%pylab inline
import Image
import re
import FPI
import glob
from lmfit import Parameters
import logging
from optparse import OptionParser
import MySQLdb as mdb
import datetime
import numpy as np
import os
import BoltwoodSensor
import X300Sensor
import FPIDisplay
import pytz
import datetime
import fpiinfo

from FPI import *

# Make default figure size bigger
matplotlib.rcParams['savefig.dpi'] = 150
matplotlib.rcParams['figure.figsize'] = (4,3)

SIGMA_V_THRESH = 30.

Populating the interactive namespace from numpy and matplotlib

/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  warnings.warn(_use_error_msg)

EMCCD 2-hour run, 5-sec integrations

2015 Feb 12, about 22-24 LT

#fn = '/home/bhardin2/Laser_20150209_2147_3sec.tif'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0098.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,1), vmax = prctile(img,99))
colorbar();

instrument = fpiinfo.get_instr_info('minime05')
ESTIMATE_BLUR = True

# Initialize values
output = []
last_chi = -999
laser_times = []
laser_redchi = []
last_t = instrument['nominal_t']
lam_laser = instrument['lam_laser']

cx,cy = FPI.FindCenter(img)

N = 512
N0 = 0
N1 = N
annuli = FPI.FindEqualAreas(img,cx,cy,N)


# Perform annular summation
laser_spectra, laser_sigma = AnnularSum(img,annuli,0)

if not np.isnan(laser_spectra).any(): # Do the inversion. Otherwise, ignore it.

    ####### Find good initial guesses for the parameters ######

    # Magnification parameter by using geometry (assuming square binning)
    alpha = instrument['pix_size']/instrument['focal_length'] * 2
    # Intensity and background by looking at fringes
    I = laser_spectra.max() - laser_spectra.min()
    B = laser_spectra.min()

    laser_params = Parameters()
    laser_params.add('n',     value = 1.0,                           vary = False)
    laser_params.add('t',     value = None,                        vary = False)
    laser_params.add('lam',   value = lam_laser,                     vary = False)
    laser_params.add('R',     value = 0.5,                           vary = False)
    laser_params.add('alpha', value = alpha,                         vary = False)
    laser_params.add('I',     value = I,                             vary = False)
    laser_params.add('B',     value = B,                             vary = False)
    laser_params.add('a1',    value = -0.1, min=-5.0, max=5.0,        vary = False)
    laser_params.add('a2',    value = 0.0,  min=-5.0, max=5.0,        vary = False)
    laser_params.add('b0',    value = 0.5,  min=-10.0, max=10.0,      vary = False)
    laser_params.add('b1',    value = 0.0,  min=-10.0, max=10.0,      vary = False)
    laser_params.add('b2',    value = 0.0,  min=-10.0, max=10.0,      vary = False)

    # To find a good initial guess for "t", we'll need to do a grid search.  Search
    # over 1 FSR around the last solved-for value (or the nominal value, if this is first trial).
    # TODO: make this grid search a separate general function.
    def goodness(t): 
        # return the correlation between the fringe with this t and the data
        laser_params['t'].value = t
        fringe = Laser_FringeModel(laser_params, annuli['r'])
        return np.dot(fringe, laser_spectra)/(norm(fringe)*norm(laser_spectra))
    t_candidates = linspace(last_t - lam_laser/4, last_t + lam_laser/4, 50)
    corrvec = np.array([goodness(t) for t in t_candidates])
    best_t = t_candidates[corrvec.argmax()]
    laser_params['t'].value = best_t
    ####### Inversion of laser image ##########

    # Now do least-squares fit, but in stages, varying only certain parameters at a time, according to:
    order = [
             ['alpha'],\
             ['t','alpha'],\
             ['B','I'],\
             ['R'],\
             ['t','alpha','B','I','R','a1','a2'], \
             ]
    if ESTIMATE_BLUR:
        order.append(['t','alpha','B','I','R','a1','a2','b0','b1','b2'])

    for group in order: # set all the params in this group to "vary=True", and then run inversion
        for param in laser_params.keys():
            if param in group:
                laser_params[param].vary = True
            else:
                laser_params[param].vary = False
        # Set falloff terms to false, if this instrument only has a couple fringes
        if not instrument['many_fringes']:
            for param in ['a2', 'b1', 'b2']:
                laser_params[param].vary=False
                laser_params[param].value=0.0

        laser_fit = Minimizer(Laser_Residual,laser_params, \
            fcn_args=(annuli['r'][N0:N1],), fcn_kws={'data': laser_spectra[N0:N1], 'sigma': laser_sigma[N0:N1]}, \
            scale_covar = True)
        laser_fit.leastsq()
    output.append(laser_fit)

i = 0
# Make default figure size shorter and fatter
figure(figsize=(8,3))
subplot(2,1,1)

