plt.colorbar()
plt.savefig('chisq_map',fmt='png')
for i in range(0,NX):
for j in range(0,NY):
chisq_min = chisq[i,j]
if chisq[i,j] > 1.0:
for ii in range(0,NX):
for jj in range(0,NY):
res = cube1[i,j]-cube2[ii,jj]
res = res*res
chisq_loc = np.sum(res[0]) + np.sum(res[3])
chisq_loc /= 1001 * 1E25
if chisq_loc < chisq_min:
chisq_min = chisq_loc
nodes[:,i,j] = np.copy(nodes[:,ii,jj])
cube2[i,j] = np.copy(cube2[ii,jj])
chisq[i,j] = chisq_min
print 'i = ',i,'done'
chi_hist = chisq.reshape(NX*NY)
plt.cla()
plt.clf()
plt.hist(chi_hist,bins=50)
plt.savefig('chisq_hist_new',fmt='png')
pyana.fzwrite('inverted_spectra_purified.f0',cube2,0,'placeholder')
pyana.fzwrite('nodes_purified.f0',nodes,0,'placeholder')
By = temp_input[0].data
for i in range(0, NX):
for j in range(0, NY):
spinor_cube[7, i, j, :] = np.sqrt(Bx[i, :, j]**2.0 + By[i, :, j]**2.0 +
Bz[i, :, j]**2.0)
spinor_cube[10, i,
j, :] = np.arccos(Bz[i, :, j] / spinor_cube[7, i, j, :])
spinor_cube[11, i, j, :] = np.arctan(By[i, :, j] / Bx[i, :, j])
d = 66
plt.clf()
plt.cla()
plt.subplot(221)
plt.imshow(spinor_cube[2, :, :, d])
plt.colorbar()
plt.subplot(222)
plt.imshow(spinor_cube[7, :, :, d])
plt.colorbar()
plt.subplot(223)
plt.imshow(spinor_cube[10, :, :, d])
plt.colorbar()
plt.subplot(224)
plt.imshow(spinor_cube[9, :, :, d] / 1e5)
plt.colorbar()
plt.savefig('test_0.png', fmt='png')
#finally we need to flip it and write it down:
spinor_cube = spinor_cube[:, :, :, ::-1]
pyana.fzwrite(output_filename, spinor_cube, 0, 'placeholder')
import numpy as np import pyana import sys file_in = sys.argv[1] + '.dat' spectrum = np.loadtxt(file_in, unpack=True) NS = 4 NL = spectrum.shape[1] print 'Number of wavelengths = ', NL cube = np.zeros([1, 1, NS, NL]) cube[0, 0] = np.copy(spectrum[1:5]) #for s in range(1,4): # cube[0,0,s] /= cube[0,0,0] pyana.fzwrite(sys.argv[1] + '.f0', cube, 0, 'placeholder')
file_in = sys.argv[1]
file_out = sys.argv[2]
to_normalize = int(sys.argv[3])
temp = pyana.fzread(file_in)
stokes_cube = temp["data"]
dims = stokes_cube.shape
NX = dims[0]
NY = dims[1]
NL = dims[3]
print NX, NY, NL
noise_level = 3E-4
I_c_mean = np.mean(stokes_cube[:, :, 0, 0])
noise_level *= I_c_mean
for i in range(0, NX):
for j in range(0, NY):
loc_noise = noise_level * np.sqrt(stokes_cube[i, j, 0, 0] / I_c_mean)
for s in range(0, 4):
random_sample = np.random.normal(0, 1.0, NL)
stokes_cube[i, j, s] += random_sample * loc_noise
if (to_normalize):
for s in range(1, 4):
stokes_cube[:, :, s, :] /= stokes_cube[:, :, 0, :]
pyana.fzwrite(file_out, stokes_cube, 0, 'placeholder')
if (NB):
B_v = an[start:end + 1] * np.cos(an[-1])
B_h = an[start:end + 1] * np.sin(an[-1])
for i in range(0, B_start):
