问题描述

import os
import numpy as np
from scipy.signal import *
import csv
import matplotlib.pyplot as plt

from scipy import signal
from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
from brainflow.data_filter import DataFilter, FilterTypes, AggOperations, WindowFunctions, DetrendOperations
from sklearn.cluster import KMeans

#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-IO' 

# Parameters and bandpass filtering
fs = 250.0

# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f] #List of all the files, Lists are randomized, its only looking for file with _data in it
print(list_of_files)
file_sig = list_of_files[file_idx] # Data File
file_stim = list_of_files[file_idx].replace('_data','_labels') #Label File, Replacing _data with _labels
print ("Reading: ", file_sig, file_stim)

# Loading data
if data_folder == 'EEG-IO' or data_folder == 'EEG-MB':
    data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2)) #data_sig would be a buffer
elif data_folder == 'EEG-VR' or data_folder == 'EEG-VV':
    data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2)) 
    data_sig = data_sig[0:(int(200*fs)+1),:] # getting data ready -- not needed for previous 2 datasets
    data_sig = data_sig[:,0:3] #
    data_sig[:,0] = np.array(range(0,len(data_sig)))/fs


############ Calculating PSD ############
index, ch = data_sig.shape[0], data_sig.shape[1]
# print(index)
feature_vectors = [[], []]
feature_vectorsa = [[], []]
feature_vectorsb = [[], []]
feature_vectorsc = [[], []]
#for x in range(ch):
#for x in range(1,3):
#while x < 
#while x>0:
x=1
while x>0 and x<3:
    if x==1:
        data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)

    elif x==2:
        data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)

    for y in range(500, 19328 ,500):
        #print(ch)
        if x==1:
            DataFilter.detrend(data_sig[y-500:y, 1], DetrendOperations.LINEAR.value)

            psd = DataFilter.get_psd_welch(data_sig[y-500:y, 1], nfft, nfft//2, 250,
                                       WindowFunctions.BLACKMAN_HARRIS.value)

            band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
            
            # Theta 4-8
            band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
            
            #Alpha 8-12
            band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
             
            #Beta 12-30
            band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
            # print(feature_vectors.shape)

            feature_vectors[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
            feature_vectorsa[x].insert(y, [band_power_delta, band_power_theta])

        elif x==2:
            DataFilter.detrend(data_sig[y-500:y, 2], DetrendOperations.LINEAR.value)

            psd = DataFilter.get_psd_welch(data_sig[y-500:y, 2], nfft, nfft//2, 250,
                                       WindowFunctions.BLACKMAN_HARRIS.value)

            band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
            
            # Theta 4-8
            band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
            
            #Alpha 8-12
            band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
             
            #Beta 12-30
            band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
            # print(feature_vectors.shape)

            # feature_vectorsc[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
            # feature_vectorsd[x].insert(y, [band_power_delta, band_power_theta])

    x = x+1

print(feature_vectorsa)
powers = np.log10(np.asarray(feature_vectors, dtype=float))
powers1 = np.log10(np.asarray(feature_vectorsa, dtype=float))
# powers2 = np.log10(np.asarray(feature_vectorsb))
# powers3 = np.log10(np.asarray(feature_vectorsc))
print(powers.shape)
print(powers1.shape)

超级困惑。当我运行我的代码时，我一直收到这个错误：

ValueError：使用序列设置数组元素。请求的数组在%1维后具有不均匀形状。检测到的形状为(2，)+不均匀部分。

回溯：

文件"；/Users/mikaelhaji/Downloads/EEG-EyeBlinks/read_data.py"；，第170行，在 POWERS=np.log10(np.asarray(FEATURE_VECTOR，dtype=FLOAT)) 文件"；/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_asarray.py"；，第102行，在一个数组中 返回数组(a，dtype，Copy=False，Order=Order) ValueError：使用序列设置数组元素。请求的数组在%1维后具有不均匀形状。检测到的形状为(2，)+不均匀部分。

如果您对为什么会发生这种情况有任何想法/答案，请让我知道。

提前感谢您的回复。

推荐答案

下面是一个生成错误消息的简单案例：

In [19]: np.asarray([[1,2,3],[4,5]],float)
Traceback (most recent call last):
  File "<ipython-input-19-72fd80bc7856>", line 1, in <module>
    np.asarray([[1,2,3],[4,5]],float)
  File "/usr/local/lib/python3.8/dist-packages/numpy/core/_asarray.py", line 102, in asarray
    return array(a, dtype, copy=False, order=order)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.

如果我省略float，它将生成一个带有警告的对象dtype数组。

In [20]: np.asarray([[1,2,3],[4,5]])
/usr/local/lib/python3.8/dist-packages/numpy/core/_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  return array(a, dtype, copy=False, order=order)
Out[20]: array([list([1, 2, 3]), list([4, 5])], dtype=object)

这篇关于用序列请求的数组设置数组元素在%1维后具有不均匀形状检测到的形状是(2，)+不均匀部分的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！