一、功能介绍

1、将水均衡数据(.ZOT)文件中的各水均衡组分提取到表格（.xls）文件

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• 按关键字进行数据分类，关键字优先级依次为：层、补\排、各均衡组分
• 合并每层中补给或排泄的越流项，并将原文件中各均衡组分的英文名称翻译为中文
• 数据导出至表格文件中的三个工作表“All”、“In”、“Out”中
  

2、绘制各层补给、排泄项水均衡组分扇形图

• 不绘制数值为0的水均衡项
• 单个扇形图中，占比最高的一项会突出显示
  

二、代码

import re import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文 def read_zot_file(in_zot): """ 寻找.ZOT文件中含有等号的行，将其中的空格删去，并将 :param in_zot: MODFLOW模拟后生成的水均衡文件(.ZOT) :return: 元素为字符串的列表，格式为["均衡组分名a,数值1","均衡组分名b,数值2",......] """ with open(in_zot) as f: lines = [line.strip().replace(' ', "").replace('=', ',') for line in f if line.find("=") != -1] return lines def class_by_layer(datas): """ :param datas: 格式为["均衡组分名a,数值1","均衡组分名b,数值2",......],由方法read_zot_file()方法 :return: in_comp：[[layer1补给项],[layer2补给项],[layer3补给项]] out_comp：[[layer1排泄项],[layer2排泄项],[layer3排泄项]] """ # 各层级的关键词 sep_layer = "PercentDiscrepancy" # 层分隔标志符 sep_in = "TotalIN" # IN标志符 sep_out = "TotalOUT" # OUT标志符 # 各级关键字所在的位置 in_index = [] out_index = [] layer_index = [] for i, v in enumerate(datas): if sep_in in v: in_index.append(i) elif sep_out in v: out_index.append(i) elif sep_layer in v: layer_index.append(i) # 分离各层 index_s = 0 in_comp = [] out_comp = [] for i in range(len(layer_index)): in_comp.append(datas[index_s:in_index[i]]) out_comp.append(datas[in_index[i] + 1:out_index[i]]) index_s = layer_index[i] + 1 return in_comp, out_comp def translate(eg_words, dict_trans): zh_words = { 
   } # 补给项、排泄项的翻译字典，默认如下 for index, words in enumerate(eg_words): list_temp = { 
   } temp = 0 for word in words: p = word.split(',') if p[0] in dict_trans.keys(): # 如果字典存在该英文单词，翻译成对应中文 list_temp[dict_trans[p[0]]] = p[1] elif re.match(r'Zone\dto\d+', p[0]): # 如果该项原格式为'Zone\dto\d+'，则记录其值至temp temp += float(word.split(',')[1]) else: # 如果字典不存在该英文单词，不翻译 list_temp[p[0]] = p[1] list_temp["越流"] = temp zh_words["layer{}".format(index+1)] = list_temp return zh_words def draw_pieplots(df1, df2): # 绘制饼状图组图 size = (2, df1.shape[1]) plt.subplots(size[0], size[1]) plt.figure(figsize=(15, 10)) for i in range(size[0] * size[1]): plt.subplot(size[0], size[1], i + 1) target = "layer{}".format(i % size[1] + 1) if i // size[1] == 0: df = df1[df1[target] != 0][target] else: df = df2[df2[target] != 0][target] highlight = list(df).index(max(df)) explode = [0.05 if j == highlight else 0 for j in range(len(df.index))] plt.pie(x=df, explode=explode, labels=df.index, autopct="%.3f%%") plt.title("{}".format(target)) plt.legend() if __name__ == "__main__": dict_in = { 
   'CONSTANTHEAD': "定水头", "WELLS": "井", "RIVERLEAKAGE": "河流入渗", "ET": "潜水蒸发", "RECHARGE": "降雨入渗", "STORAGE": "储存量", } dict_out = { 
   'CONSTANTHEAD': "定水头", "WELLS": "井", "RIVERLEAKAGE": "河流排泄", "ET": "潜水蒸发", "RECHARGE": "降雨入渗", "STORAGE": "释放量", } # 输入zot文件，并读取 # ！！！在此更改要转换的（.ZOT）文件路径，例如C:\sim_2\XXX.ZOT in_path = "zot3.ZOT" zufen = read_zot_file(in_path) # 分离补给项、排泄项 supply, consume = class_by_layer(zufen) # 将其中的英语单词翻译成中文 df_in = pd.DataFrame(translate(supply, dict_in), dtype='float') df_out = pd.DataFrame(translate(consume, dict_out), dtype='float') # 绘制饼状图组图 draw_pieplots(df1=df_in, df2=df_out) # 计算补给项和排泄项的总和 df_in.loc["总补给"] = df_in.sum() df_out.loc["总排泄"] = df_out.sum() df_all = pd.concat([df_in, df_out], axis=0) df_all.loc["补排差"] = df_all.loc["总补给"] - df_all.loc["总排泄"] # 导出至excel # ！！！在此更改要转换的（.ZOT）文件路径，此处为zot2.xls with pd.ExcelWriter(r'zot2.xls') as writer: df_all.to_excel(writer, sheet_name="All") df_in.to_excel(writer, sheet_name="In") df_out.to_excel(writer, sheet_name="Out") # 绘图，可选 plt.show()