doc/uses/EPSCs-and-IPSCs/variance method/2016-12-15 noise sample.py
def show_variances(Y,variances,varianceX,logScale=False):
    """create some fancy graphs to show color-coded variances."""
    
    plt.figure(1,figsize=(10,7))
    plt.figure(2,figsize=(10,7))
    varSorted=sorted(variances)
    
    plt.figure(1)
    plt.subplot(211)
    plt.grid()
    plt.title("chronological variance")
    plt.ylabel("original data")
    plot_shaded_data(X,Y,variances,varianceX)
    plt.margins(0,.1)   
    plt.subplot(212)
    plt.ylabel("variance (pA) (log%s)"%str(logScale))
    plt.xlabel("time in sweep (sec)")
    plt.plot(varianceX,variances,'k-',lw=2)
    
    plt.figure(2)
    plt.ylabel("variance (pA) (log%s)"%str(logScale))
    plt.xlabel("chunk number")
    plt.title("sorted variance")
    plt.plot(varSorted,'k-',lw=2)
    
    for i in range(0,100,PERCENT_STEP):
        varLimitLow=np.percentile(variances,i)
        varLimitHigh=np.percentile(variances,i+PERCENT_STEP)
        label="%2d-%d percentile"%(i,i++PERCENT_STEP)
        color=COLORMAP(i/100)
        print("%s: variance = %.02f - %.02f"%(label,varLimitLow,varLimitHigh))
        plt.figure(1)
        plt.axhspan(varLimitLow,varLimitHigh,alpha=.5,lw=0,color=color,label=label)
        plt.figure(2)
        chunkLow=np.where(varSorted>=varLimitLow)[0][0]
        chunkHigh=np.where(varSorted>=varLimitHigh)[0][0]
        plt.axvspan(chunkLow,chunkHigh,alpha=.5,lw=0,color=color,label=label)
        
    for fignum in [1,2]:
        plt.figure(fignum)
        if logScale:
            plt.semilogy()
        plt.margins(0,0)
        plt.grid()
        if fignum is 2:
            plt.legend(fontsize=10,loc='upper left',shadow=True)
        plt.tight_layout()
        plt.savefig('2016-12-15-variance-%d-log%s.png'%(fignum,str(logScale)))
    plt.show()
def show_variances(Y,variances,varianceX,logScale=False):
    """create some fancy graphs to show color-coded variances."""
    
    plt.figure(1,figsize=(10,7))
    plt.figure(2,figsize=(10,7))
    varSorted=sorted(variances)
    
    plt.figure(1)
    plt.subplot(211)
    plt.grid()
    plt.title("chronological variance")
    plt.ylabel("original data")
    plot_shaded_data(X,Y,variances,varianceX)
    plt.margins(0,.1)   
    plt.subplot(212)
    plt.ylabel("variance (pA) (log%s)"%str(logScale))
    plt.xlabel("time in sweep (sec)")
    plt.plot(varianceX,variances,'k-',lw=2)
    
    plt.figure(2)
    plt.ylabel("variance (pA) (log%s)"%str(logScale))
    plt.xlabel("chunk number")
    plt.title("sorted variance")
    plt.plot(varSorted,'k-',lw=2)
    
    for i in range(0,100,PERCENT_STEP):
        varLimitLow=np.percentile(variances,i)
        varLimitHigh=np.percentile(variances,i+PERCENT_STEP)
        label="%2d-%d percentile"%(i,i++PERCENT_STEP)
        color=COLORMAP(i/100)
        print("%s: variance = %.02f - %.02f"%(label,varLimitLow,varLimitHigh))
        plt.figure(1)
        plt.axhspan(varLimitLow,varLimitHigh,alpha=.5,lw=0,color=color,label=label)
        plt.figure(2)
        chunkLow=np.where(varSorted>=varLimitLow)[0][0]
        chunkHigh=np.where(varSorted>=varLimitHigh)[0][0]
        plt.axvspan(chunkLow,chunkHigh,alpha=.5,lw=0,color=color,label=label)
        
    for fignum in [1,2]:
        plt.figure(fignum)
        if logScale:
            plt.semilogy()
        plt.margins(0,0)
        plt.grid()
        if fignum is 2:
            plt.legend(fontsize=10,loc='upper left',shadow=True)
        plt.tight_layout()
        plt.savefig('2016-12-15-variance-%d-log%s.png'%(fignum,str(logScale)))
    plt.show()
def show_variances ( Y , variances , varianceX , logScale = False ) : plt . figure ( 1 , figsize = ( 10 , 7 ) ) plt . figure ( 2 , figsize = ( 10 , 7 ) ) varSorted = sorted ( variances ) plt . figure ( 1 ) plt . subplot ( 211 ) plt . grid ( ) plt . title ( "chronological variance" ) plt . ylabel ( "original data" ) plot_shaded_data ( X , Y , variances , varianceX ) plt . margins ( 0 , .1 ) plt . subplot ( 212 ) plt . ylabel ( "variance (pA) (log%s)" % str ( logScale ) ) plt . xlabel ( "time in sweep (sec)" ) plt . plot ( varianceX , variances , 'k-' , lw = 2 ) plt . figure ( 2 ) plt . ylabel ( "variance (pA) (log%s)" % str ( logScale ) ) plt . xlabel ( "chunk number" ) plt . title ( "sorted variance" ) plt . plot ( varSorted , 'k-' , lw = 2 ) for i in range ( 0 , 100 , PERCENT_STEP ) : varLimitLow = np . percentile ( variances , i ) varLimitHigh = np . percentile ( variances , i + PERCENT_STEP ) label = "%2d-%d percentile" % ( i , i + + PERCENT_STEP ) color = COLORMAP ( i / 100 ) print ( "%s: variance = %.02f - %.02f" % ( label , varLimitLow , varLimitHigh ) ) plt . figure ( 1 ) plt . axhspan ( varLimitLow , varLimitHigh , alpha = .5 , lw = 0 , color = color , label = label ) plt . figure ( 2 ) chunkLow = np . where ( varSorted >= varLimitLow ) [ 0 ] [ 0 ] chunkHigh = np . where ( varSorted >= varLimitHigh ) [ 0 ] [ 0 ] plt . axvspan ( chunkLow , chunkHigh , alpha = .5 , lw = 0 , color = color , label = label ) for fignum in [ 1 , 2 ] : plt . figure ( fignum ) if logScale : plt . semilogy ( ) plt . margins ( 0 , 0 ) plt . grid ( ) if fignum is 2 : plt . legend ( fontsize = 10 , loc = 'upper left' , shadow = True ) plt . tight_layout ( ) plt . savefig ( '2016-12-15-variance-%d-log%s.png' % ( fignum , str ( logScale ) ) ) plt . show ( )
create some fancy graphs to show color-coded variances.
create some fancy graphs to show color - coded variances .