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  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from scipy import stats
  4. from scipy.interpolate import UnivariateSpline
  5. import warnings
  6. warnings.filterwarnings('ignore')
  7.  
  8. plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
  9. plt.rcParams['axes.unicode_minus'] = False
  10.  
  11. class SulfosalicylateIronExperiment:
  12.     def __init__(self):
  13.         self.fe_concentration = 0.001
  14.         self.ligand_concentration = 0.001
  15.         self.hclo4_concentration = 0.01
  16.         self.hclo4_volume = 10.0
  17.         self.total_mixing_volume = 10.0
  18.         self.total_volume = self.hclo4_volume + self.total_mixing_volume
  19.         self.mole_fractions = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
  20.         self.absorbances = None
  21.         self.absorbances_all = None
  22.  
  23.     def input_data(self):
  24.         print("=== 磺基水杨酸铁配位数和稳定常数测定实验数据处理 ===")
  25.         print("\n请输入11个数据点的吸光度值 (平行实验):")
  26.         print("格式: 点1实验1 点1实验2 点2实验1 点2实验2 ... 点11实验1 点11实验2")
  27.  
  28.         data_str = input("请输入22个吸光度值: ")
  29.         data = list(map(float, data_str.split()))
  30.  
  31.         if len(data) != 22:
  32.             raise ValueError("必须输入22个吸光度值!")
  33.  
  34.         self.absorbances_all = np.array(data).reshape(11, 2)
  35.         self.absorbances = np.mean(self.absorbances_all, axis=1)
  36.         self.absorbances_std = np.std(self.absorbances_all, axis=1, ddof=1)
  37.  
  38.         print("\n数据输入完成!")
  39.         print(f"摩尔分数: {self.mole_fractions}")
  40.         print(f"平均吸光度: {self.absorbances}")
  41.  
  42.         return self.mole_fractions, self.absorbances
  43.  
  44.     def linear_fit(self, x_data, y_data):
  45.         A = np.vstack([x_data, np.ones(len(x_data))]).T
  46.         m, c = np.linalg.lstsq(A, y_data, rcond=None)[0]
  47.         return m, c
  48.  
  49.     def calculate_intersection(self, m1, c1, m2, c2):
  50.         x_intersect = (c2 - c1) / (m1 - m2)
  51.         y_intersect = m1 * x_intersect + c1
  52.         return x_intersect, y_intersect
  53.  
  54.     def find_curve_intersection(self, x_intersect):
  55.         for i in range(len(self.mole_fractions)-1):
  56.             if self.mole_fractions[i] <= x_intersect <= self.mole_fractions[i+1]:
  57.                 x1, x2 = self.mole_fractions[i], self.mole_fractions[i+1]
  58.                 y1, y2 = self.absorbances[i], self.absorbances[i+1]
  59.                 y_curve = y1 + (y2 - y1) * (x_intersect - x1) / (x2 - x1)
  60.                 return y_curve
  61.         return self.absorbances[np.argmax(self.absorbances)]
  62.  
  63.     def calculate_acid_effect(self, pH=2.5):
  64.         H_plus = 10**(-pH)
  65.         K2 = 10**(-2.6)
  66.         K3 = 10**(-11.7)
  67.  
  68.         alpha = 1 + H_plus/K3 + H_plus**2/(K2*K3)
  69.         lg_alpha = np.log10(alpha)
  70.  
  71.         return lg_alpha
  72.  
  73.     def analyze_data(self):
  74.         if self.absorbances is None:
  75.             raise ValueError("请先输入数据!")
  76.  
  77.         left_indices = [0, 1, 2]
  78.         right_indices = [8, 9, 10]
  79.  
  80.         m_left, c_left = self.linear_fit(
  81.             self.mole_fractions[left_indices], 
  82.             self.absorbances[left_indices]
  83.         )
  84.  
  85.         m_right, c_right = self.linear_fit(
  86.             self.mole_fractions[right_indices], 
  87.             self.absorbances[right_indices]
  88.         )
  89.  
  90.         x_intersect, D1 = self.calculate_intersection(m_left, c_left, m_right, c_right)
  91.  