M0 = 0
M1 = N1-N0

spectra = laser_spectra[N0:N1]
sigma   = laser_sigma[N0:N1]
model = spectra + sigma*laser_fit.residual
resid = spectra-model

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})
subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
#title(lasers[i])
print(fn)

figure(figsize=(8,1.5))
subplot(2,1,2)


plot(resid,'k.', label='residual')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8})
subplots_adjust(bottom=0.0) # or else the xlabel will be cut off, for some reason


#fl = instrument['pix_size']*d.info['XBinning']/o.params['alpha'].value
#print fl

/rdata/airglow/fpi/minime05/uao/2015/20150212/laser_X1.tif

print '%.2f m/s error from laser calibration' % (laser_params['t'].stderr*20/1e-9)

2.65 m/s error from laser calibration

for param in laser_params:
    vals = [o.params[param].value for o in output]
    print '%06s: %.3e' % (param, median(vals)) 

     n: 1.000e+00
     t: 1.500e-02
   lam: 6.328e-07
     R: 8.818e-01
 alpha: 8.845e-05
     I: 8.216e+03
     B: 6.399e+02
    a1: 1.525e-01
    a2: -7.229e-02
    b0: 1.029e+00
    b1: 2.204e-01
    b2: -1.277e-01

lasm = Laser_FringeModel(laser_params, annuli['r'])
plot(lasm)

[<matplotlib.lines.Line2D at 0x7fdc4fe631d0>]

######### Spline fit to the parameters ###########

# Grab the seconds from the first laser observation (required for spline interpolation)
dt = []
for x in laser_times:
    diff = (x - laser_times[0])
    dt.append(diff.seconds+diff.days*86400.)
dt = array(dt)

# Spline fit to the parameters.
# (This will work for both the fitted and constant params)
for param in laser_fit.params:
    # Grab the estimates and the associated (normalized) uncertanty
    p = []
    ep = []
    for o in output:
        p.append(o.params[param].value)
        ep.append(o.params[param].stderr)

    p = np.array(p)
    ep = np.array(ep)
    m = len(ep)
    s = m  # Expected value of chi^2

    if param == 't': # force 't' to fit more smoothly
        # i.e., we don't trust those small error bars, so make them bigger
        ep = ep * 3 # TODO: optimal value?  1 seems too small, 10 seems too big
    
    # set weights
    if laser_fit.params[param].vary:
        # use the error bars from the inversion, but
        # replace the values that are infinite, 
        # (i.e., where the errorbars couldn't be calculated)
        # with very large numbers.
        w = [1/stderr if stderr > 0 else 1/(1e6*paramval) for paramval,stderr in zip(p,ep) ]
    else: 
        # use arbitrary error bars (doesn't matter - it will fit exactly)
        w = np.ones(shape(ep))
    
        # Try spline, then linear, then zeroth-order interpolation
        
    try: # spline interpolation
        #sfit = interpolate.UnivariateSpline(np.array(dt),p, w=w, s=s)
        sfit = interpolate.interp1d(np.array(dt), p)
    except: # Try linear.
        notify_the_humans = True
        try: # if there's only one laser image, then even linear won't work.
            print 'Spline interpolation failed for laser param "%s". Defaulting to linear interpolation.\n' % (param)                
            sfit = interpolate.interp1d(np.array(dt), p)
        except: # all else failed. There's probably only one laser image.
            print 'Spline and linear interpolation both failed for laser param ' + \
                 '"%s". Defaulting to zeroth-order interpolation.\n' % (param)
            sfit = lambda t: p[0] # whatever the input, just return the parameter.
            
    laser_fit.params[param].spfit = sfit

Spline interpolation failed for laser param "n". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "n". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "t". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "t". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "lam". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "lam". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "R". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "R". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "alpha". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "alpha". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "I". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "I". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "B". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "B". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "a1". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "a1". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "a2". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "a2". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "b0". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "b0". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "b1". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "b1". Defaulting to zeroth-order interpolation.

