plots.py 文件源码

def fitconfmc(xdata,ydata,errx,erry,covxy,nboot=1000,bces='ort',linestyle='',conf=1.,confcolor='gray',xplot=None,front=False,**args):
    """
    This is a wrapper that given the input data performs the BCES
    fit, get the orthogonal parameters and plot the best-fit line and
    confidence band (generated using MC). I decided to put together these 
    commands in a method because I have been using them very frequently.

    Assumes you initialized the plot window before calling this method.
    This method is more stable than fitconf, which is plagued with numerical 
    instabilities when computing the gradient.

    Usage:

    >>> a1,b1,erra1,errb1,cov1=nemmen.fitconf(x[i],y[i],errx[i],erry[i],covxy[i],nboot,bces,linestyle='k',confcolor='LightGrey')

    Explanation of some arguments:
    - xplot: if provided, will compute the confidence band in the X-values provided
    with xplot
    - front: if True, then will plot the confidence band in front of the data
    points; otherwise, will plot it behind the points
    - conf: size of confidence band to be plotted in standard deviations
    """ 
    # Selects the desired BCES method
    i=whichbces(bces)

    # Performs the BCES fit
    a,b,erra,errb,cov=bcesp(xdata,errx,ydata,erry,covxy,nboot)

    # Plots best-fit
    if xplot==None:
        x=numpy.linspace(xdata.min(),xdata.max(),100)
    else:
        x=xplot
    pylab.plot(x,a[i]*x+b[i],linestyle,**args)

    fitm=numpy.array([ a[i],b[i] ]) # array with best-fit parameters
    covm=numpy.array([ (erra[i]**2,cov[i]), (cov[i],errb[i]**2) ])  # covariance matrix

    # Plots confidence band
    lcb,ucb,y=confbandmc(x,fitm,covm,10000,conf)
    if front==True:
        zorder=10
    else:
        zorder=None
    pylab.fill_between(x, lcb, ucb, alpha=0.3, facecolor=confcolor, zorder=zorder)

    return a,b,erra,errb,cov