqnLZavEyZMyMKFCzN16tTsv//+efbZZ7NmzZr06tUru+yyS7p06aJ8BQAAAAD4b7a47UBtbW323Xff3HHHHWlsbMwdd9yRG264IT169EiPHj0yc+bMtsgJAAAAANCubHHl64YNG/L5z38+S5cu3eSPbPXs2TNLliwpNBwAAAAA7GhuuummTJgwIa+//nqS5Lzzzstuu+3W+ro9YNuPLa58bWxszI9+9KOcfPLJ2XnnnXPSSSfl3HPPzdNPP52///u/z8c//vG2yAkAAAAAO4SDDz64tXhNkhEjRuS3v/1tnn766XzpS1+qYDI+qC2ufB03blyOPfbYXHvttenWrVv23XffrF69Ovfdd186d+6cESNG5Mc//nFbZAUAAACA7V5LS0t22mmnbNy4Mbvsskveeeed1q0/v/71r1c4HR/EFle+9u7dOxMmTMiRRx6Zgw46KAsXLkxtbW2mTJmSnj17brF47dKlS+69997MnTs3jzzySA488MAkyaRJk7Jw4cI8/PDD6dev30fz2wAAAABAO/fv//7vmTlzZsaOHZu7774706dPb33tv24LyrZviytfk+TCCy/Mpz/96ey///5Zt25d1qxZk/POOy9TpkzJhAkT0tjY+J7vbWpqype+9KW88847OeKII3LxxRfnpz/9abp27ZqBAwdmwIABmTx5co4//viP7JcCAAAAgPbqBz/4QUqlUj7zmc/k0ksvzcKFC1tfs+1A+7LF8rWmpiaHHnpo6urqMmvWrHzuc59Lc3NzFixYkG9/+9v52te+lu9973vv+f6NGzdm48aNSZL9998/v/nNbzJ8+PBMmzYtSbJ48eL07ds3VVVVmnsAAAAASFIqlVIqlTY7v3r16gqkYWttsXydPXt2Zs+enW7duqWmpiZ33XVX+vTpkyR5/fXXc911123xJuPHj88555yTl19+Occdd1x+/vOfZ+nSpa2vr1ixIj169MjKlSs3ed+YMWNy9tlnJ0lqa2s/0C8GOxrzAuUzL1A+8wLlMy9QPvMC7CiqkrTZctNTTz01I0aMSOfOnXPWWWfl5ZdfTpI0NDTk9NNPf9/mvlQqpb6+vq2iQrtmXqB85gXKZ16gfOYFymdeoDydOnXKunXrKh2DD2iLf3Dro3THHXekX79+Wb58eXr37t16vnv37pZMAwAAAECSvn37bnbMmzcvf/M3f5OPfexjlY7HB1DWH9z6MOrq6vLaa69l3bp1+cxnPpMXX3wxs2fPzsiRI7No0aIMGDAgS5YsKToGAAAAALQLU6ZM2ezvI33qU5/KjTfemD322CO/+tWvcsUVV1QwIeUqvHzt27dv7r777jQ2NqaxsTHjxo3LK6+8kiFDhmT+/PlpamrKqFGjio4BAAAAAO3CySefvNm5+++/v/X8r3/9a+VrO1F4+bpgwYIccsghm50fO3Zs0bcGAAAAgHanb9++m50755xzWh+PHj26LePwIRRevgIAAAAA5fuv2w587GMfy8EHH5zHH388xxxzTJLk+eefr3BCyqV8BQAAAIBtyH/fdmD33XfPlClTKpSGD6O60gEAAAAAgPe2evXqLF++vNIx2ApWvgIAAADANmT33XfPFVdckUGDBmXjxo351a9+lQkTJlQ6FlvBylcAAAAA2Ib85Cc/ybPPPptBgwblrbfeyoMPPpjvf//7lY7FVlC+AgAAAMA2ZPfdd88tt9yS9evXp6qqKg8//HD69OlT6VhsBeUrAAAAAGxDmpubs9NOO7U+/9u//du0tLRUMBFbS/kKAAAAANuQyy+/PPvss0+SZO3atfnWt76Vc845p8Kp2Br+4BYAAAAAbEMeeuih7Lzzzunfv38uvvjiPP/889m4cWOlY7EVlK8AAAAAsA055ZRTcuWVV6ZUKqVjx47527/924wdOzYLFy6sdDQ+IOUrAAAAAGxDvv3tb2fQoEF58803kyR77LFH7rjjjhxxxBEVTsYHZc9XAAAAANiGrFy5srV4TZIVK1Zk7dq1FUzE1rLyFQAAAAAqrG/fvq2PH3zwwVxxxRW5/fbbs379+px00kl56qmnKpiOraV8BQAAAIAKmzJlSqqqqtLS0tJ6bsCAAa2Pq6qqKhGLD0n5CgAAAAAVdvLJJ1c6AgWw5ysAAAAAQAGUrwAAAAAABdiuth0oHVJf6QhQtvrHS5WOAAAAAECBrHwFAAAAACiA8hUAAAAAoACFl6/V1dX53//7f2fevHlZtGhRzj///CTJhRdemFKplP/4j//IYYcdVnQMAAAAAIA2Vfierx06dMj999+fCy64INXV1Xnsscfy+OOP56ijjkp9fX3q6upy5513pr7efq0AAAAAwPaj8JWvTU1NaWhoSJI0NzfnxRdfzMCBAzN9+vQkybJly7Jq1arU1dUVHQUAAAAAoM206Z6ve+65Z3r27Jm6urosXbq09fyf/vSn9OrVa7Prx4wZk1KplFKplNra2raMCu2OeYHymRcon3mB8pkXKJ95AXYUVUla2uJGXbp0yd13353x48fna1/7Wm699dYsWLAgSfLDH/4wN998cxYvXvye7y+VSlvcmqB0iK0LaD/qHy8V9tnlzAvwF+YFymdeoHzmBcpnXoDtWZusfK2pqcntt9+e6667Lk899VSWL1+e3r17t76+1157ZdmyZW0RBQAAAACgTRRevu6000659dZbM3Xq1Dz44INJktmzZ2fEiBFJkrq6unTs2DErVqwoOgoAAAAAQJvpUPQNRo8encGDB6dnz54ZP358kmTEiBF58sknW7cdGDduXNExAAAAAADaVOHl69SpUzN16tTNzk+cODETJ04s+vYAAAAAABXRJnu+AgAAAADsaJSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABSgzcrXXXbZJX369Gmr2wEAAAAAVFTh5etuu+2WmTNn5ne/+12+/OUvt56fNGlSFi5cmIcffjj9+vUrOgYAAAAAQJvqUPQNNmzYkIkTJ6Z///6pra1NkgwbNixdu3bNwIEDM2DAgEyePDnHH3980VEAAAAAANpM4Stf165dm6eeemqTc8OHD8+0adOSJIsXL07fvn1TVVVVdBQAAAAAgDZT+MrXd1NXV5elS5e2Pl+xYkV69OiRlStXbnLdmDFjcvbZZydJ66pZ4N2ZFyifeYHymRcon3mB8pkXYEdRlaSlLW40atSo1NbW5vrrr8+sWbNy1lln5eWXX06SNDQ05PTTT8/q1avf8/2lUin19fXve4/SIe//OmxL6h8vFfbZ5cwL8BfmBcpnXqB85gXKZ16A7Vnh2w68m+XLl6d3796tz7t37/6+xSsAAAAAQHtTkW0HZs+enZEjR2bRokUZMGBAlixZUokYAFAW36ygvSny2xUAAFBJffr0yRFHHJHbbrut0lHKUnj52r1798ycOTO9evVKx44dc8IJJ2T06NEZMmRI5s+fn6ampowaNaroGAAAAABAO/fJT34yhx12mPL1r9bLlB6NAAAgAElEQVSsWZMhQ4Zsdn7s2LFF3xoAAAAA2AadcMIJueiii1JdXZ2ZM2dm7733zte//vUkyU9+8pN8//vfz29/+9v89Kc/TZ8+fdLY2JjzzjsvkydPTs+ePbPPPvvkuOOOy5FHHpnLL788SfLaa69lzJgxef311zNv3rz86le/yjHHHJMNGzbkmmuuySWXXJKPf/zjOffcc/PQQw+lS5cu+f73v5++ffumpaUl55xzTv7whz/k6aefzowZMzJ48OAceeSRH+r3rMierwAAAADAjukTn/hELrnkkhx33HE5/PDD88wzz7zrdccdd1yWLl2aww8/PMOHD8/vf//7nH/++bnzzjtz3HHHpXv37vne976X4cOH5wtf+ELuv//+1iI2SV555ZUMHjw4zzzzTCZMmJCjjz46I0aMyEUXXZQkufTSSzN9+vQcddRRueCCC/Kd73wnSdKlS5c8+uijH7p4TSq05ysAAAAAsGM6+uij87Of/Sxr165Nkrz99tvvet2TTz6Z73znO3nuuefys5/9bLPXDzvssDzwwAN5/fXXkyTTp0/PggULWl9/4IEHkiS/+c1v8sorr6S5uTm//e1vs9deeyVJvvjFL+bwww/PFVdckSR56623kiQtLS1paGj4SH5X5SsAAAAA0Ga6dOmSN998s/X5hg0bstNOO7U+79ixY5LkxRdfzODBg3PBBRdk3rx5GTx48Caf06FDh7S0tGxyrrm5ufVxU1NTkmTjxo1Zt25d6+O/3qtDhw455phjWl/7q3feeWeTz/kwbDsAAAAAALSZefPmZeTIkampqUmSvPTSSznggAOS/KUQHThwYJKke/fuWbVqVSZMmJDq6up069Yt77zzTnbdddckyaOPPpoTTjgh3bp1S5KceeaZratdy7