def interpirr(re,ta):
"interpolate over irradiance to get the spectrum at ta and re"
sp = np.zeros(lut.wvl.size)
print re,ta
for w in xrange(lut.wvl.size):
irrdnfx = interpolate.RectBivariateSpline(lut.refsp[lut.refsp>5],lut.tausp,lut.sp_irrdn[1,w,0,lut.refsp>5,:],kx=1,ky=1)
sp[w] = irrdnfx(re,ta)
return smooth(sp,20)
def param(sp,wvlin):
" Calculates the parameters from a spectrum."
from linfit import linfit
from Sp_parameters import nanmasked, norm2max, smooth, deriv, find_closest
npar = 16
spc, mask = nanmasked(sp)
wvl = wvlin[mask]
try:
norm = norm2max(spc)
except ValueError:
par = np.zeros(npar)*np.nan
return par
[i1000,i1077,i1493,i1600,i1200,i1300,i530,i610,
i1565,i1634,i1193,i1198,i1236,i1248,i1270,i1644,
i1050,i1040,i1065,i600,i870,i515] = find_closest(wvl,np.array([1000,1077,1493,1600,1200,1300,530,
610,1565,1634,1193,1198,1236,1248,
1270,1644,1050,1040,1065,600,870,515]))
if np.isnan(spc[i1000]) or not spc[i1000]:
par = np.zeros(npar)*np.nan
return par
norm2 = spc/spc[i1000]
dsp = smooth(deriv(norm2,wvl/1000),2,nan=False)
imaxwvl = np.argmax(spc)
maxwvl = wvl[mask[imaxwvl]]
# now calculate each parameter
fit0 = np.polyfit(np.array([wvl[i1000],wvl[i1077]]),np.array([norm2[i1000],norm2[i1077]]),1)
fit0_fn = np.poly1d(fit0)
fit7 = np.polyfit(np.array([wvl[i1493],wvl[i1600]]),np.array([norm2[i1493],norm2[i1600]]),1)
fit7_fn = np.poly1d(fit7)
fit8,z = linfit(wvl[i1000:i1077],dsp[i1000:i1077])
fit9,z = linfit(wvl[i1200:i1300],dsp[i1200:i1300])
fit10,z = linfit(wvl[i530:i610]/1000,norm[i530:i610])
fit14,z = linfit(wvl[i1565:i1634],spc[i1565:i1634]/norm[i1565])
par = [sum(norm2[i1000:i1077]-fit0_fn(wvl[i1000:i1077])), # 1 curvature of rad normed to 1000 nm for 1000 nm - 1077 nm
dsp[i1198], # 2 deriv of rad normed to 1000 nm at 1198 nm (!=IDL version)
dsp[i1493], # 3 deriv of rad normed to 1000 nm at 1493 nm
norm[i1198]/norm[i1236], # 4 ratio of normalized rad of 1198 nm / 1236 nm
np.nanmean(norm[i1248:i1270]), # 5 mean of normalized rad between 1248 nm - 1270 nm
np.nanmean(norm[i1565:i1644]), # 6 mean of normalized rad between 1565 nm - 1644 nm
np.nanmean(norm[i1000:i1050]), # 7 mean of normalized rad between 1000 nm - 1050 nm
sum(norm2[i1493:i1600]-fit7_fn(wvl[i1493:i1600])), # 8 curvature of rad normed to 1000 nm for 1493 nm - 1600 nm
fit8[0], # 9 slope of deriv of rad normed to 1000 nm, 1000 nm - 1077 nm
fit9[0], # 10 slope of deriv of rad normed to 1000 nm, 1200 nm - 1300 nm
fit10[0], # 11 slope of normalized radiance between 530 nm - 610 nm
norm[i1040], # 12 normalized radiance at 1040 nm
norm[i1000]/norm[i1065], # 13 ratio of normalized radiance at 1000 nm / 1065 nm
norm[i600]/norm[i870], # 14 ratio of normalized radiance at 600 nm / 870 nm
np.nanmin([0.003,fit14[0]]), # 15 slope of radiance / rad at 1565 between 1565 nm - 1634 nm
spc[i515]] # 16 radiance at 515 nm
# do a check for bad points
if np.all(np.isnan(par[0:13])):
par[14] = np.nan
par[15] = np.nan
return par
from Sp_parameters import smooth
# In[98]:
for i,daystr in enumerate(dds):
plt.figure()
ax1 = plt.subplot(211)
ax2 = plt.subplot(212,sharex=ax1)
ax1.plot(rts[i]['utc'],rts[i]['tau'],'b.')
