method="linear") ### Plotting plotter = torch.Plotter(nx, ny, plot_size, figformat, DPI) plotparams = torch.PlotParams(None, None, None, False, 'linear', (1, 1), None, tight=False, detail="all") grid = plotter.getGrid(plotparams) plotter.modifyGrid(grid, True) ax = grid[0] cbax = grid.cbar_axes[0] var = deni im = ax.imshow(var, vmin=var.min(), vmax=var.max(), origin='lower', extent=[x[0].min(), x[0].max(), x[1].min(), x[1].max()], interpolation="bicubic", cmap=hgspy.get_par_cmap()) ax.set_xlabel('$x\ \\left[\mathrm{kpc}\\right]$') ax.set_ylabel('$y\ \\left[\mathrm{kpc}\\right]$') cbax.colorbar(im) cblax = cbax.axis[cbax.orientation] #cbax.set_ylim([-0.1, 0.7]) cblax.label.set_text(plotter.format_label(torch.VarType('n_\mathrm{e}', units='cm^{-3}'))) plotter.save_plot("galaxy-density-xz.png") ### Plotting #plotter = torch.Plotter(nx, ny, plot_size, figformat, DPI) #plotparams = torch.PlotParams(None, None, None, False, 'linear', (1, 1), None, tight=False, detail="all") #grid = plotter.getGrid(plotparams)
inputfile = args.inputfile outputfile = 'four-panel-champagne.' + figformat ### Data set up. datacubes = [] vs_types = [] vsminmax = [] for i in range(4): datacubes.append(torch.CFD_Data(inputfile, axial=False)) color_maps = [] color_maps.append(hgspy.get_density_cmap()) color_maps.append(hgspy.get_water_cmap()) color_maps.append(hgspy.get_temperature_cmap()) color_maps.append(hgspy.get_par_cmap()) vs_types = [] vs_types.append(torch.VarType("nh", isLog10=datacubes[0].appropriate_to_log("nh"))) vs_types.append(torch.VarType("pre", isLog10=datacubes[1].appropriate_to_log("pre"))) vs_types.append(torch.VarType("tem", isLog10=datacubes[2].appropriate_to_log("tem"))) vs_types.append(torch.VarType("hii", isLog10=datacubes[3].appropriate_to_log("hii"))) vsminmax = [] #vsminmax.append([3.5, 4.8]) #vsminmax.append([-10, -5]) #vsminmax.append([2 , 4]) #vsminmax.append([1, 5]) vsminmax.append([None, None])
DPI = 300 figformat = 'png' plot_size = 2.5 fontsize = 8 torch.set_font_sizes(fontsize) inputfile = [] inputfile.append("data/shadowing-square/data2D_002.txt") inputfile.append("data/shadowing-square/data2D_011.txt") inputfile.append("data/shadowing-square/data2D_100.txt") outputfile = 'multi-shadow' + '.' + figformat cmap = hgspy.get_par_cmap() ### Data set up. datacubes = [] vs_types = [] vsminmax = [] color_maps = [] for i in range(3): datacubes.append(torch.CFD_Data(inputfile[i], axial=True)) color_maps.append(cmap) vs_types.append(torch.VarType("hii", isLog10=datacubes[i].appropriate_to_log("hii"))) vsminmax.append([0, 1]) plotparams = torch.PlotParams(datacubes, vs_types, vsminmax, True, 'linear', (3, 1), color_maps, tight=False, detail="all")
inputfile.append(dir + "data2D_025.txt") inputfile.append(dir + "data2D_050.txt") inputfile.append(dir + "data2D_100.txt") outputfile = "multi-champagne" + '.' + figformat ### Data set up. datacubes = [] vs_types = [] vsminmax = [] color_maps = [] for i in range(4): datacubes.append(torch.CFD_Data(inputfile[i], axial=False)) vs_types.append(torch.VarType("nhii", isLog10=datacubes[i].appropriate_to_log("nhii"))) vsminmax.append([2, 4]) color_maps.append(hgspy.get_par_cmap(isReversed=False)) plotparams = torch.PlotParams(datacubes, vs_types, vsminmax, True, "cubic", (2, 2), color_maps, tight=False, detail="all") #plotparams.xminmax = [0, 0.5] #plotparams.yminmax = [0.1, 0.6] ### Plotting. plotter = torch.Plotter(1.0, 1.0, plot_size, figformat, DPI) ### Image. grid = plotter.multi(plotparams) ts = 0.04 for i in range(len(datacubes)): timestring = str(datacubes[i].t) + " yr" grid[i].text(1-ts, 1-ts, timestring, fontsize=int(1.5*fontsize), color='white', horizontalalignment='right', verticalalignment='top', transform = grid[i].transAxes)
torch.set_font_sizes(fontsize) inputfile = [] inputfile.append(dir + "data2D_001.txt") inputfile.append(dir + "data2D_025.txt") inputfile.append(dir + "data2D_050.txt") outputfile = 'multi-champagne' + '.' + figformat ### Data set up. datacubes = [] vs_types = [] vsminmax = [] color_maps = [] cmap = hgspy.get_par_cmap(isReversed=False) for i in range(3): datacubes.append(torch.CFD_Data(inputfile[i], axial=False)) color_maps.append(cmap) vs_types.append(torch.VarType(var_type, isLog10=datacubes[i].appropriate_to_log(var_type))) vsminmax.append([2.0, 4.0]) plotparams = torch.PlotParams(datacubes, vs_types, vsminmax, True, 'linear', (3, 1), color_maps, tight=False, detail="all") plotparams.xminmax = [0, 0.6] plotparams.yminmax = [0, 0.6] ### Plotting plotter = torch.Plotter(datacubes[0].nx, datacubes[0].ny, plot_size, figformat, DPI) ### Image.