In [1]:

    

from iuvs import io


    

In [2]:

    

iuvs_fnames = []
for fname in io.l1a_filenames():
    iuvs_fnames.append(io.Filename(fname))


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-579f7e4cd999> in <module>()
      1 iuvs_fnames = []
----> 2 for fname in io.l1a_filenames():
      3     iuvs_fnames.append(io.Filename(fname))

TypeError: l1a_filenames() missing 1 required positional argument: 'pattern'

In [3]:

    

import pandas as pd


    

In [4]:

    

s = pd.Series(iuvs_fnames)


    

In [5]:

    

df = pd.DataFrame()


    

In [6]:

    

for item in ['basename','phase', 'cycle_orbit', 'mode', 'channel', 'time', 'level', 'version', 'revision']:
    df[item] = s.map(lambda x: getattr(x, item))


    

In [7]:

    

df['channel']=df.channel.astype('category')


    

In [8]:

    

df.dtypes


    

    
Out[8]:





basename         object
phase            object
cycle_orbit      object
mode             object
channel        category
time             object
level            object
version          object
revision         object
dtype: object

In [9]:

    

df.head()


    

    
Out[9]:






  

    

      

      
basename
      
phase
      
cycle_orbit
      
mode
      
channel
      
time
      
level
      
version
      
revision
    
  
  

  

In [14]:

    

df.channel.value_counts()


    

    
Out[14]:





fuv        12928
muv        12362
fuvdark     4966
muvdark     4551
echdark     1730
ech         1087
dtype: int64

In [11]:

    

df.set_index('time', inplace=True)
df.sort_index(inplace=True)


    

In [12]:

    

df.head()


    

    
Out[12]:






  

    

      

      
basename
      
phase
      
cycle_orbit
      
mode
      
channel
      
level
      
version
      
revision
    
    

      
time
      

      

      

      

      

      

      

      

    
  
  

    

      
2013-12-04 19:02:17
      
 mvn_iuv_l1a_checkout-muvdark_20131204T190217_v...
      
 checkout
      
 N/A
      
 N/A
      
 muvdark
      
 l1a
      
 v00
      
 r00
    
    

      
2013-12-04 19:02:17
      
 mvn_iuv_l1a_checkout-fuvdark_20131204T190217_v...
      
 checkout
      
 N/A
      
 N/A
      
 fuvdark
      
 l1a
      
 v00
      
 r00
    
    

      
2013-12-04 19:03:04
      
 mvn_iuv_l1a_checkout-muvdark_20131204T190304_v...
      
 checkout
      
 N/A
      
 N/A
      
 muvdark
      
 l1a
      
 v00
      
 r00
    
    

      
2013-12-04 19:03:04
      
 mvn_iuv_l1a_checkout-fuvdark_20131204T190304_v...
      
 checkout
      
 N/A
      
 N/A
      
 fuvdark
      
 l1a
      
 v00
      
 r00
    
    

      
2013-12-04 19:03:42
      
 mvn_iuv_l1a_checkout-muv_20131204T190342_v00_r...
      
 checkout
      
 N/A
      
 N/A
      
     muv
      
 l1a
      
 v00
      
 r00
    
  

In [13]:

    

df.tail()


    

    
Out[13]:






  

    

      

      
basename
      
phase
      
cycle_orbit
      
mode
      
channel
      
level
      
version
      
revision
    
    

      
time
      

      

      

      

      

      

      

      

    
  
  

    

      
2015-01-09 14:27:01
      
 mvn_iuv_l1a_apoapse-orbit00542-fuv_20150109T14...
      
 apoapse
      
 orbit00542
      
 N/A
      
 fuv
      
 l1a
      
 v00
      
 r00
    
    

      
2015-01-09 14:32:21
      
 mvn_iuv_l1a_apoapse-orbit00542-fuv_20150109T14...
      
 apoapse
      
 orbit00542
      
 N/A
      
 fuv
      
 l1a
      
 v00
      
 r00
    
    

      
2015-01-09 14:37:43
      
 mvn_iuv_l1a_apoapse-orbit00542-fuv_20150109T14...
      
 apoapse
      
 orbit00542
      
 N/A
      
 fuv
      
 l1a
      
 v00
      
 r00
    
    