ax1.plot(rts[i]['utc'][rts[i]['fl']],rts[i]['tau'][rts[i]['fl']],'g+')
try:
ax1.plot(rts[i]['utc'][rts[i]['fl']],smooth(rts[i]['tau'][rts[i]['fl']],6),'kx')
except:
pass
ax1.set_ylabel('tau')
ax2.plot(rts[i]['utc'],rts[i]['ref'],'b.')
ax2.plot(rts[i]['utc'][rts[i]['fl']],rts[i]['ref'][rts[i]['fl']],'g+')
try:
ax2.plot(rts[i]['utc'][rts[i]['fl']],smooth(rts[i]['ref'][rts[i]['fl']],6),'kx')
except:
pass
ax2.set_ylabel('ref')
ax2.set_xlabel('UTC')
ax1.set_title(daystr)
# <codecell>
print meas.utc.shape
print len(meas.good), max(meas.good)
# <codecell>
reload(Sp)
from Sp_parameters import smooth
# <codecell>
fig,ax = plt.subplots(4,sharex=True)
ax[0].set_title('Retrieval results time trace')
ax[0].plot(meas.utc,tau,'rx')
ax[0].plot(meas.utc[meas.good[:,0]],smooth(tau[meas.good[:,0],0],20),'k')
ax[0].set_ylabel('$\\tau$')
ax[1].plot(meas.utc,ref,'g+')
ax[1].set_ylabel('R$_{ef}$ [$\\mu$m]')
ax[1].plot(meas.utc[meas.good[:,0]],smooth(ref[meas.good[:,0],0],20),'k')
ax[2].plot(meas.utc,phase,'k.')
ax[2].set_ylabel('Phase')
ax[2].set_ylim([-0.5,1.5])
ax[2].set_yticks([0,1])
ax[2].set_yticklabels(['liq','ice'])
ax[3].plot(meas.utc,ki)
ax[3].set_ylabel('$\\chi^{2}$')
ax[3].set_xlabel('UTC [Hours]')
ax[3].set_xlim([17,19.05])
plt.savefig(fp+'plots/TCAP_retri_results.png',dpi=600)
#plt.savefig(fp+'plots/TCAP_retri_results.eps')
# In[36]:
dc8_header
# # Ploting of the combine effective radius
# ## Now make a plot of the time series for easier referencing
# In[39]:
plt.figure(figsize=(9,7))
ax1 = plt.subplot(211)
ax1.plot(star_utc,smooth(modis_tau,6),'m->',label='MODIS',markeredgecolor='none')
ax1.plot(emas_utc_full,smooth(emas_tau_full,60),'ko',label='eMAS',markeredgecolor='none')
ax1.plot(ssfr_utc,smooth(ssfr_tau,2),'g-x',label='SSFR')
ax1.plot(rsp_utc,smooth(rsp_tau,70),'c-+',label='RSP')
ax1.plot(goes_utc,smooth(goes_tau,2),'b-*',label='GOES',markeredgecolor='none')
ax1.plot(star_utc,smooth(star_tau,40),'r-s',label='4STAR',markeredgecolor='none')
ax1.errorbar(star_utc,smooth(modis_tau,6),yerr=modis_tau_std*2.0,color='m')
ax1.errorbar(ssfr_utc,smooth(ssfr_tau,2),yerr=ssfr_tau_std*2.0,color='g')
ax1.errorbar(rsp_utc,smooth(rsp_tau,70),yerr=rsp_tau_std*2.0,color='c')
ax1.errorbar(goes_utc,smooth(goes_tau,2),yerr=goes_tau_std*2.0,color='b')
ax1.errorbar(star_utc,smooth(star_tau,40),yerr=star_tau_std*2.0,color='r')
ax1.legend(frameon=False,numpoints=1)
ax1.grid()
#ax1.set_xlabel('UTC [H]')
from Sp_parameters import deriv,smooth
# In[82]:
s['ext'] = np.zeros_like(s['tau_aero'])
# In[161]:
for l,w in enumerate(s['w'][0]):
s['ext'][it,l] = smooth(deriv(smooth(s['tau_aero'][it,l],3,nan=False,old=True),
s['Alt'][it][:,0])*-1000000.0,5,nan=False,old=True)
# In[162]:
plt.figure()
plt.plot(s['ext'][it,400],s['Alt'][it])
# In[170]:
fig = plt.figure(figsize=(11,8))
ax = plt.subplot2grid((4,4),(0,0),colspan=3,rowspan=3)
cb = ax.pcolorfast(s['w'].flatten()*1000.0,s['Alt'][it].flatten(),s['ext'][it,:][:-1,:-1],
emas_tau_full = m_dict['emas'][1]; emas_ref_full = m_dict['emas'][3]; emas_utc_full = m_dict['emas'][5]
