我试图在Spyder IDE（Python 3.6）中运行Python脚本，随着我的进步，我开始将它重构为更多的OOP（面向对象），并希望是一个成熟的包。
我有一个名为（Epoch）的课程，见下文：
class Epoch:
def __init__(self):
self.Epoch_Counter = 0
self.Timings = []
self.Timings_millisec_ticks = []
self.Duration_milli_secs = 0
self.Raw = []
self.Stimulia = []
self.Majority_stimulia = "NA"
self.epoch = datetime.datetime.utcfromtimestamp(0)
#median, positive area, negative area, geometric,max,ratio positive vs negative
self._median = 0
self._mean = 0
self._positive_area = 0
self._negative_area = 0
self._geometric_area = 0
self._max = 0
self._min = 0
self._max_amplitude = 0
self._integral = 0
self._variance= 0
self._positive_to_negative_ratio = 0
self._skew = 0
self._kurtosis = 0
self.Components = []
#mean
def mean(self,value):
self._mean = value
def mean(self):
return self._mean
#median
def median(self,value):
self._median = value
def median(self):
return self._median
#positive_area
def positive_area(self,value):
self._positive_area = value
def positive_area(self):
return self._positive_area
#negative_area
def negative_area(self,value):
self._negative_area = value
def negative_area(self):
return self._negative_area
#geometric_area
def geometric_area(self,value):
self._geometric_area = value
def geometric_area(self):
return self._geometric_area
def integral(self,value):
self.integral = value
def integral(self):
return self.integral
def max_amplitude(self,value):
self._max_amplitude = value
def max_amplitude(self):
return self._max_amplitude
def max_(self,value):
self._max = value
def max_(self):
return self._max
#min
def min_(self,value):
self.min = value
def min_(self):
return self._min
def positive_to_negative_ratio(self,value):
self.positive_to_negative_ratio = value
def positive_to_negative_ratio(self,value):
return self._positive_to_negative_ratio
#Timings_millisec_ticks
def timings_millisec_ticks(self):
return self.Timings_millisec_ticks
def timings_millisec_ticks_append(self,value):
self.Timings_millisec_ticks.append(value)
#start_time
def timings(self):
return self.Timings
def timings_append(self,value):
self.Timings.append(value)
#end_time
def end_time(self):
return self.Timings[len(self.Timings)-1]
#start_time
def start_time(self):
return self.Timings[0]
#duration
def duration_milli_secs(self):
return self.Duration_milli_secs
def duration_milli_secs(self):
return self.unix_time_millis(self.str_to_datetime_(self.end_time())) - self.unix_time_millis(self.str_to_datetime_(self.start_time()))
#raw
def raw(self):
return self.Raw
def raw_append(self,value):
self.Raw.append(value)
#components
def components(self):
return self.Components
def components_append(self,value):
self.Components.append(value)
#stimulia
def stimulia_append(self,value):
self.Stimulia.append(value)
def stimulia(self):
return self.Stimulia
#variance
def variance(self):
return self.variance
def variance(self,value):
self.variance = value
#skew
def skew(self,value):
self._skew = value
def skew(self):
return self._skew
def unix_time_millis(self,dt):
return (dt - self.epoch).total_seconds() * 1000.0
""" covert datetime of this format 2017-10-13 19:22:50:525 to datetime object"""
def str_to_datetime_(self,datetime_str):
return datetime.datetime.strptime(datetime_str, '%Y-%m-%d %H:%M:%S:%f')
def _print(self):
return str(self.Epoch_Counter)+","+str(len(self.Timings))+","+str(self.duration_milli_secs())+","+str(len(self.Raw))+","+str(len(self.Stimulia))+","+str(self.majority_stimulia)
我的脚本有函数，它计算汇总统计信息并尝试设置Epoch对象中的统计值（平均值，中位数等），见下文：
def get_epoch_summary_stats(eeg_record_epochs_):
import numpy as np
import scipy.stats as stats
import pylab as pl
##stimulia
stimulia_raw_median = []
stimulia_raw_mean = []
stimulia_raw_positive_area = []
stimulia_raw_negative_area = []
stimulia_raw_geometric_area = []
stimulia_raw_max = []
stimulia_raw_min = []
stimulia_raw_positive_to_negative = []
stimulia_variance = []
stimulia_max_amplitude = []
stimulia_integral = []
stimulia_raw_kurtosis = []
stimulia_raw_skew = []
##no stimulia
no_stimulia_raw_median = []
no_stimulia_raw_mean = []
no_stimulia_raw_positive_area = []
no_stimulia_raw_negative_area = []
no_stimulia_raw_geometric_area = []
no_stimulia_raw_max = []
no_stimulia_raw_min = []
no_stimulia_raw_positive_to_negative = []
no_stimulia_variance = []
no_stimulia_max_amplitude = []
no_stimulia_integral = []
no_stimulia_raw_kurtosis = []
no_stimulia_raw_skew = []
