From 16a0b4e7c542e7d893bd37cf2a684f9562673749 Mon Sep 17 00:00:00 2001 From: marslbr Date: Tue, 11 Nov 2025 17:33:22 +0800 Subject: [PATCH] =?UTF-8?q?251111=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- KANO/main.py | 557 ++++++++++------------------------------------- KANO/result.xlsx | Bin 0 -> 5518 bytes 2 files changed, 121 insertions(+), 436 deletions(-) create mode 100644 KANO/result.xlsx diff --git a/KANO/main.py b/KANO/main.py index 2c1a272..bfb536c 100644 --- a/KANO/main.py +++ b/KANO/main.py @@ -5,9 +5,12 @@ 本脚本用于KANO需求分析，能够处理问卷数据并输出需求分类结果 """ +import re + +import numpy import pandas -import prettytable +from utils.pandas_extension import save_as_workbook print("1 打开并读取Excel文件...", end="") @@ -37,9 +40,9 @@ try: raise Exception("样本数为0") # 统计列数 - columns_counts = dataset.shape[1] + columns = dataset.shape[1] # 若列数非奇数则抛出异常 - if columns_counts % 2 != 0: + if columns % 2 != 0: raise Exception("列数为奇数") print(f"已完成，样本数为{samples_size}") @@ -48,451 +51,133 @@ except Exception as exception: print(f"发生异常：{str(exception)}") exit() -# 读取问卷题目和答案（从第7列开始为题目或答案） -DataSet = DataSet.iloc[:, 6:] +print("2 生成KANO评价结果...", end="") -# 统计数据集中样本数量和题目数量 -Sample_Size, Question_Amount = DataSet.shape +# KANO评价结果 +kano = pandas.DataFrame() -# 判断题目数量是否为偶数，若为偶数则计算问卷中涉及需求数量，若为奇数则终止脚本 -if Question_Amount % 2 == 0: +for column in range(columns): + if column % 2 != 0: + continue - # 计算问卷中涉及需求数量 - Requirement_Amount = int(Question_Amount / 2) + # 功能名称 + feature = re.search( + pattern="【(?P.*?)】", string=dataset.columns[column] + ).group("feature") -else: + # 生成某功能的选项分布 + distribution = pandas.crosstab( + index=dataset.iloc[:, column], # 行索引为如果该产品有{{功能名称}}的选项 + columns=dataset.iloc[:, column + 1], # 列索引为如果该产品没有{{功能名称}}的选项 + rownames=[""], + colnames=[""], + normalize=True, # 转为占比 + ).reindex(index=alternatives, columns=alternatives, fill_value=0) - print("题目数量为奇数，请检查") - print("") - - exit() - -print( - "数据集中包含 %d 份样本， %d 个问题（涉及 %d 个需求）" - % (Sample_Size, Question_Amount, Requirement_Amount) -) -print("") - -print("*" * 100) -print("") - -print("2 数据预处理") -print("") - -print("2.1 检查并删除不规范样本") -print("") - -# 定义问卷中备选答案列表 -Alternatives = ["我很喜欢", "理所应当", "无所谓", "勉强接受", "我很不喜欢"] - -# 检查答案是否在指定范围，若否则将该答案定义为空值 -DataSet = DataSet.applymap(lambda x: x if x in Alternatives else None) - -# 删除包含缺失值的样本 -DataSet.dropna(axis="index", how="any", inplace=True) - -# 删除答案全部相同的样本 -DataSet = DataSet[DataSet.apply(pandas.Series.nunique, axis="columns") != 1] - -# 统计有效样本数量 -Sample_Size = DataSet.shape[0] - -print("处理后，有效样本数量为 %d 份" % (Sample_Size)) -print("") - -print("*" * 100) -print("") - -print("3 数据处理") -print("") - -Requirement_Labels = DataSet.columns.tolist() - -# 通过问题截取需求名称（截取'有'右侧、'，'左侧字符串） -Requirement_Labels = [ - x[x.find("有") + 1 : x.find("，")] - for x in Requirement_Labels - if isinstance(x, str) and "具有" in x -] - -print("3.1 绘制KANO评价结果分类对照表") -print("") - -for Question_Number in range(Requirement_Amount): - - # 创建KANO评价结果分类对照表 - KANO = pandas.DataFrame(data=[], index=Alternatives, columns=Alternatives) - - for Column_Label in Alternatives: - - for Index_Label in Alternatives: - - # 统计并赋值 - KANO.loc[Index_Label, Column_Label] = DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin([Index_Label])) - & (DataSet.iloc[:, Question_Number + 1].isin([Column_Label])), - :, - ].shape[0] - - # 修改行名 - KANO.index = [ - "Provide_Like", - "Provide_Should", - "Provide_Indifferent", - "Provide_Grudging", - "Provide_Hate", - ] - - # 重置索引 - KANO.reset_index(inplace=True) - - # 修改列名 - KANO.columns = [ - "", - "Not_Provide_Like", - "Not_Provide_Should", - "Not_Provide_Indifferent", - "Not_Provide_Grudging", - "Not_Provide_Hate", - ] - - # 打印表格 - - PrintTable = prettytable.PrettyTable() - - PrintTable.field_names = KANO.columns.tolist() - - for Index in KANO.index.tolist(): - - PrintTable.add_row(KANO.loc[Index]) - - PrintTable.align = "r" - - PrintTable.align[""] = "l" - - PrintTable.float_format = "." - - print( - "附表 需求%d：%s的KANO评价结果分类对照表：" - % (Question_Number + 1, Requirement_Labels[Question_Number]) - ) - - print(PrintTable) - - print("") - -print("字段说明：") - -print( - "1）Not_Provide_Like为不提供该需求、用户表示“我很喜欢”，Not_Provide_Should为不提供该需求、用户表示“理所应当”，Not_Provide_Indifferent为不提供该需求、用户表示“无所谓”，Not_Provide_Grudging为不提供该需求、用户表示“勉强接受”，Not_Provide_Hate为不提供该需求、用户表示“我很不喜欢”。" -) - -print( - "1）Provide_Like为提供该需求、用户表示“我很喜欢”，Provide_Should为提供该需求、用户表示“理所应当”，Provide_Indifferent为提供该需求、用户表示“无所谓”，Provide_Grudging为提供该需求、用户表示“勉强接受”，Provide_Hate为不提供该需求、用户表示“我很不喜欢”。" -) - -print("") - -print("3.2 计算KANO评价维度") -print("") - -# 创建KANO各维度分数表 -KANO_Report = pandas.DataFrame( - data=[], - columns=[ - "Requirement_Label", - "A_Score", - "O_Score", - "M_Score", - "I_Score", - "R_Score", - "Q_Score", - ], - dtype="float", -) - -KANO_Report["Requirement_Label"] = Requirement_Labels - -for Question_Number in range(Requirement_Amount): - - # 计算兴奋型需求分数 - A_Score = round( - DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) - & ( - DataSet.iloc[:, Question_Number + 1].isin( - ["理所应当", "无所谓", "勉强接受"] - ) + kano = pandas.concat( + objs=[ + kano, + pandas.DataFrame( + [ + { + "功能名称": feature, + "魅力属性": distribution.loc[ + "非常满意", ["理应如此", "无所谓", "勉强接受"] + ].sum(), + "期望属性": distribution.loc["非常满意", "不满意"].sum(), + "必备属性": distribution.loc[ + ["理应如此", "无所谓", "勉强接受"], "不满意" + ].sum(), + "无差异属性": distribution.loc[ + ["理应如此", "无所谓", "勉强接受"], + ["理应如此", "无所谓", "勉强接受"], + ] + .sum() + .sum(), + "反向属性": distribution.loc[ + ["理应如此", "无所谓", "勉强接受", "不满意"], "非常满意" + ].sum() + + distribution.loc[ + ("不满意", ["理应如此", "无所谓", "勉强接受"]) + ].sum(), + "可疑结果": distribution.loc["非常满意", "非常满意"] + + distribution.loc["不满意", "不满意"], + } + ] ), - :, - ].shape[0] - / Sample_Size - * 100, - 2, + ], + ignore_index=True, ) - KANO_Report.loc[Question_Number, "A_Score"] = A_Score - - # 计算期望型需求分数 - O_Score = round( - DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) - & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), - :, - ].shape[0] - / Sample_Size - * 100, - 2, +# 计算better-worse分数，并贴标签 +kano = ( + kano.assign( + 满意系数=lambda dataframe: (dataframe["魅力属性"] + dataframe["期望属性"]) + / ( + dataframe["魅力属性"] + + dataframe["期望属性"] + + dataframe["必备属性"] + + dataframe["无差异属性"] + ), + 不满意系数=lambda dataframe: (dataframe["必备属性"] + dataframe["期望属性"]) + / ( + dataframe["魅力属性"] + + dataframe["期望属性"] + + dataframe["必备属性"] + + dataframe["无差异属性"] + ), ) - - KANO_Report.loc[Question_Number, "O_Score"] = O_Score - - # 计算必备型需求分数 - M_Score = round( - DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"])) - & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), - :, - ].shape[0] - / Sample_Size - * 100, - 2, - ) - - KANO_Report.loc[Question_Number, "M_Score"] = M_Score - - # 计算无差型需求分数 - I_Score = round( - DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"])) - & ( - DataSet.iloc[:, Question_Number + 1].isin( - ["理所应当", "无所谓", "勉强接受"] - ) + .pipe( + lambda dataframe: dataframe.assign( + 需求标签=numpy.select( + condlist=[ + (dataframe["满意系数"] >= dataframe["满意系数"].mean()) + & (dataframe["不满意系数"] >= dataframe["不满意系数"].mean()), + (dataframe["满意系数"] >= dataframe["满意系数"].mean()) + & (dataframe["不满意系数"] < dataframe["不满意系数"].mean()), + (dataframe["满意系数"] < dataframe["满意系数"].mean()) + & (dataframe["不满意系数"] < dataframe["不满意系数"].mean()), + (dataframe["满意系数"] < dataframe["满意系数"].mean()) + & (dataframe["不满意系数"] >= dataframe["不满意系数"].mean()), + ], + choicelist=[ + "期望型需求(P2)", + "魅力型需求(P3)", + "无差异需求(P4)", + "必备型需求(P1)", + ], + default="未分类", ), - :, - ].shape[0] - / Sample_Size - * 100, - 2, + ) ) - - KANO_Report.loc[Question_Number, "I_Score"] = I_Score - - # 计算反向型需求分数 - R_Score = round( - DataSet.loc[ - ( - DataSet.iloc[:, Question_Number].isin( - ["理所应当", "无所谓", "勉强接受", "我很不喜欢"] - ) - ) - & ( - DataSet.iloc[:, Question_Number + 1].isin( - ["我很喜欢", "理所应当", "无所谓", "勉强接受"] - ) - ), - :, - ].shape[0] - / Sample_Size - * 100 - - I_Score, - 2, - ) - - KANO_Report.loc[Question_Number, "R_Score"] = R_Score - - # 计算可疑型需求分数 - Q_Score = round( - DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) - & (DataSet.iloc[:, Question_Number + 1].isin(["我很喜欢"])), - :, - ].shape[0] - / Sample_Size - * 100 - + DataSet.loc[ - (DataSet.iloc[:, Question_Number].isin(["我很不喜欢"])) - & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), - :, - ].shape[0] - / Sample_Size - * 100, - 2, - ) - - KANO_Report.loc[Question_Number, "Q_Score"] = Q_Score - -# 打印表格 - -PrintTable = prettytable.PrettyTable() - -PrintTable.field_names = KANO_Report.columns.tolist() - -for Index in KANO_Report.index.tolist(): - - PrintTable.add_row(KANO_Report.loc[Index]) - -PrintTable.align["Requirement_Label"] = "l" - -PrintTable.align["A_Score"] = "r" - -PrintTable.align["O_Score"] = "r" - -PrintTable.align["M_Score"] = "r" - -PrintTable.align["I_Score"] = "r" - -PrintTable.align["R_Score"] = "r" - -PrintTable.align["Q_Score"] = "r" - -PrintTable.align["Better_Score"] = "r" - -PrintTable.align["Worse_Score"] = "r" - -PrintTable.float_format["A_Score"] = ".2" - -PrintTable.float_format["O_Score"] = ".2" - -PrintTable.float_format["M_Score"] = ".2" - -PrintTable.float_format["I_Score"] = ".2" - -PrintTable.float_format["R_Score"] = ".2" - -PrintTable.float_format["Q_Score"] = ".2" - -PrintTable.float_format["Better_Score"] = ".2" - -PrintTable.float_format["Worse_Score"] = ".2" - -print("附表 各需求的KANO评价维度计算结果：") - -print( - PrintTable.get_string( - fields=[ - "Requirement_Label", - "A_Score", - "O_Score", - "M_Score", - "I_Score", - "R_Score", - "Q_Score", - ] + .assign( + 魅力属性=lambda dataframe: dataframe["魅力属性"].apply( + lambda cell: f"{cell:.2f}" + ), + 期望属性=lambda dataframe: dataframe["期望属性"].apply( + lambda cell: f"{cell:.2f}" + ), + 必备属性=lambda dataframe: dataframe["必备属性"].apply( + lambda cell: f"{cell:.2f}" + ), + 无差异属性=lambda dataframe: dataframe["无差异属性"].apply( + lambda cell: f"{cell:.2f}" + ), + 反向属性=lambda dataframe: dataframe["反向属性"].apply( + lambda cell: f"{cell:.2f}" + ), + 可疑结果=lambda dataframe: dataframe["可疑结果"].apply( + lambda cell: f"{cell:.2f}" + ), + 满意系数=lambda dataframe: dataframe["满意系数"].apply( + lambda cell: f"{cell:.2f}" + ), + 不满意系数=lambda dataframe: dataframe["不满意系数"].apply( + lambda cell: f"{cell:.2f}" + ), ) ) -print("字段说明：") +save_as_workbook(worksheets=[("Sheet1", kano)], workbook_name="result.xlsx") -print( - "1）Requirement_Label为需求名称，A_Score为兴奋型需求分数，O_Score为期望型需求分数，M_Score为必备型需求分数，I_Score为无差型需求分数，R_Score为反向型需求分数，Q_Score为可疑型需求分数。" -) - -print("") - -print("3.3 定义需求类型和Better-Worse分数") -print("") - -# 以KANO评价维度中最高分定义需求类型 -Requirement_Types = list( - KANO_Report[ - ["A_Score", "O_Score", "M_Score", "I_Score", "R_Score", "Q_Score"] - ].idxmax(axis="columns") -) - -# 通过列名截取需求类型（第一位、'_'左侧字符串） -Requirement_Types = [ - x[0 : x.find("_")] for x in Requirement_Types if isinstance(x, str) -] - -KANO_Report["Requirement_Type"] = Requirement_Types - -# 计算Better分数 -KANO_Report["Better_Score"] = ( - (KANO_Report["A_Score"] + KANO_Report["O_Score"]) - / ( - KANO_Report["A_Score"] - + KANO_Report["O_Score"] - + KANO_Report["M_Score"] - + KANO_Report["I_Score"] - ) - * 100 -) - -# 计算Worse分数 -KANO_Report["Worse_Score"] = ( - -1 - * (KANO_Report["O_Score"] + KANO_Report["M_Score"]) - / ( - KANO_Report["A_Score"] - + KANO_Report["O_Score"] - + KANO_Report["M_Score"] - + KANO_Report["I_Score"] - ) - * 100 -) - -# 打印表格 - -PrintTable = prettytable.PrettyTable() - -PrintTable.field_names = KANO_Report.columns.tolist() - -for Index in KANO_Report.index.tolist(): - - PrintTable.add_row(KANO_Report.loc[Index]) - -PrintTable.align["Requirement_Label"] = "l" - -PrintTable.align["Requirement_Type"] = "r" - -PrintTable.align["Better_Score"] = "r" - -PrintTable.align["Worse_Score"] = "r" - -PrintTable.float_format["Better_Score"] = ".2" - -PrintTable.float_format["Worse_Score"] = ".2" - -print("附表 各需求的KANO评价维度计算结果：") - -print( - PrintTable.get_string( - fields=["Requirement_Label", "Requirement_Type", "Better_Score", "Worse_Score"] - ) -) - -print("字段说明：") - -print("1）Requirement_Label为需求名称，Requirement_Type为需求类型。") - -print( - "2）A为兴奋型需求：表示产品具有该种需求，则用户满意度会提高；没有该种需求，则用户满意度不会降低。建议给予P3关注。" -) - -print( - "3）O为期望型需求：表示产品具有该种需求，则用户满意度会提高；没有该种需求，则用户满意度会降低。建议给予P1关注。" -) - -print( - "4）M为必备型需求：表示产品具有该种需求，则用户满意度不会提高；没有该种需求，则用户满意度会降低。建议给予P2关注。" -) - -print( - "5）I为无差型需求：表示产品具有该种需求，则用户满意度不会提高；没有该种需求，则用户满意度不会降低。建议给予P4关注。" -) - -print("6）R为反向型需求：建议给予关注。") - -print("7）Q为可疑型需求：建议给予关注。") - -print( - "8）Better_Score为Better分数。表示如果产品具有某种需求，则用户满意度会提高，数值越大提高越大。" -) - -print( - "9）Worse_Score为Worse分数。表示如果产品没有某种需求，则用户满意度会下降，数值越小下降越大。" -) - -print("") +print("已完成") diff --git a/KANO/result.xlsx b/KANO/result.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..6c91fbc27e005b336151a0587387be4695c68ec3 GIT binary patch literal 5518 zcmZ`-1yq|^w+&j{DHNv^C~l#+yGx-2cPkLwp}4ygDAwXmaBXq7;BLhkpcF06OJ~;m zXNLE`B;WU~td)Ik&R%z)bM97EKtLn_001Ze+-MtJ>3${IkFal}unPxvnc17FI@voo zvl%-$uzJ|pDnXUdyVx<1*W1Q39V~Wnw+z1a3T{%U&4vSl(foLw(k;V66szEmcp$Edf%gm1HVhV1XmklLTM z5UKtvg`07r&I8zAABF<}aQ~TtslAi=?;Hvelx(`$F+v~Z2Zhvx_l){33QUVIk(q1{ zEzIgMqsUEdUfut4u6eUE4@NL7|20ALLg5_#R#YYD7eP}&6N%k!u4wyU?1%wCQZR9* zS~ghWdmHvU=ax_ekC@{dQPtFKOpu8k&?YUo*)V!R+uA|xCT@HbK1h^Y+lefPk+G>p zv9nKvbjOUkf1Y)k9iw-AL~pS;aIQaW*Yoyoh1%>GfgwDK<4+BSz+#>~jqfZ0lPTtiLA-jJ$po098_a9AV8JP!_EON_2Lyzcbc zw)bvvRoEQ5&ri-70A?ozQTdJ87i}r)gn!rQ>gjlIDe}&ye_~SBKXA}D8L36?GH;3y zg%eYNKMOSG=5yI|^f`KH(FJHnvc7rZ!P_uA8RbBRxUSZk-IaB2+nz6mT9;=%ZZ?0p zSeZ~*8uM0V>X+}@L55W(T8ysBl#Tn!RBS|F#kdD%+lfk8cV$Fh*|^JrH68tZrA_|+ zb1QM_%>yqy!uoB|DJlTS!EJVA5+TKa;l?$YWP!YOMNID}=m%c}?n7TM;;i-Lixi-= zQ&v0@7J#Q`LQ&NRBGwSO-jz717e5PSs8=OHNgoKfkO`#jU54CWq_z&`b9rU?@{idh z`&pO46&`k2!p0Mm@Qu(KQaE2Mgdk@snAK`#>)*6AEZKp~u%~ z2g-t$l}V&N)|E?;@1=?im}ah|qxR)ovh*vd&jTziTe!USa+sX>A}Cp* zY426Z;PX*AQgTAC>iWuY*YSND+fWA9V#0rw*{2I><$&ZCgRgL;Nj5T_cbq3|Z=V%N zPh4t#UCv7d5yGVsuK`QBnR{nFK6ac`WD1hwu=tQU zCHga&?bVHZLY#FiO_2-T=pGkIr!<7rHOy*K(CY!|I+GiXEP<39-jcLdnmX9J^(O`7 zRcv}i7cP^yu*r)zBu@3u0~#Dlf{k?W`X)S-C+l$Uw9C1ZgpCFcM-yDfW)rY*6AgG^ zza5K;$8Dzw<&{rA%5?-G-e((h%XRJr4qMzeeF&Z5vSp6_B{NMVzlBX#B(l#5e3qB4 zjruM4-TTj_VL;(x3QjbnlV%FxZ7&Zro|L|?s+ED>aE}va`Lvmb% znW5K$hP!p6=v)$wP6~fCB)8Sm@yef9k2ok(3}Xmb3MP^_xPKw%zP;3U+oCC8f14`N zL?YgXVAqnh%+H&hme7ngr5aV&F3duJUtqJXzCZqC)xHvlnmMcA4aQGfUK|dhakOgS{=VYk00NQTgF= zW%CA{Bp|Dp>Dh}5=OP79^N|esIT7cU>W{>DWPG^MivIn~F0sL`{Bz!Ms|guXV5H%bERMap!