示例#1
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						   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)
示例#2
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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])
示例#3
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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")
示例#4
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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)
示例#5
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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.