no_stimulia_class_count = 0
stimulia_class_count = 0
epoch_durations = []
stimulia_raws = []
no_stimulia_raws = []
for item in eeg_record_epochs:
epoch_durations.append(item.duration_milli_secs())
epoch_durations_sorted = sorted(epoch_durations)
mean_duration = np.mean(epoch_durations_sorted)
std_duration = np.std(epoch_durations_sorted)
print("Input data:",path)
print("Epoch duration(millisecs) - mean_duration:",mean_duration)
print("Epoch duration(millisecs) - std_duration:",std_duration)
#remove epoch that are more than 1 standard deviation away from the mean in epoch size
counter = 0
for item in eeg_record_epochs_:
##DURATION SELECTION RULE
if (item.duration_milli_secs() > (mean_duration + std_duration) or item.duration_milli_secs() < (mean_duration - std_duration)):
del eeg_record_epochs[counter]
##print("epoch duration_milli_secs - REMOVED:",item.duration_milli_secs())
else:
##print("epoch duration_milli_secs:",item.duration_milli_secs())
#median, positive area, negative area, geometric area, max , ratio positive vs negative, ratio negative vs positive
raw_float_array = np.array(item.raw()).astype(np.float)
#median
item.median(np.median(raw_float_array))
#mean
item.mean(np.mean(raw_float_array))
##print("raw median: ",item.median)
positive_area = 0
negative_area = 0
#positive area
for value in raw_float_array:
if value > 0:
positive_area = positive_area + value
item.positive_area(positive_area)
##print("raw positive_area:", item.positive_area)
#negative area
for value in raw_float_array:
if value < 0 :
negative_area = negative_area + abs(value)
item.negative_area(negative_area)
##print("raw negative_area:", item.negative_area)
#geometric area
abs_raw_float_array = np.abs(raw_float_array)
item.geometric_area(sum(abs_raw_float_array))
##print("raw geometric_area:", item.geometric_area)
#max_
item.max(max(raw_float_array))
##print("raw max_:",item.max_)
#min
item.min(min(raw_float_array))
item.max_amplitude(max(max(raw_float_array),abs(min(raw_float_array))))
item.integral(item.positive_area - abs(item.negative_area))
##print("raw min_:",item.min_)
#min
#positive_to_negative_ratio
try:
item.positive_to_negative_ratio=abs(item.positive_area/item.negative_area)
except ZeroDivision as err:
continue
#variance
item.variance(np.var(raw_float_array))
#skew
item.skew(stats.skew(raw_float_array))
#kurtosis
item.kurtosis(stats.kurtosis(raw_float_array))
##print("raw positive_to_negative:",item.positive_to_negative_ratio)
item.majority_stimulia()
##default NO_STIMULIA
stimulia_class = "NO_STIMULIA"
if item.majority_stimulia().upper() == "ON":
stimulia_class = "ON"
print(stimulia_class)
item.plot()
for raw_value in item.raw():
stimulia_raws.append(int(raw_value))
stimulia_raw_median.append(item.median)
stimulia_raw_mean.append(item.mean)
stimulia_raw_positive_area.append(item.positive_area)
stimulia_raw_negative_area.append(item.negative_area)
stimulia_raw_geometric_area.append(item.geometric_area)
stimulia_raw_max.append(item.max_)
stimulia_raw_min.append(item.min_)
stimulia_raw_mean.append(item.mean)
stimulia_raw_skew.append(item.skew)
stimulia_raw_kurtosis.append(item.kurtosis)
stimulia_max_amplitude.append(item.max_amplitude)
stimulia_integral.append(item.integral)
##append only if the number is not inf or nan but just a number
if is_not_inf_or_is_nan(item.positive_to_negative_ratio) != 1:
stimulia_raw_positive_to_negative.append(item.positive_to_negative_ratio)
##print("item.positive_to_negative_ratio:",item.positive_to_negative_ratio)
stimulia_variance.append(item.variance)
stimulia_class_count= stimulia_class_count + 1
#P3 component stats +/- estimated peek for P3
else:
no_stimulia_class_count = no_stimulia_class_count + 1
print(stimulia_class)
item.plot()
for raw_value in item.raw():
no_stimulia_raws.append(int(raw_value))
no_stimulia_raw_median.append(item.median)
no_stimulia_raw_mean.append(item.mean)
no_stimulia_raw_positive_area.append(item.positive_area)
no_stimulia_raw_negative_area.append(item.negative_area)
no_stimulia_raw_geometric_area.append(item.geometric_area)
no_stimulia_raw_max.append(item.max_)
no_stimulia_raw_min.append(item.min_)
no_stimulia_raw_mean.append(item.mean)
no_stimulia_raw_skew.append(item.skew)
no_stimulia_raw_kurtosis.append(item.kurtosis)
no_stimulia_max_amplitude.append(item.max_amplitude)
no_stimulia_integral.append(item.integral)
