import numpy as np
import plotc
import pyfits as pf
it=0
graddir="/home/jishnu/project/magnetic_inversions/working/data/update/"
grad=pf.getdata(graddir+"gradient_vectorpsi_0"+str(it)+".fits")
grad/=grad.max()
z=np.loadtxt('/home/jishnu/project/magnetic_inversions/back/polytrope')[:,0];Rsun=695.8e3;z=(z-z[-1])*Rsun
x=np.linspace(-200,200,256,endpoint=False)
Atruedir="/home/jishnu/project/magnetic_inversions/working/true/"
Astartdir="/home/jishnu/project/magnetic_inversions/working/start/"
Atrue=pf.getdata(Atruedir+'true_vectorpsi.fits')
Astart=pf.getdata(Astartdir+'model_vectorpsi.fits')
Adiff=Atrue-Astart
plotc.colorplot(Adiff,x=x,y=z,yr=[-0.8e3,None],xr=[-100,100],vmax=1,centerzero=True,xbins=5,
title=r"$\Delta A$",xlabel="Horizontal Distance (Mm)",ylabel="Depth (km)",usetex=True,y_sci=False
,sp=121)
plotc.colorplot(grad,x=x,y=z,yr=[-0.8e3,None],xr=[-100,100],vmax=1,centerzero=True,xbins=5,
title=r"Normalized Vector Potential Kernel",xlabel="Horizontal Distance (Mm)",ylabel="Depth (km)",usetex=True,y_sci=False
,sp=122)
plotc.figuresize(13,5)
plotc.tight_layout()
plotc.savefig('/home/jishnu/project/magnetic_inversions/report/vector_potential_kernel_'+str(it)+'.eps')
#~ plotc.show()
for j in xrange(nt):
if (f[j] < f_low+df):
fmode[:,0,j] = fmode[:,0,j]*0.5*(1.+cos(pi*(f[j]-(f_low+df))/df) )
if (f[j] < f_low): fmode[:,0,j] = 0.
fmode=np.squeeze(fmode).T
g=plotc.gridlist(1,2)
good_quadrant=powsp[:nt/2,:nx/2]
goodk=kf[:nx/2]*Rsun
goodf=ff[:nt/2]
plotc.colorplot(good_quadrant,x=goodk,y=goodf,subplot_index=next(g),
xlabel="k$R_\odot$ ",ylabel="$\omega$ (mHz)",title="Power spectrum",cbar_sci=True,
yr=[0,5],xr=[0,1.5*Rsun],usetex=True,x_sci=False)
plotc.gca().text(550,2,"f-mode",fontsize="15")
plotc.gca().text(550,3.4,"p1-mode",fontsize="15")
#~ filter_low_cutoff=np.where(f0>f_low)[0][0]
#~ k_filt_line_low=k[filter_low_cutoff-1:nx/2]*Rsun
#~ f0_filt_line_low=f0[filter_low_cutoff-1:nx/2]
#~ plt.plot(k_filt_line_low,f0_filt_line_low,zorder=2,color='brown')
#~ plt.plot(-k_filt_line_low,f0_filt_line_low,zorder=2,color='brown')
#~ plt.plot(k_filt_line_low,-f0_filt_line_low,zorder=2,color='brown')
#~ plt.plot(-k_filt_line_low,-f0_filt_line_low,zorder=2,color='brown')
#~
#~ filter_low_cutoff=np.where(f1>f_low)[0][0]
#~ k_filt_line_low=k[filter_low_cutoff-1:nx/2]*Rsun
vzccf.append(vzccfarr)
vzccparr = np.real(np.fft.ifft2(np.fft.fft2(vzccarr) * pmode)) / datapmax
vzccp.append(vzccparr)
vzcc = None
Nrec = 12
recs = [105 + 5 * i for i in xrange(Nrec)]
plotc.colorplot(dataf,
x=x,
y=time,
vmax=0.2,
centerzero=True,
sp=121,
xr=[x[recs[0]] - 30, x[recs[-1]] + 30],
yr=[0, 100],
axes_properties=dict(locator_properties_x=dict(nbins=5),
title="f mode"),
colorbar=False)
plotc.draw_vlines(x=x[recs], ls='dashed')
plotc.colorplot(datap,
x=x,
y=time,
vmax=0.2,
