import numpy as np
from tqdm import tqdm


def ssnmf_core(V, rdim, iter_num, W0, H0):
    if (V < 0).any():
        raise ValueError("Negative values in data!")

    vdim = V.shape[0]
    samples = V.shape[1]

    W = W0
    H = H0

    for iteration in range(1, iter_num + 1):

        # Update using standard NMF multiplicative update rule
        H = H * (W.T @ V) / (W.T @ W @ H + 1e-9)

        # Re-normalize so that rows of H have constant energy
        norms = np.sqrt(np.sum(H.T ** 2, 0))
        H = H / np.outer(norms.T, np.ones((1, samples)))
        W = W * np.outer(np.ones((vdim, 1)), norms)
    
        # Update using standard NMF multiplicative update rule
        W = W * (V @ H.T) / (W @ H @ H.T + 1e-9)

    return (W, H)


def ssnmf(spectrogramData2D, number_basis, number_iter): 
    '''
    2022-01-30 (v01)
        - Fuh-Cherng (Fuh) Jeng converted part of the nmfsc(), that was originally written by Tzu-Hao (Harry) Lin in Matlab, to Python
        - Fuh made a few minor changes
    2022-02-22 (v02)
        - Fuh modified this script so that the input spectrogramData2D must be a 2D matrix. Otherwise, this script will not work.
        - As such, no reshape() is needed within this function. 
        - In other words, any reshape() must be performed prior to feeding spectrogramData2D into this function.
    '''
  
    data = 20 * np.log10(spectrogramData2D)   # convert nV to dB (adopted from Lin et al., 2017). 
    # IMPORTANT: This converion is just for the purpose of optimizing W and H. It has no effect on the amplitude unit of ssnmf() output. The unit of ssnmf() output will always be the same the unit of the input data. For this study, the unit of the input data is nV.

    data[data < 0] = 0                        

    n = data.shape[1]
    weight = np.ones((1, n))

    W = abs(np.random.randn(data.shape[0], number_basis))
    H = abs(np.random.randn(number_basis, n))
    H = H / np.outer(np.sqrt(np.sum(H ** 2, 1)), np.ones((1, n)))

    # NMF decomposition
    print('performing ssnmf(), please wait...', flush=True)
    for run in tqdm(range(number_iter)):
        H[0, :] = weight * np.sum(H[0, :]) / np.sum(weight)
        (W, H) = ssnmf_core(data, number_basis, 1, W, H)

    output = np.zeros((data.shape[0], data.shape[1], number_basis))
    np.seterr(invalid='ignore')     
    for run in range(number_basis):
        output[:, :, run] = spectrogramData2D * np.outer(W[:, run], H[run, :]) / (W @ H)
        
    np.seterr(invalid='warn')   
    output[np.isnan(output)] = 0

    return (output, W, H)

import numpy as np
from tqdm import tqdm


def ssnmf_core(V, rdim, iter_num, W0, H0):
    if (V < 0).any():
        raise ValueError("Negative values in data!")

    vdim = V.shape[0]
    samples = V.shape[1]

    W = W0
    H = H0

    for iteration in range(1, iter_num + 1):

        # Update using standard NMF multiplicative update rule
        H = H * (W.T @ V) / (W.T @ W @ H + 1e-9)

        # Re-normalize so that rows of H have constant energy
        norms = np.sqrt(np.sum(H.T ** 2, 0))
        H = H / np.outer(norms.T, np.ones((1, samples)))
        W = W * np.outer(np.ones((vdim, 1)), norms)
    
        # Update using standard NMF multiplicative update rule
        W = W * (V @ H.T) / (W @ H @ H.T + 1e-9)

    return (W, H)


def ssnmf(spectrogramData2D, number_basis, number_iter): 
    '''
    2022-01-30 (v01)
        - Fuh-Cherng (Fuh) Jeng converted part of the nmfsc(), that was originally written by Tzu-Hao (Harry) Lin in Matlab, to Python
        - Fuh made a few minor changes
    2022-02-22 (v02)
        - Fuh modified this script so that the input spectrogramData2D must be a 2D matrix. Otherwise, this script will not work.
        - As such, no reshape() is needed within this function. 
        - In other words, any reshape() must be performed prior to feeding spectrogramData2D into this function.
    '''
  
    data = 20 * np.log10(spectrogramData2D)   # convert nV to dB (adopted from Lin et al., 2017). 
    # IMPORTANT: This converion is just for the purpose of optimizing W and H. It has no effect on the amplitude unit of ssnmf() output. The unit of ssnmf() output will always be the same the unit of the input data. For this study, the unit of the input data is nV.

    data[data < 0] = 0                        

    n = data.shape[1]
    weight = np.ones((1, n))

    W = abs(np.random.randn(data.shape[0], number_basis))
    H = abs(np.random.randn(number_basis, n))
    H = H / np.outer(np.sqrt(np.sum(H ** 2, 1)), np.ones((1, n)))

    # NMF decomposition
    print('performing ssnmf(), please wait...', flush=True)
    for run in tqdm(range(number_iter)):
        H[0, :] = weight * np.sum(H[0, :]) / np.sum(weight)
        (W, H) = ssnmf_core(data, number_basis, 1, W, H)

    output = np.zeros((data.shape[0], data.shape[1], number_basis))
    np.seterr(invalid='ignore')     
    for run in range(number_basis):
        output[:, :, run] = spectrogramData2D * np.outer(W[:, run], H[run, :]) / (W @ H)
        
    np.seterr(invalid='warn')   
    output[np.isnan(output)] = 0

    return (output, W, H)

# Update using standard NMF multiplicative update rule # Re-normalize so that rows of H have constant energy # Update using standard NMF multiplicative update rule 2022-01-30 (v01)         - Fuh-Cherng (Fuh) Jeng converted part of the nmfsc(), that was originally written by Tzu-Hao (Harry) Lin in Matlab, to Python         - Fuh made a few minor changes     2022-02-22 (v02)         - Fuh modified this script so that the input spectrogramData2D must be a 2D matrix. Otherwise, this script will not work.         - As such, no reshape() is needed within this function.          - In other words, any reshape() must be performed prior to feeding spectrogramData2D into this function. # convert nV to dB (adopted from Lin et al., 2017). # IMPORTANT: This converion is just for the purpose of optimizing W and H. It has no effect on the amplitude unit of ssnmf() output. The unit of ssnmf() output will always be the same the unit of the input data. For this study, the unit of the input data is nV. # NMF decomposition