二向无常 遇而无往

Echo of Starsong

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Tafel曲线的绘制相关内容

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本代码用于物理化学实验中Tafel曲线的绘制，包含了曲线、切线和交点绘制等功能，效果如图。

第一部分：从实验软件生成的txt中批量提取内容到csv表格中

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import pandas as pd
import os

def extract_data_and_save_to_csv(txt_file_path):
    # 加载数据
    try:
        data = pd.read_csv(txt_file_path, skiprows=17, header=None, names=['Potential/V', 'Current/A', 'log(i/A)'])
    except Exception as e:
        print(f"Failed to load data from {txt_file_path}: {str(e)}")
        return

    # 构建输出CSV文件的路径和名称
    output_file_path = os.path.splitext(txt_file_path)[0] + '.csv'

    # 保存到CSV文件
    try:
        data.to_csv(output_file_path, index=False)
        print(f"Data from {txt_file_path} has been successfully saved to {output_file_path}")
    except Exception as e:
        print(f"Failed to save data to {output_file_path}: {str(e)}")

def main(directory_path):
    # 获取目录下所有的TXT文件
    txt_files = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f)) and f.lower().endswith('.txt')]

    # 对每个TXT文件执行数据提取和保存操作
    for txt_file in txt_files:
        txt_file_path = os.path.join(directory_path, txt_file)
        extract_data_and_save_to_csv(txt_file_path)

if __name__ == "__main__":
    directory_path = input("Please enter the directory path: ")
    main(directory_path)

第一步输出和第二步输入的csv格式如图：

第二部分演示程序见：

http://47.122.18.217:8501/

（结构化学课程服务器）

阿尔图罗Arturia
arturia.fun:8501(备案已完成)

2023.11.17起因为负载问题，此端口关闭。欢迎查看其他端口的程序。

（个人服务器，长期）

第二部分：选择csv表格进行绘图操作

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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress
from scipy.interpolate import interp1d
import tempfile
import shutil
import os

# 定义颜色方案
colors = {
    "color1": np.array([99, 178, 238]) / 255,
    "color2": np.array([118, 218, 145]) / 255,
    "color3": np.array([248, 203, 127]) / 255,
    "color4": np.array([248, 149, 136]) / 255,
    "color5": np.array([124, 214, 207]) / 255,
    "color6": np.array([145, 146, 171]) / 255,
    "color7": np.array([120, 152, 225]) / 255,
    "color8": np.array([239, 166, 102]) / 255,
    "color9": np.array([237, 221, 134]) / 255,
    "color10": np.array([153, 135, 206]) / 255
}


def plot_tafel(data, A_1, A_2):
    data['abs_Current/A'] = np.abs(data['Current/A'])

    # 1. 找到电流最接近0的点
    min_current_row = data.loc[data['abs_Current/A'].idxmin()]

    # 2. 在这个点的正负A_1, A_2范围内，找到对应的两个点
    mask = (data['Potential/V'] >= min_current_row['Potential/V'] - A_1) & (data['Potential/V'] <= min_current_row['Potential/V'] + A_2)
    range_data = data[mask]

    # 找到正负A_1, A_2对应的点
    point1 = range_data.loc[range_data['Potential/V'].idxmin()]
    point2 = range_data.loc[range_data['Potential/V'].idxmax()]

    # 3. 在这两个位置的三个点上分别计算切线
    def calculate_tangent_line(data, center_index):
        subset = data.loc[center_index-1:center_index+1]
        slope, intercept, _, _, _ = linregress(subset['Potential/V'], subset['log(i/A)'])
        return slope, intercept

    slope1, intercept1 = calculate_tangent_line(data, point1.name)
    slope2, intercept2 = calculate_tangent_line(data, point2.name)

    # 4. 求出这两条切线的交点
    x_intersect = (intercept2 - intercept1) / (slope1 - slope2)
    y_intersect = slope1 * x_intersect + intercept1