modis_tau = m_dict['modis'][1]; modis_ref = m_dict['modis'][3]
ssfr_tau = m_dict['ssfr'][1]; ssfr_ref = m_dict['ssfr'][3]; ssfr_utc = m_dict['ssfr'][5]
rsp_tau = m_dict['rsp'][1]; rsp_ref = m_dict['rsp'][3]; rsp_utc = m_dict['rsp'][5]
star_tau = m_dict['star'][1]; star_ref = m_dict['star'][3]
goes_tau = m_dict['goes'][1]; goes_ref = m_dict['goes'][3]
goes_utc = m_dict['goes'][5]; star_utc = m_dict['star'][5]
# # Now make a plot of the time series for easier referencing
# In[7]:
plt.figure(figsize=(9,7))
ax1 = plt.subplot(211)
ax1.plot(star_utc,smooth(modis_tau,6),'m->',label='MODIS',markeredgecolor='none')
ax1.plot(emas_utc_full,smooth(emas_tau_full,60),'ko',label='eMAS',markeredgecolor='none')
ax1.plot(ssfr_utc,smooth(ssfr_tau,2),'g-x',label='SSFR')
ax1.plot(rsp_utc,smooth(rsp_tau,70),'c-+',label='RSP')
ax1.plot(goes_utc,smooth(goes_tau,2),'b-*',label='GOES',markeredgecolor='none')
ax1.plot(star_utc,smooth(star_tau,40),'r-s',label='4STAR',markeredgecolor='none')
ax1.errorbar(star_utc,smooth(modis_tau,6),yerr=modis_tau_std*2.0,color='m')
ax1.errorbar(ssfr_utc,smooth(ssfr_tau,2),yerr=ssfr_tau_std*2.0,color='g')
ax1.errorbar(rsp_utc,smooth(rsp_tau,70),yerr=rsp_tau_std*2.0,color='c')
ax1.errorbar(goes_utc,smooth(goes_tau,2),yerr=goes_tau_std*2.0,color='b')
ax1.errorbar(star_utc,smooth(star_tau,40),yerr=star_tau_std*2.0,color='r')
ax1.legend(frameon=False,numpoints=1)
ax1.grid()
#ax1.set_xlabel('UTC [H]')
# <markdowncell>
# Create the desired arrays which are used in creating the parameters
# <codecell>
s_0_5_6 = lut.sp[0,:,0,5,6]
s,n = nanmasked(s_0_5_6)
snorm = norm2max(s_0_5_6)
[i1000,i1077,i1493,i1600,i1200,i1300,i530,i610,
i1565,i1634,i1193,i1198,i1236,i1248,i1270,i1644,
i1050,i1040,i1065,i600,i870,i515] = find_closest(lut.wvl,np.array([1000,1077,1493,1600,1200,1300,530,
610,1565,1634,1193,1198,1236,1248,
1270,1644,1050,1040,1065,600,870,515]))
norm2 = s_0_5_6/s_0_5_6[i1000]
dsp = smooth(np.gradient(norm2,lut.wvl/1000.),2)
# <codecell>
norm2_uni = UnivariateSpline(lut.wvl/1000.0,norm2,k=5)
norm2_uni.set_smoothing_factor(1)
dnorm2 = norm2_uni.derivative()
# <codecell>
norm2_bspline = splrep(lut.wvl/1000.0,norm2,k=5)
norm2_b = splev(lut.wvl/1000.0,norm2_bspline,der=0)
dbnorm2 = splev(lut.wvl/1000.0,norm2_bspline,der=1)
# <codecell>
# ## Do the combined plot # In[519]: profiles[k]['point'] # In[520]: len(profiles) # In[407]: smooth(profiles[k]['ams'],4) # In[463]: k['so2'] # In[464]: k['alt_so2'] # In[534]: fig = plt.figure(figsize=(8,10))
# <codecell>
(meas.tau,meas.ref,meas.phase,meas.ki) = rk.run_retrieval(meas,lut)
# <codecell>
print meas.utc.shape
print len(meas.good), max(meas.good)
# <codecell>
from Sp_parameters import smooth
fig,ax = plt.subplots(4,sharex=True)
ax[0].set_title('Retrieval results time trace')
ax[0].plot(meas.utc,meas.tau,'rx')
ax[0].plot(meas.utc[meas.good],smooth(meas.tau[meas.good],20),'k')
ax[0].set_ylabel('$\\tau$')
ax[1].plot(meas.utc,meas.ref,'g+')
ax[1].set_ylabel('R$_{ef}$ [$\\mu$m]')
ax[1].plot(meas.utc[meas.good],smooth(meas.ref[meas.good],20),'k')
ax[2].plot(meas.utc,meas.phase,'k.')
