1 Jun 11:26 2009

### Re: How to use pcolor and scatter plot in one image?

wierob <wierob83 <at> googlemail.com>

2009-06-01 09:26:38 GMT

2009-06-01 09:26:38 GMT

Hi,

thanks for your help. Unfortunately, your example does not work for me. The line

histo = np.histogram(z.ravel(), bins=r_[Z.ravel(),2*n**2]) produeces the following error message:

Traceback (most recent call last): File "/mnt/VBoxShare/eg.py", line 15, in <module> histo = np.histogram(z.ravel(), bins=r_[Z.ravel(),2*n**2]) NameError: name 'r_' is not defined I'm very new to Scipy and have no idea what your intended to do there.

What I'm trying to do is the following:

from scipy import polyval, zeros import pylab a, b = fetch_data(...) pylab.plot(a, b, "g.") # scatter plot # regression line regression = regression_analysis(...) xr = polyval([regression[0], regression[1]], b) pylab.plot(b, xr, "r-") pylab.gca().set_xlim([0,max(b)]) pylab.gca().set_ylim([0,max(a)]) # calculate grid (10x10) xlim = pylab.gca().get_xlim()[1] ylim = pylab.gca().get_ylim()[1] block_x = int(xlim / 10.0 + 1) block_y = int(ylim / 10.0 + 1) grid_x = [ block_x * i for i in range(11) ] grid_y = [ block_y * i for i in range(11) ] density_map = zeros((10, 10)) # matrix for points per cell inc = 1.0 / number_of_data_points for i in range(10): for j in range(10): cell = [ grid_x[i], grid_x[i+1], grid_y[j], grid_y[j+1] ] density_map[j][i] += points_in(cell) * inc # plot the 'density map' pylab.pcolor(density_map, cmap=pylab.get_cmap("hot")) pylab.show()

This only creates the scatter plot and the regression line.

kind regards

robert

thanks for your help. Unfortunately, your example does not work for me. The line

histo = np.histogram(z.ravel(), bins=r_[Z.ravel(),2*n**2]) produeces the following error message:

Traceback (most recent call last): File "/mnt/VBoxShare/eg.py", line 15, in <module> histo = np.histogram(z.ravel(), bins=r_[Z.ravel(),2*n**2]) NameError: name 'r_' is not defined I'm very new to Scipy and have no idea what your intended to do there.

What I'm trying to do is the following:

from scipy import polyval, zeros import pylab a, b = fetch_data(...) pylab.plot(a, b, "g.") # scatter plot # regression line regression = regression_analysis(...) xr = polyval([regression[0], regression[1]], b) pylab.plot(b, xr, "r-") pylab.gca().set_xlim([0,max(b)]) pylab.gca().set_ylim([0,max(a)]) # calculate grid (10x10) xlim = pylab.gca().get_xlim()[1] ylim = pylab.gca().get_ylim()[1] block_x = int(xlim / 10.0 + 1) block_y = int(ylim / 10.0 + 1) grid_x = [ block_x * i for i in range(11) ] grid_y = [ block_y * i for i in range(11) ] density_map = zeros((10, 10)) # matrix for points per cell inc = 1.0 / number_of_data_points for i in range(10): for j in range(10): cell = [ grid_x[i], grid_x[i+1], grid_y[j], grid_y[j+1] ] density_map[j][i] += points_in(cell) * inc # plot the 'density map' pylab.pcolor(density_map, cmap=pylab.get_cmap("hot")) pylab.show()

This only creates the scatter plot and the regression line.

kind regards

robert

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