an_conv[i] = flt.medfilt(an[i], 11)
if (sigma):
an_conv[i] = filters.gaussian_filter(an_conv[i], sigma)
if (NB):
for i in range(B_start, B_end + 1):
if (sigma):
B_v[i - B_start] = filters.gaussian_filter(B_v[i - B_start], sigma)
B_h[i - B_start] = filters.gaussian_filter(B_h[i - B_start], sigma)
an_conv[i] = np.sqrt(B_v[i - B_start]**2.0 + B_h[i - B_start]**2.0)
if (NB):
an_conv[-1] = np.cos(an_conv[-1])
an_conv[-1] = filters.gaussian_filter(an_conv[-1], sigma)
an_conv[-1] = np.clip(an_conv[-1], -0.99, 0.99)
an_conv[-1] = np.arccos(an_conv[-1])
for i in range(0, 0):
plt.clf()
plt.cla()
plt.imshow(an_conv[i])
plt.colorbar()
plt.savefig('node_' + str(i) + '.png', fmt='png')
pyana.fzwrite(file_out, an_conv, 0, 'bla')
spectrum_mean[3] += spectrum_mean[1]+spectrum_mean[2]
spectrum_mean[1] -= spectrum_mean[1]
spectrum_mean[2] -= spectrum_mean[2]
#but also for the whole cube:
for s in range (1,4):
cube[:,:,s,:] -= cube[:,:,0,:] * zero_level[s]/zero_level[0]
cube[:,:,3,:] += cube[:,:,1,:]+cube[:,:,2,:]
cube[:,:,1,:] = cube[:,:,2,:] = 0
for s in range (1,4):
cube[:,:,s,:] /= cube[:,:,0,:]
wherenan = np.isnan(cube)
cube[wherenan] = 0.0
spectrum_to_plot=spectrum_mean
plt.subplot(221)
plt.plot(spectrum_to_plot[0])
plt.subplot(222)
plt.plot(spectrum_to_plot[1])
plt.subplot(223)
plt.plot(spectrum_to_plot[2])
plt.subplot(224)
plt.plot(spectrum_to_plot[3])
plt.tight_layout()
plt.savefig('mean_spectrum.png',fmt='png')
pyana.fzwrite(file_out,cube,0,'bla')
#regularize
cH[:,:] = 0.0
cV[:,:] = 0.0
cD[:,:] = 0.0
#cA[47:57,:] = 0.0
#reconstruct
reconstructed = pywt.idwt2(coeffs,'db8')
plt.clf()
plt.cla()
plt.subplot(311)
plt.imshow(nodes[p])
plt.colorbar()
plt.subplot(312)
plt.imshow(reconstructed)
plt.colorbar()
plt.subplot(313)
plt.imshow(nodes[p]-reconstructed)
plt.colorbar()
plt.tight_layout()
plt.savefig('test'+str(p),fmt='png',bbox_inches='tight')
plt.close('all')
nodes[p] = reconstructed
pyana.fzwrite(sys.argv[3],nodes,0,'placeholder')
vt = f(vt_nodes) nodes[index:index+N_vt,i,j] = vt index+=N_vt f = interpol.interp1d(atmos[i,j,0],atmos[i,j,9]) v = f(v_nodes) nodes[index:index+N_v,i,j] = v index+=N_v f = interpol.interp1d(atmos[i,j,0],atmos[i,j,7]) B = f(B_nodes) nodes[index:index+N_B,i,j] = B index+=N_B f = interpol.interp1d(atmos[i,j,0],atmos[i,j,10]) theta = f(theta_nodes) nodes[index:index+N_theta,i,j] = theta index+=N_theta f = interpol.interp1d(atmos[i,j,0],atmos[i,j,11]) phi = f(phi_nodes) nodes[index:index+N_phi,i,j] = phi #additional polishing - hardcode #nodes[4,:,:] = 2E4 nodes = np.transpose(nodes,(0,2,1)) print nodes.shape #write down nodes in the file: output_file = sys.argv[2] pyana.fzwrite(output_file,nodes,0,'placeholder')