py:binning

gistfile1.txt

def binning(x,y,bins,log_10=False,confinter=5):
    '''makes a simple binning'''
     
    x = np.array(x);y = np.array(y)
    
    if isinstance(bins,int) or isinstance(bins,float):
        bins = np.linspace(np.min(x)*0.9,np.max(x)*1.1,bins)
    else:
        bins = np.array(bins)
        
    if log_10:
        bins = bins[bins>0]
        c = x > 0
        x = x[c]
        y = y[c]
        bins = np.log10(bins)
        x = np.log10(x)
        y = np.log10(y)

    Tbins = []
    Median = []
    Mean = []
    Sigma =[]
    Perc_Up = []
    Perc_Down = []
    Points=[]


    for i,ix in enumerate(bins):
        if i+2>len(bins):
            break
            
        c1 = x >= ix
        c2 = x < bins[i+1]
        c=c1*c2

        if len(y[c])>0:
            Tbins = np.append(Tbins,np.median(x[c]))
            Median =  np.append(Median,np.median(y[c]))
            Mean = np.append(Mean,np.mean(y[c]))
            Sigma = np.append(Sigma,np.std(y[c]))
            Perc_Down = np.append(Perc_Down,np.percentile(y[c],confinter))
            Perc_Up = np.append(Perc_Up,np.percentile(y[c],100 - confinter))
            Points = np.append(Points,len(y[c]))


    return {'bins' : Tbins, 
            'median' : Median, 
            'mean' : Mean, 
            'stdDev' : Sigma, 
            'percDown' :Perc_Down, 
            'percUp' :Perc_Up,
            'nPoints' : Points}