python代码生成图片

import matplotlib.pyplot as plt import numpy as np def simulate_ecosystem(gender_ratio_variation): # 模拟七鳃鳗雄性比例的变化 if gender_ratio_variation == 'equal': initial_ratio = 0.5 # 初始雄性比例为 50% elif gender_ratio_variation == 'male_dominant': initial_ratio = 0.7 # 初始雄性比例为 70% elif gender_ratio_variation == 'female_dominant': initial_ratio = 0.3 # 初始雄性比例为 30% else: raise ValueError("Invalid gender ratio variation") generations = 50 gender_ratio = np.zeros(generations) gender_ratio[0] = initial_ratio predator_population = np.ones(generations) # 初始捕食者数量为 1 reproductive_success = np.ones(generations) # 初始繁殖成功率为 1 food_resources = np.ones(generations) # 初始食物资源为 1 composite_indicator = np.zeros(generations) # 初始综合指标为 0 for gen in range(1, generations): # 模拟雄性比例的变化 gender_ratio[gen] = gender_ratio[gen - 1] # 模拟繁殖成功率的变化，根据性别比例调整 reproductive_success[gen] = reproductive_success[gen - 1] - 0.005 * abs(gender_ratio[gen] - 0.5) # 根据捕食者数量和繁殖成功率更新下一代捕食者数量 predator_population[gen] = predator_population[gen - 1] * reproductive_success[gen] # 模拟食物资源的变化 food_resources[gen] = food_resources[gen - 1] - 0.02 # 计算综合指标（加权求和） # 这里简单地加权求和，你可以根据实际情况调整权重 composite_indicator[gen] = 0.4 * gender_ratio[gen] + 0.3 * predator_population[gen] + 0.2 * reproductive_success[gen] + 0.1 * food_resources[gen] return composite_indicator def evaluate_population(gender_ratio_variation): # 模拟七鳃鳗种群 composite_indicator = simulate_ecosystem(gender_ratio_variation) # 计算各个评价指标 predation_control = np.mean(composite_indicator) # 综合指标平均值表示对寄生虫的控制 food_chain_role = np.max(composite_indicator) # 综合指标最大值表示在食物链中的重要角色 ecosystem_provider = np.sum(composite_indicator) # 综合指标总和表示生态系统服务提供者 adaptability = np.min(composite_indicator) # 综合指标最小值表示环境适应性 # 模拟其他劣势因素 predation_threat = np.random.uniform(0.1, 0.5) # 捕食其他物种的威胁程度 reproduction_threat = np.random.uniform(0.1, 0.5) # 繁殖受到威胁的程度 overfishing_risk = np.random.uniform(0.1, 0.5) # 过度捕捞的风险程度 environmental_pressure = np.random.uniform(0.1, 0.5) # 环境压力程度 # 计算各个评价指标的权重 weight_predation_control = 0.2 weight_food_chain_role = 0.2 weight_ecosystem_provider = 0.2 weight_adaptability = 0.1 weight_predation_threat = 0.1 weight_reproduction_threat = 0.1 weight_overfishing_risk = 0.1 weight_environmental_pressure = 0.1 # 计算综合评分 overall_score = (weight_predation_control * predation_control + weight_food_chain_role * food_chain_role + weight_ecosystem_provider * ecosystem_provider + weight_adaptability * adaptability - weight_predation_threat * predation_threat - weight_reproduction_threat * reproduction_threat - weight_overfishing_risk * overfishing_risk - weight_environmental_pressure * environmental_pressure) # 可视化结果 labels = ['Predation Control', 'Food Chain Role', 'Ecosystem Provider', 'Adaptability', 'Predation Threat', 'Reproduction Threat', 'Overfishing Risk', 'Environmental Pressure'] values = [predation_control, food_chain_role, ecosystem_provider, adaptability, -predation_threat, -reproduction_threat, -overfishing_risk, -environmental_pressure] # 绘制直方图 plt.figure(figsize=(12, 8)) # 上半部分展示正面因素 plt.subplot(2, 1, 1) plt.barh(labels[:4], values[:4], color=['green' if v >= 0 else 'red' for v in values[:4]]) plt.title(f'Positive Aspects - {gender_ratio_variation.capitalize()} Gender Ratio Variation') plt.xlabel('Score') # 在最大的两个指标上标注红线 max_positive_indices = np.argpartition(values[:4], -2)[-2:] for idx in max_positive_indices: plt.axvline(x=values[idx], color='red', linestyle='--', label=f'Max {labels[idx]}') # 下半部分展示负面因素 plt.subplot(2, 1, 2) plt.barh(labels[4:], values[4:], color=['green' if v >= 0 else 'red' for v in values[4:]]) plt.title(f'Negative Aspects - {gender_ratio_variation.capitalize()} Gender Ratio Variation') plt.xlabel('Score') max_negative_indices = np.argpartition(values[4:], -2)[-2:] for idx in max_negative_indices: plt.axvline(x=values[idx + 4], color='red', linestyle='--', label=f'Max {labels[idx + 4]}') plt.tight_layout() plt.show() # 输出对应的最大优势和劣势的标签 max_positive_labels = [labels[idx] for idx in max_positive_indices] max_negative_labels = [labels[idx + 4] for idx in max_negative_indices] print(f'{gender_ratio_variation.capitalize()} Gender Ratio Variation:') print(f'Max Positive Aspects: {max_positive_labels}') print(f'Max Negative Aspects: {max_negative_labels}') return overall_score # 模拟性别比例为1:1的情况 evaluate_population('equal') # 模拟性别比例为70%雄性的情况 evaluate_population('male_dominant') # 模拟性别比例为30%雄性的情况 evaluate_population('female_dominant')