html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/500#issuecomment-126445362,https://api.github.com/repos/pydata/xarray/issues/500,126445362,MDEyOklzc3VlQ29tbWVudDEyNjQ0NTM2Mg==,2443309,2015-07-30T19:24:33Z,2015-07-30T19:24:33Z,MEMBER,"I have this mostly working on my branch now.  A few questions before I issue a PR.

@clarkfitzg - do you happen to have any example code that creates intervals from a list?  I also think it would be better to only use ticks defined by the user, so your case would need to be: `myarray.plot_imshow(cmap_intervals=[-np.inf, -1, 1, np.inf])`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,97861940
https://github.com/pydata/xarray/issues/500#issuecomment-125974756,https://api.github.com/repos/pydata/xarray/issues/500,125974756,MDEyOklzc3VlQ29tbWVudDEyNTk3NDc1Ng==,2443309,2015-07-29T14:45:51Z,2015-07-29T14:45:51Z,MEMBER,"Exactly.

I've used something like this to generate the discrete colormap:

``` python
def cmap_discretize(cmap, n_colors):
    """"""Return a discrete colormap from the continuous colormap cmap.

    Parameters
    ----------
    cmap : str or colormap object
        Colormap to discretize.
    n_colors : int
        Number of discrete colors to divide `cmap` into.

    Returns
    ----------
    discrete_cmap : LinearSegmentedColormap
        Discretized colormap.
    """"""
    from matplotlib.colors import LinearSegmentedColormap
    import matplotlib.pyplot as plt

    if type(cmap) == basestring:
        cmap = plt.get_cmap(cmap)
    colors_i = np.concatenate((np.linspace(0, 1., n_colors), (0., 0., 0., 0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., n_colors + 1)
    cdict = {}
    for ki, key in enumerate(('red', 'green', 'blue')):
        cdict[key] = [(indices[i], colors_rgba[i - 1, ki], colors_rgba[i, ki])
                      for i in range(n_colors + 1)]
    # Return colormap object.
    return LinearSegmentedColormap(cmap.name + ""_%d"" % n_colors, cdict, 1024)
```

Typically, I've manually constructed the colorbar by hand with the discrete colormap:

``` python
cmap = cmap_discretize(cmap, n_colors=10)
cnorm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cticks = np.linspace(vmin, vmax, num=cn + 1)

cb = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=cnorm,
                               orientation='vertical',
                               extend=cbar_extend,
                               ticks=cticks)
```

I'm sure this later part can be improved.  Possibly by using [`matplotlib.ticker.MaxNLocator`](http://matplotlib.org/api/ticker_api.html#matplotlib.ticker.MaxNLocator) to find/set the ticks and intervals.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,97861940