      
2015-01-09 14:43:03
      
 mvn_iuv_l1a_apoapse-orbit00542-fuv_20150109T14...
      
 apoapse
      
 orbit00542
      
 N/A
      
 fuv
      
 l1a
      
 v00
      
 r00
    
    

      
2015-01-09 14:48:23
      
 mvn_iuv_l1a_apoapse-orbit00542-fuv_20150109T14...
      
 apoapse
      
 orbit00542
      
 N/A
      
 fuv
      
 l1a
      
 v00
      
 r00
    
  

In [18]:

    

ds  =io.L1AReader(str(io.level1apath) + '/' + df.basename[df.index[-1]])


    

In [33]:

    

ds.integration.data


    

    
Out[33]:






  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
...
      
11
      
12
      
13
      
14
      
15
      
16
      
17
      
18
      
19
      
20
    
  
  

    

      
TIMESTAMP
      
                      4.740869e+08
      
                      4.740869e+08
      
                      4.740869e+08
      
                      4.740869e+08
      
                      4.740869e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
...
      
                       4.74087e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
    
    

      
ET
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                       4.74087e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
                      4.740871e+08
      
...
      
                      4.740871e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740872e+08
      
                      4.740873e+08
      
                      4.740873e+08
    
    

      
UTC
      
 2015/009 Jan 09 14:48:23.76188UTC
      
 2015/009 Jan 09 14:48:38.76189UTC
      
 2015/009 Jan 09 14:48:53.76190UTC
      
 2015/009 Jan 09 14:49:08.76190UTC
      
 2015/009 Jan 09 14:49:23.76191UTC
      
 2015/009 Jan 09 14:49:38.76192UTC
      
 2015/009 Jan 09 14:49:53.76193UTC
      
 2015/009 Jan 09 14:50:08.76194UTC
      
 2015/009 Jan 09 14:50:23.76194UTC
      
 2015/009 Jan 09 14:50:38.76195UTC
      
...
      
 2015/009 Jan 09 14:51:08.76197UTC
      
 2015/009 Jan 09 14:51:23.76197UTC
      
 2015/009 Jan 09 14:51:38.76198UTC
      
 2015/009 Jan 09 14:51:53.76199UTC
      
 2015/009 Jan 09 14:52:08.76200UTC
      
 2015/009 Jan 09 14:52:23.76201UTC
      
 2015/009 Jan 09 14:52:38.76201UTC
      
 2015/009 Jan 09 14:52:53.76202UTC
      
 2015/009 Jan 09 14:53:08.76203UTC
      
 2015/009 Jan 09 14:53:23.76204UTC
    
    

      
MIRROR_DN
      
                             29342
      
                             29552
      
                             29762
      
                             29972
      
                             30182
      
                             30392
      
                             30602
      
                             30812
      
                             31022
      
                             31232
      
...
      
                             31652
      
                             31862
      
                             32072
      
                             32282
      
                             32492
      
                             32702
      
                             32912
      
                             33122
      
                             33332
      
                             33542
    
    

      
MIRROR_DEG
      
                          45.05219
      
                          45.62897
      
                          46.20575
      
                          46.78253
      
                          47.35931
      
                           47.9361
      
                          48.51288
      
                          49.08966
      
                          49.66644
      
                          50.24323
      
...
      
                          51.39679
      
                          51.97357
      
                          52.55035
      
                          53.12714
      
                          53.70392
      
                           54.2807
      
                          54.85748
      
                          55.43427
      
                          56.01105
      
                          56.58783
    
    

      
FOV_DEG
      
                          90.10437
      
                          91.25793
      
                           92.4115
      
                          93.56506
      
                          94.71863
      
                          95.87219
      
                          97.02576
      
                          98.17932
      
                          99.33289
      
                          100.4865
      
...
      
