Python 电导率计算：从CSV数据到结果分析

import numpy as np import pandas as pd

def load_data(pandas_dir): '读取CSV数据并进行预处理' raw_data = pd.read_csv(pandas_dir, encoding='utf-8') regular_data = {'name': [], 'magnitude': [], 'phase': [], 'frequency': []} freq_list = [1, 2, 3, 7, 11, 17, 23, 31, 43, 61, 89, 127, 179, 251, 349]

for it in freq_list:
    regular_data['name'].extend(
        raw_data['name'] + raw_data['name'].isin(['saline']) * raw_data['property']
    )  # 盐水考虑浓度属性，脑脊液不考虑, 在新数据中命名为盐水+浓度
    regular_data['magnitude'].extend(raw_data[str(it) + 'kHz_mag'])
    regular_data['phase'].extend(raw_data[str(it) + 'kHz_ang'])
    for i in range(raw_data.shape[0]):
        regular_data['frequency'].append(it)

regular_data['logFreq'] = np.log10(regular_data['frequency'])  # 将频率以对数形式展现
regular_data['R'] = np.array(regular_data['magnitude']) * np.cos(
    np.array(regular_data['phase']) * 3.14 / 180
)  # np.cos默认弧度
regular_data['X'] = np.array(regular_data['magnitude']) * np.sin(
    np.array(regular_data['phase']) * 3.14 / 180
)
regular_data['logMag'] = np.log10(regular_data['magnitude'])  # 在bode图中，幅度也是用对数表示的
# 创建电导率空列表保持行数一致
regular_data['conductivity'] = [0] * raw_data.shape[0] * len(freq_list)
df = pd.DataFrame(regular_data)
generate_conductivity(df)
return df

def generate_conductivity(df): '利用已知浓度盐水的R和电导率映射关系计算待测物体的电导率' conductivity_list = [976, 1987, 3850, 5650, 7450, 9238] # 该电导率列表对应浓度为0.1,0.5的盐水 concentration_list = [0.05, 0.10, 0.2, 0.3, 0.4, 0.50] saline_real_dict = {} # 每个频率，每个浓度的盐水的实部，其中同浓度对应同一个电导率

for it in df['frequency'].unique():  # 对每一个频率
    one_freq_saline_real_list = []

    for con in concentration_list:
        saline_concentration = 'saline' + format(con, '.2f')
        filted_df = df[
            df['frequency'].isin([it]) * df['name'].isin([saline_concentration])
        ]
        avg = np.mean(filted_df['R'])
        one_freq_saline_real_list.append(avg)

    saline_real_dict[it] = one_freq_saline_real_list
    df['conductivity'] += df['frequency'].isin([it]) * np.interp(
        df['R'], one_freq_saline_real_list[::-1], conductivity_list[::-1]
    )

def save_20230213NO1_conductivity(): '将处理后的数据保存到CSV文件' df = load_data(pandas_dir='./2023_2_13No1/2023_2_13_12.csv') filtered_df = df[~df['name'].isin(['air'])] rslt_df = filtered_df[filtered_df['property'].str.contains('A|B|C|D|E|F|G|H') & filtered_df['name'].str.contains('saline|normal|tumor|peritumor')] rslt_df.to_csv('2023_2_13No1_data1.csv', index=True)

def calculate_conductivity(freq_list): '计算特定条件下肿瘤组织的平均电导率' df = pd.read_csv('2023_2_13No1_data1.csv', index_col=0) my_df = [] for i in range(len(freq_list)): the_df = df[ (df['name'] == 'tumor') & (df['property'] == 'A|B|C|D|E|F|G|H') & (df['frequency'] == freq_list[i]) & (df['conductivity'] > 0) ] my_df.append(the_df)

result = []
for item in my_df:
    result.append((sum(item['conductivity'])) / (item.shape[0]))

df_result = pd.DataFrame({'frequency': freq_list, 'conductivity': result})
df_result.to_csv('2023_2_13No1_data2.csv', index=False)

freq_list = [1, 2, 3, 7, 11, 17, 23, 31, 43, 61, 89, 127, 179, 251, 349] save_20230213NO1_conductivity() calculate_conductivity(freq_list)