How to add new rows in each for loop to an array in order to create a matrix (m,n) in python?

Question

I am working on creating a matrix of features from a database of signals.

I want to calculate some features in order to end up with a matrix. Each row corresponding to each signal, and 4 columns corresponding to each assessed feature.

I have searched and I can't understand how to properly insert or add a new row with the features for each signal, for every for loop while I assess the features.

This is the code I'm following on:

The.mat file is attached to this link HERE

import numpy as np
import scipy.io as sio
from scipy import stats

mat=sio.loadmat('signal_1.mat')

size=mat['signal_1']
a,b=size.shape
calc=[]

for i in range(a):

    signal=mat['signal_1'][i][0]

    def function(signal):


        x = signal

        mu=np.mean(x)                          
        mini=np.min(x)
        maxi=np.max(x)
        ran=maxi-mini

        values = np.column_stack((mu,mini,maxi,ran))

        return values

    calc.append(function(signal)) 

Which creates a list as follows:

That is inconvenient because I need to have an array with the shape (n,4), being n= a (number of signals).

This is is the desired result:

To sum up,

-How can I create the calc list as a float64 array with size (n,5)?

-How can I replace this line calc.append(function(signal)) to add each row to the array of the assessed features corresponding to each for loop?

-or what is the most efficient way to properly add each row?

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PD: if I try this conversion calc=np.array(calc) ,it doesn't work and gives me a very weird float64 array with size (9,1,4)

Answer 1

Just create an empty array features_mat and fill it with your features by iterating on all your signals:

import numpy as np
import scipy.io as sio

mat = sio.loadmat('signal_1.mat')

# number of signals in .mat file
n = mat['signal_1'].shape[0]
# get the signals
signals = mat['signal_1'][:,0]

def get_features(signal):
    mu = np.mean(signal)                          
    mini  = np.min(signal)
    maxi = np.max(signal)
    ran = maxi-mini
    return  np.array([mu,mini,maxi,ran])

# pre-allocate memory without initializing it
features_mat = np.empty((n,4))
for i, signal in enumerate(signals):
    features_mat[i,:] = get_features(signal)


>>> np.array([[ 4.07850385e+00, -2.10251071e-01,  7.06541344e+00, 7.27566451e+00],
              [ 8.31759999e-02, -2.61125020e-03,  1.50838105e-01, 1.53449355e-01],
              [-5.55470935e+00, -5.81185396e+00, -5.17208787e+00, 6.39766089e-01],
              [-1.36478103e+01, -1.46263278e+02,  1.46379425e+02, 2.92642704e+02],
              [ 3.22094459e+00,  1.00760787e+00,  5.55007608e+00, 4.54246820e+00],
              [ 4.36753757e+01,  3.57114093e+01,  4.93010863e+01, 1.35896770e+01],
              [ 1.71242787e+00, -2.25392323e-01,  3.59933423e+00, 3.82472655e+00],
              [-1.73530851e+00, -2.00324815e+00, -1.35313746e+00, 6.50110688e-01],
              [-5.83099184e+00, -6.98125270e+00, -4.75522063e+00, 2.22603207e+00]])

Output has desired shape and seems to contain the features you're looking for. Tell me if this works.

Hope this helps.