Spline interpolation failed for laser param "b2". Defaulting to linear interpolation.

Spline and linear interpolation both failed for laser param "b2". Defaulting to zeroth-order interpolation.

#fn = '/rdata/airglow/fpi/minime05/uao/2015/20150207/UAO_L_20150208_100608_138.img'
fn = '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0002_X0004.tif'
d = Image.open(fn)
img = array(d)
imshow(img, vmin = prctile(img,2), vmax = prctile(img,98))
colorbar()

<matplotlib.colorbar.Colorbar instance at 0x7fdc4fc0b8c0>

Analyze Sky Images

sky = glob.glob('/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003*')
sky.sort()
sky = sky[:]
sky

['/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0001.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0002.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0003.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0004.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0005.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0006.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0007.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0008.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0009.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0010.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0011.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0012.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0013.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0014.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0015.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0016.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0017.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0018.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0019.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0020.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0021.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0022.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0023.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0024.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0025.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0026.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0027.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0028.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0029.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0030.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0031.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0032.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0033.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0034.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0035.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0036.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0037.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0038.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0039.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0040.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0041.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0042.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0043.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0044.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0045.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0046.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0047.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0048.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0049.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0050.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0051.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0052.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0053.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0054.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0055.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0056.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0057.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0058.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0059.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0060.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0061.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0062.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0063.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0064.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0065.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0066.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0067.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0068.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0069.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0070.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0071.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0072.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0073.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0074.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0075.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0076.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0077.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0078.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0079.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0080.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0081.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0082.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0083.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0084.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0085.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0086.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0087.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0088.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0089.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0090.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0091.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0092.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0093.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0094.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0095.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0096.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0097.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0098.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0099.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0100.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0101.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0102.tif',
 '/rdata/airglow/fpi/minime05/uao/2015/20150212/zenith_05_sec_0003_X0103.tif',
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 ...]

def ReadIMG(fname): # Override FPI behavior
    d = Image.open(fname)
    return d

# Constants
c = 299792458.
k = 1.3806503e-23
m = 16/6.0221367e26


# Initialize data holder
sky_times = []
az = []
ze = []
LOSwind = []
sigma_LOSwind = []
T = []
sigma_T = []
direction = []
sky_redchi = []
skyI = []
skyB = []
ccdB = []
sigma_skyI = []
sigma_skyB = []
sigma_ccdB = []
sky_out = []
sky_intT = []
sky_temperature = []


lam0 = instrument['lam0']
last_lamc = lam0 # nominal value of line center
lamc_start_set = False # Turns true when a reasonable search interval center is found

my_params = dict()

for i, fname in enumerate(sky):
    # Read in the sky image
    d = ReadIMG(fname)
    img = np.asarray(d)
    
    # 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)
    
    # See if there are any NaNs in the spectra.  If so, skip this one.
    if np.isnan(sky_spectra).sum() == 0:
        
        # Append the time of the sky image
        sky_times.append(nan)
        sky_intT.append(nan)
        sky_temperature.append(nan)

        # Set the fitting parameter guesses
        sky_params = Parameters()

        # Use the spline-interpolated values for the instrument function at this time
        for param in laser_fit.params:
            # Hack to just use last laser image:
            sky_params.add(param, value = laser_fit.params[param].value, vary = False)
       
        # But change the sky wavelength to lam0
        sky_params['lam'].value = lam0
        
        # Set up the forward model        
        L = 301
        A_1D, lamvec = get_conv_matrix_1D(sky_params, annuli['r'][N0:N1], L, lam0)
        
        # Come up with good initial guesses for the sky parameters
        skyI_guess = sky_spectra[N0:N1].max() - sky_spectra[N0:N1].min()
        skyB_guess = 0.0
        ccdB_guess = sky_spectra[N0:N1].min()
                
        sky_params.add('lamc', value = lam0,       vary = True ) # We'll do a grid search to find a good value for lamc
        sky_params.add('T',    value = 1000,       vary = True , min = 0.1, max = 5000.0)
        sky_params.add('skyI', value = skyI_guess, vary = True , min = 0.0)
        sky_params.add('skyB', value = skyB_guess, vary = True )
        sky_params.add('skym', value = 0.0,        vary = False) # Don't try to estimate skym (or should we?)
        sky_params.add('ccdB', value = ccdB_guess, vary = True )
        sky_params.add('lam0', value = lam0,       vary = False) # This is determined by chemistry so it can't change.