centerzero=True,
sp=122,
xr=[x[recs[0]] - 30, x[recs[-1]] + 30],
rho = -(hz + dzp) / (grav * convert)
rho -= rho[0] - rho0
drho = rho / rho0 - 1
if drho.min() < -1:
print "Negative density"
sys.exit(0)
soundspeed_squared = c2 * rho0 / p0 * p / rho # This works even if c2 etc are 1D, since x is the leading dimension and multiplication is along z
soundspeed = np.sqrt(soundspeed_squared)
alfven_speed = np.sqrt((bx**2 + bz**2) / rho)
alf_c_ratio = alfven_speed / soundspeed
plotc.plt.figure()
plotc.colorplot(bz.T * np.sqrt(4 * np.pi), y=z, x=x, sp=221, title="Bz true")
plotc.colorplot(bz_start.T * np.sqrt(4 * np.pi),
y=z,
x=x,
sp=222,
title="Bz start")
plotc.colorplot(bx.T * np.sqrt(4 * np.pi), y=z, x=x, sp=223, title="Bx true")
plotc.colorplot(bx_start.T * np.sqrt(4 * np.pi),
y=z,
x=x,
sp=224,
title="Bx start")
#~ plotc.plt.figure()
#~ plotc.colorplot(dp.T,x=x,y=z,title="Relative Pressure Difference",sp=121,
#~ ylim=[-2.5,-0.39],
surf_cutoff = -1
nrows = 3
ncols = 2
gridlist = plotc.gridlist(nrows, ncols)
#~ plotc.colorplot(KBx,y=z,subplot_index=next(gridlist),yr=[z[inner_cutoff],yax_ul],
#~ title="KBx",ylabel="Depth from surface (Mm)")
#~ plotc.colorplot(KBz,y=z,subplot_index=next(gridlist),yr=[z[inner_cutoff],yax_ul],
#~ title="KBz",ylabel="Depth from surface (Mm)")
KA_inner_cutoff = 270
KA_plot = KA[KA_inner_cutoff:]
plotc.colorplot(KA_plot,
y=z[KA_inner_cutoff:],
subplot_index=next(gridlist),
title="A Kernel",
vmin=-KA_plot.max())
dA = A_true - A_start
dA = dA[KA_inner_cutoff:]
plotc.colorplot(dA,
y=z[KA_inner_cutoff:],
subplot_index=next(gridlist),
title="A true - A start")
Kcplot = Kc[inner_cutoff:surf_cutoff]
plotc.colorplot(Kcplot,
y=z[inner_cutoff:surf_cutoff],
subplot_index=next(gridlist),
cbar_sci=True,
title="c Kernel",
int_Bx_true=int_Bx_true.T A_start=int_Bz_start - int_Bx_start A_true=int_Bz_true - int_Bx_true Bz_true_fromA=fc.differentiate(A_true,axis=1,period=L,discontinuity=A_true[:,-1]-A_true[:,0]) Bx_true_fromA=-zderiv(A_true,z) z-=1 yax_ll=z[200] yax_ul=None inner_cutoff=270 surf_cutoff=-1 gridlist=plotc.gridlist(2,3) ploty=z[inner_cutoff:]*Rsun/1e8 dA=A_true-A_start;dA=dA[inner_cutoff:] plotc.colorplot(A_start[inner_cutoff:],y=ploty,subplot_index=next(gridlist),title="A start") plotc.colorplot(dA,y=ploty,subplot_index=next(gridlist),title="A true - A start") plotc.colorplot(Bz_true[inner_cutoff:],y=ploty,subplot_index=next(gridlist),title="Bz true") plotc.colorplot(Bz_true_fromA[inner_cutoff:],y=ploty,subplot_index=next(gridlist),title="Bz true from A") plotc.colorplot(Bx_true[inner_cutoff:],y=ploty,subplot_index=next(gridlist),title="Bx true") plotc.colorplot(Bx_true_fromA[inner_cutoff:],y=ploty,subplot_index=next(gridlist),title="Bx true from A") plotc.plt.show() write_vector_potential=False if write_vector_potential: pf.writeto(os.path.join(start_field_dir,'model_vectorpsi_ls00.fits'),A_start,clobber=True) pf.writeto(os.path.join(true_field_dir,'true_vectorpsi.fits'),A_true,clobber=True)
