 # order of plotting for 'layer' of data

Rob,

Have you tried the zorder argument. It is an integer that controls the relative ‘height’ of a plotting element: higher numbers are plotted over lower numbers. For example, the following code plots the scatter points on top of the plotted line (even though scatter was called first):

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-1, 1, 10)
y = np.random.rand(10)

plt.scatter(x, y, s=50, zorder=2)
plt.plot(x, y, ‘r’, lw=2, zorder=1)
plt.show()

Also, looking over your code, I noticed you had a for loop for your plot commands. The plot command can take two dimensional arrays for x and y as well. In this case, the x and y data for each individual plot are the columns of the 2D arrays. For your problem in particular, you may find Numpy’s ‘tile’ command to be useful as well. Here’s some code that does something similar to what you are doing (I think).

import numpy as np
import matplotlib.pyplot as plt

z = np.tile( range(5), (5,1) )
plt.plot(z, np.random.rand(5, 5), ‘o’)
plt.show()

This may not make a big difference in your code, but if you have a lot of data, it may speed things up a little. (As I understand it, the Python for loops can be a little slow.)

Hope this helps a little.

Ryan

···

Date: Fri, 02 Sep 2011 14:18:39 -0230

From: Rob Briggs <rdbriggs@…3768…2…>

Subject: [Matplotlib-users] order of plotting for ‘layer’ of data

Message-ID: <1314982119.4902.118.camel@…42…>

Content-Type: text/plain; charset=“us-ascii”

Hello,

I’m not sure of the correct way to ask this question…I’m trying to

create a plot that has a number of layers. I plot a standard plot, then

a scatterplot over that. See attachment. I expected the scatter plot to

‘render/draw’ after the standard plot command, but the scatter plot data

is buried under the standard command.

I tried changing the order, i.e. scatterplot first but that had no

effect. How do I ensure the scatterplot data is plotted above/over the

other data?

The following code extract is after all the data has been read and

sorted.

plt.close()

# first EAIS data

stitle = ‘plot showing cumulative paleoHmodel and paleoHscore for EAIS’

# set up index range for plotting

il=0 # index for lower bound to plot

iu=idx_splt # index for upper bound to plot

fig = plt.figure(5,figsize=(18,12))

plt.title(stitle, fontsize=16)

plt.xlabel(“paleoH data point”)

plt.ylabel(“thickness [m]”)

ii=np.empty(num_rows)

# plot the model results

for i in range(il,iu):

``````ii[:] = i+1

plt.plot(ii,a[:,i],'o',color='0.9')
``````

# set axis limits

ymin=-1800

ymin=-500

ax1.set_xlim(il,iu+1)

top = 3000

bottom=-500

ax1.set_ylim(bottom, top)

# plot the labels

for i in range(il,iu):

``````plt.text(i+1,ymin,datn[i], horizontalalignment='center',
``````

fontsize=10,rotation=‘vertical’, verticalalignment=‘bottom’)

#cmap = cm.get_cmap(‘PRGn’, 10)

#cmap = cm.get_cmap(‘autumn_r’, 100)

#cmap = cm.get_cmap(‘gray’, 100)

#plt.scatter(obs[il:iu,0],obs[il:iu,1],c=time[il:iu],marker=‘s’,s=50,cmap=cmap)

plt.scatter(obs[il:iu,0], obs[il:iu,1], c=time[il:iu],marker=‘s’,s=100)

plt.colorbar()

# plot the observation dp with error bars

#plt.errorbar(obs[il:iu,0], obs[il:iu,1], yerr=obs[il:iu,2], fmt=‘r.’)

plt.grid(which=‘both’)

fname=“scoreVSpaleoHsite.png”

plt.savefig(fname)

plt.show()

Regards

Rob

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