                          102.7936
      
                          103.9471
      
                          105.1007
      
                          106.2543
      
                          107.4078
      
                          108.5614
      
                           109.715
      
                          110.8685
      
                          112.0221
      
                          113.1757
    
    

      
LYA_CENTROID
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
...
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
      
                                 1
    
  

7 rows × 21 columns

In [36]:

    

ds.binning.header


    

    
Out[36]:





XTENSION= 'BINTABLE'           /Binary table written by MWRFITS v1.11           
BITPIX  =                    8 /Required value                                  
NAXIS   =                    2 /Required value                                  
NAXIS1  =                 2147 /Number of bytes per row                         
NAXIS2  =                    1 /Number of rows                                  
PCOUNT  =                    0 /Normally 0 (no varying arrays)                  
GCOUNT  =                    1 /Required value                                  
TFIELDS =                    9 /Number of columns in table                      
COMMENT                                                                         
COMMENT  *** End of mandatory fields ***                                        
COMMENT                                                                         
EXTNAME = 'Binning'                                                             
COMMENT                                                                         
COMMENT  *** Column names ***                                                   
COMMENT                                                                         
TTYPE1  = 'SPABINWIDTH'        /                                                
TTYPE2  = 'SPABINTRANSMIT'     /                                                
TTYPE3  = 'SPEBINWIDTH'        /                                                
TTYPE4  = 'SPEBINTRANSMIT'     /                                                
TTYPE5  = 'SPAPIXLO'           /                                                
TTYPE6  = 'SPAPIXHI'           /                                                
TTYPE7  = 'SPEPIXLO'           /                                                
TTYPE8  = 'SPEPIXHI'           /                                                
TTYPE9  = 'BINTABLENAME'       /                                                
COMMENT                                                                         
COMMENT  *** Column formats ***                                                 
COMMENT                                                                         
TFORM1  = '12I     '           /                                                
TFORM2  = '12I     '           /                                                
TFORM3  = '256I    '           /                                                
TFORM4  = '256I    '           /                                                
TFORM5  = '10I     '           /                                                
TFORM6  = '10I     '           /                                                
TFORM7  = '256I    '           /                                                
TFORM8  = '256I    '           /                                                
TFORM9  = '11A     '           /                                                
COMMENT SPABINWIDTH: "Width of each spatial bin in pixels, one entry for each   
COMMENT   bin, transmitted or not"                                              
COMMENT SPABINTRANSMIT: "1 if bin is transmitted, 0 if not. Total number of 1   
COMMENT   entries set equals the number of spatial bins in the file"            
COMMENT SPEBINWIDTH: "Width of each spectral bin in pixels, one entry for each  
COMMENT   bin, transmitted or not"                                              
COMMENT SPEBINTRANSMIT: "1 if bin is transmitted, 0 if not. Total number of 1   
COMMENT   entries set equals the number of spatial bins in the file"            
COMMENT SPAPIXLO: Lowest numbered pixel in each transmitted spatial bin         
COMMENT SPAPIXHI: Highest numbered pixel in each transmitted spatial bin        
COMMENT SPEPIXLO: Lowest numbered pixel in each transmitted spectral bin        
COMMENT SPEPIXHI: Highest numbered pixel in each transmitted spectral bin       

In [126]:

    

import glob
fnames = glob.glob(root+'/*.fits.gz')


    

In [132]:

    

from IPython import parallel
rc = parallel.Client()


    

In [133]:

    

dview = rc.direct_view()


    

In [134]:

    

lview = rc.load_balanced_view()


    

In [140]:

    

%%px 
key_errors=[]
results = []


    

In [188]:

    

def find_big_deltas(fname):
    fitsdata = IUVSReader(fname)
    mir_deg1 = fitsdata.img_header['MIR_DEG']
    mir_deg2 = fitsdata.integration.data.get_value('MIRROR_DEG', 0)
    return mir_deg1/mir_deg2


    

In [189]:

    

ratios = []
n = 100
for i,fname in enumerate(fnames[:n]):
    print(i/n)
    ratio = find_big_deltas(fname)
    if abs(ratio) > 2:
        print(fname)
    ratios.append(ratio)


    

    




