Ryan,
I have tried setting c=locations (after converting to float) and gotten
inconsistent results. For a dataset with ~32,000 points it seems to work,
but a 2nd dataset of ~100,000 points colors everything the same even though
there are at least 10 locations.
Your second idea works nicely and I'm going to try applying it to my data.
The only real issue is that I don't know how many locations there will be
for each plot so I can't hard code the colors list. I think I can figure
that part out though.
Thanks!
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
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