def comparison_stats(df, els=None):
    """
    Compute comparison stats for test and LAtools data.
    
    Population-level similarity assessed by a Kolmogorov-Smirnov test.
    
    Individual similarity assessed by a pairwise Wilcoxon signed rank test.
    
    Trends in residuals assessed by regression analysis, where significance of
    the slope and intercept is determined by t-tests (both relative to zero).
    
    Parameters
    ----------
    df : pandas.DataFrame
        A dataframe containing reference ('X/Ca_r'), test user 
        ('X/Ca_t') and LAtools ('X123') data.
    els : list
        list of elements (names only) to plot.
    
    Returns
    -------
    pandas.DataFrame
    
    """
    if els is None:
        els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',
               'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',
               'U']
        
    yl_stats = []
    
    for i, e in enumerate(els):
        x = df.loc[:, e + '_rd'].values
        yl = df.loc[:, e + '_la'].values
        
        yl_stats.append(summary_stats(x, yl, e))
    
    yl_stats = pd.concat(yl_stats).T
    
    return yl_stats.T
def comparison_stats(df, els=None):
    """
    Compute comparison stats for test and LAtools data.
    
    Population-level similarity assessed by a Kolmogorov-Smirnov test.
    
    Individual similarity assessed by a pairwise Wilcoxon signed rank test.
    
    Trends in residuals assessed by regression analysis, where significance of
    the slope and intercept is determined by t-tests (both relative to zero).
    
    Parameters
    ----------
    df : pandas.DataFrame
        A dataframe containing reference ('X/Ca_r'), test user 
        ('X/Ca_t') and LAtools ('X123') data.
    els : list
        list of elements (names only) to plot.
    
    Returns
    -------
    pandas.DataFrame
    
    """
    if els is None:
        els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',
               'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',
               'U']
        
    yl_stats = []
    
    for i, e in enumerate(els):
        x = df.loc[:, e + '_rd'].values
        yl = df.loc[:, e + '_la'].values
        
        yl_stats.append(summary_stats(x, yl, e))
    
    yl_stats = pd.concat(yl_stats).T
    
    return yl_stats.T
def comparison_stats ( df , els = None ) : if els is None : els = [ 'Li' , 'Mg' , 'Al' , 'P' , 'Ti' , 'Y' , 'La' , 'Ce' , 'Pr' , 'Nd' , 'Sm' , 'Eu' , 'Gd' , 'Tb' , 'Dy' , 'Ho' , 'Er' , 'Tm' , 'Yb' , 'Lu' , 'Hf' , 'Pb' , 'Th' , 'U' ] yl_stats = [ ] for i , e in enumerate ( els ) : x = df . loc [ : , e + '_rd' ] . values yl = df . loc [ : , e + '_la' ] . values yl_stats . append ( summary_stats ( x , yl , e ) ) yl_stats = pd . concat ( yl_stats ) . T return yl_stats . T
Compute comparison stats for test and LAtools data.
    
    Population-level similarity assessed by a Kolmogorov-Smirnov test.
    
    Individual similarity assessed by a pairwise Wilcoxon signed rank test.
    
    Trends in residuals assessed by regression analysis, where significance of
    the slope and intercept is determined by t-tests (both relative to zero).
    
    Parameters
    ----------
    df : pandas.DataFrame
        A dataframe containing reference ('X/Ca_r'), test user 
        ('X/Ca_t') and LAtools ('X123') data.
    els : list
        list of elements (names only) to plot.
    
    Returns
    -------
    pandas.DataFrame
Compute comparison stats for test and LAtools data . Population - level similarity assessed by a Kolmogorov - Smirnov test . Individual similarity assessed by a pairwise Wilcoxon signed rank test . Trends in residuals assessed by regression analysis where significance of the slope and intercept is determined by t - tests ( both relative to zero ) . Parameters ---------- df : pandas . DataFrame A dataframe containing reference ( X / Ca_r ) test user ( X / Ca_t ) and LAtools ( X123 ) data . els : list list of elements ( names only ) to plot . Returns ------- pandas . DataFrame