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
def build_lut_2wvl(model,wvls,sza_norm):
" function that builds the 2 wavelength Reflectance lut from the model Sp object (lut) "
import numpy as np
import math
from Sp_parameters import find_closest
iw = find_closest(model.wvl,np.array(wvls))
if hasattr(model,'sza') & sza_norm:
sza_factor = 1.0/math.cos(math.radians(model.sza))
else:
sza_factor = 1.0
return np.reshape(model.reflect[:,iw,1,:,:],(2,2,model.ref.size,model.tau.size))*sza_factor #ph, wvl, z, ref, tau
def nearest_neighbor(X, Y, Xnew, dist=1):
"""
Purpose:
To return a nearest neighbor linear interpolation, but with a limit on the possible distance between the two points
Input:
X: initial independent variable
Y: initial dependant variable
Xnew: new independant variable to interpolate over
dist: max distance allowed
Output:
Ynew: new dependent variable interpolate using nearest neighbor
Keywords:
dist: (default 1) see above
Dependencies:
Numpy
Sp_parameters
Example:
...
Modification History:
Written: Samuel LeBlanc, NASA Ames, Santa Cruz, 2016-04-04
"""
from Sp_parameters import find_closest
import numpy as np
i = find_closest(X, Xnew)
Ynew = Y[i]
i_bad = abs(X[i] - Xnew) > dist
try:
Ynew[i_bad] = np.nan
except ValueError:
YYnew = Ynew.astype('float64')
YYnew[i_bad] = np.nan
Ynew = YYnew
return Ynew
def nearest_neighbor(X,Y,Xnew,dist=1):
"""
Purpose:
To return a nearest neighbor linear interpolation, but with a limit on the possible distance between the two points
Input:
X: initial independent variable
Y: initial dependant variable
Xnew: new independant variable to interpolate over
dist: max distance allowed
Output:
Ynew: new dependent variable interpolate using nearest neighbor
Keywords:
dist: (default 1) see above
Dependencies:
Numpy
Sp_parameters
Example:
...
Modification History:
Written: Samuel LeBlanc, NASA Ames, Santa Cruz, 2016-04-04
"""
from Sp_parameters import find_closest
import numpy as np
i = find_closest(X,Xnew)
Ynew = Y[i]
i_bad = abs(X[i]-Xnew) > dist
try:
Ynew[i_bad] = np.nan
except ValueError:
YYnew = Ynew.astype('float64')
YYnew[i_bad] = np.nan
Ynew = YYnew
return Ynew
star['good'] = np.where((star['utc']>20.0667) &
(star['utc']<20.4667) &
(star['Str'].flatten()!=0) &
(star['sat_time'].flatten()==0) &
(np.isfinite(star['tau_aero'][:,319])))[0]
len(star['good'])
# ## Plot time traces at select wavelengths
# In[7]:
print star['tau_aero'].shape
print star['w'].shape
iw = find_closest(star['w'][0],np.array([380,400,430,500,515,635,875,1000,1140,1200,1600])/1000.0)
print star['w'][0].shape
print iw
# In[8]:
plt.figure()
color.cycle_cmap(len(iw)+1,cmap=plt.cm.gist_ncar)
for i in iw:
plt.plot(star['utc'][star['good']],star['tau_aero'][star['good'],i], label=str('%.3f $\mu$m' % star['w'][0,i]))
plt.xlabel('UTC [h]')
plt.ylabel('AOD')
plt.legend(bbox_to_anchor=[1,0.1],loc=3)
plt.savefig(fp+datestr+filesep+'AOD_time.png',dpi=600)
ax4[1].set_xlabel('Parameters')
ax4[1].legend()
# <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)