        
        # Do a grid search to find a good starting value for lamc.
        # The instrument bias might mean that lam0 is a really bad value to start with.
        # Search over one FSR around the last solved-for value (or the nominal value, if it's the first run).
        # It's not really important to sort by direction, since the instrument bias dominates.
        def goodness(lamc): 
            # return the correlation between the fringe with this lamc and the data
            sky_params['lamc'].value = lamc
            fringe = Sky_FringeModel(sky_params, annuli['r'][N0:N1], lamvec, A_1D)
            return np.dot(fringe, sky_spectra[N0:N1])/(norm(fringe)*norm(sky_spectra[N0:N1]))
        FSR = lam0**2/(2*sky_params['t'].value)
        lamc_candidates = linspace(last_lamc - FSR/2, last_lamc + FSR/2, 50)
        corrvec = np.array([goodness(lamc) for lamc in lamc_candidates])
        best_lamc = lamc_candidates[corrvec.argmax()]
        sky_params['lamc'].value = best_lamc
        
        # Make a copy for future debugging
        sky_params_init = Parameters()
        for p in sky_params:
            sky_params_init.add(p, value = sky_params[p].value)
        
        # Take the inversion in steps
        order = [
                 ['skyI'],\
                 ['skyI','ccdB'],\
                 ['skyI', 'ccdB', 'skyB'], \
                 ['skyI', 'ccdB', 'skyB', 'lamc', 'T'], \
                 ]
        
        for group in order: # set all the params in this group to "vary=True", and then run inversion
            for param in sky_params.keys():
                if param in group:
                    sky_params[param].vary = True
                else:
                    sky_params[param].vary = False
                    
            sky_fit = Minimizer(Sky_Residual,sky_params,fcn_args=(annuli['r'][N0:N1],lamvec,A_1D), \
                      fcn_kws={'data': sky_spectra[N0:N1], 'sigma': sky_sigma[N0:N1]}, scale_covar=True)
            sky_fit.prepare_fit()
            sky_fit.leastsq()
        
        # if the inversion failed, replace values with nans and set error bars to inf
        if not sky_fit.success or not sky_fit.errorbars:
            for p in sky_fit.params:
                sky_fit.params[p].value = np.nan
                sky_fit.params[p].stderr = np.inf
        
        sky_redchi.append(sky_fit.redchi)
        sky_out.append(sky_fit)

        # Transform from lamc to v and collect all parameters
        lamc = sky_params['lamc'].value
        sigma_lamc = sky_params['lamc'].stderr
        v = c * (lamc/lam0 - 1)
        sigma_v = c * sigma_lamc / lam0
        LOSwind.append(v) # negative sign
        sigma_LOSwind.append(sigma_v)
        T.append(sky_fit.params['T'].value)
        sigma_T.append(sky_fit.params['T'].stderr)
        skyI.append(sky_params['skyI'].value)
        sigma_skyI.append(sky_params['skyI'].stderr)
        skyB.append(sky_params['skyB'].value)
        sigma_skyB.append(sky_params['skyB'].stderr)
        #logfile.write(datetime.datetime.now().strftime('%m/%d/%Y %H:%M:%S %p: ') + '%s reduced chisqr: %.4f\n' % (fname, sky_fit.redchi))
        print('%s reduced chisqr: %.4f. Message:"%s"' % (fname, sky_fit.redchi, sky_fit.message))
        
        # use the lamc value to center the next grid search, if the error bars are small enough
        if sigma_v < SIGMA_V_THRESH and sigma_v > 0 and not lamc_start_set: # sigma_v==0 means it didn't converge
            last_lamc = lamc # save for next time
            lamc_start_set = True
        

    else:
        #logfile.write(datetime.datetime.now().strftime('%m/%d/%Y %H:%M:%S %p: ') + '%s is invalid and not analyzed\n' % fname)
        print '%s is invalid and not analyzed' % fname


