81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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plt.rcParams['font.family'] = ['Microsoft YaHei']
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def process(df, col):
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df = df[col]
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df = df.sort_values(by=col[1]).to_numpy()
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name = df[:-1, 0]
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data = df[:-1, 1]
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dSum = df[-1, 1]
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lng = len(data) - 1
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value = []
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label = []
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for k in range(lng):
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if data[lng - k] / dSum > 0.01:
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value.append(data[lng - k])
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label.append(name[lng - k])
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else:
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break
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value.append(dSum - np.sum(value))
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label.append('else')
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return value, label
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df = pd.read_csv('result/anl-P.csv')
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value, label = process(df, ['Particle', 'energy deposition'])
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plt.title('能量沉积（按粒子划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/eDep-P.png')
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plt.close()
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value, label = process(df, ['Particle', 'absorbed dose'])
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plt.title('吸收剂量（按粒子划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/aDoes-P.png')
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plt.close()
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value, label = process(df, ['Particle', 'equivalent dose'])
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plt.title('当量剂量（按粒子划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/eqDose-P.png')
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plt.close()
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value, label = process(df, ['Particle', 'effective dose'])
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plt.title('有效剂量（按粒子划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/efDose-P.png')
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plt.close()
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df = pd.read_csv('result/anl-O.csv')
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value, label = process(df, ['Organ', 'energy deposition'])
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plt.title('能量沉积（按器官划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/eDep-O.png')
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plt.close()
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value, label = process(df, ['Organ', 'absorbed dose'])
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plt.title('吸收剂量（按器官划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/aDoes-O.png')
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plt.close()
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value, label = process(df, ['Organ', 'equivalent dose'])
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plt.title('当量剂量（按器官划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/eqDose-O.png')
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plt.close()
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value, label = process(df, ['Organ', 'effective dose'])
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plt.title('有效剂量（按器官划分）')
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plt.pie(value, labels=label, autopct='%0.2f%%')
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plt.savefig('docs/efDose-O.png')
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plt.close()
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