  92.         D2 = self.find_curve_intersection(x_intersect)
  93.  
  94.         coordination_number = x_intersect / (1 - x_intersect)
  95.  
  96.         if D1 > 0:
  97.             dissociation_degree = (D1 - D2) / D1
  98.         else:
  99.             dissociation_degree = 0
  100.  
  101.         fe_initial_moles = self.fe_concentration * (1 - x_intersect) * (self.total_mixing_volume / 2) / 1000
  102.         complex_concentration = fe_initial_moles / (self.total_volume / 1000)
  103.  
  104.         if dissociation_degree > 0 and dissociation_degree < 1:
  105.             apparent_constant = (1 - dissociation_degree) / (complex_concentration * dissociation_degree**2)
  106.  
  107.             lg_alpha = self.calculate_acid_effect()
  108.             stability_constant = apparent_constant * (10**lg_alpha)
  109.         else:
  110.             stability_constant = float('inf')
  111.             apparent_constant = 0
  112.  
  113.         results = {
  114.             'D1': D1,
  115.             'D2': D2,
  116.             'x_intersect': x_intersect,
  117.             'coordination_number': coordination_number,
  118.             'dissociation_degree': dissociation_degree,
  119.             'stability_constant': stability_constant,
  120.             'apparent_constant': apparent_constant,
  121.             'lg_alpha': lg_alpha,
  122.             'left_fit': (m_left, c_left),
  123.             'right_fit': (m_right, c_right),
  124.             'complex_concentration': complex_concentration
  125.         }
  126.  
  127.         return results
  128.  
  129.     def plot_results(self, results):
  130.         if self.absorbances is None:
  131.             raise ValueError("请先输入数据!")
  132.  
  133.         plt.figure(figsize=(12, 8))
  134.  
  135.         confidence_sizes = 100 + 200 * (1 / (self.absorbances_std + 0.001) / np.max(1 / (self.absorbances_std + 0.001)))
  136.         plt.scatter(self.mole_fractions, self.absorbances, s=confidence_sizes, alpha=0.7, color='blue', label='实验数据点')
  137.  
  138.         for i, (x, y) in enumerate(zip(self.mole_fractions, self.absorbances)):
  139.             plt.annotate(f'{y:.3f}', (x, y), textcoords="offset points", xytext=(0,10), ha='center', fontsize=9)
  140.  
  141.         if len(self.mole_fractions) > 3:
  142.             spline = UnivariateSpline(self.mole_fractions, self.absorbances, s=0.001)
  143.             x_smooth = np.linspace(0, 1, 200)
  144.             y_smooth = spline(x_smooth)
  145.             plt.plot(x_smooth, y_smooth, 'b-', alpha=0.7, linewidth=2, label='实验曲线')
  146.  
  147.         x_left = np.array([0.0, results['x_intersect']])
  148.         x_right = np.array([results['x_intersect'], 1.0])
  149.  
  150.         m_left, c_left = results['left_fit']
  151.         m_right, c_right = results['right_fit']
  152.  
  153.         x_fit_left = np.linspace(0.0, results['x_intersect'], 50)
  154.         y_fit_left = m_left * x_fit_left + c_left
  155.  
  156.         x_fit_right = np.linspace(results['x_intersect'], 1.0, 50)
  157.         y_fit_right = m_right * x_fit_right + c_right
  158.  
  159.         plt.plot(x_fit_left, y_fit_left, 'r--', linewidth=2, label='左侧拟合直线')
  160.         plt.plot(x_fit_right, y_fit_right, 'g--', linewidth=2, label='右侧拟合直线')
  161.  
  162.         plt.text(0.1, 0.1, f'左侧: y = {m_left:.3f}x + {c_left:.3f}', transform=plt.gca().transAxes, fontsize=10, color='red')
  163.         plt.text(0.1, 0.05, f'右侧: y = {m_right:.3f}x + {c_right:.3f}', transform=plt.gca().transAxes, fontsize=10, color='green')
  164.  
