In [257]:

    

from iuvs import io, scaling
from astropy.io import fits
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (32/3, 8)


    

In [255]:

    

summaryfile = io.l1b_filenames('summary', ext='.txt')[0]


    

In [262]:

    

summary = pd.read_table(summaryfile, skiprows=26, skipinitialspace=True, sep=' ',index_col=0)


    

In [263]:

    

summary.columns


    

    
Out[263]:





Index(['filename', 'OBS_ID', 'XUV', 'INT_TIME', 'NX', 'NY', 'NZ', 'X1', 'X2', 'Y1', 'Y2', 'BINX', 'BINY', 'MCP_HV', 'SHUT_STATE', 'PHASE', 'MODE', 'CYCLE', 'DET_TEMP', 'CASE_TEMP', 'FOV_DEG', 'STIM_STATE', 'FILL_BINS', 'TARGET', 'PURPOSE'], dtype='object')

In [267]:

    

pattern = 'cruisecal2-mode080-muv'
pattern = 'orbit00490'
pattern = 'cruisecal2-mode070'
fname = io.l1b_filenames(pattern, stage=False)
fname


    

    
Out[267]:





['/Users/maye/data/iuvs/level1b/mvn_iuv_l1b_cruisecal2-mode070-muv_20140521T080545_v01_r01.fits.gz']

In [266]:

    

fname


    

    
Out[266]:





['/Users/maye/data/iuvs/level1b/mvn_iuv_l1b_cruisecal2-mode070-muv_20140521T080545_v01_r01.fits.gz']

In [268]:

    

l1b = io.L1BReader(fname)


    

In [270]:

    

l1b.DarkIntegration


    

    
Out[270]:






  

    

      

      
0
      
1
    
  
  

    

      
TIMESTAMP
      
                      4.539315e+08
      
                      4.539318e+08
    
    

      
ET
      
                      4.539316e+08
      
                      4.539319e+08
    
    

      
UTC
      
 2014/141 May 21 08:05:31.94743UTC
      
 2014/141 May 21 08:10:27.94270UTC
    
    

      
MIRROR_DN
      
                             15235
      
                             15235
    
    

      
MIRROR_DEG
      
                          6.306152
      
                          6.306152
    
    

      
FOV_DEG
      
                           12.6123
      
                           12.6123
    
    

      
LYA_CENTROID
      
                                 1
      
                                 1
    
  

In [271]:

    

dark1, dark2 = l1b.detector_dark


    

In [272]:

    

dark1.mean()


    

    
Out[272]:





40.964488636363633

In [273]:

    

dark2.mean()


    

    
Out[273]:





38.852982954545453

In [17]:

    

fits.info('dark_fitting.fits')


    

    




Filename: dark_fitting.fits
No.    Name         Type      Cards   Dimensions   Format
0    PRIMARY     PrimaryHDU       8   (341, 62)    int32   
1    DARK2       ImageHDU         9   (341, 62)    int32   
2    RANK-1      ImageHDU        16   (341, 62)    float64   
3    RANK0       ImageHDU        16   (341, 62)    float64   
4    RANK1       ImageHDU        16   (341, 62)    float64   
5    RANK2       ImageHDU        16   (341, 62)    float64   
6    RANK3       ImageHDU        16   (341, 62)    float64   
7    RANK4       ImageHDU        16   (341, 62)    float64   

In [229]:

    

def do_plot(data, title, header=None):
    fig, axes = plt.subplots(nrows=2)
    if header is not None:
        coeffs = eval(header['COEFFS'])
        coeffs = [round(i, 4) for i in coeffs]
        print(coeffs)
    else:
        coeffs = 0.0
    axes[0].plot(data[30])
    axes[0].set_title("{}, profile at spatial bin 30".format(title))
    axes[0].set_ylim(-500, 500)
    imax = axes[1]
    im = imax.imshow(data,vmin=-500, vmax=500, cmap='gray')
    plt.colorbar(im, ax=imax, orientation='horizontal')
    imax.set_title("{}, stddev: {:.1f} DN, {}".format(title, data.std(), coeffs),
                  fontsize=16)
#     fig.tight_layout()
    fig.savefig(title+'.png', dpi=150)


    

In [230]:

    

do_plot(dark2-dark1,'dark2-dark1')


    

In [231]:

    

header = fits.getheader('dark_fitting.fits', 'RANK-1')
do_plot(fits.getdata('dark_fitting.fits', 'RANK-1'), 'additive',
       header)


    

    




[235.8248]






    

In [232]:

    

do_plot(fits.getdata('dark_fitting.fits', 'RANK0'), 'multiplicative (no offset)',
        fits.getheader('dark_fitting.fits', "RANK0"))


    

    




[1.4511]






    

In [233]:

    

do_plot(fits.getdata('dark_fitting.fits', 'RANK1'), 'degree 1 polynom',
       fits.getheader('dark_fitting.fits', "RANK1"))


    

    




[1.2536, 129.0344]






    

In [234]:

    

do_plot(fits.getdata('dark_fitting.fits', 'RANK2'), 'degree 2 polynom',
       fits.getheader('dark_fitting.fits', 'rank2'))


    

    




[-0.0001, 1.4888, 58.9136]






    

In [237]:

    

do_plot(fits.getdata('dark_fitting.fits', 'RANK3'), 'degree 3 polynom',
       fits.getheader('dark_fitting.fits', 'rank3'))


    

    




[0.0, -0.0002, 1.542, 47.5132]






    

In [242]:

    

ranks = [-1,0,1,2,3]
for rank in ranks:
    header = fits.getheader('dark_fitting.fits', 'rank'+str(rank))
    print(header['COEFFS'])


    

    




[235.82480370850152]
[1.4510501785354721]
[1.2535862923342835, 129.03443681032883]
[-0.00010518865504002474, 1.4888327725011952, 58.913645957805009]
[1.203598772902105e-08, -0.00015978889457175823, 1.5420174307729337, 47.513171648014264]

In [ ]: