def fit_shown_data(f="<STR_LIT>", p="<STR_LIT>", axes="<STR_LIT>", verbose=True, **kwargs):
<EOL>if axes=="<STR_LIT>": axes = _pylab.gca()<EOL>_pylab.sca(axes)<EOL>xmin,xmax = axes.get_xlim()<EOL>ymin,ymax = axes.get_ylim()<EOL>if '<STR_LIT>' not in kwargs: kwargs['<STR_LIT>'] = axes.figure.number+<NUM_LIT:1><EOL>fitters = []<EOL>for l in axes.lines:<EOL><INDENT>x,y = l.get_data()<EOL>x,y = _s.fun.trim_data_uber([x,y],[xmin<x,x<xmax,ymin<y,y<ymax])<EOL>fitters.append(_s.data.fitter(**kwargs).set_functions(f=f, p=p))<EOL>fitters[-<NUM_LIT:1>].set_data(x,y)<EOL>fitters[-<NUM_LIT:1>].fit()<EOL>fitters[-<NUM_LIT:1>].autoscale_eydata().fit()<EOL>if verbose:<EOL><INDENT>print(fitters[-<NUM_LIT:1>])<EOL>print("<STR_LIT>")<EOL><DEDENT>if not axes.lines[-<NUM_LIT:1>] == l: fitters[-<NUM_LIT:1>].ginput(timeout=<NUM_LIT:0>)<EOL><DEDENT>return fitters<EOL>
Fast-and-loos quick fit:

Loops over each line of the supplied axes and fits with the supplied
function (f) and parameters (p). Assumes uniform error and scales this
such that the reduced chi^2 is 1.

Returns a list of fitter objects

**kwargs are sent to _s.data.fitter()