Compute tick angles by advancing a small amount in axes coordinates

This handles cases when a plot extends over a small number of pixels in the WCS projection, but a large distance or number of pixels in display coordinates. Addresses #45.

Here is a modified test script. The ticks are red and extra-long to make them stand out better.

    #!/usr/bin/env python
    from matplotlib import pyplot as plt
    from wcsaxes import WCSAxes
    import astropy.wcs

    wcs = astropy.wcs.WCS()
    wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
    wcs.wcs.crval = [90, 70]
    wcs.wcs.cdelt = [16, 16]
    wcs.wcs.crpix = [1, 1]
    wcs.wcs.radesys = 'ICRS'
    wcs.wcs.equinox = 2000.0
    fig = plt.figure(figsize=(3, 3), dpi=72)
    ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], wcs=wcs)
    fig.add_axes(ax)
    ax.set_xlim(1, -1)
    ax.set_ylim(-1, 1)
    ax.grid(color='gray', alpha=0.5)
    ax.coords['ra'].set_ticks(color='red', size=20)
    ax.coords['dec'].set_ticks(color='red', size=20)
    fig.savefig('test.png', dpi=72)

![test](https://cloud.githubusercontent.com/assets/728407/3264181/83ad6656-f27b-11e3-9172-9411842a69d4.png)
> If these are always going to be affine transformations with no rotation, I wonder whether it would be more efficient to simply find out what the scale from axes to data coordinates is and multiply/divide by these later on?

Yes, but it would be clearer and nearly as efficient to pass all of the ticks to `transform()` as one big vector.

You might be able to eliminate the multiple transformation objects by using a [blended transform](http://matplotlib.org/users/transforms_tutorial.html#blended-transformations).