ax[2].set_ylabel('Phase')
ax[2].set_ylim([-0.5,1.5])
ax[2].set_yticks([0,1])
ax[2].set_yticklabels(['liq','ice'])
ax[3].plot(meas.utc,meas.ki)
ax[3].set_ylabel('$\\chi^{2}$')
ax[3].set_xlabel('UTC [Hours]')
ax[3].set_xlim([18.5,19.05])
plt.savefig(fp+'plots\\SEAC4RS_20130913_retri_results.png',dpi=600)
plt.savefig(fp+'plots\\SEAC4RS_20130913_retri_results.pdf',bbox='tight')
plt.figure(figsize=(11,6))
plt.plot(m2.variables['LONGITUDE'].data[0,:],m2.variables['RE_CLIMOMEAN'].data[0,:],
'*-',color='r',label='MODIS climatology',zorder=50,lw=2.5)
plt.plot(m2.variables['LONGITUDE'].data[0,:],m2.variables['RE_YRMEAN'].data[0,:,0],
'x-',color='grey',alpha=0.1,zorder=10,label='MODIS yearly means')
plt.plot(m2.variables['LONGITUDE'].data[0,:],m2.variables['RE_YRMEAN'].data[0,:,:],'x-',color='grey',alpha=0.1,zorder=10)
means = []
cls = ['green','blue','yellow','cyan','magenta','orange']
for j,d in enumerate(d_irtn):
m = make_boxplot(s['REF'][s['days']==d],s['LON'][s['days']==d],lims3,pos3,color=cls[j],
label='{}/{} 4STAR'.format(d_rtn[j][4:6],d_rtn[j][6:8]),alpha=0.3)
means.append(m)
plt.plot(pos3,np.nanmean(np.array(means),axis=0),'s-k',lw=3.5,label='4STAR mean',zorder=180)
plt.plot(pos3,smooth(np.nanmean(np.array(means),axis=0),6),'s--',color='grey',lw=2.5,label='4STAR smoothed mean',zorder=200)
plt.ylabel('R$_{{eff}}$ [$\\mu$m]')
plt.ylim(0,25)
plt.xlabel('Longitude [$^\\circ$]')
plt.title('Effective radius for clouds along routine flight path')
box = plt.gca().get_position()
plt.gca().set_position([box.x0, box.y0, box.width * 0.78, box.height])
prelim()
plt.legend(numpoints=1,bbox_to_anchor=(1.45,1.0))
plt.savefig(fp+'plot\\MODIS_Climatology_vs_4STAR_cld_ref_days.png',transparent=True,dpi=600)
vert_speed_ft = np.diff(arctas['GPS_ALT'])
arctas['GPS_ALT']
# In[45]:
vert_speed = vert_speed_ft*0.3084
# In[55]:
plt.plot(smooth(vert_speed,10),smooth(arctas['GPS_ALT'][1:]*0.3084,10),'b.')
plt.xlabel('Vertical speed [m/s]')
plt.ylabel('Altitude [m]')
plt.title('P-3 vertical speed for ARCTAS')
# ## P3 during DISCOVER-AQ Denver
# In[58]:
discover = lm.load_ict(fp+'discoveraq-pds_p3b_20140807_r1.ict')
# In[59]:
