Note
Go to the end to download the full example code.
Get PHI Data Noise Levels#
This example demonstrates how to compute the noise levels in the polarimetric (L2 Stokes) and line-of-sight magnetic field (L2 Blos) data from the Solar Orbiter PHI instrument.
Helper functions#
def find_nearest(array, value):
"""
return index of nearest value in array to the desired value
"""
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
def gaus(x,a,x0,sigma):
"""
return Gauss function
"""
return a*np.exp(-(x-x0)**2/(2*sigma**2))
def gaussian_fit(a):
"""
gaussian fit for data 'a' from np.histogram
"""
xx=a[1][:-1] + (a[1][1]-a[1][0])/2
y=a[0][:]
p0=[0.,sum(xx*y)/sum(y),np.sqrt(sum(y * (xx - sum(xx*y)/sum(y))**2) / sum(y))] #weighted avg of bins for avg and sigma inital values
p0[0]=y[find_nearest(xx,p0[1])-5:find_nearest(xx,p0[1])+5].mean() #find init guess for ampltiude of gauss func
p,cov=spo.curve_fit(gaus,xx,y,p0=p0)
return p
def blos_noise(blos_values, show=False):
"""
plot blos hist + Gaussian fit
"""
fig = plt.figure(figsize = (8,6))
hi = plt.hist(blos_values.flatten(), bins=np.linspace(-2e2,2e2,100), histtype='stepfilled', alpha=0.5, edgecolor='black')
if not show:
plt.close(fig)
tmp = [0,0]
tmp[0] = hi[0].astype('float64')
tmp[1] = hi[1].astype('float64')
#guassian fit + label
p = gaussian_fit(tmp)
xx=hi[1][:-1] + (hi[1][1]-hi[1][0])/2
lbl = r'$\mu=$' + f'{p[1]:.2f} G' + '\n' + r'$\sigma=$' + f'{p[2]:.2f} G'
if show:
plt.plot(xx,gaus(xx,*p),'r--', label=lbl)
plt.xlabel('BLOS [G]')
plt.ylabel('Counts')
plt.yscale('log')
plt.ylim(1e0,np.max(hi[0])*10)
plt.legend(loc='upper right', fontsize=20)
plt.tight_layout()
plt.show()
return p
def stokes_noise(stokes_values, show=False):
"""
plot stokes v hist + Gaussian fit
"""
fig = plt.figure(figsize = (8,6))
hi = plt.hist(stokes_values.flatten(), bins=np.linspace(-1e-2,1e-2,200), histtype='stepfilled', alpha=0.5, edgecolor='black')
if not show:
plt.close(fig)
tmp = [0,0]
tmp[0] = hi[0].astype('float64')
tmp[1] = hi[1].astype('float64')
#guassian fit + label
p = gaussian_fit(tmp)
if show:
xx=hi[1][:-1] + (hi[1][1]-hi[1][0])/2
lbl = r'$\mu=$' + f'{p[1]:.5f}'+r' $V/I_c$' + '\n' + r'$\sigma=$' + f'{p[2]:.5f}'+r' $V/I_c$'
plt.plot(xx,gaus(xx,*p),'r--', label=lbl)
plt.xlabel(r'Stokes $V/I_c$')
plt.ylabel('Counts')
plt.yscale('log')
plt.ylim(1e0,np.max(hi[0])*10)
plt.legend(loc='upper right', fontsize=20)
plt.tight_layout()
plt.show()
return p
Searching for PHI-HRT Blos and Stokes Data#
(Everything also applies to FDT data) We first search for Solar Orbiter PHI-HRT (High Resolution Telescope) Blos data in a given time range. The search results will return metadata about available files.
