Mike,
You may want to look into the matplotlib.cm and matplotlib.colors modules.
I've had good success with matplotlib.colors.LinearSegmentedColormap and its
'from_list' method. The documentation is the best location for information
on this topic. If you have a large number of locations, then the color
differences will be pretty small, unless you use a colormap with lots of
different colors. Below is your example using the 'from_list' method and the
built-in colormap 'hsv' (you'll just have to flip around the comments). For
the matplotlib.cm colormaps, be sure to passed in normalized values (which
is why the call to the colormap is slightly complex).
Maybe this is a bit more help.
Ryan
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as plc
import matplotlib.cm as mcm
IDs = np.array([47, 33, 47, 12, 50, 50, 27, 27, 16, 27])
locations = np.array(['201', '207', '207', '205', '204', '201', '209',
'209', \
'207','207'])
dates = np.array([ 733315.83240741, 733315.83521991, 733315.83681713,
733315.83788194, 733336.54554398, 733336.54731481,
733337.99842593, 733337.99943287, 733338.00070602,
733338.00252315])
fig = plt.figure()
ax = fig.add_subplot(111)
locs_un = np.unique(locations)
# The variable assignment below can be removed if you use the mcm module.
cs = plc.LinearSegmentedColormap.from_list('Colormap name', ['r', 'g', 'b'],
N=len(locs_un) )
for n, i in enumerate(locs_un):
# Reverse the comments here to use the mcm module 'hsv' colormap.
ax.plot(dates[locations==i],IDs[locations==i],'d', c=cs(n), label=i)
#ax.plot(dates[locations==i],IDs[locations==i],'d',
# c=mcm.hsv( float(n)/(len(locs_un)-1) ), label=i)
ax.xaxis_date()
fig.autofmt_xdate()
plt.legend(numpoints=1)
plt.grid(True)
plt.show()
On Tue, Oct 4, 2011 at 5:25 PM, Michael Castleton <fatuhe...@yahoo.com>wrote:
>
> Ryan,
> I should clarify my color issue. Your code is smart enough to generate
> however many colors are needed but I want to make sure the colors are all
> unique.
> Thanks again!
>
> Mike
>
>
>
> Mike, sorry to send this twice... I should have sent it to the list as
> well...
> _______________________________
> Mike,
>
> If your locations were integers or floats rather than strings, you could
> just change the scatter call to the following:
> ax.scatter(dates,IDs,c=
> locations,marker='d')
> I don't know about a legend... I don't know if that is possible with a
> scatter plot (?). Because scatter plots get their colors based off of a
> color map, you could generate a color bar for your data. You may need to
> capture the collection object returned from the scatter plot function call,
> though. Here's your code with these modifications:
>
> # Of course, you need to change your locations list to integers rather than
> strings.
>
> fig = plt.figure()
> ax = fig.add_subplot(111)
> sc = ax.scatter(dates,IDs,c=locations,marker='d')
> ax.xaxis_date()
> fig.autofmt_xdate()
> plt.colorbar(sc)
> plt.grid(True)
> plt.show()
>
> If you really need a legend, then you could do a loop of plot commands for
> each set of unique locations. Using some fancy Numpy masking makes the
> process easier...
>
> import numpy as np
> import matplotlib.pyplot as plt
>
> IDs = np.array([47, 33, 47, 12, 50, 50, 27, 27, 16, 27])
> locations = np.array(['201', '207', '207', '205', '204', '201', '209',
> '209', \
> '207','207'])
> dates = np.array([ 733315.83240741, 733315.83521991, 733315.83681713,
>
> 733315.83788194, 733336.54554398, 733336.54731481,
> 733337.99842593, 733337.99943287, 733338.00070602,
> 733338.00252315])
>
>
> fig = plt.figure()
> ax = fig.add_subplot(111)
> cs = ['r', 'b', 'g', 'k', 'c']
> for n, i in enumerate(np.unique(locations)):
> ax.plot(dates[locations==i],IDs[locations==i],'d', c=cs[n%len(cs)],
> label=i)
> ax.xaxis_date()
> fig.autofmt_xdate()
> plt.legend(numpoints=1)
> plt.grid(True)
> plt.show()
>
> Not sure if this is exactly what you wanted, but I hope it helps a little.
>
> Ryan
>
>
>
> --
> View this message in context:
> http://old.nabble.com/color-problems-in-scatter-plot-tp32584727p32592799.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
>
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