##append only if the number is not inf or nan but just a number
if is_not_inf_or_is_nan(item.positive_to_negative_ratio) != 1:
no_stimulia_raw_positive_to_negative.append(item.positive_to_negative_ratio)
##print("item.positive_to_negative_ratio:",item.positive_to_negative_ratio)
no_stimulia_variance.append(item.variance)
##print("majority stimulia:",item.Majority_stimulia)
counter = counter + 1
##component_extraction(item,"ON",300,200,800)
print("ON summary stats-")
mean_plot = float(sum(stimulia_raw_mean)/counter)
std_plot = float(math.sqrt(sum(stimulia_variance)/counter))
fit = stats.norm.pdf(sorted(stimulia_raws), mean_plot, std_plot) #this is a fitting indeed
pl.plot(sorted(stimulia_raws),fit,'-o')
pl.hist(sorted(stimulia_raws),normed=True) #use this to draw histogram of your data
pl.show()
print("stimulia_class_count:",stimulia_class_count)
print("average stimulia_raw_mean:",sum(stimulia_raw_mean)/counter)
print("average stimulia_raw_median:",sum(stimulia_raw_median)/counter)
print("average stimulia_raw_positive_area:",sum(stimulia_raw_positive_area)/counter)
print("average stimulia_raw_negative_area:",sum(stimulia_raw_negative_area)/counter)
print("average stimulia_raw_geometric_are:",sum(stimulia_raw_geometric_area)/counter)
print("average stimulia_raw_max:",sum(stimulia_raw_max)/counter)
print("average stimulia_raw_min:",sum(stimulia_raw_min)/counter)
print("average stimulia_max_amplitude:",sum(stimulia_max_amplitude)/counter)
print("average stimulia_integral:",sum(stimulia_integral)/counter)
print("average stimulia_variance:",sum(stimulia_variance)/counter)
print("average stimulia_std:",math.sqrt(sum(stimulia_variance)/counter))
print("average stimulia_skew:",sum(stimulia_raw_skew)/counter)
print("average stimulia_kurtosis:",sum(stimulia_raw_kurtosis)/counter)
print("average stimulia_raw_positive_to_negative:",sum(stimulia_raw_positive_to_negative)/counter)
print("NO_STIMULIA summary stats-")
mean_plot = float(sum(no_stimulia_raw_mean)/counter)
std_plot = float(math.sqrt(sum(no_stimulia_variance)/counter))
fit = stats.norm.pdf(sorted(no_stimulia_raws), mean_plot, std_plot) #this is a fitting indeed
pl.plot(sorted(no_stimulia_raws),fit,'-o')
pl.hist(sorted(no_stimulia_raws),normed=True) #use this to draw histogram of your data
pl.show()
print("no_stimulia_class_count:",no_stimulia_class_count)
print("average no_stimulia_raw_mean:",sum(no_stimulia_raw_mean)/counter)
print("average no_stimulia_raw_median:",sum(no_stimulia_raw_median)/counter)
print("average no_stimulia_raw_positive_area:",sum(no_stimulia_raw_positive_area)/counter)
print("average no_stimulia_raw_negative_area:",sum(no_stimulia_raw_negative_area)/counter)
print("average no_stimulia_raw_geometric_are:",sum(no_stimulia_raw_geometric_area)/counter)
print("average no_stimulia__raw_max:",sum(no_stimulia_raw_max)/counter)
print("average no_stimulia_raw_min:",sum(no_stimulia_raw_min)/counter)
print("average no_stimulia_max_amplitude:",sum(no_stimulia_max_amplitude)/counter)
print("average no_stimulia_integral:",sum(no_stimulia_integral)/counter)
print("average no_stimulia_variance:",sum(no_stimulia_variance)/counter)
print("average no_stimulia_std:",math.sqrt(sum(no_stimulia_variance)/counter))
print("average no_stimulia_skew:",sum(no_stimulia_raw_skew)/counter)
print("average no_stimulia_kurtosis:",sum(no_stimulia_raw_kurtosis)/counter)
print("average no_stimulia_raw_positive_to_negative:",sum(no_stimulia_raw_positive_to_negative)/counter)
运行脚本时，出现错误：
文件＆＃34;＆＃34;，第1行，in get_epoch_summary_stats（eeg_record_epochs）
文件＆＃34;＆＃34;，第425行，在get_epoch_summary_stats中 item.median（np.median（raw_float_array））
TypeError：median（）需要1个位置参数但是2个被赋予
如何修复Epoch类或我对item.median（）的调用，以便设置Epoch的中位数属性而不会出现此错误。
万分感谢！
答案 0 :(得分：2)
python中没有方法重载。如果定义median函数两次，将使用第二个定义。因此，在您的代码中，使用第二个定义，即
def median(self):
return self._median
所以它只需要一个位置参数（self）。
你想要定义的似乎是一个getter和一个setter。在python中，您可以通过添加功能装饰器来实现。例如：
@property
def median(self):
return self._median
@median.setter
def median(self, value):
self._median = value
第一个是median的吸气剂，第二个是定位器。当您调用print(self.median)时，将调用第一个函数，而如果执行self.median = 5，则将调用第二个函数。
您可以阅读本文，了解有关python中getter和setter的更多信息：http://stackabuse.com/python-properties/
答案 1 :(得分：1)
您要定义median两次：
#median
def median(self,value):
self._median = value
def median(self):
return self._median
使用第二个定义，它不接受名为value的第二个参数。
答案 2 :(得分：0)
添加其他答案，因为他是对的。尝试专注于将功能更改为getter和setter。从长远来看应该有所帮助。