[yt-users] coordinate question

Tue Aug 23 14:48:57 PDT 2011

Slight amendment: line plots imply this is an imshow thing, not a 
covering_grid problem. That actually makes it a python problem, not a yt 
one but I'd still really appreciate any insight. Thank you :)

Elizabeth Tasker wrote:
> Hi Matt,
>
> Thank you, that helped, but it did not entirely solve the problem. 
> What I've found is that a slice through my covering_grid does not look 
> the same as a slice done using pc.add_slice.
>
> I've attached links to two images to demonstrate the issue.
>
> http://i.imgur.com/zKVzY.png
>
> shows the slice through the extracted region with the positions of the 
> cores (found using extract_connected_sets on the whole hierarchy) 
> which don't line up.
>
> However, if you can imagine putting those same cores onto
>
> http://i.imgur.com/sOEvG.png
>
> which was made via add_slice, you can see they'd align perfectly.
>
> It looks like the extracted section is flipped through the line y = x. 
> Do you know if this is a feature of imshow or of covering_grid? I'm 
> thinking it *might* be the covering_grid that is inverted, since the 
> results from my data analysis don't look as expected (but it was a big 
> calculation that I could totally have just messed it up).
>
> I've put my calls for both below (and a more complete code is at the 
> bottom of the email).
>
> extractCube = pf.h.covering_grid(extract_level,
>                                        left_edge=extractLE,
>                                        right_edge=extractRE,
>                                        # How many dimensions along 
> each axis
>                                        dims=extractDims,
>                                        # And any fields to preload 
> (this is optional!)
>                                        num_ghost_zones = 3)
>
> pylab.imshow(extractCube["NegEscapeVelocity"][:,:,int(extractDims[2]/2)], 
> origin='lower', interpolation='nearest', aspect=aspect, extent=[min_x, 
> max_x, min_y, max_y])
>
> Thanks!
>
> Elizabeth
>
>
> Matthew Turk wrote:
>> 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 at 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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>>>
>>>
>>>     
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