Python，接受1个位置参数，但给出了2个
我将尝试在Spyder IDE Python 3.6中运行一个Python脚本，随着我的进步，我将开始将其重构为更多面向对象的OOP，希望是一个成熟的包 我有一个名为Epoch的课程，见下文：Python，接受1个位置参数，但给出了2个,python,python-3.x,oop,Python,Python 3.x,Oop,我将尝试在Spyder IDE Python 3.6中运行一个Python脚本，随着我的进步，我将开始将其重构为更多面向对象的OOP，希望是一个成熟的包 我有一个名为Epoch的课程，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 stimuli
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)
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)
#median
def median(self,value):
self._median = value
def median(self):
return self._median
使用第二个定义，该定义不接受第二个名为value的参数。因为他是正确的，所以添加到另一个答案中。尝试将您的函数更改为getter和setter。从长远来看应该会有所帮助 python中没有方法重载。如果定义两次中值函数，将使用第二个定义。因此，在您的代码中，使用了第二个定义，即
def median(self):
return self._median
@property
def median(self):
return self._median
@median.setter
def median(self, value):
self._median = value
有关python中的getter和setter的更多信息，请阅读本文：是什么让您认为您的类可以有两个同名的方法？您有两个def media-一个版本接受参数，另一个版本不接受参数。请提供一个简单可行的示例。此外，您正在尝试进行python中不存在的方法多态性，至少您尝试的方式不是这样。为此，您应该看看属性。@StefanPochmann我怀疑这是因为他们学会了用另一种语言编写代码：Python不支持重载。@TemporalWolf对推测不太感兴趣，对不起。这不是OP当前使用self.media的方式，但这是OP想要做的事情的正确方式。没错。感谢您的澄清。@TemporalWolf我想说，正确的方法是在需要实际控制访问之前不要使用属性。就目前情况而言，蟒蛇式的方法就是省略所有这些methods@TemporalWolf，我更新了我的Epoch类，现在当我运行我得到的代码时：item.mediannp.medianraw\u float\u数组TypeError:'int'对象不可调用，我在stackoverflow上读到，由于在构造函数中位数=0中中位数被设置为0，所以中位数被视为int而不是函数。@AryanAIM，因为它是一个属性，而不是函数：item.median=np.medianraw\u float\u数组