FgwYJ89atfbb3vfvvt95H9jn9l5SsAAAAA0GaefPLJ/Pu//3seeeSR/PnPf87Pf/7zLFy4MDNmzMjLL7+cF154IUkycODAfPe7382aNWvS0NCQ119/PU888UT23Xff/PKXv8wJJ5yQyy67LLNnz86f//znvPjiiznvvPPKzjFx4sT85Cc/yYgRI9LS0pLLL788S5Ys+Uh/V+UrAAAAANCmpk6dmqlTp27xutmzZ2/yfN26dRk0aFDr81/+8pf55S9/udn7hgwZ0vp42rRpm7x24IEHJklWrVqVk046abP3/vX1j4JtBwAAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACtCh0gGAyigdUl/pCFC2+sdLlY4AAAAAH5iVrwAAAAAABVC+AgAAAADbheHDh6djx46bnNt3333zrW99K126dEmS7LzzzunXr1/q68v7VvBBBx2Uzp07b1Ue2w4AAAAAAO3annvumdtvvz177LFHLrvssrzxxhu56KKLctVVV+WRRx7Jz372s7z99ttpaGjIhg0bsnDhwtx22225+OKLc8wxx2TvvffOH/7wh1x11VWZN29errnmmjQ1NSVJ9tlnn1RXV+eFF15IktTU1GTChAll5VK+AgAAAAAfmY/678yU83dAXn311dxyyy159tln8/rrr+eUU05JTU1NJk2alDlz5rRe19LSks6dO2fw4MEZNmxYjj766Dz11FM56aST8tOf/jQLFy5MklRXV+fKK6/MOeeck5tvvjkrVqxo/Ywrrrii7OzKVwAAAACgXdtll13y9ttvZ9y4cdm4cWMWLFiQLl26pK6uLvvtt1+S5E9/+lP++Mc/Zv78+UmSY489Np/61Kfy6U9/OrfeemtGjBjRWr7+8Y9/zJlnnpmLLrooZ5xxRpKkU6dOWblyZe69996ycylfAQAAAIB2r7GxMc8//3yampqybNmy1NTUpG/fvtl7771TXV2dqVOn5ne/+12eeeaZbNiwIQ888ED23HPPjBo1KieeeGI6derU+llr165NS0tLrr766rS0tGT9+vXp06dPfvjDH2bVqlVlZ1K+AgAAAADt2sEHH5yJEyeme/fuaW5uTmNjY2bNmpV77703Xbt2TYcOHfLSSy+lqqoqEyZMSKlUSlVVVRYsWJD77rsvTz75ZL74xS8mSXbbbbd07NgxH/vYx7LTTju1FrGrVq3K8OHD884772Tu3Ll55ZVXtphL+QoAAAAAtGvz58/PmWeemYMPPjjr1q3Lc889l89//vNZsWJFunbt2npdt27dsueee+a0005Lc3Nz7r333jz00EN54IEH8qlPfSrJX1bQ3nHHHbnnnnve9V4nnnhiNmzYUFauipavX/nKV3LBBRdkw4YNufbaa3PXXXdVMg4AAAAA0E5985vfTENDQ9avX59/+qd/yhtvvJHbbruttVStrq5uXRFbVVWVpqamvPrqq/mnf/qn7Lnnnmlqakrylz/K9eabb+aRRx7JZZddtsk9vvOd72Tt2rVlZ6pY+brrrrvmG9/4RgYNGpROnTplwYIFmTVrVusvCQAAAABQjk9/+tP5/e9/n/Xr12fDhg350Y9+lNGjR29yzYEHHpjHH388Rx99dIYNG5bm5uYsXLgwM2bMyK9+9atceuml6dOnT1566aUkyZlnnpnDDz98k8/Ye++9Nytk30/Fytejjz4699xzT5qamtLU1JT58+fn0EMPzSOPPFKpSAAAAADAh1T/eKnN7/nss8/mueeey5FHHpkk6d27d+bOnbvJNU8++WSS5MEHH2w9V11dnebm5iTJNddcs8n1n/jEJz50roqVr3V1dVm6dGnr8z/96U/p1avXJteMGTMmZ599dpJkv/32S6nU9v9wJLW1tVm5cmWlY2x3yvnfvHLlyhx77LFlfZ552TaYl2KYl+2TeSnOlv5Hm5f2x7xUjnlpf8xL5ZiX9se8VM4HmRfK19LSkjlz5mx2/qGHHnrP9/y1eC1KVZKWQu/wHi666KIsW7Yst912W5JkwoQJefbZZzNz5sxKxOF9lEql1NfXVzoGtAvmBcpnXqB85gXKZ16gfOYFilddqRsvX748vXv3bn2+1157ZdmyZZWKAwAAAADwkapY+drQ0JDTTjstHTp0SNeuXdO/f39fMwAAAAAAths7JZlYiRuvXbs21dXVmTJlSkaNGpXLLrssv//97ysRhTIsXry40hGg3TAvUD7zAuUzL1A+8wLlMy9QrIrt+QoAAAAAsD2r2LYDAAAAAADbM+UrAAAAAMB7OOigg9K5c+etem+HjzgLAAAAAECbmzx5cq6++uqsXLkySXLqqadm1apV+b//9/9uct1Xv/rVTJ8+PQceeGA+9alP5Re/+MVmn3XNNdekqakpSbLPPvukuro6L7zwQpKkpqYmEyZMKCuTPV8BAAAAgI/M0d++/SP9vAeuPqOs63bbbbd8+ctfzoknnpiuXbtmn332aS1Mk+T888/Pq6++mvnz52fp0qXZdddd06lTp9aydsaMGbnpppuSJP/rf/2vXHLJJTnnnHNy5513ZsWKFa2fc8UVV+TKK68sK5OVrwAAAABAu1ZbW5svfOELmTp1ambPnp2zzjorN954Y5qamnLCCSfkF7/4RV544YX867/+aw466KC89dZbGTx4cGpqatLQ0LDZ5/3xj3/MmWeemYsuuihnnPGX8vevRe29995bdi7lKwAAAADQrq1cuTLPPfdcPve5z2XvvffOkiVL0tzcnMWLF+c3v/lN9txzz9TW1ubrX/963nrrrSTJ/2fv7qO0ruv88T+Hu8G0IWBQGgfUFHMt70ikKPWgtJl+TdJ21RXFs4qIdmPJV21JUTO/6gHjKyu6urnyba32rCDCrqAoJIILDNGqlaJmSQY63DWIhsPN9ftjT/Nb1hsuzM91MfB4nPM6zHyu65rPc069/nmej+/p0aNHfvvb377jz9uwYUNKpVJuuOGGlEqlbNq0KX369Mndd9+dNWvWlJ1L+QoAAAAAtHu//OUvM2DAgPz+979PbW1tTjjhhDzwwANtxwQcccQRmTx5ctv7DzrooCTJiy++2HZt5MiRaW5uTufOnbPnnnumY8eObUXsmjVrMnTo0GzcuDFz5szJypUrt5tJ+QoAAAAAtHuf//zn07Vr19TW1ub5559/2+tPPfVUBg8enCSpqanJXXfdlbfeeiuXXXZZNm/e3Pa+mpqaTJkyJdOnT3/H+5x66qnbvP+9dHgfvwcAAAAAwE7ltNNOy7/927/luOOOyy9/+cv3fO+NN96Y22+/PbfeemtuvPHG1NTUtL1WKpXy+uuvZ/78+Rk8ePA2M3/+/GzYsCEbN24sK5PyFQAAAABo1w499NAsWLAgZ5xxRh566KFtnkzt2rVr5s6dmy984Qv52Mc+lkmTJuXBBx/Mf/7nf+all17K5MmTc/vtt6dfv37b/Mxzzz03c+fO3WbOPffcHcpVk6T0QfyCAAAAAAA7q4MOOihf+tKXcuedd+bNN9/c5rU99tgjl156af71X/81L7/88gd2T+UrAAAAAEAB2s2xAzNnzqx2BGg37AuUz75A+ewLlM++QPnsC7Arazfla319fbUjQLthX6B89gXKZ1+gfPYFymdfgF1ZuylfAQAAAADaE+UrAAAAAEABlK8AAAAAAAVQvgIAAAAAFED5CgAAAADwLg4//PB07dr1fX220wecBQAAAACg4saOHZtbbrklgwYNSpLMnTs3V1xxRW666aa29xx99NEZP358tm7dmrq6unTu3Dlr1qxJTU1NbrzxxjzyyCNJku9973tpbW1Nkhx44IHp0KFDXnjhhSRJly5dMmbMmLIyKV8BAAAAgA/M2f/vzA/05/34vH/Z7ntuvfXW/OY3v8l5552X+vr6JMlBBx2UN954I7feemu+9a1vJUk6dOiQmTNn5je/+U0OOuigfPjDH87Pf/7z9OjRIx07dmz7eR06dMh1112XkSNH5o477khzc3Pba2PHji07u/IVAAAAAGjX3njjjaxfvz7nn3/+Ntd/9KMf5Y033kiSfPazn80NN9zQ9tqfnnwdOHBg27WtW7fm4Ycfzm9/+9uce+65ueKKK3LWWWclSWpra7N69erMmDGj7FzKVwAAAACgXZs9e3YGDx6ck046KW+99Vbb9f/zf/5PZs6cmSRZsGBBvvzlL2fYsGFZtWrVNp/v0aNHpk6dmtdeey1JsmHDhpRKpdxwww0plUrZtGlT+vTpk7vvvjtr1qwpO5fyFQAAAABo1+bNm5fOnTvn4osvTmNjYzp37pzf/OY3+elPf5p58+a1va9bt2752te+lhUrVmzz+X322Sfz5s3La6+9lo985CPp3Llz9txzz3Ts2LGtiF2zZk2GDh2ajRs3Zs6cOVm5