# Convert sky_params to array
n = len(sky_out)
sky_value = {}
sky_stderr = {}
for p in sky_out[-1].params:
    sky_value[p] = np.zeros(n)
    sky_stderr[p] = np.zeros(n)
    for i in range(0,n):
        sky_value[p][i] = sky_out[i].params[p].value
        sky_stderr[p][i] = sky_out[i].params[p].stderr
                

i = 0

M0 = N0
M1 = N1

# Make default figure size shorter and fatter
figure(figsize=(8,3))
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})
subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason
plt.title('%s' % sky[i].split('/')[-1])

subplot(2,1,2)

plot(o.residual[M0:M1],'k.', label='residual')
xlabel('Radial Bin')
ylabel('Detector Counts')
legend(loc='best', prop={'size':8})
#subplots_adjust(bottom=0.25) # or else the xlabel will be cut off, for some reason

i = i + 1

figure(figsize=(8,1.5));
exp_t = int(sky[0].split('/')[-1].split('_')[1]);
dref = mean(LOSwind);
t = array([i*exp_t for i in range(len(LOSwind))]);
errorbar(t,LOSwind-dref,yerr=sigma_LOSwind,fmt='k.',markersize=1);
xlabel('Time [s]');
ylabel('Vertical Wind [m/s]');
title('%i-second integrations' % exp_t);
#savefig('/home/bhardin2/ipython_figures/emccd/20150212_%i_sec.eps' % exp_t)

<matplotlib.text.Text at 0x7fdc4720dc90>

plot(t,sigma_LOSwind,'k.-')
xlabel('Time [s]')
ylabel('Wind Error [m/s]')
title('%i-second integrations' % exp_t)
#savefig('/home/bhardin2/ipython_figures/emccd/20150212_%i_sec.eps')

<matplotlib.text.Text at 0x7fdc47830290>

figure(figsize=(8,1.5))
exp_t = int(sky[0].split('/')[-1].split('_')[1])
dref = mean(LOSwind)
t = array([i*exp_t for i in range(len(LOSwind))])
plot(t,LOSwind-dref,'k.')
xlabel('Time [s]')
ylabel('Vertical Wind [m/s]')

<matplotlib.text.Text at 0x7fdc47fefe50>

LOSwind = array(LOSwind)
sigma_LOSwind = array(sigma_LOSwind)
LOSwind2 = (LOSwind[::2] + LOSwind[1::2])/2
sigma_LOSwind2 = (sigma_LOSwind[::2])/sqrt(2)
t2 = t[::2]

figure(figsize=(8,1.5))
plot(t2,LOSwind2-dref,'k.')
xlabel('Time [s]')
ylabel('Vertical Wind [m/s]')

<matplotlib.text.Text at 0x7fdc47f24090>

LOSwind4 = (LOSwind2[2::2] + LOSwind2[1::2])/2
sigma_LOSwind4 = (sigma_LOSwind2[2::2])/sqrt(2)
t4 = t2[2::2]

figure(figsize=(8,1.5))
plot(t4,LOSwind4-dref,'k.')
xlabel('Time [s]')
ylabel('Vertical Wind [m/s]')

<matplotlib.text.Text at 0x7fdc47ec9c90>

LOSwind8 = (LOSwind4[::2] + LOSwind4[1::2])/2
sigma_LOSwind8 = (sigma_LOSwind4[::2])/sqrt(2)
t8 = t4[::2]

figure(figsize=(8,1.5))
#plot(t8,LOSwind8-dref,'k.')
errorbar(t8,LOSwind8-dref,yerr=sigma_LOSwind8,fmt='k.',markersize=1)
xlabel('Time [s]')
ylabel('Vertical Wind [m/s]')

<matplotlib.text.Text at 0x7fdc47c659d0>

Downsampled by 8: --> 40 sec integrations

import subprocess
import shutil
nb_name = 'EMCCD_Testing_Long_Run.ipynb' # There's probably a way to get this programmatically
cmd = ['ipython', 'nbconvert', '--to', 'slides', nb_name, \
       '--reveal-prefix', '"http://cdn.jsdelivr.net/reveal.js/2.6.2"',\
      '--template','output_toggle']
# The --template command points to a custom template which opens and closes the input code cells with a click
subprocess.call(cmd)
html_name = '%s.slides.html' % (nb_name.split('.')[0])
shutil.copy(html_name, '/home/bhardin2/public_html/slideshows/')