The __compression rate__ 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 and work out a better estimation of compression rate 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, the vertical curve means data packets dumped to ground station. So in general, known a raster's start and end time can calculate actual data volume, and then compare it with the designed data volume of this raster to get data compression rate.

[{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 rate vs. slit/slot, rate vs. target, rate vs. exposures

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!!Some preliminary results (plots):\\


(The investigation here is for dates 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 rate for each scheme are listed in a %%(color:#c00;)Sortable Table%% here (how ever, only a single number cannot describe the varation of the rate.)

%%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| | | | 
%%


There is difference between the number shown here and other previous results, for example, Hara'san work:

{{{
> 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. }}}

!As I mentioned above, this is a preliminary study on EIS compression factor. The method and results shown here need to check, confirm and compare with other number. There are still mystery hide behind these numbers. It is really just a start, so more contributions are very welcome!

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

However, there is not always having value for SCI_OBJ in EIS fits header, also the range of value of SCI_OBJ keyword is varying, ([SCI_OBJ_Example]), sometimes we have to throw away some EIS data as it's hard to judge the SCI_OBJ belong to QS,AR or CH, eg. LMB, or FIL.
%%
%%information
There is a compensation for paused (and aborted) raster. This is implemented by comparing %%(color:#cc0000;)NEXP%% and %%(color:#cc0000;)RAST_REQ%% and times the ration of two values
%%
%%information
The related information for investigated EIS data set has been stored in a structure array, which has elements 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 the IDL sav file [here|http://msslxr.mssl.ucl.ac.uk:8080/eiswiki/images/newgifs/20071201_20071215.sav.tar.gz]. Please download it and play it with your self, 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 find those records having 'QS' SCI_OBJ and using DPCM compression scheme.
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