Question

我想请你帮忙

我有一个数据框，其中包含来自不同受试者（eda1，eda2，eda3等）的皮肤电活动（EDA）变量。我手动计算主题的Cross Recurrence Quantification Plot和复发率作为同步的量度。

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但是我需要针对所有受试者（eda1raw1，eda1raw2，... eda1rawN）的复发率（缩写：RR）的自动函数或迭代函数。我有许多EDA变量，我不想写：

from pyunicorn.timeseries import CrossRecurrencePlot, RecurrencePlot
from nolitsa import data, dimension, delay
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import pylab

eda1 = data['eda1raw'] 
eda2 = data['eda2raw']

eda1z = stats.zscore(eda1)
eda2z = stats.zscore(eda2)

DIM = 5  
TAU = 10  
METRIC = "supremum" 
THRESHOLD=0.5 
crp = CrossRecurrencePlot(eda1z, eda2z, dim=DIM, tau=TAU, metric=METRIC, normalize=False, threshold=THRESHOLD) 
print "Recurrence rate - RReda1vs2z:", crp.recurrence_rate()

最终结果应为所有受试者的EDA复发率矩阵：

我尝试了一些事情：

crp = CrossRecurrencePlot(eda1z, eda2z, dim=DIM, tau=TAU, metric=METRIC, normalize=False, threshold=THRESHOLD) 
crp1 = CrossRecurrencePlot(eda3z, eda4z, dim=DIM, tau=TAU, metric=METRIC, normalize=False, threshold=THRESHOLD) 
...

然后转置到数组。

Answer 1

MikulášMuroň的解决方案：

# Save data to list
input_data = [xd, yd, cd, dd, ed, fd, gd, hd, iid, jd, kd, ld, md, nd, od, pd]
# transform to np.array
input_data_numpy_array = [np.array(x) for x in input_data]
# counting zscore
zscore_data = [stats.zscore(x) for x in input_data_numpy_array]
# counting phasic diemnsion of EDA
eda_data = [cvxEDA(x, delta=1) for x in zscore_data]

# processing data - actual and next element
for eda_data1,eda_data2 in zip(eda_data, eda_data[1:]):
    crp = CrossRecurrencePlot(eda_data1.next(), eda_data2.next(), dim=DIM, tau=TAU, metric=METRIC, normalize=False, threshold=THRESHOLD)
    pylab.imshow(crp.recurrence_matrix())
    pylab.xlabel("$n$")
    pylab.ylabel("$n$")
    pylab.show()

    print("Recurrence rate:", crp.recurrence_rate())

交叉递归图计算迭代的问题

1 个答案: