src/pys5p/s5p_msm.py
def pad_rows(arr1, arr2):
    """
    Pad the array with the least numer of rows with NaN's
    """
    if arr2.ndim == 2:
        if arr1.shape[0] < arr2.shape[0]:
            buff = arr1.copy()
            arr1 = np.full_like(arr2, np.nan)
            arr1[0:buff.shape[0], :] = buff
        elif arr1.shape[0] > arr2.shape[0]:
            buff = arr2.copy()
            arr2 = np.full_like(arr1, np.nan)
            arr2[0:buff.shape[0], :] = buff
    else:
        if arr1.shape[1] < arr2.shape[1]:
            buff = arr1.copy()
            arr1 = np.full_like(arr2, np.nan)
            arr1[:, 0:buff.shape[1], :] = buff
        elif arr1.shape[1] > arr2.shape[1]:
            buff = arr2.copy()
            arr2 = np.full_like(arr1, np.nan)
            arr2[:, 0:buff.shape[1], :] = buff

    return (arr1, arr2)

    Pad the array with the least numer of rows with NaN's
    
Pad the array with the least numer of rows with NaN's
Pad the array with the least numer of rows with NaN ' s
def pad_rows(arr1, arr2):
    if arr2.ndim == 2:
        if arr1.shape[0] < arr2.shape[0]:
            buff = arr1.copy()
            arr1 = np.full_like(arr2, np.nan)
            arr1[0:buff.shape[0], :] = buff
        elif arr1.shape[0] > arr2.shape[0]:
            buff = arr2.copy()
            arr2 = np.full_like(arr1, np.nan)
            arr2[0:buff.shape[0], :] = buff
    else:
        if arr1.shape[1] < arr2.shape[1]:
            buff = arr1.copy()
            arr1 = np.full_like(arr2, np.nan)
            arr1[:, 0:buff.shape[1], :] = buff
        elif arr1.shape[1] > arr2.shape[1]:
            buff = arr2.copy()
            arr2 = np.full_like(arr1, np.nan)
            arr2[:, 0:buff.shape[1], :] = buff
    return (arr1, arr2)
def pad_rows ( arr1 , arr2 ) : if arr2 . ndim == 2 : if arr1 . shape [ 0 ] < arr2 . shape [ 0 ] : buff = arr1 . copy ( ) arr1 = np . full_like ( arr2 , np . nan ) arr1 [ 0 : buff . shape [ 0 ] , : ] = buff elif arr1 . shape [ 0 ] > arr2 . shape [ 0 ] : buff = arr2 . copy ( ) arr2 = np . full_like ( arr1 , np . nan ) arr2 [ 0 : buff . shape [ 0 ] , : ] = buff else : if arr1 . shape [ 1 ] < arr2 . shape [ 1 ] : buff = arr1 . copy ( ) arr1 = np . full_like ( arr2 , np . nan ) arr1 [ : , 0 : buff . shape [ 1 ] , : ] = buff elif arr1 . shape [ 1 ] > arr2 . shape [ 1 ] : buff = arr2 . copy ( ) arr2 = np . full_like ( arr1 , np . nan ) arr2 [ : , 0 : buff . shape [ 1 ] , : ] = buff return ( arr1 , arr2 )
Pad the array with the least numer of rows with NaN's
Pad the array with the least numer of rows with NaN ' s
"""
    Pad the array with the least numer of rows with NaN's
    """
arr1
arr2