# deprecated/phabc2-post

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 """ Program to calculate the magnetic lines of force and plot them Version 0.2: WJH 17 Jan 2008 - extend to central xy cuts of 3d models Version 0.1: WJH 30 Dec 2007 Method is to first calculate the magnetic vector potential and then contour it. This is only valid for 2D simulations in slab cartesian symmetry. Or for symmetry planes of a 3d model, where the field can be guaranteed to be in the plane. In the latter case, we use the central xy plane. """ import numpy as N import pyfits import os, sys execname = os.path.split(sys.argv[0])[-1] # Parse command line arguments try: runid = sys.argv[1] itime = int(sys.argv[2]) except IndexError, ValueError: print "Usage: %s RUNID ITIME [VNORM]" % execname exit # Box size try: # look for fourth arg in pc worldwidth = float(sys.argv[4]) except IndexError, ValueError: # otherwise, assume 2 pc worldwidth = 2.0 # must be float!!! # Velocity normalization try: # look for third arg in km/s vnorm = 1.e5*float(sys.argv[3]) except IndexError, ValueError: # otherwise, assume 30 km/s vnorm = 3.e6 # Check whether model is 2d or 3d is2d = runid.find("2D") != -1 if is2d: zcut = 0 else: zcut = None # Read in data arrays for B field varlist = ["bx", "by", "vx", "vy", "dd", "xn", "pp"] data = {} for var in varlist: f = pyfits.open('%s-%s%4.4i.fits' % (runid, var, itime)) if zcut is None: zcut = f[0].data.shape[0]/2 # half way along first axis data[var] = f[0].data[zcut,:,:] # Subtract off mean velocity of neutral gas vmean = (data['vx']*data['dd']*data['xn']).sum() / (data['dd']*data['xn']).sum() data['vx'] -= vmean print "Mean x-velocity of neutral gas: %i km/s" % (vmean/1.e5) vmax = N.sqrt(data['vx']**2 + data['vy']**2).max()/1.e5 # km/s bmax = N.sqrt(data['bx']**2 + data['by']**2).max()*N.sqrt(4.*N.pi)/1.e-6 # micro G bmin = N.sqrt(data['bx']**2 + data['by']**2).min()*N.sqrt(4.*N.pi)/1.e-6 # micro G if runid.endswith('2D1024x'): time = float(itime)*100.0 else: time = float(itime)*1000.0 plottitle = r''' \shortstack{Model \texttt{%s}, $t = %.3f$ Myr,\\ $V_\mathrm{max} = %.1f$~km/s, $V_\mathrm{glob} = %.1f$~km/s, $B = [%.0f\ldots %.0f]~\mu$G} ''' % (runid, 1.e-6*time, vmax, vmean/1.e5, bmin, bmax) # Calculate vector potential: A = \int B_y dx - \int B_x dy # First stab - simply use cumulative sum for the integration fx = N.cumsum(data['bx'][:,0])[:,N.newaxis] # \int B_x dy fy = N.cumsum(data['by'], axis=1) # \int B_y dx vecpot = fy - fx # Do the graph import pyxgraph, pyx pyx.text.preamble(r"""\usepackage{mathpazo}""") # set up colors and other stuff graymap = pyxgraph.ColMapper.ColorMapper("pm3d", exponent=0.3, pm3d=[3,3,3]) orangemap = pyxgraph.ColMapper.ColorMapper("white-yellow-red-black", exponent=1.6, brightness=0.4) yellowgreen = pyx.color.rgb(0.8,1.0,0.0) midblue = pyx.color.rgb(0.3,0.2,1.0) darkgreen = pyx.color.rgb(0.2,0.5,0.0) midgreen = pyx.color.rgb(0.3,1.0,0.0) cyan = pyx.color.rgb(0.0,1.0,1.0) opaque = pyx.color.transparency(0.1) opaqueish = pyx.color.transparency(0.2) # map colors to graph elements imagecolmap = orangemap fieldlinecolor = midblue ifrontcolor = cyan velocitycolor = midgreen ny, nx = vecpot.shape figwidth = 10.0 worldheight = worldwidth*ny/nx figheight = ny*figwidth/nx g = pyxgraph.pyxgraph(xlimits=(0, worldwidth), ylimits=(0, worldheight), width=figwidth, height=figheight, key=None, title=plottitle, xlabel='$x$ (pc)', ylabel='$y$ (pc)', ) # image of the pressure imagedata = N.log10(data['dd']/(1.3*1.67262158e-24)) # log10 number density imagemin = 1.0 imagemax = 5.0 g.pyxplotarray(imagedata[::-1,:], colmap=imagecolmap, xpos=0.0, ypos=0.0, width=worldwidth, height=worldheight, graphcoords=True) cb = pyxgraph.pyxcolorbar(lut=imagecolmap.generate_lut(), frame=g, pos=(1.03,0.0), orientation="vertical2", width=12*pyx.unit.x_pt, minlabel="$n = 10^{%.1f}$" % imagemin, maxlabel="$n = 10^{%.1f}$" % imagemax, textattrs=[pyx.trafo.scale(0.7)]) g.insert(cb) # Plot vectors of velocity v = N.sqrt(data['vx']**2 + data['vy']**2)/vnorm # normalized magnitude of velocity theta = N.arctan2(data['vy'], data['vx']) # angle of v with x-axis mx, my = 64, 64*ny/nx # fixed grid of arrows, independent of resolution skip = nx/mx # we abuse a parametric function below, so we express everything in # terms of a parameter k import random x = lambda k: random.randint(skip/4,3*skip/4) + skip*(int(k)/my) y = lambda k: random.randint(skip/4,3*skip/4) + skip*(int(k)%my) # s = lambda k: 3.e4*B[y(k),x(k)] # s = lambda k: 0.5 # all arrows the same size s = lambda k: v[y(k),x(k)] a = lambda k: theta[y(k),x(k)]*180/N.pi arrowfunc = pyx.graph.data.paramfunction( "k", 0, mx*my-1, "x, y, size, angle = (worldwidth/nx)*x(k), (worldheight/ny)*y(k), s(k), a(k)", points=mx*my, context=locals()) vectorstyles = [velocitycolor, opaque] g.plot(arrowfunc, [pyx.graph.style.arrow(arrowsize=0.1, arrowattrs=vectorstyles, lineattrs=vectorstyles, )]) # Plot contours of vector potential mycolmap = pyxgraph.ColMapper.ColorMapper("pm3d", exponent=1.0, brightness=0.2) xx = N.arange(nx) + 0.5 yy = N.arange(ny) + 0.5 # add 1 to positions to align with image g.pyxplotcontour(vecpot, (worldwidth/nx)*(xx+1.0), (worldwidth/nx)*(yy+1.0), levels=100, colors='color', color=fieldlinecolor, lw=1.5, lineattrs=[opaqueish] ) # Plot contour at ionization front g.pyxplotcontour(data['xn'], (worldwidth/nx)*(xx+0.5), (worldwidth/nx)*(yy+0.5), levels=[0.1,0.5,0.9], colors='color', color=ifrontcolor, lw=1.0, lineattrs=[opaqueish] ) # Write file g.writePDFfile('%s-%s-%i' % (execname.split('.')[0], runid, itime))