&91V?GaN?5l9GJrl z5eVX;Wr#^yAVcw!aN>YkW2IL1{pgd?RdpZx~-gD)hTd?nlAcz{se4 zVb9xpW*?d+Kb_T-oiB`UDE9R^Uq=k*j!OyInZ;uYf^+t=PsKX0-ej&r*s+M1N?*H3 zV|7|cPW2RmLm-P|v6y=!03vME(W z);u7fj9KMEKVKnu_;R>b;f0IJUtgnw_GjY!tRvp@xxbBAPTpM62o#h@o)(9%=q9dBw_`^7(jMQdOX-8cYlU7pH`v8KnGrMs0Q|4CxBPj{aPB|A>AhRFcG!OJ=8FqDHt!(uJHTbh*T}l9kzk_0t38&<(b;K zSW7up-~HE(9YW{3g5`;`H4|o~8ruC0j*S&}5FXIeo(#Jd`6l;!@Bn}%CIA5Y z{2u9?T|8~goqrE=D_TpDOYAuJgqzsK_+jZyJpmQes#e2YKO_u4f*O!&wuYQ!FAvHR zd{)es>)1m0nG<$KKJk&nY#mlkXek@Y%h$V?G+E)bPq#b}AdZ&g6Iu%oq@x9GscTAR zlrRPszDqX4jAx_>F4H8?2uAg5$G3AWcG=7_V{+Mm_^{0%J26O4^|>Q&Dlq<$g{pFw z?BJ#`!j}|zQdS!_>y)U+s6xsOJeixF{>m5T%7av*Uml5CQqJvQy-M$`9_?`8a2Tjq zQ@mlN0l~WbRD?!}&L34<4HYN5ok{bEKJ$!-Hf}GAnAL~~`ox=9ziP5_b)l1iM;NI$ z_g&`EQJRKtn94d~Y(_>+E5xXaJa>_e=Ha!b^bD2Ax%bqEe(TBr2uN))Af=w=+qBL* z_90@1d@8W7kezfRdHPjL9eL;s4`5ArB8cGN5GXXjUKw=tjA6{XrU`6>=AN6`E}Bev zt@^D#!gWx{J-*HC5<@KMT;lC1c(2O^e*lQN8>q4YeqaTUj;Z^BNAZHQ9&HN>p^-a> zCcCLV1pXn0ao@YGJxu9cM z4|5g9BJackIG8rD7h++}!!2XscF@$%xhj!l zbNyUg8%vT3FrinBk-Y6;Rr%>PYwE$B zyR9^J%c))$s)pz%E2=^5Fs>JWq{7rb4nVg%%UBh4_pG)Xr)8^q-Le%jgb+N9!|NCg z1f-+$?iGF^M|v5rmt<(%h;ztQiX*gxu1c1N17w)|rWQ=d7>HeCxJSKcyFnZPkTmO1 zVMPo?Xa(eOilU%hi2Pjr1`T`=B8q<0Ir4kbq#9)oB0FGGcn*^VI_zoaWNz!s#`^o7 zm8bxQ=fDt!;F%jLmh`}Ll+Ra5grhe_?B;#yNigj>zl@phpt8bMc+AdbfnWo_xRE(8 z71;K%_vkIWKf_a$GJ;axKei5A(sJ`DE434 z|J{vE?CouSt3M`wLK$|bTdZ^nBa|S3&6I7!)@` z==8FUGk7|yyVF2WXJqMWV3Lmx zL2YKIm6%go?^s#DC1uJnvCEMzpxQ!IJ4Ts>D6dv)>~uI5vGnS#MqZ5+@AX>9yoJnp zlSI&|cIo+>FPu?WEZI4%nmL?nlluw8&@IUfrCu||61r|9bY{?f0?))w>*{rW`0BUb zk%DwWxOpmXInoG7&>&6lcFsY_j|r5Y^5}x>7vwFzUBSfHQn4rTDW=iGQ{{NXKT9khrLdJd2MDiZ4Z@kD*N{oa0GaT5sdoVYOoW5#e%gYWww% zQ}ln6%XP5XQUN3N7Dh11U*!I^k^V{XpPQ;aK?o~|9U}ySw+$dJp3Z?UWGyEo3J<)- z4UnuZ>pCLs1jv%Nuxmq8+JBi@vH>H#1=QvQ@XoC0hRpj9DFFEbWQ0<)-wEhqF7h_0 ze9@Kn#p+S4$<{h4U(^i5Dyp$yJyI_g?sE%Qe2C*c!DjaGlGdEPGse*d_{9xlrk{O}m!KAPR z=U_A4Z$m&=!XE5m4t6or@N_VD*8jbWDw3eTcTt~V!5^;gxp*B!4_Kp_4m;3;d-U^m zKAYIP*vBo^$bYHE{HT!Eclpctj<&4P6mf*#Qs|wh1Oa7;=!}07C=?}|##L4s@3Al| zT)p1u*~W)v9~?#xcon1zl54N}0G%H!=1_9`Jk+)!e9%a35l6M9W^?oe32nsY#`*=e z{E5`-W~YJ9K1n?X@$87&aO!iW?;2NL{>jmywoLfnWc#giYvK2;;D(1uEL$*K?Fjb1 zT*fNMN2BKVA^T^t$V~JS0#D+N?^Z{_M)A$EThn%ExC`}&e!E$=`(;rB;_(A|d|Sb0 zM+@}9tB#mA?|4