def parseNoise(rawArray): 
    '''
    Function returns indices that contain non-noisy genes as an integer array
    :param rawArray: numpy ndarray of data set
    :return nnGenes : numpy ndarray of non-noise gene indices
    '''
    nnGenes=[] 
    for i in range(0,(rawArray.shape[1])): #Checks all genes
        count0=np.asarray(np.where(rawArray[:,i]==0))
        count1=np.asarray(np.where(rawArray[:,i]==1))
        if ((count1.shape[1]+count0.shape[1])<rawArray.shape[0]):
            nnGenes=np.append(nnGenes,i)
    return nnGenes.astype(int)
def parseNoise ( rawArray ) : nnGenes = [ ] for i in range ( 0 , ( rawArray . shape [ 1 ] ) ) : count0 = np . asarray ( np . where ( rawArray [ : , i ] == 0 ) ) count1 = np . asarray ( np . where ( rawArray [ : , i ] == 1 ) ) if ( ( count1 . shape [ 1 ] + count0 . shape [ 1 ] ) < rawArray . shape [ 0 ] ) : nnGenes = np . append ( nnGenes , i ) return nnGenes . astype ( int )
Function returns indices that contain non-noisy genes as an integer array
    :param rawArray: numpy ndarray of data set
    :return nnGenes : numpy ndarray of non-noise gene indices