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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def plot2dhist(xdata,ydata,cmap='binary',interpolation='nearest', fig=None,logscale=True,xbins=None,ybins=None, nbins=50,pts_only=False,**kwargs):
 """Plots a 2d density histogram of provided data :param xdata,ydata: (array-like) Data to plot. :param cmap: (optional) Colormap to use for density plot. :param interpolation: (optional) Interpolation scheme for display (passed to ``plt.imshow``). :param fig: (optional) Argument passed to :func:`setfig`. :param logscale: (optional) If ``True`` then the colormap will be based on a logarithmic scale, rather than linear. :param xbins,ybins: (optional) Bin edges to use (if ``None``, then use ``np.histogram2d`` to find bins automatically). :param nbins: (optional) Number of bins to use (if ``None``, then use ``np.histogram2d`` to find bins automatically). :param pts_only: (optional) If ``True``, then just a scatter plot of the points is made, rather than the density plot. :param **kwargs: Keyword arguments passed either to ``plt.plot`` or ``plt.imshow`` depending upon whether ``pts_only`` is set to ``True`` or not. """


    setfig(fig)
    if pts_only:
        plt.plot(xdata,ydata,**kwargs)
        return

    ok = (~np.isnan(xdata) & ~np.isnan(ydata) & 
           ~np.isinf(xdata) & ~np.isinf(ydata))
    if ~ok.sum() > 0:
        logging.warning('{} x values and {} y values are nan'.format(np.isnan(xdata).sum(),
                                                                     np.isnan(ydata).sum()))
        logging.warning('{} x values and {} y values are inf'.format(np.isinf(xdata).sum(),
                                                                     np.isinf(ydata).sum()))

    if xbins is not None and ybins is not None:
        H,xs,ys = np.histogram2d(xdata[ok],ydata[ok],bins=(xbins,ybins))
    else:
        H,xs,ys = np.histogram2d(xdata[ok],ydata[ok],bins=nbins)        
    H = H.T

    if logscale:
        H = np.log(H)

    extent = [xs[0],xs[-1],ys[0],ys[-1]]
    plt.imshow(H,extent=extent,interpolation=interpolation,
               aspect='auto',cmap=cmap,origin='lower',**kwargs)