<p>如果不想从类外显式调用函数read_file。然后,您可以将程序转换为:</p>
<pre><code>class Result_analysis():
def __init__(self, confidence_interval):
self.confidence_interval = confidence_interval
def read_file(self, file_number):
dict_ = {1: 'Ten_Runs_avg-throughput_scalar.csv',
2: 'Thirty_Runs_avg-throughput_scalar.csv',
3: 'Hundred_Runs_avg-throughput_scalar.csv',
4: 'Thousand_Runs_avg-throughput_scalar.csv'}
cols = ['run', 'ber', 'timelimit', 'repetition', 'Module', 'Avg_Throughput']
data = pd.read_csv(dict_[file_number], delimiter=',', skiprows=[0], names=cols)
df = pd.DataFrame(data)
return df
def extract_arrays(self,file_number):
df = Result_analysis().read_file(file_number)
avgTP_10s_arr = []
avgTP_100s_arr = []
avgTP_1000s_arr = []
for i in range(len(data)):
if (df['timelimit'][i] == 10):
avgTP_10s_arr.append(df['Avg_Throughput'][i])
elif (df['timelimit'][i] == 100):
avgTP_100s_arr.append(df['Avg_Throughput'][i])
elif (df['timelimit'][i] == 1000):
avgTP_1000s_arr.append(df['Avg_Throughput'][i])
return avgTP_10s_arr, avgTP_100s_arr, avgTP_1000s_arr
</code></pre>
<p>调用函数<code>extract_arrays</code>,并将<code>file_number</code>作为参数传递</p>