    # 计算切线交点到切点的距离
    distance_1 = x_intersect - point1['Potential/V']
    distance_2 = x_intersect - point2['Potential/V']

    # 对数据进行插值
    interp_function = interp1d(data['Potential/V'], data['log(i/A)'], kind='cubic')
    potential_interpolated = np.linspace(data['Potential/V'].min(), data['Potential/V'].max(), 1000000)
    current_interpolated = interp_function(potential_interpolated)

    # 5. 绘制塔菲尔曲线（使用插值后的数据）、切线和交点
    plt.figure(figsize=(10, 5))
    plt.plot(potential_interpolated, current_interpolated, label='Tafel Plot', color=colors["color1"])
    # 在x坐标轴上标注电流最接近0的点
    plt.annotate(f'Min Current\n({min_current_row["Potential/V"]:.3f}, {min_current_row["log(i/A)"]:.3f})',
                xy=(min_current_row['Potential/V'], min_current_row['log(i/A)']),
                xytext=(min_current_row['Potential/V']-0.15, min_current_row['log(i/A)']+0.05),
                arrowprops=dict(facecolor=colors["color4"], shrink=0.1),
                color=colors["color4"])
    # 标记切线的交点
    plt.scatter(x_intersect, y_intersect, color=colors["color2"])
    # 确定切线最小范围
    mm=0.03
    # 绘制切线，确保切线可见
    x_values_1 = np.linspace(point1['Potential/V'] - max(abs(distance_1),mm), point1['Potential/V'] + max(abs(distance_1),mm), 1000)
    y_values_1 = slope1 * x_values_1 + intercept1
    x_values_2 = np.linspace(point2['Potential/V'] - max(abs(distance_2),mm), point2['Potential/V'] + max(abs(distance_2),mm), 1000)
    y_values_2 = slope2 * x_values_2 + intercept2

    plt.plot(x_values_1, y_values_1, label='Tangent Line 1', linestyle='--', color=colors["color3"])
    plt.plot(x_values_2, y_values_2, label='Tangent Line 2', linestyle='--', color=colors["color4"])
    # 标记切点
    plt.scatter([point1['Potential/V'], point2['Potential/V']], [point1['log(i/A)'], point2['log(i/A)']], color=colors["color5"], alpha=0.5)
    # 在图的右下角显示交点坐标
    plt.text(0.78, 0.05, f'Intersect\n({x_intersect:.3f}, {y_intersect:.3f})', color=colors["color2"], ha='left', va='bottom', transform=plt.gca().transAxes)

    # 设置图表标题和坐标轴标签
    plt.title('Tafel Plot with Tangent Lines and Intersection')
    plt.xlabel('Potential (V)')
    plt.ylabel('log(i/A)')

    # 显示图例
    plt.legend(loc='upper left')

    # 显示图表
    plt.grid(True)
    st.pyplot(plt)  # 使用st.pyplot()来显示图表

# Streamlit 应用
st.title("Tafel Plot Analysis")

#文件上传
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
    # 获取上传文件的文件名（不包含扩展名）
    file_name, _ = os.path.splitext(uploaded_file.name)

    data = pd.read_csv(uploaded_file)

    # 自定义参数
    A_1 = st.number_input("输入左切点偏离值", value=0.06)
    A_2 = st.number_input("输入右切点偏离值", value=0.06)

    # 绘制图表
    plot_button = st.button("Plot Tafel Curve")
    if plot_button:
        plot_tafel(data, A_1, A_2)
        # 保存到临时文件
        temp_filename = tempfile.mktemp(suffix=".png")
        plt.savefig(temp_filename)
        st.session_state.temp_filename = temp_filename

    # 添加保存图片的按钮
    if "temp_filename" in st.session_state:
        save_button = st.button("Save Plot")
        if save_button:
            # 将临时文件移动到目标位置
            shutil.move(st.session_state.temp_filename, f"{file_name}.png")
            st.success("Plot saved successfully!")
            # 清除session状态中的临时文件名
            del st.session_state.temp_filename
else:
    st.write("Please upload a CSV file to proceed.")