The __compression factor__ of on board EIS data may vary depending on a few factors such as the observing target (QS, AR, CH, etc), slit/slot selection, exposures, etc.

The purpose of this study is trying to investigate how different compression schemes effect EIS data volume on board (MDP) and work out a better estimation of compression factor for compression scheme (eg. DPCM, JPEG98, JPEG95, etc.)

!The approach:

1. to get actual data volume from MDP status information: the inclined curve means data packets from EIS on MDP increasing, the vertical curve means data packets dumped to ground station. So in general, knowing a raster's start and end time one can fitstly calculate actual data volume in that duration, and then compare it with the designed data volume in raster's definition, to get data compression factor.

[{Image src='images/factor/dataVolume_eis.png}]

2. to get related information from planning database/eis catalogue/fits header, for example: raster ID, compression scheme, designed data volume, SCI_OBJ, TARGET, slit/slot, exposures, etc.

3. prepare plots based on various factor combinations: compression factor vs. slit/slot, factor vs. target, factor vs. exposures

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!!Some preliminary results (plots):

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''The investigation here is for date sets obtained mainly between %%(color:#cc0000;)2007-Sep-15 and 2007-Dec-15%%, as EIS is operationally stable over this duration. The other data sets is in %%(color:#cc0000;)2008-Mar%% (for JPEG85), and %%(color:#cc0000;)2006-Dec%% (for JPEG75)
''

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* [DPCM Compression Scheme]


* [JPEG98 Compression Scheme]


* [JPEG95 Compression Scheme]


* [JPEG90 Compression Scheme]


* [JPEG85 Compression Scheme]


* [JPEG75 Compression Scheme]

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The __average values__ of compression factor for each scheme are listed in following %%(color:#c00;)Sortable Table%% (although only a single number can't show the compression factor varation over a large dataset).

%%sortable
|| Scheme || Total || QS || AR || CH || 1" || 2" || 40" || 266" || 10s || 15s || 20s || 30s || 60S
|DPCM |2.757|2.826|2.657|2.676|2.900|2.947|2.538|2.620|3.105|2.950|2.629|2.586|2.578
|JPEG98|2.553|2.672|2.449|3.436|2.803|2.365|2.553| |3.205|2.528| |2.401| 
|JPEG95|5.809|5.990|5.451| |5.342|6.077| | |4.852|4.131| | |
|JPEG90|8.273|7.387|3.533|7.790|2.753| |3.678|8.329| | | |3.678|2.293
|JPEG85|5.528|5.578|4.922|6.148| | |5.539| | | | | | 
|JPEG75|11.294| | | | | | |11.294|11.294| | | | 
%%


However, the number shown above has difference from previous work done by others, eg, Hara'san result:

{{{
> 40" SLOT
> DPCM      2.36
> JPEG98    2.70
> JPEG95    3.47
> JPEG92    4.22
> JPEG90    4.63
> JPEG85    5.74
> JPEG75    7.63
> JPEG65    9.43
> JPEG50    12.0
> For 10s exposure time. }}}

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This work is just a start, the method and results shown here need to check again, and compare with other numbers. !As I mentioned above, these are only preliminary results on EIS compression factor. Still there are more need to be done, so any contributions, comments and suggestions are very helpful!\\


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%%information
As for target selection, in this case, I uses value of %%(color:#cc0000;)SCI_OBJ%% keyword instead of %%(color:#cc0000;)TARGET%% keyword as the later is only meaningful at (study/raster) design stage, the actual target is chosen during the EIS planning. 

However, sometimes there is no values for SCI_OBJ in EIS fits header, and sometimes there are two many values (see [SCI_OBJ vaules examples]). Lots of EIS dataset are thrwon away as hardly to decide to which catagroy the SCI_OBJ belongs to, such as QS,AR or CH.

%%
%%information
Compensation has been applied for those paused (and aborted) rasters. This is implemented by comparing %%(color:#cc0000;)NEXP%% and %%(color:#cc0000;)RAST_REQ%% and timing the ratio of two values.
%%
%%information
There is a structure array to store all related information for the EIS data investigated here. The array has element with the following format:

{{{
compFactor={compression_factor, $
          study_ACR     :'', $  ;string
          study_id      :'', $  ;string
          rast_ACR      :'', $  ;string
          rast_id       :'', $  ;string
          ll_ACR        :'',$   ;string
          ll_id         :'',$   ;string
          start_time    :'', $  ;string
          end_time      :'', $  ;string
          fitsname      :'',$   ;string
          target        :'',$   ;string
          sci_obj       :'',$   ;string
          slit          :'',$   ;string
          def_volume    :0LL,$  ;long64 int, unit: bits
          mdp_volume    :0.0,$  ;float, unit: kbits
          comp_scheme   :0,$    ;int
          nexp          :0,$    ;int
          rast_req      :0,$    ;int
          exposures     :fltarr(8) $    ;float, unit: sec
        }
}}}

I attached an IDL sav file [here|http://msslxr.mssl.ucl.ac.uk:8080/eiswiki/images/newgifs/20071201_20071215.sav.tar.gz]. You may download and play it, for example, I use: 

{{{if (str1[i].SCI_OBJ eq 'QS') && (str1[i].COMP_SCHEME eq 1) && (str1[i].MDP_VOLUME gt 0.) then ind[i]=1}}}

to extract records associated with 'QS' SCI_OBJ and using DPCM compression scheme.
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