Hi Stephanie,

No idea what's happening here. Does it still look weird when you zoom in?

If I were in your shoes right now, I'd look very carefully right here after this for loop:

https://github.com/yt-project/yt/blob/master/yt/visualization/plot_modifications.py#L1034

In particular, I'd probably make an imshow plot of the "buff" array. I'd also put a breakpoint inside the for loop look carefully at the data being passed to the pixelize_cartesian function, which is accepting the x, dx, y, and dy fields for the each clump (e.g. the x, and y positions of all the zones in each clump as well as the cell spacing in x and y for the same zones), along with an integer mask.

-Nathan

On Tue, Aug 28, 2018 at 10:20 PM Stephanie Tonnesen <stonnes@gmail.com> wrote:

Dear yt-users,

I am having some trouble with the yt clump finder. Mostly, my issue is that I don't understand what I am getting when I annotate the projectionplot.  Instead of the nice lines in the example, I get strange filled-in circles?

My figure is attached, and here is my code:

##################

ds = yt.load("/mnt/ceph/users/stonnesen/runs/JO204wind/DD"+loop[i]+"/JO204cw"+loop[i])

    ad = ds.all_data()

    data_source = ad.cut_region(["obj['z'] > 0.27"]) #ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],(80, 'kpc'), (145, 'kpc'))

    # the field to be used for contouring

    field = ("gas", "density")

    # This is the multiplicative interval between contours.

    step = 2.0

    # Now we set some sane min/max values between which we want to find contours

    # This is how we tell the clump finder what to look for -- it won't look for

    # contours connected below or above these threshold values.

    c_min = 2e-25 #10**np.floor(np.log10(data_source[field]).min()+3 )

    c_max = 10**np.floor(np.log10(data_source[field]).max()+1)

    # Now find get our 'base' clump -- this one just covers the whole domain.

    master_clump = Clump(data_source, 'density')

    # Add a "validator" to weed out clumps with less than 16 cells.

    # As many validators can be added as you want.

    master_clump.add_validator("min_cells", 16)

    

    # Calculate center of mass for all clumps.

    master_clump.add_info_item("center_of_mass")

    # Begin clump finding.

    find_clumps(master_clump, c_min, c_max, step)

    # Save the clump tree as a reloadable dataset

    fn = master_clump.save_as_dataset(fields=["density"])

    # Traverse the clump hierarchy to get a list of all of the 'leaf' clumps

    leaf_clumps = get_lowest_clumps(master_clump)

    # To visualize these clumps, a list of clumps can be supplied to

    # the "clumps" callback on a plot.  First, we create a projection plot:

    prj = yt.ProjectionPlot(ds, 0, 'density', center=[0.5,0.5,0.5],width=(160,'kpc'))

    prj.set_buff_size(4000)

    # Next we annotate the plot with contours on the borders of the clumps

    prj.annotate_clumps(leaf_clumps)

    

    # Save the plot to disk.

    prj.save('JO204cw'+loop[i]+'_clumps_16pix')

##########################

Also, when I have 

> print (leaf_clumps[0]["gas","density"])

I get 22 outputs, which seems like a lot less than what is getting put on the figure?

Anyway, I am lost and any help would be greatly appreciated.

Best,

Stephanie

--
Dr. Stephanie Tonnesen

Associate Research Scientist

CCA, Flatiron Institute

New York, NY

stonnes@gmail.com

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