densitypoly2D = np.atleast_2d(densitypoly).T
densitypoly2D = np.tile(densitypoly2D, [1, 256])
drho = density - densitypoly2D
pressurepoly2D = np.atleast_2d(pressurepoly).T
pressurepoly2D = np.tile(pressurepoly2D, [1, 256])
dp = pressure - pressurepoly2D
plotc.colorplot(dp / pressurepoly2D,
x=x,
y=z,
sp=121,
yr=[-0.2e3, None],
xr=[-100, 100],
xlabel="Horizontal Distance (Mm)",
ylabel="Depth (km)",
usetex=True,
title="Relative Pressure Difference",
titley=1.02)
plotc.colorplot(drho / densitypoly2D,
x=x,
y=z,
sp=122,
yr=[-0.2e3, None],
xr=[-100, 100],
xlabel="Horizontal Distance (Mm)",
ylabel="Depth (km)",
usetex=True,
title="Relative Density Difference",
import plotc
from matplotlib import pyplot as plt
import pyfits as pf
Btruedir="/home/jishnu/project/magnetic_inversions/working/true/"
z=np.loadtxt('/home/jishnu/project/magnetic_inversions/back/polytrope')[:,0];Rsun=695.8e3;z=(z-z[-1])*Rsun
x=np.linspace(-200,200,256,endpoint=False)
Bztrue=pf.getdata(Btruedir+'B_z2D.fits')
Bxtrue=pf.getdata(Btruedir+'B_x2D.fits')
soundspeed=pf.getdata(Btruedir+'soundspeed2D.fits')
density=pf.getdata(Btruedir+'density2D.fits')
calf=np.sqrt((Bztrue**2+Bxtrue**2)/density/(4*np.pi))
cratio=calf/soundspeed
plotc.quiver2D(Bxtrue,Bztrue,x=x,y=z,every=[3,1],sp=121,yr=[-0.5e3,None],xr=[-100,100],key=True,
keysuffix=" Gauss",title="Magnetic Field",titley=1.01,usetex=True,
xlabel="Horizontal Distance (Mm)",ylabel="Depth (km)")
plotc.colorplot(cratio,x=x,y=z,sp=122,yr=[-0.5e3,None],xr=[-100,100],vmax=2,title="$c_A/c_S$",titley=1.01,usetex=True,
xlabel="Horizontal Distance (Mm)")
plt.setp(plt.gca().get_yticklabels(),visible=False)
plt.subplots_adjust(wspace=0)
plotc.figuresize(13,5)
plotc.show()
#~ plotc.savefig('/home/jishnu/project/departmental_talk/field_cratio.eps')
sourcedir = 'forward_src' + str(sourceno + 1).zfill(2) + '_ls00'
vzcc = np.squeeze(pf.getdata(os.path.join(datadir, sourcedir,
'data.fits')))
Nx = vzcc.shape[1]
sourcepix = int(np.floor(sourceloc / Lx * Nx) + Nx / 2 - 1)
if fmode:
plotc.plt.figure()
fmode_filter = np.squeeze(
pf.getdata(os.path.join(codedir, 'fmode_filter.fits')))
vzcc_f = np.real(np.fft.ifft2(np.fft.fft2(vzcc) * fmode_filter))
vzcc_f /= abs(vzcc_f).max()
plotc.colorplot(vzcc_f, vmax=0.01, centerzero=True)
datacursor(display='multiple', draggable='True')
Npoints = 5
xpix = np.array([-30 - ind * 6 for ind in xrange(Npoints)]) + sourcepix
xpoints = (xpix - 127) / 256 * 400
plotc.draw_vlines(xpix)
t_cutoff = np.zeros((Npoints, 2))
#~ Line fit to bottom of wavepacket
def bot_time(x):
return (89.2 - 161) / (89.2 - 60.1) * (x - sourcepix) + 20
#~ Line fit to top of wavepacket