cuV2cylfAQAAAIB27Qtf+EKuuuqqJP9VsHbs2DFHHHFEkqRjx4556KGH2t772GOP5eGHH97m88cff3zb1y0tLZkyZUqmT5/+jvc69dRTs3nz5rJyKV8BAAAAgHatd+/eufDCC/PrX/86n/3sZ7PXXnvl4Ycfzn777ZchQ4Zs894TTzwxf/EXf7HNtX322Sc/+MEPkiSlUimvv/565s+fn6uvvnqb9333u9/Nhg0bys6lfAUAAAAAdjlDhgzJddddl7FjxyZJunTpkoMOOig/+tGP0qlTp1x33XWpra3Nt771rTQ3N6empmabz5977rn53Oc+t821Aw444G2F7HtRvgIAAAAAH5gfn/cvFb/n5MmT33bt0UcfzaOPPtr2fceOHVMqlXLzzTdn48aNSZLNmzfnu9/9bvbaa6/85V/+ZX7961/nj3/8Y5Jk//33/7NzKV8BAAAAgF3GggUL3vH6H//4x8yZM+cdX9uwYUOmTp36gWfp8IH/RAAAAAAAKlO+9uzZM9OmTcvChQvb/pLYuHHjsmjRosybNy/9+vWrRAwAAAAAgIqpyLEDkyZNyh133NFWvA4ZMiR1dXUZOHBg+vfvnwkTJuSUU06pRBQAAAAAgIoo/MnX3r1758Mf/nBb8ZokQ4cObTsEd+nSpenbt+/b/poYAAAAAEB7VviTr5/85CezcuXK3H///dl7773zwx/+MI2NjVm+fHnbe5qbm9OzZ8+sXr16m8+OGDEiF110UZKkvr6+6KjQrtkXKJ99gfLZFyiffYHy2Rdgd1Iqcs4666zS4sWLS3V1daWuXbuWnnjiidKcOXNKDQ0Nbe+ZPXt2qUePHu/5c5qamgrNacyuNPbFmPLHvhhT/tgXY8of+2JM+WNfjDG78hR+7MCqVavyxBNPZP369dm4cWMefvjh7L///mloaGh7T/fu3bN27dqiowAAAAAAu6iePXu+r8916tQphx12WE466aR3fP3www9P165d39/Pfl+f2gELFy7Mtddem9ra2rS2tuYzn/lM7rzzzgwbNixLlixJ//79s2zZsqJjAAAAAAC7sHPPPTcHHHBA3nzzzXzmM59JqVRqe23//ffPAQcckKeeeipbtmzJyJEjM2nSpHTt2jVdu3bNfffdl3/7t39re//3vve9tLa2JkkOPPDAdOjQIS+88EKSpEuXLhkzZkxZmQovX994442MHz8+c+bMydatW3Pfffflrrvuyu23354FCxaktbU1w4cPLzoGAAAAAFABE86+8wP9eZf9+OLtvqdjx46ZNm1a1q1bl8suuyznnHNOtmzZktdffz0f/ehHc8455yRJpk6dmp/+9Kc54IADcu211yZJzjzzzDQ1NWWfffZJXV1d1q9fnw4dOuS6667LyJEjc8cdd6S5ubntXmPHji07e+Hla5JMmzYt06ZN2+baqFGjKnFrAAAAAGAXd+ihh+bf//3fc8IJJ+TJJ5/MwQcfnA4dOuTFF1/M8ccfn8WLFydJzj///AwdOjRXXHFFVq9enTVr1uS4447Lc889lz322CNvvvlmkuS3v/1tzj333FxxxRU566yzkiS1tbVZvXp1ZsyYUXauipSvAAAAAABFeeaZZ3LWWWdl9OjRufjiizN48OBs3bq17fWZM2cmSe6999789Kc/zT777JOvfOUr+fGPf5z169dn4MCB+eQnP5lbb701q1evzoYNG1IqlXLDDTekVCpl06ZN6dOnT+6+++6sWbOm7FzKVwAAAACg3XvyySfzyiuvpFOnTunRo0defvnlt73nT0++Xn755Xn55Zfz4osv5rOf/Wx+/etfp0uXLvnDH/6Qj3zkI+ncuXP23HPPdOzYsa2IXbNmTYYOHZqNGzdmzpw5Wbly5XYzKV8BAAAAgF3C2LFjM2LEiPTv3z+zZ89O9+7dkyS9evXK3nvv3fbk6+bNm7N58+YMGDAgH//4x/Pyyy+ntrY2mzdvTktLS6ZMmZLp06e/4z1OPfXUbN68uaw8ylcAAAAAoN376le/mh//+Mc56KCD8pGPfCTr169vK18PPPDA9OzZs+3J169+9au5/fbbkyRHHnlkHnjggZRKpSRJqVTK66+/nvnz5+fqq6/e5h7f/e53s2HDhrIzKV8BAAAAgHbt0EMPzT777JPp06fn//7f/5vzzjsvSbJ27dpceumlaW1tzRlnnJF77703s2fPzpAhQ3LDDTckSfbbb78cd9xxSZIrrrgiTU1NSZJzzz03n/vc57a5zwEHHPC2Qva91CQpfQC/X+GampoyYMCAaseAdsG+QPnsC5TPvkD57AuUz74AH5SampqUSqV07NgxW7Zsecf37LPPPnnttdcqlqlDxe4EAAAAAFCQPx0b8G7Fa5KKFq+J8hUAAAAAoBDKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAAChARcrXF198MXPnzs3cuXMzbty4JMm4ceOyaNGizJs3L/369atEDAAAAACAiulUiZv88Y9/zODBg9u+HzJkSOrq6jJw4MD0798/EyZMyCmnnFKJKAAAAAAAFVGVYweGDh2ayZMnJ0mWLl2avn37pqamphpRAAAAAAAKUZHyde3atZk/f36mT5+eQw89NI2NjVm+fHnb683NzenZs2clogAAAAAAVERFjh04/vjjkyQDBgzIj370o6xYsSJbtmxpe33r1q3ZunXr2z43YsSIXHTRRUmS+vr6SkSFdsu+QPnsC5TPvkD57AuUz74Au4uaJKVK3nDx4sX5xS9+kUmTJmXJkiVJkiVLluToo49+z881NTVlwIABlYgI7Z59gfLZFyiffYHy2Rcon30BdmWFHzvQpUuX7LHHHkmSj33sY6mpqcmsWbMybNiwJEn//v2zbNmyomMAAAAAAFRU4ccO1NXV5eGHH87rr7+eTZs25YILLsgvfvGLDB48OAsWLEhra2uGDx9edAwAAAAAgIoqvHxdvXp1PvWpT73t+qhRo4q+NQAAAABA1RR+7AAAAAAAwO5I+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABRA+QoAAAAAO4mampokSX19ffr06ZMk6dWrV7p161bNWLxPnaodAAAAAAD4L1dccUX+4R/+IUkyatSodOjQIQsXLszJJ5+cV155JYsXL86sWbOqnJJyefIVAAAAAHYSd955Z2644YasWbMmCxcuzK9+9asce+yxeeqppzJ//vy0trZWOyI7QPkKAAAAADuJlpaW3HzzzbntttvSvXv3LFy4MM8991xKpVLGjx9f7XjsIMcOAAAAAMBO4oILLsh9992Xr33taznllFMycODAPP3002lpaUlzc3NWrFhR7YjsAOUrAAAAAOwkHnrooVxzzTXZf//9s3LlyvTo0SOXXHJJli1blksvvTQzZszICSecUO2YlMmxAwAAAACwk1i5cmUeeOCBHHbYYbn99tszffr0vPnmm3nwwQdz4YUXplu3btWOyA6oSPlaW1ubX/7yl7n88suTJOPGjcuiRYsyb9689OvXrxIRAAAAAKBd+PjHP55Vq1a1fV9TU5OamprMmzcvnTr5D9nbk4qUr1dffXWampqSJEOGDEldXV0GDhyYyy67LBMmTKhEBAAAAADY6Z1++ulZs2ZN1qxZk29+85s544wz0q1bt5xxxhl5/vnns3r16mpHZAcUXpUfdthh6d27d+bOnZv6+voMHTo0kydPTpIsXbo0ffv2TU1NTUqlUtFRAAAAAGCn9swzz+SFF17IzJkz3/H1733vexVOxJ+j0Cdfa2pqctNNN+XKK69su9bY2Jjly5e3fd/c3JyePXu+4+dHjBiRpqamNDU1pb6+vsio0O7ZFyiffYHy2Rcon32B8tkXeHcvvPBCkuTYY499x9fnzJlTyTh8AEpFzTe+8Y3SeeedV0pSGj58eOnyyy8vPfTQQ6WGhoa298yePbvUo0eP7f6spqamwnIas6uNfTGm/LEvxpQ/9sWY8se+GFP+2Bdj3nnmzJlTSlI6+OCDS0lKBxxwQOnkk08uzZo1q+rZTPlT6LEDZ555ZlpaWnL22Wdn3333TefOnVNbW5uGhoasWLEiSdK9e/esXbu2yBgAAAAA0K786YjOzZs35/jjj8+RRx6ZiRMntv1Be9qHQsvXQYMGtX09fPjw1NfX53e/+12GDRuWJUuWpH///lm2bFmREQAAAACg3ampqUmSjB49OqNHj87xxx+frVu3VjkVO6rQM1/fyf3335/a2tosWLAg48ePz7e//e1KRwAAAACAndaAAQNSV1eXY445JoMHD843v/nNHHLIIbn++uvTvXv3fOUrX6l2RMpU6JOv/93kyZPbvh41alSlbgsAAAAA7Urv3r3TpUuX7LPPPnnllVeyZMmSNDQ05LbbbsvQoUPz2GOPVTsiZar4k68AAAAAwLubMWNGVq1alRkzZuRXv/pVunXrlkmTJmXjxo1ZtWpV1q1bV+2IlEn5CgAAAAA7mT+d+XrjjTemtrY2p59+ejZs2NB2nfahYscOAAAAAADlKZVKSZK//du/ze23355u3brlxBNPbLtO+6B8BQAAAICdzE033ZQkGTduXDZt2pT169fnd7/7XZYsWVLlZOwIxw4AAAAAwE5m9uzZSZJNmzZtc72lpaUacXiftlu+duvWbZvvO3funCT5/Oc/X0wiAAAAAIBdwHbL16lTp+bEE09Mnz59UldXl9GjRydJrrrqqsLDAQAAAAC0V9stX2tqavLYY4/l/PPPz2mnnZZ77rmn7ToAAAAAUIyjjjpqm39pf7ZbvpZKpey1116ZOnVq3nrrrfzd3/1d23UAAAAAoBjjx4/f5l/an/csX7t3755evXqla9euueSSS9LS0pJDDz00p556anr16pVTTz21UjkBAAAAYLfyp4cfPQTZfr1n+XriiSdm8+bN+frXv57u3bvn6KOPTmNjY1577bW0trbm1VdfrVROAAAAANitOPaz/XvP8vX+++/PunXrcs011+S5557L97///cydOzeLFy/O+vXr09TUVKmcAAAAALBb8cRr+7fdM1+TpEOHDmlpacmAAQMybty4JP7HBwAAAIAiPf7449v8S/uz3fJ106ZNOemkkzJ9+vQ8/vjj6dSpUxKPPQMAAABAka6//vpt/qX96bS9N9xyyy2ZM2dO2/fPP/98kmTs2LFJkn79+uWFF14oKB4AAAAAQPu03fJ1zJgxWbRoUe66664ccsghefbZZ7Nu3br07t07e+21V/bYYw/lKwAAAADA/7DdYwfq6+tz8MEHZ8qUKWlpacmUKVPy/e9/Pz179kzPnj0zderUSuQEAAAAAGhXtlu+bt68Occdd1xKpdI2f2SrV69eWbZsWaHhAAAAAGB3s/fee+fuu+/Oz3/+8/zhD3/I4sWLM3369Hzxi1+sdjR20HbL15aWlvzjP/5jTjvttHzoQx/Kl770pVx66aV55rQoH7IAACAASURBVJln8uUvfzkf/ehHK5ETAAAAAHYL99xzT+65554cddRROfXUUzNt2rRcfPHFOeuss/K///f/rnY8dsB2y9dLLrkkX/ziF3PTTTflsssuy5w5czJnzpz8zd/8Ta6//vqcfPLJlcgJAAAAALuFrl275j/+4z+SJE888UT+8i//MitWrMjw4cNz9tlnVzkdO2K75WtDQ0PGjBmTE044IYcffngWLVqU+vr6TJw4Mb169coPfvCD9/z8HnvskRkzZmTOnDmZP39+DjvssCTJuHHjsmjRosybNy/9+vX7YH4bAAAAAGjnmpubc/bZZ2fffffNV7/61bz00kttr23YsKGKydhR2y1fk+Tyyy9PTU1NDjnkkHz605/OunXr8vWvfz1XXnllunXr9p6fbW1tzV/91V/lhBNOyLe//e1ceeWVGTJkSOrq6jJw4MBcdtllmTBhwgfyywAAAABAe3fhhRfmkEMOyR133JGGhoZ87Wtfa3vtyiuvrGIydlSn7b2hS5cuOeaYY9LY2JiHHnoon/70p7N169Y8+eST+c53vpOLL744N99887t+fsuWLdmyZUuS5JBDDsnPf/7zDB06NJMnT06SLF26NH379k1NTc02f9ALAAAAAHZHb775ZsaOHfuOr/3pOALah+2Wr7NmzcqsWbPSrVu3dOnSJdOmTUufPn2SJH/4wx9yyy23bPcmo0ePzsiRI7NixYqcfPLJue+++7J8+fK215ubm9OzZ8+sXr16m8+NGDEiF110UZKkvr5+h34x2N3YFyiffYHy2Rcon32B8tkXYHdRk6Rij5ueccYZOeecc9K1a9dceOGFWbFiRZJk9uzZOfPMM7N27dp3/WxTU1MGDBhQqajQrtkXKJ99gfLZFyiffYHy2RcoT21tbd56661qx2AHlXXm6wdlypQp6devX1599dU0NDS0Xe/evft7Fq8AAAAAsLvo27fv22bu3LnZb7/9sueee1Y7Hjtgu8cO/LkaGxuzatWqvPXWWznyyCPz0ksvZdasWRk2bFiWLFmS/v37Z9myZUXHAAAAAIB2YeLEiW/7+0gHHXRQbrvttuy999555JFH3vVMWHYuhZevffv2zYMPPpiWlpa0tLTkkksuycqVKzN48OAsWLAgra2tGT58eNExAAAAAKBdOO200952bebMmW3Xn3jiCeVrO1F4+frkk0/mU5/61Nuujxo1quhbAwAAAEC707dv37ddGzlyZNvXF1xwQSXj8GcovHwFAAAAAMr3348d2HPPPXPEEUfkZz/7WU466aQkyfPPP1/lhJRL+QoAAAAAO5H/eexAjx49MnHixCql4c/RodoBAAAAAIB3t3bt2rz66qvVjsH74MlXAAAAANiJ9OjRI2PHjs2gQYOyZcuWPPLIIxkzZky1Y/E+ePIVAAAAAHYi//RP/5Rnn302gwYNyhtvvJFHH300f//3f1/tWLwPylcAAAAA2In06NEjd955ZzZt2pSamprMmzcvffr0qXYs3gflKwAAAADsRLZu3ZqOHTu2ff+JT3wipVKpiol4v5SvAAAAALATueaaa3LggQcmSTZs2JCrrroqI0eOrHIq3g9/cAsAAAAAdiKPP/54PvShD+Woo47KlVdemeeffz5btmypdizeB+UrAAAAAOxETj/99Fx33XVpampK586d84lPfCKjRo3KokWLqh2NHaR8BQAAAICdyHe+850MGjQor7/+epJk7733zpQpU3LsscdWORk7ypmvAAAAALATWb16dVvxmiTNzc3ZsGFDFRPxfnnyFQAAAACqrG/fvm1fP/rooxk7dmx+8pOfZNOmTfnSl76Up59+uorpeL+UrwAAAABQZRMnTkxNTU1KpVLbtf79+7d9XVNTU41Y/JmUrwAAAABQZaeddlq1I1AAZ74CAAAAABRA+QoAAAAAUADlKwAAAABAAZSvAAAAAAAFUL4CAAAAABSg8PK1Q4cOufXWWzN37twsWbIkl112WZLk8ssvT1NTU/7jP/4jn/nMZ4qOAQAAAABQUZ0Kv0GnTpk5c2a+9a1vpUOHDlm8eHF+9rOf5fOf/3wGDBiQxsbGPPDAAxkwYEDRUQAAAAAAKqbwJ19bW1sze/bsJMnWrVvz0ksvZeDAgfnhD3+YJHnllVeyZs2aNDY2Fh0FAAAAAKBiKnrm6z777JNevXqlsbExy5cvb7v++9//Pr17937b+0eMGJGmpqY0NTWlvr6+klGh3bEvUD77AuWzL1A++wLlsy/A7qRUidljjz1KjzzySOnwww8vTZo0qTRo0KC21+6+++5S//793/PzTU1NFclpzK4w9sWY8se+GFP+2Bdjyh/7Ykz5Y1+MMbvyVOTJ1y5duuQnP/lJbrnlljz99NN59dVX09DQ0Pb6vvvum1deeaUSUQAAAAAAKqLw8rVjx47553/+59x111159NFHkySzZs3KOeeckyRpbGxM586d09zcXHQUAAAAAICK6VT0DS644IIcf/zx6dWrV0aPHp0kOeecc/LUU0/lySefTJJccsklRccAAAAAAKiowsvXu+66K3fdddfbrl977bW59tpri749AAAAAEBVVOTMVwAAAACA3Y3yFQAAAACgAMpXAAAAAIACKF8BAAAAAAqgfAUAAAAAKIDyFQAAAACgAMpXAAAAAIACKF8BAAAAAAqgfAUAAAAAKIDyFQAAAACgAMpXAAAAAIACKF8BAAAAAAqgfAUAAAAAKIDyFQAAAACgAMpXAAAAAIACKF8BAAAAAAqgfAUAAAAAKIDyFQAAAACgABUrX/faa6/06dOnUrcDAAAAAKiqwsvXj3zkI5k6dWpefPHF/PVf/3Xb9XHjxmXRokWZN29e+vXrV3QMAAAAAICK6lT0DTZv3pxrr702Rx11VOrr65MkQ4YMSV1dXQYOHJj+/ftnwoQJOeWUU/7se539/878s38GVMqPz/uXakcAAAAAoECFP/m6YcOGPP3009tcGzp0aCZPnpwkWbp0afr27ZuampqiowAAAAAAVExV/uBWY2Njli9f3vZ9c3NzevbsWY0oAAAAAACFKPzYgXfSpUuXbNmype37rVu3ZuvWrW9734gRI3LRRRclSduRBcA7sy9QPvsC5bMvUD77AuWzL8DuoiZJqRI3Gj58eOrr6zN+/Pjcc889mTRpUpYsWZIkWbJkSY4++uj3/HxTU1MGDBjwnu9x5ivtSZFnvpazL8B/sS9QPvsC5bMvUD77AuzKqnLswKxZszJs2LAkSf/+/bNs2bJqxAAAAAAAKEzhxw507949U6dOTe/evdO5c+f8r//1v3LBBRdk8ODBWbBgQVpbWzN8+PCiYwAAAAAAVFTh5eu6desyePDgt10fNWpU0bcGAAAAAKiaqhw7AAAAAACwq1O+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFCATtUOAFTH2f/vzGpHgLL9+Lx/qXYEAAAA2GGefAUAAAAAKIDyFQAAAACgAI4dAADgAzPh7DurHQF2yGU/vrjaEQCAXZgnXwEAAAAAClDV8vVv/uZvsmTJkixcuDBDhw6tZhQAAAAAgA9U1Y4d+PCHP5xvfOMbGTRoUGpra/Pkk0/moYceSmtra7UiAQAAAAB8YKr25OsXvvCFTJ8+Pa2trXn99dezYMGCHHPMMdWKAwAAAADwgarak6+NjY1Zvnx52/e///3v07t3723eM2LEiFx00UVJko9//ONpamqqaEb+S319fVavXl3tGLucbzWN3u57Vq9enS9+8Ytl/Tz7snOwL8WwL7sm+1I99qX9sS/FafrWe/9/2r60P/aleuxL+2NfqmdH9oX2rSZJqRo3vuKKK/LKK6/kRz/6UZJkzJgxefbZZzN16tRqxOE9NDU1ZcCAAdWOAe2CfYHy2Rcon32B8tkXKJ99geJV7diBV199NQ0NDW3f77vvvnnllVeqFQcAAAAA4ANVtfJ19uzZ+cpXvpJOnTqlrq4uRx11lP/MAAAAAADYZXRMcm01brxhw4Z06NAhEydOzPDhw3P11Vfn17/+dTWiUIalS5dWOwK0G/YFymdfoHz2BcpnX6B89gWKVbUzXwEAAAAAdmVVO3YAAAAAAGBXpnwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAKoHwFAAAAACiA8hUAAAAAoADKVwAAAACAAihfAQAAAAAK0G7K15kzZ1Y7ArQb9gXKZ1+gfPYFymdfoHz2BdiVtZvytb6+vtoRoN2wL1A++wLlsy9QPvsC5bMvwK6s3ZSvAAAAAADtifIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAJUpHzt2bNnpk2bloULF+bhhx9OkowbNy6LFi3KvHnz0q9fv0rEAAAAAAComE6VuMmkSZNyxx13tBWvQ4YMSV1dXQYOHJj+/ftnwoQJOeWUUyoRBQAAAACgIgp/8rV379758Ic/3Fa8JsnQoUMzefLkJMnSpUvTt2/f1NTUFB0FAAAAAKBiCn/y9ZOf/GRWrlyZ+++/P3vvvXd++MMfprGxMcuXL297T3Nzc3r27JnVq1dv89kRI0bkoosuSpLU19cXHRXaNfsC5bMvUD77AuWzL1A++wLsTkpFzllnnVVavHhxqa6urtS1a9fSE088UZozZ06poaGh7T2zZ88u9ejR4z1/TlNTU6E5jdmVxr4YU/7YF2PKH/tiTPljX4wpf+yLMWZXnsKPHVi1alWeeOKJrF+/Phs3bszDDz+c/fffPw0NDW3v6d69e9auXVt0FAAAAACAiim8fF24cGGOOeaY1NbWpqamJp/5zGdy5513ZtiwYUmS/v37Z9myZUXHAAAAAACoqMLPfH3jjTcyfvz4zJkzJ1u3bs19992Xu+66K7fffnsWLFiQ1tbWDB8+vOgYAAAAAAAVVXj5miTTpk3LtGnTtrk2atSoStwaAAAAAKAqCj92AAAAAABgd6R8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAIoXwEAAAAACqB8BQAAAAAogPIVAAAAAKAAylcAAAAAgAJUpHx98cUXM3fu3MydOzfjxo1LkowbNy6LFi3KvHnz0q9fv0rEAAAAAAComE6VuMkf//jHDB48uO37IUOGpK6uLgMHDkz//v0zYcKEnHLKKZWIAgAAAABQEVU5dmDo0KGZPHlykmTp0qXp27dvampqqhEFAAAAAKAQFSlf165dm/nz52f69Ok59NBD09jYmOXLl7e93tzcnJ49e77tcyNGjEhTU1OamppSX19fiajQbtkXKJ99gfLZFyiffYHy2Rdgd1Kq1AwYMKD0n//5n6WHHnqo1NDQ0HZ99uzZpR49erznZ5uamiqW05j2PvbFmPLHvhhT/tgXY8of+2JM+WNfjDG78lT02IGmpqa0trbm1VdfTUNDQ9v17t27Z+3atZWMAgAAAABQqMLL1y5dumSPPfZIknzsYx9LTU1NZs2alWHDhiVJ+vfvn2XLlhUdAwAAAACgojoVfYO6uro8/PDDef3117Np06ZccMEF+cUvfpHBgwdnwYIFaW1tzfDhw4uOAQAAAABQUYWXr6tXr86nPvWpt10fNWpU0bcGAAAAAKiaip75CgAAAACwu1C+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAAAUQPkKAAAAAFAA5SsAAAAAQAGUrwAAAAAABVC+AgAAAMBOoqamJklSX1+fPn36JEl69eqVbt26VTMW71OnagcAAAAAAP7LFVdckX/4h39IkowaNSodOnTIwoULc/LJJ+eVV17J4sWLM2vWrCqnpFyefAUAAACAncSdd96ZG264IWvWrMnChQvzq1/9Kscee2yeeuqpzJ8/P62trdWOyA5QvgIAAADATqKlpSU333xzbrvttnTv3j0LFy7Mc889l1KplPHjx1c7HjvIsQMAAAAAsJO44IILct999+VrX/taTjnllAwcODBPP/10Wlpa0tzcnBUrVlQ7IjtA+QoAAAAAO4mHHnoo11xzTfbff/+sXLkyPXr0yCWXXJJly5bl0ksvzYwZM3LCCSdUOyZlcuwAAAAAAOwkVq5cmQceeCCHHXZYbr/99kyfPj1vvvlmHnzwwVx44YXp1q1btSOyAypSvtbW1uaXv/xlLr/88iTJuHHjsmjRosybNy/9+vWrRAQAAAAAaBc+/vGPZ9WqVW3f19TUpKamJvPmzUunTv5D9vakIuXr1VdfnaampiTJkCFDUldXl4EDB+ayyy7LhAkTKhEBAAAAAHZ6p59+etasWZM1a9bkm9/8Zs4444x069YtZ5xxRp5//vmsXr262hHZAYVX5Ycddlh69+6duXPnpr6+PkOHDs3kyZOTJEuXLk3fvn1TU1OTUqlUdBQAAAAA2Kk988wzeeGFFzJz5sx3fP173/tehRPx5yj0ydeamprcdNNNufLKK9uuNTY2Zvny5W3fNzc3p2fPnu/4+REjRqSpqSlNTU2pr68vMiq0e/YFymdfoHz2BcpnX6B89gXe3QsvvJAkOfbYY9/x9Tlz5lQyDh+AUlHzjW98o3TeeeeVkpSGDx9euvzyy0sPPfRQqaGhoe09s2fPLvXo0WO7P6upqamwnMbsamNfjCl/7Isx5Y99Mab8sS/GlD/2xZh3njlz5pSSlA4++OBSktIBBxxQOvnkk0uzZs2qejZT/hR67MCZZ56ZlpaWnH322dl3333TuXPn1NbWpqGhIStWrEiSdO/ePWvXri0yBgAAAAC0K386onPz5s05/vjjc+SRR2bixIltf9Ce9qHQ8nXQoEFtXw8fPjz19fX53e9+l2HDhmXJkiXp379/li1bVmQEAAAAAGh3ampqkiSjR4/O6NGjc/zxx2fr1q1VTsWOKvTM13dy//33p7a2NgsWLMj48ePz7W9/u9IRAAAAAGCnNWDAgNTV1eWYY47J4MGD881vfjOHHHJIrr/++nTv3j1f+cpXqh2RMhX65Ot/N3ny5LavR40aVanbAgAAAEC70rt373Tp0iX77LNPXnnllSxZsiQNDQ257bbbMnTo0Dz22GPVjkiZKv7kKwAAAADw7mbMmJFVq1ZlxowZ+dWvfpVu3bpl0qRJ2bhxY1atWpV169ZVOyJlUr4CAAAAwE7mT2e+3njjjamtrc3pp5+eDRs2tF2nfajYsQMAAAAAQHlKpVKS5G//9m9z++23p1u3bjnxxBPbrtM+KF8BAAAAYCdz0003JUnGjRuXTZs2Zf369fnd736XJUuWVDkZO8KxAwAAAACwk5k9e3aSZNOmTdtcb2lpqUYc3qftlq/dunXb5vvOnTsnST7/+c8XkwgAAAAAYBew3fJ16tSpOfHEE9OnT5/U1dVl9OjRSZKrrrqq8HAAAAAAAO3VdsvXmpqaPPbYYzn//PNz2mmn5Z577mm7DgAAAAAU46ijjtrmX9qf7ZavpVIpe+21V6ZOnZq33norf/d3f9d2HQAAAAAoxvjx47f5l/bnPcvX7t27p1evXunatWsuueSStLS05NBDD82pp56aXr165dRTT61UTgAAAADYrfzp4UcPQbZf71m+nnjiidm8eXO+/vWvp3v37jn66KPT2NiY1157La2trXn11VcrlRMAAAAAdiuO/Wz/3rN8vf/++7Nu3bpcc801ee655/L9738/c+fOzeLFi7N+/fo0NTVVKicAAAAA7FY88dr+bffM1yTp0KFDWlpaMmDAgIwbNy6J//EBAAAAoEiPP/74Nv/S/my3fN20aVNOOumkTJ8+PY8//ng6deqUxGPPAAAAAFCk66+/fpt/aX86be8Nt9xyS+bMmdP2/fPPP58kGTt2bJKkX79+eeGFFwqKBwAAAADQPm23fB0zZkwWLVqUu+66K4ccckieffbZrFu3Lr17985ee+2VPfbYQ/kKAAAAAPA/bPfYgfr6+hx88MGZMmVKWlpaMmXKlHz/+99Pz54907Nnz0ydOrUSOQEAAABgt3fIIYdUOwI7YLvl6+bNm3PcccelVCpt80e2evXqlWXLlhUaDgAAAAB2N926dXvX1yZNmlTBJPy5tlu+trS05B//8R9z2mmn5UMf+lC+9KUv5dJLL80zzzyTL3/5y/noRz9aiZwAAAAAsFuYMWNGfvjDH+b4449/22v//eFIdn7bPfP1kksuyRe/+MXcdNNN6datWw4++OCsXbs2//7v/56uXbvmnHPOyQ9+8INKZAUAAACAXd5xxx2Xww47LBdeeGFuvvnmTJ06Nffee2+am5tTU1NT7XjsgO0++drQ0JAxY8bkhBNOyOGHH55Fixalvr4+EydOTK9evbZbvO6xxx6ZMWNG5syZk/nz5+ewww5LkowbNy6LFi3KvHnz0q9fvw/mtwEAAACAXcAzzzyTb3zjGzn22GOzfPny3HvvvfnXf/3XNDY2VjsaO2C7T74myeWXX56/+Iu/yCGHHJK33nor69aty9e//vVMnDgxY8aMSUtLy7t+trW1NX/1V3+VjRs35thjj82VV16Ze++9N3V1dRk4cGD69++fCRMm5JRTTvnAfikAAAAA2BVs2rQpP/nJT/KTn/wk++23X84///xqR2IHbPfJ1y5duuRzn/tcjjjiiDzxxBM59NBD8+lPfzo9e/bMd77znVx88cXv+fktW7Zk48aNSf7rr7H9/Oc/z9ChQzN58uQkydKlS9O3b1+PTAMAAADAe3j55Zdz3XXXVTsGO2C7T77OmjUrs2bNSrdu3dKlS5dMmzYtffr0SZL84Q9/yC233LLdm4wePTojR47MihUrcvLJJ+e+++7L8uXL215vbm5Oz549s3r16m0+N2LEiFx00UVJkvr6+h36xWB3Y1+gfPYFymdfoHz2BcpnX4DdRU2Siv2JtDPOOCPnnHNOunbtmgsvvDArVqxIksyePTtnnnlm1q5d+66fbWpqyoABAyoVFdo1+wLlsy9QPvsC5bMvUD77AuWpra3NW2+9Ve0Y7KDtHjvwQZoyZUr69euXV199NQ0NDW3Xu3fv/p7FKwAAAADsLvr27fu2mTt3bvbbb7/sueee1Y7HDijrD279ORobG7Nq1aq89dZbOfLII/PSSy9l1qxZGTZsWJYsWZL+/ftn2bJlRccAAAAAgHZh4sSJqampSan0//8H6wcddFBuu+227L333nnkkUcyduzYKiakXIWXr3379s2DDz6YlpaWtLS05JJLLsnKlSszePDgLFiwIK2trRk+fHjRMQAAAACgXTjttNPedm3mzJlt15944gnlaztRePn65JNP5lOf+tTbro8aNaroWwMAAABAu9O3b9+3XRs5cmTb1xdccEEl4/BnKLx8BQAAAADK99+PHdhzzz1zxBFH5Gc/+1lOOumkJMnzzz9f5YSUS/kKAAAAADuR/3nsQI8ePTJx4sQqpeHP0aHaAQAAAACAd7d27dq8+uqr1Y7B++DJVwAAAADYifTo0SNjx47NoEGDsmXLljzyyCMZM2ZMtWPxPnjyFQAAAAB2Iv/0T/+UZ599NoMGDcobb7yRRx99NH//939f7Vi8D8pXAAAAANiJ9OjRI3feeWc2bdqUmpqazJs3L3369Kl2LN4H5SsAAAAA7ES2bt2ajh07tn3/iU98IqVSqYqJeL+UrwAAAACwE7nmmmty4IEHJkk2bNiQq666KiNHjqxyKt4Pf3ALAAAAAHYijz/+eD70oQ/lqKOOypVXXpnnn38+W7ZsqXYs3gflKwAAAADsRE4//fRcd911aWpqSufOnfOJT3wio0aNyqJFi6odjR2kfAUAAACAnch3vvOdDBo0KK+//nqSZO+9986UKVNy7LHHVjkZO8qZrwAAAACwE1m9enVb8Zokzc3N2bBhQxUT8X558hWA/6+9uw+yqrDvx/++sIAmurCwGLouJO0MxlpNyyoSnViGiDHGh9KEPCnTzdRCi5mqEWq0PtGkjQ/ByNTBGmLTUJMYO4pPI2BQiSQQ8RJaSRtFjckQEgnyUHxI44rc3x+d7q98tXIgnnt24fWaec/uPffePR/Ej3+8PXsuAAAAFRszZkzv9w8++GCuuuqqfOtb38qrr76as846K+vWratwOvaV8hUAAAAAKnbjjTemVqul0Wj0Huvq6ur9vlarVTEWvyHlKwAAAABU7I/+6I+qHoESuOcrAAAAAEAJlK8AAAAAACVQvgIAAAAAlED5CgAAAABQgtLL1wEDBuRLX/pSli9fnjVr1uTCCy9MksyaNSv1ej3f//73c8IJJ5Q9BgAAAABAU7WUfoKWlixZsiQXXXRRBgwYkMceeyw/+MEPcsopp2T8+PHp7OzMXXfdlfHjx5c9CgAAAABA05R+5WtPT0+WLVuWJNm1a1eeffbZTJgwIbfeemuSZOPGjdm6dWs6OzvLHgUAAAAAoGmaes/Xd7zjHRk5cmQ6OzuzYcOG3uM///nPM2rUqGaOAgAAAABQqqaVrwcffHBuvfXWXHDBBRk8eHBee+213ud27dqVXbt2ve4906dPT71eT71eECz0MgAAIABJREFUT3t7e7NGhX7JvkBx9gWKsy9QnH2B4uwLcCBplJ3Bgwc37rnnnsbkyZMbSRpXXnllY+rUqb3PL168uHHYYYe96c+o1+ulzymyv8S+iBSPfREpHvsiUjz2RaR47IuI7M8p/crXgQMH5utf/3oWLFiQBx98MEmydOnSnHPOOUmSzs7ODBo0KJs3by57FAAAAACApmkp+wTnnntuJk6cmJEjR2b27NlJknPOOSePP/54Vq1alSQ577zzyh4DAAAAAKCpSi9fFyxYkAULFrzu+Jw5czJnzpyyTw8AAAAAUImmfeAWAAAAAMCBRPkKAAAAAFAC5SsAAAAAQAmUrwAAAAAAJVC+AgAAAACUQPkKAAAAAFAC5SsAAAAAQAmUrwAAAAAAJVC+AgAAAACUQPkKAAAAAFAC5SsAAAAAQAmUrwAAAAAAJVC+AgAAAACUQPkKAAAAAFAC5SsAAAAAQAmUrwAAAAAAJVC+AgAAAACUQPkKAAAAAFAC5SsAAAAAQAmaVr4ecsghGT16dLNOBwAAAABQqdLL12HDhmXRokV55pln8rGPfaz3+Ny5c7N69eqsWLEiY8eOLXsMAAAAAICmain7BDt37sycOXMybty4tLe3J0kmT56c1tbWTJgwIV1dXZk3b15OP/30skcBAAAAAGia0q98femll7Ju3brdjk2ZMiULFy5MkqxduzZjxoxJrVYrexQAAAAAgKYp/crXN9LZ2ZkNGzb0Pt68eXNGjBiRLVu27Pa66dOnZ8aMGUnSe9Us8MbsCxRnX6A4+wLF2Rcozr4AB5JGM9Ld3d2YNWtWI0lj8eLFjY6Ojt7nli1b1hg+fPibvr9erzdlTpH9IfZFpHjsi0jx2BeR4rEvIsVjX0Rkf07ptx14I5s2bUpHR0fv47a2tmzbtq2KUQAAAAAASlFJ+bp06dJMmzYtSdLV1ZX169dXMQYAAAAAQGlKv+drW1tbFi1alFGjRmXQoEE544wzcu6552bSpElZuXJlenp60t3dXfYYAAAAAABNVXr5un379kyaNOl1x2fOnFn2qQEAAAAAKlN6+dpMn/znj1c9AhR225/cXvUIAAAAAJSoknu+AgAAAADs75SvAAAAAAAlUL4CAAAAAJRA+QoAAAAAUALlKwAAAABACZSvAAAAAAAlUL4CAAAAAJRA+QoAAAAAUALlKwAAAABACZSvAAAAAAAlUL4CAAAAAJRA+QoAAAAAUALlKwAAAABACZSvAAAAAAAlUL4CAAAAAJRA+QoAAAAAUIKWqgcAgL7uk//88apHgL1y25/cXvUIAABAXPkKAAAAAFAK5SsAAAAAQAkqve3A2WefnYsuuig7d+7MNddck7vvvrvKceCA4teo6U/8CjUAAAD9UWXl66GHHpoLLrggJ554YoYMGZJVq1Zl8eLF6enpqWokAAAAAIC3TGW3HTj11FNz7733pqenJy+++GJWrlyZ448/vqpxAAAAAADeUrUkjSpOfOGFF2br1q259dZbkyRXXHFFnnjiidxxxx29r5k+fXpmzJiRJHn3u9+d9evXVzHqAa+9vT1btmypeowD0pYtW3LaaacVeq