0.0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.4
0.41
0.42
0.43
0.44
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52
0.53
0.54
0.55
0.56
0.57
0.58
0.59
0.6
0.61
/maven_iuvs/stage/products/level1a/mvn_iuv_l1a_IPH1-cycle00023-mode060-fuv_20140410T141518_v00_r00.fits.gz
0.62
/maven_iuvs/stage/products/level1a/mvn_iuv_l1a_IPH2-cycle00203-mode060-muv_20140630T035343_v00_r00.fits.gz
0.63
0.64
0.65
0.66
0.67
0.68
0.69
0.7
0.71
0.72
0.73
0.74
0.75
0.76
0.77
0.78
0.79
0.8
0.81
0.82
0.83
0.84
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99

In [184]:

    




    

    
Out[184]:





count    100.000000
mean       0.798660
std        1.421604
min       -9.102222
25%        1.000058
50%        1.000071
75%        1.002890
max        1.011358
dtype: float64

In [190]:

    

ds = IUVSReader('/maven_iuvs/stage/products/level1a/mvn_iuv_l1a_IPH1-cycle00023-mode060-fuv_20140410T141518_v00_r00.fits.gz')


    

In [196]:

    

ds.img_header['MIR_DEG']


    

    
Out[196]:





-0.025

In [193]:

    

df = ds.integration.data


    

In [194]:

    

df.get_value('MIRROR_DEG', 0)


    

    
Out[194]:





0.00274658203125

In [195]:

    

ratios


    

    
Out[195]:





[1.011358024691358,
 1.0000610388817677,
 0.9954787585092881,
 1.0000741804419213,
 1.0000610388817677,
 1.011358024691358,
 1.0000610388817677,
 1.011358024691358,
 1.011358024691358,
 1.0000956122562226,
 1.0000610388817677,
 0.9102222222222223,
 1.0004109076109076,
 1.011358024691358,
 1.0000426922721817,
 1.0000426922721817,
 1.0000610388817677,
 1.0057704112952732,
 1.0000610388817677,
 1.0000610388817677,
 1.0000737973674572,
 0.9102222222222223,
 1.011358024691358,
 1.011358024691358,
 1.000043535246735,
 1.0000426922721817,
 1.0000610388817677,
 0.9996287972144013,
 1.0000592110319881,
 1.011358024691358,
 1.0000610388817677,
 1.0002442002442002,
 1.0002442002442002,
 1.000454473374049,
 1.0000610388817677,
 1.0000610388817677,
 1.0000610388817677,
 1.0000411445128576,
 1.0008275487701441,
 1.011358024691358,
 0.99544339497682,
 0.9996287972144013,
 0.9996287972144013,
 1.0000610388817677,
 1.0000610388817677,
 1.000092264416315,
 1.000454473374049,
 1.011358024691358,
 1.000043535246735,
 1.011358024691358,
 1.000092264416315,
 1.011358024691358,
 1.0000610388817677,
 1.011358024691358,
 1.011358024691358,
 1.0005406643757158,
 1.0000610388817677,
 1.011358024691358,
 0.99544339497682,
 0.9991679126788259,
 1.011358024691358,
 -9.102222222222222,
 -9.102222222222222,
 1.0000516802366892,
 1.0000737973674572,
 1.0000956122562226,
 1.011358024691358,
 1.0024463223787168,
 1.000593928287826,
 1.0000737973674572,
 1.0000610388817677,
 1.000052647159285,
 1.0057704112952732,
 1.0000741804419213,
 1.0039033214324353,
 1.0000741804419213,
 1.0000674622750414,
 1.0000737973674572,
 1.011358024691358,
 1.0000426922721817,
 1.0000610388817677,
 1.000043535246735,
 1.0000610388817677,
 1.0057704112952732,
 0.9989009268795056,
 1.0000610388817677,
 1.0000674622750414,
 1.0000741804419213,
 1.0000610388817677,
 1.0057704112952732,
 1.000043535246735,
 1.0000610388817677,
 1.000052647159285,
 1.0000956122562226,
 1.011358024691358,
 1.011358024691358,
 1.0000426922721817,
 1.0000741804419213,
 1.002552419840335,
 1.000454473374049]

In [202]:

    

pd.Series(ratios).describe()


    

    
Out[202]:





count    100.000000
mean       0.798660
std        1.421604
min       -9.102222
25%        1.000058
50%        1.000071
75%        1.002890
max        1.011358
dtype: float64

In [203]:

    

pd.Series(ratios).median()


    

    
Out[203]:





1.0000706298212494