setting an array element with a sequence requested array has an inhomogeneous shape after 1 dimensions The detected shape was (2,)+inhomogeneous part

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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)

Super confused. When I run my code, I keep on getting this error:

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.

Traceback:

File "/Users/mikaelhaji/Downloads/EEG-EyeBlinks/read_data.py", line 170, in powers = np.log10(np.asarray(feature_vectors, dtype=float)) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-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.

If you have any thoughts/ answers as to why this may be occurring, please let me know.

Thanks in advance for the responses.

k-means python-3.x numpy arrays python
2 Answers

Here's a simple case that produces your error message:

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.

If I omit the float, it makes an object dtype array - with warning.

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)
user_9019250

I was getting the same error. I was opening a txt file that contains a table of values, and saving it into a NumPy array defining the dtype as float since otherwise, the numbers would be strings.

with open(dirfile) as fh:
    next(fh)
    header = next(fh)[2:]
    next(fh)
    data = np.array([line.strip().split() for line in fh], float)

For the previous files, it worked perfectly however for the last file it did not: The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (35351,) + inhomogeneous part.

However when I ran data = nploadtxt(fh) a new error appeared: Wrong number of columns at line 35351

So, my problem was that the last line of the file was missing the values of the two last columns. I corrected it in the txt file since I wanted to have the same structure of a numpy.array(dtype=float) and everything worked fine.

user_94616000
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