196RvsS3XsS/9jX6pjX/of+1Id+9L/2Jfq2Jf+x75UZ2/2hf6tsvL14osvzsaNG/PNb34zSXLZZZfliSeeyKJFi6oYhzdRr9czfvz4qseAfsG+QHH2BYqzL1CcfYHi7AuUr7LbDmzatCkdHR29jw8//PBs3LixqnEAAAAAAN5SlZWvy5Yty9SpU9PS0pLW1taMGzcu9Xq9qnEAAAAAAN5SA5PMqeLEL730UgYMGJAbb7wx3d3dueKKK/LjH/+4ilEoYO3atVWPAP2GfYHi7AsUZ1+gOPsCxdkXKFdl93wFAAAAANifVXbbAQAAAACA/ZnyFQAAAACgBMpXAAAAAIASKF8BAAAAAEqgfAUAAAAAKIHyFQAAAACgBMpXAAAAAIASKF8BAAAAAEqgfAUAAAAAKIHyFQAAAACgBMpXAAAAAIASKF8BAAAAAErQb8rXJUuWVD0C9Bv2BYqzL1CcfYHi7AsUZ1+A/Vm/KV/b29urHgH6DfsCxdkXKM6+QHH2BYqzL8D+rN+UrwAAAAAA/YnyFQAAAACgBMpXAAAAAIASKF8BAAAAAEqgfAUAAAAAKIHyFQAAAACgBMpXAAAAAIASKF8BAAAAAEqgfAUAAAAAKIHyFQAAAACgBMpXAAAAAIASKF8BAAAAAEqgfAUAAAAAKIHyFQAAAACgBMpXAAAAAIASKF8BAAAAAErQlPJ1xIgRufvuu/Poo4/mgQceSJLMnTs3q1evzooVKzJ27NhmjAEAAAAA0DQtzTjJTTfdlH/4h3/oLV4nT56c1tbWTJgwIV1dXZk3b15OP/30ZowCAAAAANAUpV/5OmrUqBx66KG9xWuSTJkyJQsXLkySrF27NmPGjEmtVit7FAAAAACApin9ytejjz46zz33XO64444cdthhufXWW9PZ2ZkNGzb0vmbz5s0ZMWJEtmzZstt7p0+fnhkzZiRJ2tvbyx4V+jX7AsXZFyjOvkBx9gWKsy/AgaRRZj7xiU80HnvssUZra2vjoIMOanz3u99tPPzww42Ojo7e1yxbtqwxfPjwN/059Xq91DlF9qfYF5HisS8ixWNfRIrHvogUj30Rkf05pd924Pnnn893v/vdvPDCC/n1r3+dBx54IO9617vS0dHR+5q2trZs27at7FEAAAAAAJqm9PL10UcfzfHHH58hQ4akVqvlhBNOyM0335xp06YlSbq6urJ+/fqyxwAAAAAAaKrS7/n68ssv5/rrr8/DDz+cXbt25Rvf+EYWLFiQ+fPnZ+XKlenp6Ul3d3fZYwAAAAAANFXp5WuS3H333bn77rt3OzZz5sxmnBoAAAAAoBKl33YAAAAAAOBApHwFAAAAACiB8hUAAAAAoATKVwAAAACAEihfAQAAAABKoHwFAAAAACiB8hUAAAAAoATKVwAAAACAEihfAQAAAABKoHwFAAAAACiB8hUAAAAAoATKVwAAAACAEihfAQAAAABKoHwFAAAAACiB8hUAAAAAoATKVwAAAACAEihfAQAAAABKoHwFAAAAACiB8hUAAAAAoATKVwAAAACAEjSlfH3mmWeyfPnyLF++PHPnzk2SzJ07N6tXr86KFSsyduzYZowBAAAAANA0Lc04yX/9139l0qRJvY8nT56c1tbWTJgwIV1dXZk3b15OP/30ZowCAAAAANAUldx2YMqUKVm4cGGSZO3atRkzZkxqtVoVowAAAAAAlKIp5eu2bdvyve99L/fee2+OOuqodHZ2ZsOGDb3Pb968OSNGjHjd+6ZPn556vZ56vZ729vZmjAr9ln2B4uwLFGdfoDj7AsXZF+BA0mhWxo8f3/i3f/u3xuLFixsdHR29x5ctW9YYPnz4m763Xq83bU6R/h77IlI89kWkeOyLSPHYF5HisS8isj+nqbcdqNfr6enpyaZNm9LR0dF7vK2tLdu2bWvmKAAAAAAApSq9fB08eHAOPvjgJMnv/M7vpFarZenSpZk2bVqSpKurK+vXry97DAAAAACApmop+wStra154IEH8uKLL+bVV1/Nueeem3//93/PpEmTsnLlyvT09KS7u7vsMQAAAAAAmqr08nXLli059thjX3d85syZZZ8aAAAAAKAyTb3nKwAAAADAgUL5CgAAAABQAuUrAAAAAEAJlK8AAAAAACVQvgIAAAAAlED5CgAAAABQAuUrAAAAAEAJlK8AAAAAACVQvgIAAAAAlED5CgAAAAB9RK1WS5K0t7dn9OjRSZKRI0dm6NChVY7FPmqpegAAAAAA4L9dfPHF+fKXv5wkmTlzZgYMGJBHH300H/rQh7Jx48Y89thjWbp0acVTUpQrXwEAAACgj7j55pvzt3/7t9m6dWseffTR/OhHP8pJJ52Uxx9/PN/73vfS09NT9YjsBeUrAAAAAPQRO3bsyLXXXpu///u/T1tbWx599NE8+eSTaTQauf7666sej73ktgMAAAAA0Eece+65+cY3vpG//Mu/zOmnn54JEyZk3bp12bFjRzZv3pxf/OIXVY/IXlC+AgAAAEAfsXjx4lx55ZV517veleeeey7Dhw/Peeedl/Xr1+fTn/507rvvvrz//e+vekwKctsBAAAAAOgjnnvuudx111055phjMn/+/Nx777351a9+lXvuuSd/9md/lqFDh1Y9IntB+QoAAAAAfci73/3uPP/8872Pa7VaarVaVqxYkZYWv8jen/jbAgAAAIA+4sMf/nC2bt2arVu35jOf+Uza2toydOjQfOQjH8mMGTOyZcuWqkdkLyhfAQAAAKCP+OEPf5inn346S5YsecPn/+7v/q7JE/GbaMptB4YMGZL/+I//yKxZs5Ikc+fOzerVq7NixYqMHTu2GSMAAAAAQJ/39NNPJ0lOOumkN3z+4YcfbuY4/IaaUr5eccUVqdfrSZLJkyentbU1EyZMyIUXXph58+Y1YwQAAAAA6Df+5m/+JklyxBFHJEl++7d/Ox/60IeydOnSKsdiL5Vevh5zzDEZNWpUli9fniSZMmVKFi5cmCRZu3ZtxowZk1qtVvYYAAAAANBvNBqNJMnOnTszceLEnHXWWVm6dGkGDRpU8WTsjVLL11qtlmuuuSaf/exne491dnZmw4YNvY83b96cESNGlDkGAAAAAPQr/3Ox4uzZs1Ov1zN06NDs2rWr4qnYW6WWr+eff35uv/32bN26tffY4MGD89prr/U+3rVr1//5L8706dNTr9dTr9fT3t5e5qjQ79kXKM6+QHH2BYqzL1CcfYE3N378+LS2tub444/PpEmT8pnPfCZHHnlkPve5z6WtrS1Tp06tekT2QqOsrFq1qrFkyZLGkiVLGuvWrWs88cQTjWeffbZx3HHH9b5mzZo1hX5WvV4vbU6R/S32RaR47ItI8dgXkeKxLyLFY19EXp8zzzyzsW7dusaZZ57ZWLZsWePUU09tzJ8/v9He3t5Yt25do62trfIZpVhaUqITTzyx9/vu7u60t7fnZz/7WaZNm5Y1a9akq6sr69evL3MEAAAAAOhX7rvvvlx44YW57777Mnny5AwdOjQ33XRTfv3rX+f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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "#--- 繪圖\n", "df_seg = df_seg.rename(columns = {'recency_cate':'近因'})\n", "df_seg = df_seg.rename(columns = {'frequency_cate':'頻率'})\n", "\n", "g = sns.FacetGrid(df_seg, # 來源資料表\n", " col=\"近因\", # X資料來源欄位\n", " row=\"頻率\" , # Y資料來源欄位\n", " col_order= recency_label, # X資料順序\n", " row_order= frequency_label, # Y資料順序\n", " palette='Set1', #畫布色調\n", " margin_titles=True,\n", " hue='customer'\n", " )\n", "#小圖表部分\n", "g = g.map_dataframe(sns.barplot, y ='顧客數量')\n", "g = g.set_axis_labels('近因','頻率').add_legend()\n", "g.savefig(\"RFplot.png\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 顧客產品推薦長條圖（依性別分類）" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "######### 製圖 #######\n", "\n", "df_seg = df2.groupby(['頻率', '近因','gender'], as_index = False)['顧客數量'].sum()\n", "\n", "df_seg = df_seg.merge(df2[['頻率', '近因','customer']],\n", " on =['頻率', '近因'], how = 'left')\n", "df_seg = df_seg.drop_duplicates()\n", "\n", "# 顧客產品推薦圖 - 長條圖\n", "g = sns.FacetGrid(df_seg, # 來源資料表\n", " col=\"近因\", # X資料來源欄位\n", " row=\"頻率\" , # Y資料來源欄位\n", " col_order= recency_label, # X資料順序\n", " row_order= frequency_label, # Y資料順序\n", " sharex=False,\n", " sharey=False,\n", " size=2.2, aspect=1.6,\n", " palette='Set1', #畫布色調\n", " margin_titles=True,\n", " hue='customer'\n", " )\n", "#小圖表部分\n", "g = g.map(sns.barplot, 'gender' ,'顧客數量')\n", "g = g.add_legend()\n", "g.savefig(\"顧客產品推薦長條圖（依性別分類）.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 顧客產品推薦堆疊圖（依產品分類）" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", "
)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 顧客產品推薦圖 - 堆疊圖\n", "del df2['顧客數量']\n", "RFM.RFM_stackedplot(df2, frequency_label,recency_label,'gender')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 個人商品推薦" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "# 商品個別推薦\n", "\n", "product = orders['product'].unique().tolist()\n", "\n", "recom_list = []\n", "for i in range(len(df2[product])):\n", " aa = df2[product].iloc[i,::].rank().sort_values(ascending = False)\n", " recom_list.append('、'.join(aa.index))\n", " \n", "df2['recommend_product'] =recom_list\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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productclientIdorderdaterecencygenderbottled watercoffeeoatmeal breadfrequency近因頻率customerrecommend_product
012018-04-038male2.00.02.050-17 day5 freq新顧客bottled water、oatmeal bread、coffee
122018-03-3111male4.00.01.010-17 day1 freq新顧客bottled water、oatmeal bread、coffee
232018-04-101female0.01.00.060-17 day6 freq常貴客coffee、bottled water、oatmeal bread
342018-02-0169female2.01.01.01>69 day1 freq流失客bottled water、oatmeal bread、coffee
452018-03-2616male0.00.01.040-17 day2-4 freq新顧客oatmeal bread、bottled water、coffee
.......................................
2842962018-03-1329male1.00.02.0428-40 day2-4 freq新顧客oatmeal bread、bottled water、coffee
2852972018-04-083female4.01.01.020-17 day1 freq新顧客bottled water、oatmeal bread、coffee
2862982018-03-2121female1.00.02.0318-27 day2-4 freq新顧客oatmeal bread、bottled water、coffee
2872992018-01-2774female2.00.00.01>69 day1 freq流失客bottled water、oatmeal bread、coffee
2883002018-02-0367male1.00.00.0257-68 day1 freq流失客bottled water、oatmeal bread、coffee
\n", "

289 rows × 12 columns

\n", "
" ], "text/plain": [ "product clientId orderdate recency gender bottled water coffee \\\n", "0 1 2018-04-03 8 male 2.0 0.0 \n", "1 2 2018-03-31 11 male 4.0 0.0 \n", "2 3 2018-04-10 1 female 0.0 1.0 \n", "3 4 2018-02-01 69 female 2.0 1.0 \n", "4 5 2018-03-26 16 male 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "284 296 2018-03-13 29 male 1.0 0.0 \n", "285 297 2018-04-08 3 female 4.0 1.0 \n", "286 298 2018-03-21 21 female 1.0 0.0 \n", "287 299 2018-01-27 74 female 2.0 0.0 \n", "288 300 2018-02-03 67 male 1.0 0.0 \n", "\n", "product oatmeal bread frequency 近因 頻率 customer \\\n", "0 2.0 5 0-17 day 5 freq 新顧客 \n", "1 1.0 1 0-17 day 1 freq 新顧客 \n", "2 0.0 6 0-17 day 6 freq 常貴客 \n", "3 1.0 1 >69 day 1 freq 流失客 \n", "4 1.0 4 0-17 day 2-4 freq 新顧客 \n", ".. ... ... ... ... ... \n", "284 2.0 4 28-40 day 2-4 freq 新顧客 \n", "285 1.0 2 0-17 day 1 freq 新顧客 \n", "286 2.0 3 18-27 day 2-4 freq 新顧客 \n", "287 0.0 1 >69 day 1 freq 流失客 \n", "288 0.0 2 57-68 day 1 freq 流失客 \n", "\n", "product recommend_product \n", "0 bottled water、oatmeal bread、coffee \n", "1 bottled water、oatmeal bread、coffee \n", "2 coffee、bottled water、oatmeal bread \n", "3 bottled water、oatmeal bread、coffee \n", "4 oatmeal bread、bottled water、coffee \n", ".. ... \n", "284 oatmeal bread、bottled water、coffee \n", "285 bottled water、oatmeal bread、coffee \n", "286 oatmeal bread、bottled water、coffee \n", "287 bottled water、oatmeal bread、coffee \n", "288 bottled water、oatmeal bread、coffee \n", "\n", "[289 rows x 12 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 在recommend_product裏面看到每一個人的推薦產品\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 後續練習：大家可以嘗試看看如何將不同區隔的顧客區分出來，找出各自的顧客推薦清單\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.13 ('MDS')", "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": "3.7.13" }, "orig_nbformat": 2, "vscode": { "interpreter": { "hash": "05081d663d3c7e77b16d56896a7222a402a5e78e9a8349e40c1beeee6f0e5825" } } }, "nbformat": 4, "nbformat_minor": 2 }