  165.         plt.axvline(x=results['x_intersect'], color='gray', linestyle=':', alpha=0.7, label=f'交点X坐标: {results["x_intersect"]:.3f}')
  166.  
  167.         plt.scatter([results['x_intersect']], [results['D1']], color='red', s=100, label='理论交点')
  168.         plt.scatter([results['x_intersect']], [results['D2']], color='green', s=100, label='实际交点')
  169.  
  170.         plt.annotate(f'理论: {results["D1"]:.3f}', 
  171.                     (results['x_intersect'], results['D1']), 
  172.                     textcoords="offset points", xytext=(10,10), ha='left', fontsize=9, color='red')
  173.  
  174.         plt.annotate(f'实际: {results["D2"]:.3f}', 
  175.                     (results['x_intersect'], results['D2']), 
  176.                     textcoords="offset points", xytext=(10,-15), ha='left', fontsize=9, color='green')
  177.  
  178.         plt.xlabel('磺基水杨酸的摩尔分数')
  179.         plt.ylabel('吸光度 (A)')
  180.  
  181.         plt.title('磺基水杨酸铁(Ⅲ)配合物组成与稳定常数测定结果图', fontsize=20, pad=20)
  182.  
  183.         plt.text(0.98, 0.98, "由何同学与D老师联合制作", 
  184.                 transform=plt.gca().transAxes,
  185.                 fontsize=6, color='gray', 
  186.                 ha='right', va='top', 
  187.                 bbox=dict(boxstyle="round,pad=0.2", facecolor="white", alpha=0.8))
  188.  
  189.         textstr = '\n'.join([
  190.             f'配合物组成: ML{results["coordination_number"]:.1f}',
  191.             f'交点X坐标: {results["x_intersect"]:.3f}',
  192.             f'解离度: {results["dissociation_degree"]:.4f}',
  193.             f'稳定常数: {results["stability_constant"]:.2e} L/mol'
  194.         ])
  195.         props = dict(boxstyle='round', facecolor='wheat', alpha=0.8)
  196.         plt.text(0.65, 0.25, textstr, transform=plt.gca().transAxes, fontsize=12,
  197.                 verticalalignment='top', bbox=props)
  198.  
  199.         plt.grid(True, alpha=0.3)
  200.         plt.legend(loc='upper left')
  201.         plt.tight_layout()
  202.         plt.show()
  203.  
  204.         return plt.gcf()
  205.  
  206.     def run_analysis(self):
  207.         try:
  208.             self.input_data()
  209.             results = self.analyze_data()
  210.  
  211.             print("\n" + "="*60)
  212.             print("实验结果分析")
  213.             print("="*60)
  214.             print(f"配合物组成: ML{results['coordination_number']:.1f}")
  215.             print(f"交点X坐标: {results['x_intersect']:.3f}")
  216.             print(f"解离度: {results['dissociation_degree']:.4f}")
  217.             print(f"表观稳定常数: {results['apparent_constant']:.2e} L/mol")
  218.             print(f"酸效应系数 lgα: {results['lg_alpha']:.3f}")
  219.             print(f"热力学稳定常数: {results['stability_constant']:.2e} L/mol")
  220.  
  221.             print("\n生成图表中...")
  222.             self.plot_results(results)
  223.             print("图表已显示，请查看弹出窗口。")
  224.  
  225.         except Exception as e:
  226.             print(f"错误: {e}")
  227.             print("请检查输入数据格式是否正确。")
  228.  
  229. if __name__ == "__main__":
  230.     experiment = SulfosalicylateIronExperiment()
  231.     experiment.run_analysis()
Success #stdin #stdout 1.25s 69580KB
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=== 磺基水杨酸铁配位数和稳定常数测定实验数据处理 ===

请输入11个数据点的吸光度值 (平行实验):
格式: 点1实验1 点1实验2 点2实验1 点2实验2 ... 点11实验1 点11实验2
请输入22个吸光度值: 错误: EOF when reading a line
请检查输入数据格式是否正确。
https://ideone.com/tgnXBr
language:
Python 3 nbc (python 3.7.3)
created:
10 days ago
visibility:
public

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