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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%%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 %%(background-color:#009900;)NEXP%% and %%(background-color:#009900;)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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