Collapse 2-D array into one dimension.

mytotal.py

import numpy 

# Uses MEANCLIP from above 
from meanclip import meanclip 

def mytotal(inarray, axis, type='meanclip'): 
    """ 
    Collapse 2-D array in one dimension. 
    
    .. note:: MYTOTAL routine from ACS library. 
    
    :History: 
        * Obtained from M. Sirianni. 
        * Modified and converted to Python by P. L. Lim in 2009. 
    
    Examples 
    -------- 
    >>> collapsed_array = mytotal(inarray, 1, type='median') 
    
    Parameters 
    ---------- 
    inarray: array_like 
        Input 2-D array. 
    axis: {1, 2} 
        Axis to collapse. 
            * 1: Return values along Y. 
            * 2: Return values along X. 
    type: {'median', 'meanclip', 'stdev'} 
        Algorithm to use. 
    
    Returns 
    ------- 
    out_arr: array_like 
        1-D array collapsed along desired axis with desired 
        algorithm. 
    """ 
    func_name = 'MYTOTAL' 
    out_arr = 0.0 
    # Check inarray 
    if inarray.ndim != 2: 
        print '%s: Input array must be 2D' % func_name 
        return out_arr 
    # Check axis 
    if axis == 1: 
        n_out = inarray.shape[0] 
    elif axis == 2: 
        n_out = inarray.shape[1] 
    else: 
        print func_name, ': Axis not supported -', axis 
        return out_arr 
    # Initialize output array 
    out_arr = numpy.zeros(n_out) 
    out_rng = range(0, n_out) 
    # Check type 
    if type == 'meanclip': 
        for i in out_rng: 
            if axis == 1: 
                im_i = inarray[i,:] 
            else: 
                im_i = inarray[:,i] 
            mmean, msigma = meanclip(im_i, maxiter=10, converge_num=0.001) 
            out_arr[i] = mmean 
    elif type == 'stdev': 
        for i in out_rng: 
            if axis == 1: 
                im_i = inarray[i,:] 
            else: 
                im_i = inarray[:,i] 
            mmean, msigma = meanclip(im_i, maxiter=10, converge_num=0.001) 
            out_arr[i] = msigma 
    elif type == 'median': 
        for i in out_rng: 
            if axis == 1: 
                im_i = inarray[i,:] 
            else: 
                im_i = inarray[:,i] 
            out_arr[i] = numpy.median(im_i) 
    else: 
        print func_name, ': Type not supported -', type 
        out_arr = 0.0 
    return out_arr