t_start_hrt = Time('2024-10-15T18:00', format='isot', scale='utc')
t_end_hrt = Time('2024-10-15T18:05', format='isot', scale='utc')
search_results_phi_hrt = Fido.search(a.Instrument('PHI'), a.Time(t_start_hrt.value, t_end_hrt.value), (a.soar.Product('phi-hrt-stokes') | a.soar.Product('phi-hrt-blos')))
print(search_results_phi_hrt)
sr_stokes = search_results_phi_hrt[0]
sr_blos = search_results_phi_hrt[1]
blos_file = Fido.fetch(sr_blos)#, path='../data/') - testing use case
stokes_file = Fido.fetch(sr_stokes)#, path='../data/')
blos = fits.getdata(blos_file[0])
stokes = fits.getdata(stokes_file[0])
print(blos.shape), print(stokes.shape)
Results from 2 Providers:
1 Results from the SOARClient:
Instrument Data product Level Start time End time Filesize SOOP Name Detector Wavelength
Mbyte
---------- -------------- ----- ----------------------- ----------------------- -------- ---------------------------- -------- ----------
PHI phi-hrt-stokes L2 2024-10-15 18:00:03.834 2024-10-15 18:01:26.596 308.52 L_BOTH_HRES_HCAD_Major-Flare HRT 6173.341
1 Results from the SOARClient:
Instrument Data product Level Start time End time Filesize SOOP Name Detector Wavelength
Mbyte
---------- ------------ ----- ----------------------- ----------------------- -------- ---------------------------- -------- ----------
PHI phi-hrt-blos L2 2024-10-15 18:00:03.834 2024-10-15 18:01:26.596 13.084 L_BOTH_HRES_HCAD_Major-Flare HRT 6173.341
Files Downloaded: 0%| | 0/1 [00:00<?, ?file/s]
Files Downloaded: 100%|██████████| 1/1 [00:00<00:00, 3.25file/s]
Files Downloaded: 100%|██████████| 1/1 [00:00<00:00, 3.25file/s]
Files Downloaded: 0%| | 0/1 [00:00<?, ?file/s]Exception ignored in: <function BaseEventLoop.__del__ at 0x70fc7362df80>
Traceback (most recent call last):
File "/home/docs/.asdf/installs/python/3.12.10/lib/python3.12/asyncio/base_events.py", line 732, in __del__
self.close()
File "/home/docs/.asdf/installs/python/3.12.10/lib/python3.12/asyncio/unix_events.py", line 71, in close
self.remove_signal_handler(sig)
File "/home/docs/.asdf/installs/python/3.12.10/lib/python3.12/asyncio/unix_events.py", line 160, in remove_signal_handler
signal.signal(sig, handler)
File "/home/docs/.asdf/installs/python/3.12.10/lib/python3.12/signal.py", line 58, in signal
handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: signal only works in main thread of the main interpreter
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(1792, 1792)
(6, 4, 1792, 1792)
(None, None)
Blos noise#
- Select an area of Quiet Sun (i.e. no significant magnetic structures)
Avoid the limb (unless you care about that)
Avoid the edges of the FoV if possible (although our example here is alredy cropped so no need to worry)
It will be difficult to avoid some network structures, so there will be remnant signal in the area you choose. Hence any ‘noise’ value we will now generate will be an overestimate.
plt.imshow(blos, cmap='hmimag', vmin=-1500, vmax=1500, origin="lower")
plt.show()
The region bounded by (x,y) = (0,1200) and (x,y) = (500,1700) is a suitable region for analyzing the Blos noise.
Stokes noise#
Typically when referring to the polarimetric noise, one refers to the noise level in Stokes V at the continuum wavelength.
As before:
- Select an area of Quiet Sun (ie no significant magnetic structures)
Avoid the limb (unless you care about that)
Avoid the edges of the FoV
Find and select the continuum wavelength of the Stokes V
To find the continuum wavelength point, look at the WAVEXX keywords in the fits header. It is either the 0 or 5 wavelength position The continuum wavelength is the one separated by 0.3A from the neighbour file.
hdr = fits.getheader(stokes_file[0])
contpos = hdr['CONTPOS']-1 #header is 1 based index, but python needs 0 based
#WARNING: 'CONTPOS' may not exist in some 2022 or 2023 datasets. They will be reprocessed with updated headers soon.
plt.imshow(stokes[contpos,3,:,:], cmap='gist_heat', vmin=-0.01, vmax = 0.01, origin="lower")
plt.colorbar()
plt.show()
stokes_fit = stokes_noise(stokes[contpos,3,:,:][region_slice], show=True)
Total running time of the script: (4 minutes 19.821 seconds)