On 26 Nov 2014, at 07:53 pm, Chris Barker <chris.bar...@noaa.gov> wrote:
> On Wed, Nov 26, 2014 at 1:30 AM, Todd <toddr...@gmail.com> wrote:
>> About this, I am not expert so forgive me if this is nonsensical. However,
>> it would seem to me that these requirements are basically the same as the
>> requirements for the new default colormap that prompted this whole
>> discussion. So, rather than create two inconsistent set of colors that
>> accomplish similar goals, might it be better to instead use the default
>> colormap for the line colors? You could pick "N" equally-spaced colors from
>> the colormap and use those as the line colors.
>>
> I'm no expert either, but while similar principles about colorblind
> compatibility, etc apply, you want to sue a different scheme to represent a
> continuous range of colors and a set of distinct colors that aren't intended
> to be ranked.
>
I’ve also become throughly annoyed with the default colour cycle, especially
with its
glaring cyan-magenta contrast, and found it desirable to have an easier way to
customise this either explicitly or by changing color_cycle.
As there are already a couple of sequences existing in the available colourmaps
that
could be useful for different purposes or tastes, what’s lacking in particular
in my view
is an easier-to-use interface to draw colours from those maps; I think that’s
along the
lines of what Todd also has suggested further down in his mail.
I’ve written a little utility I’m simply appending because it’s so short, which
returns an
array of colours of specified length that could be passed to axes.color_cycle
or just
explicitly used as crange[i]. Also useful to colour scatter plot markers
according to a
certain quantity (pass this quantity as “values” to crange).
Regarding to the above, I think sometimes the line colour requirements are
similar to
those for a general colourmap, e.g. I often want to plot a series of lines like
different
spectra, which are easily enough distinguishable, but should IMO reflect a
certain
continuous trend like different temperatures - are ranked, IOW - and thus would
be well
represented by a sequence of values from “heat" or “coolwarm". However there
are still
some additional requirements, as you’d generally want every colour to have
enough
contrast on a white or bright background canvas. In the example below I’ve
added a
“max_lum” keyword to darken whitish or yellow colours appropriately.
This is probably not extremely sophisticated in terms of colour physiology, but
if you
have a suggestion if and where it could be added to matplotlib, I could go
ahead and
make a pull request (and try to find the time to add some tests and examples).
Cheers,
Derek
def crange(cmap, values, max_lum=1, start=0, stop=255, vmin=None, vmax=None):
"""
Returns RGBA colour array of length values from colormap cmap
cmap: valid matplotlib.cm colormap name or instance
values: either int - number of colour values to return or
array of values to be mapped on colormap range
max_lum: restrict colours to maximum brightness (1=white)
start,stop: range of colormap to use (full range 0-255)
vmin,vmax: input values mapped to start/stop (default actual data limits)
"""
try:
if np.isscalar(values):
vrange = np.linspace(start,stop,np.int(values))
else:
v = np.array(values).astype(np.float)
vmin = vmin or v.min()
vmax = vmax or v.max()
vrange = start+(v-vmin)*(stop-start)/(vmax-vmin)
except (ValueError, TypeError) as err:
print("invalid input values: must be no. of colours or array: %s" %
err)
return None
vrange = np.uint8(np.round(vrange))
cmap = matplotlib.cm.get_cmap(cmap)
lcor = (1.0-max_lum) / 9
crange = cmap(vrange)
crange[:,:3] *= (1-crange[:,:3].sum(axis=1)**2*lcor).reshape(-1,1)
return crange
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