In [2]:
#fn = '/home/bhardin2/Laser_20150209_2147_3sec.tif'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150214/laser_X1.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,1), vmax = prctile(img,99))
colorbar();
In [5]:
print '%.2f m/s error from laser calibration' % (laser_params['t'].stderr*20/1e-9)
180 Seconds¶
In [8]:
#fn = '/rdata/airglow/fpi/minime05/uao/2015/20150207/UAO_L_20150208_100608_138.img'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150214/180sec_X1.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,2), vmax = prctile(img,98))
colorbar()
Out[8]:
10 Seconds¶
In [9]:
#fn = '/rdata/airglow/fpi/minime05/uao/2015/20150207/UAO_L_20150208_100608_138.img'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150214/10sec_X1.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,2), vmax = prctile(img,98))
colorbar()
Out[9]:
2 Seconds¶
In [10]:
#fn = '/rdata/airglow/fpi/minime05/uao/2015/20150207/UAO_L_20150208_100608_138.img'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150214/2sec_X1.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,2), vmax = prctile(img,98))
colorbar()
Out[10]:
In [14]:
i = 2
M0 = N0
M1 = N1
# Make default figure size shorter and fatter
figure(figsize=(6,2.5))
subplot(2,1,1)
o = sky_out[i]
spectra = o.userkws['data']
sigma = o.userkws['sigma']
model = spectra + sigma*o.residual
plot(spectra[M0:M1],'k.', label='data')
plot(model[M0:M1],'r',label='model')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8},ncol=2)
subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
plt.title('%s' % sky[i].split('/')[-1])
xlim((0,N1))
subplot(2,1,2)
plot(o.residual[M0:M1],'k.', label='residual')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8})
xlim((0,N1))
#subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
i = i + 1
In [15]:
i = 0
In [23]:
i = 6
M0 = N0
M1 = N1
# Make default figure size shorter and fatter
figure(figsize=(6,2.5))
subplot(2,1,1)
o = sky_out[i]
spectra = o.userkws['data']
sigma = o.userkws['sigma']
model = spectra + sigma*o.residual
plot(spectra[M0:M1],'k.', label='data')
plot(model[M0:M1],'r',label='model')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8},ncol=2)
subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
plt.title('%s' % sky[i].split('/')[-1])
xlim((0,N1))
subplot(2,1,2)
plot(o.residual[M0:M1],'k.', label='residual')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8})
xlim((0,N1))
#subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
i = i + 1
In [24]:
exp_t = zeros(len(sky))
for i in range(len(sky)):
exp_t[i] = float(sky[i].split('/')[-1].split('sec')[0])
dref = mean(LOSwind)
LOSwindref = LOSwind - dref
figure(figsize=(5,5))
subplot(2,1,1)
errorbar(exp_t,LOSwindref, yerr=sigma_LOSwind,fmt='k.')
grid()
xscale('log')
xlim((0.9,200))
#xlabel('Exposure Time [sec]')
ylabel('Vertical Wind [m/s]')
subplot(2,1,2)
plot(exp_t, sigma_LOSwind,'k.',markersize=5)
xscale('log')
yscale('log')
xlim((0.9,200))
xlabel('Exposure Time [sec]')
ylabel('Wind Uncertainty [m/s]')
grid()
Comparison with conventional CCD¶
This comparison is not apples-to-apples, since the exposures were taken on different nights. The red dots were taken from the historical database with the following requirements:
- February 2012-2015
- 20:00 - 21:00 LT
- Zenith-looking
- Clear skies $(c < -25 K)$
In [116]:
import FPIprocess
def get_exp_time_samples(instr_name, year, doy):
'''
Return exposure times and wind error bars for samples
satisfying the conditions.
'''
conv_exp_t = []
conv_sigma_LOSwind = []
r = FPIprocess.load_level0(instr_name, year, doy)
fpir = r['FPI_Results']
t = fpir['sky_times']
d = fpir['direction']
c = fpir['Clouds']['mean']
sw = fpir['sigma_LOSwind']
et = fpir['sky_intT']
for i in range(len(t)):
if d[i] == 'Zenith' and c[i] < -25 and t[i].hour in [20]:
conv_exp_t.append(et[i])
conv_sigma_LOSwind.append(sw[i])
#print d[i],c[i],t[i].hour,et[i],sw[i]
return conv_exp_t, conv_sigma_LOSwind
print '((Click here for code))'
In [122]:
plot(conv_exp_t, conv_sigma_LOSwind,'rx',markersize=5,label='Conventional CCD')
plot(exp_t, sigma_LOSwind,'k.',markersize=5,label='EMCCD')
xscale('log')
yscale('log')
xlim((0.9,200))
#xlim((20,400))
xlabel('Exposure Time [sec]')
ylabel('Wind Uncertainty [m/s]')
legend(loc='best',prop={'size':8},numpoints=1)
grid()
Same plot, with historical data binned and averaged:¶
In [126]:
t2 = list(set(conv_exp_t))
e2 = zeros(len(t2))
for i in range(len(t2)):
t = t2[i]
idx = conv_exp_t == t
e2[i] = mean(conv_sigma_LOSwind[idx])
plot(t2, e2,'r.',markersize=5,label='Conventional CCD')
plot(exp_t, sigma_LOSwind,'k.',markersize=5,label='EMCCD')
xscale('log')
yscale('log')
xlim((0.9,200))
#xlim((20,400))
xlabel('Exposure Time [sec]')
ylabel('Wind Uncertainty [m/s]')
legend(loc='best',prop={'size':8},numpoints=1)
grid()
Bonus: 732-nm exposure¶
200 seconds
In [99]:
#fn = '/rdata/airglow/fpi/minime05/uao/2015/20150207/UAO_L_20150208_100608_138.img'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150214/IR_732_200sec_X1.tif'
d = Image.open(fn)
img = array(d)
figure(figsize=(3,3))
imshow(img, vmin = prctile(img,2), vmax = prctile(img,98))
colorbar();
In [100]:
# Calculate the annuli to use for this time
annuli = FindEqualAreas(img,cx,cy,N)
# Perform the annular summation
sky_spectra, sky_sigma = AnnularSum(img,annuli,0)
figure(figsize=(6,1))
plot(sky_spectra,'k.')
xlim((0,N1))
grid()
In [ ]: