Hi Elizabeth, The imshow command in matplotlib normally puts the origin in the upper left. Change your call to imshow to include the argument: origin='lower' and it may fix your problem. If you look at the yt source, this is scattered throughout to account for this. You probably also want to supply: interpolation='nearest' -Matt On Mon, Aug 22, 2011 at 7:09 PM, Elizabeth Tasker <taskere@mcmaster.ca> wrote:

Hi,

I feel I have a co-ordinate problem.

I have a yt script that uses extract_connected_sets to find a bunch of contoured objects, for which I calculate the centre of mass (core_com below).

I then cut out a covering_grid centred on each object that is 2 kpc in width.

I image the slice of the covering_grid through its and over-plot the positions of the centre of mass of the contoured objects on it.

The problem is, they don't line up!

cloud50.png shows the process centred on object 50. Object 50 itself is nicely over a blob but the others are randomly scattered. Although this is only a slice, the z-direction of the objects differs only slightly: it shouldn't be possible to have an object sitting over nothing (especially when the slice effectively shows potential, which varies smoothly).

cloud51.png shows the same process now centred on object 51. 51 is now over a clear blob, but you can see that the image has moved differently to the numbers. (The image has shifted down and the numbers way up).

It looks to me that this should be flipped somehow (although experimental inverting the x-y axes have failed!), but I cannot see where the mistake is.

The (abbreviated) code looks like:

contours = dd.extract_connected_sets("NegEscapeVelocity", 1, 30.0, maxv, log_space=False) allcloudcores = contours[1][0] #cores defined by contour 0 thiscore = allcloudcores[c]

core_com = [] for c in range(ncores):   core_com.append(thiscore.quantities["CenterOfMass"]())

extractLE = [] extractRE = [] extractDims = []

min_x, max_x = thiscore.quantities["Extrema"]("x")[0] min_y, max_y = thiscore.quantities["Extrema"]("y")[0] min_z, max_z = thiscore.quantities["Extrema"]("z")[0]

extractLE.append(max(min_x-1.0, 0.0)) extractLE.append(max(min_y-1.0, 0.0)) extractLE.append(max(min_z-1.0, 0.0))

extractRE.append(min(max_x+1.0, thiscore.pf.domain_right_edge[0])) extractRE.append(min(max_y+1.0, thiscore.pf.domain_right_edge[1])) extractRE.append(min(max_z+1.0, thiscore.pf.domain_right_edge[2]))

for dim in range(3):

extractDims.append(round((extractRE[dim]-extractLE[dim])/cellsize))

extractCube = pf.h.covering_grid(extract_level,                                        left_edge=extractLE,                                        right_edge=extractRE,                                        dims=extractDims,                                        num_ghost_zones = 3)

plotfig = pylab.figure() pylab.imshow(extractCube["NegEscapeVelocity"][:,:,int(extractDims[2]/2)], extent=[extractLE[0], extractRE[0], extractLE[1], extractRE[1]]) colorbar = pylab.colorbar() colorbar.set_label("Negative escape velocity [km/s]") pylab.xlabel('x [kpc]') pylab.ylabel('y [kpc]')

for m in range(ncores):     pylab.annotate('%s' % (m), xy=(core_com[m][0], core_com[m][1]), xycoords='data')

pylab.savefig('clouds_novel%s' % (c))

Is this a problem of where the "zero" point is in yt / enzo / python? I was assuming (0,0,0) is the bottom left?

Elizabeth

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