Question

我看到有一个array_split和split methods但是当你必须拆分一个长度不是块大小的整数倍的数组时，这些都不是很方便。而且，这些方法输入是切片的数量而不是切片大小。我需要更像Matlab的buffer方法，它更适合信号处理。

例如，如果我想将信号缓冲到大小为60的块，我需要这样做：np.vstack(np.hsplit(x.iloc[0:((len(x)//60)*60)], len(x)//60))这很麻烦。

Answer 1

我编写了以下例程来处理我需要的用例，但是我还没有实现/测试“underlap”。

请随时提出改进建议。

def buffer(X, n, p=0, opt=None):
    '''Mimic MATLAB routine to generate buffer array

    MATLAB docs here: https://se.mathworks.com/help/signal/ref/buffer.html

    Parameters
    ----------
    x: ndarray
        Signal array
    n: int
        Number of data segments
    p: int
        Number of values to overlap
    opt: str
        Initial condition options. default sets the first `p` values to zero,
        while 'nodelay' begins filling the buffer immediately.

    Returns
    -------
    result : (n,n) ndarray
        Buffer array created from X
    '''
    import numpy as np

    if opt not in [None, 'nodelay']:
        raise ValueError('{} not implemented'.format(opt))

    i = 0
    first_iter = True
    while i < len(X):
        if first_iter:
            if opt == 'nodelay':
                # No zeros at array start
                result = X[:n]
                i = n
            else:
                # Start with `p` zeros
                result = np.hstack([np.zeros(p), X[:n-p]])
                i = n-p
            # Make 2D array and pivot
            result = np.expand_dims(result, axis=0).T
            first_iter = False
            continue

        # Create next column, add `p` results from last col if given
        col = X[i:i+(n-p)]
        if p != 0:
            col = np.hstack([result[:,-1][-p:], col])
        i += n-p

        # Append zeros if last row and not length `n`
        if len(col) < n:
            col = np.hstack([col, np.zeros(n-len(col))])

        # Combine result with next row
        result = np.hstack([result, np.expand_dims(col, axis=0).T])

    return result

Answer 2

def buffer(X = np.array([]), n = 1, p = 0):
    #buffers data vector X into length n column vectors with overlap p
    #excess data at the end of X is discarded
    n = int(n) #length of each data vector
    p = int(p) #overlap of data vectors, 0 <= p < n-1
    L = len(X) #length of data to be buffered
    m = int(np.floor((L-n)/(n-p)) + 1) #number of sample vectors (no padding)
    data = np.zeros([n,m]) #initialize data matrix
    for startIndex,column in zip(range(0,L-n,n-p),range(0,m)):
        data[:,column] = X[startIndex:startIndex + n] #fill in by column
    return data

Answer 3

与其他答案相同，但速度更快。

def buffer(X, n, p=0):

    '''
    Parameters
    ----------
    x: ndarray
        Signal array
    n: int
        Number of data segments
    p: int
        Number of values to overlap

    Returns
    -------
    result : (n,m) ndarray
        Buffer array created from X
    '''
    import numpy as np

    d = n - p
    m = len(X)//d

    if m * d != len(X):
        m = m + 1

    Xn = np.zeros(d*m)
    Xn[:len(X)] = X

    Xn = np.reshape(Xn,(m,d))
    Xne = np.concatenate((Xn,np.zeros((1,d))))
    Xn = np.concatenate((Xn,Xne[1:,0:p]), axis = 1)

    return np.transpose(Xn[:-1])

Answer 4

ryanjdillon的答案进行了重写，以显着提高性能；它会追加到列表中，而不是连接数组，后者会迭代复制数组，并且速度慢得多。

def buffer(x, n, p=0, opt=None):
    if opt not in ('nodelay', None):
        raise ValueError('{} not implemented'.format(opt))

    i = 0
    if opt == 'nodelay':
        # No zeros at array start
        result = x[:n]
        i = n
    else:
        # Start with `p` zeros
        result = np.hstack([np.zeros(p), x[:n-p]])
        i = n-p
    # Make 2D array, cast to list for .append()
    result = list(np.expand_dims(result, axis=0))

    while i < len(x):
        # Create next column, add `p` results from last col if given
        col = x[i:i+(n-p)]
        if p != 0:
            col = np.hstack([result[-1][-p:], col])

        # Append zeros if last row and not length `n`
        if len(col):
            col = np.hstack([col, np.zeros(n - len(col))])

        # Combine result with next row
        result.append(np.array(col))
        i += (n - p)

    return np.vstack(result).T

Answer 5

def buffer(X, n, p=0):
'''
Parameters:
x: ndarray, Signal array, input a long vector as raw speech wav
n: int, frame length
p: int, Number of values to overlap
-----------
Returns:
result : (n,m) ndarray, Buffer array created from X
'''
import numpy as np
d = n - p
#print(d)
m = len(X)//d
c = n//d
#print(c)
if m * d != len(X):
    m = m + 1
#print(m)

Xn = np.zeros(d*m)
Xn[:len(X)] = X
Xn = np.reshape(Xn,(m,d))
Xn_out = Xn
for i in range(c-1):
    Xne = np.concatenate((Xn,np.zeros((i+1,d))))
    Xn_out = np.concatenate((Xn_out, Xne[i+1:,:]),axis=1)
#print(Xn_out.shape)  
if n-d*c>0:
    Xne = np.concatenate((Xn, np.zeros((c,d))))
    Xn_out = np.concatenate((Xn_out,Xne[c:,:n-p*c]),axis=1)

return np.transpose(Xn_out)

这是Ali Khodabakhsh的示例代码的改进代码，在我的情况下不起作用。随时发表评论并使用它。

Answer 6

通过运行比较建议答案的执行时间

x = np.arange(1,200000)
start = timer()
y = buffer(x,60,20)
end = timer()
print(end-start)

结果是：

Andrzej May，0.005595300000095449

OverLordGoldDragon，0.06954789999986133

ryanjdillon，2.427092700000003

Answer 7

此Keras函数可被视为与MATLAB Buffer（）等效的Python。

请参见示例代码：

import numpy as np
S = np.arange(1,99) #A Demo Array

See Output Here

import tensorflow.keras.preprocessing as kp
list(kp.timeseries_dataset_from_array(S, targets = None,sequence_length=7,sequence_stride=7,batch_size=5))

See the Buffered Array Output Here

参考：See This

是否有一个Matlab的缓冲区相当于numpy？

7 个答案: