Hi All, I'm new to FLASH, and new to YT. We've been running 2D cylindrically symmetric laser-irradiated target simulations in FLASH. The computational domain is 1200e-4 cm (1200 microns) in R, and 2400e-4 cm (2400 microns) in Z. Nblockx =2, Nblocky=2 with lrefine_max = 5 (number of AMR refinement levels). We have been using We've been using the following Python to read in with YT: data = yt.load(filename) slc = data.slice(2,0) xmin, ymin, thetamin = data.domain_left_edge.d # minimum domain coordinates in cm xmax, ymax, thetamax = data.domain_right_edge.d # maximum domain coordinates in cm Lx = (xmax - xmin)*1e4 # length of x-axis (r-axis in R-Z cylindrical geometry) in microns Ly = (ymax - ymin)*1e4 # length of y-axis (z-axis in R-Z cylindrical geometry) in microns Nx = int(Lx + 1) #number of pixels in r-axis Ny = int(Ly + 1) #number of pixels in z-axis and using "FixedResolutionBuffer" to map the variables of choice to a 2D array with dimensions of our choosing (here Nx=1201 and Ny=2401, where each pixel is effectively 1-micron x 1-micron) with the following: frb = yt.FixedResolutionBuffer(slc,(xmin,xmax,ymin,ymax),(Ny,Nx), antialias=True) dens = np.nan_to_num(np.array(frb["flash","dens"])) eden = np.nan_to_num(np.array(frb["gas", "El_number_density"])) Te = np.nan_to_num(np.array(frb["flash", "tele"]))/11605.0 # convert to eV The images we get for dens, eden and Te look decent enough, but since we are trying to map to a uniform grid that is higher resolution than the actual simulation, we get "stair stepping" in some regions, especially where there's not much refinement occurring. We think that our solution somehow might be in using "smooth_covering_grid" because we want the data to be interpolated on a finer grid, but so far have been having troubles. Is there a way to use FixedResolutionBuffer so that it will interpolate to a finer resolution between the simulation grid cells, or how would be implement "smoothed_covering_grid" such that it will create 2D arrays with the desired number of rows and columns (Ny, Nx)?