Changeset 510
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- Apr 22, 2004, 6:06:00 PM (22 years ago)
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trunk/doc/design/design.tex (modified) (7 diffs)
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trunk/doc/design/design.tex
r508 r510 1 %%% $Id: design.tex,v 1. 7 2004-04-23 02:44:14price Exp $1 %%% $Id: design.tex,v 1.8 2004-04-23 04:06:00 price Exp $ 2 2 \documentclass[panstarrs]{panstarrs} 3 3 … … 191 191 \resizebox{8cm}{!}{\includegraphics{pics/overview}} 192 192 \caption{ \label{overview} IPP System Overview. \tbd{``Processing 193 Jobs'' should be renamed ``Analysis Pipelines''.} }193 Jobs'' should be renamed ``Analysis Stages''.} } 194 194 \end{center} 195 195 \end{figure} 196 196 197 \subsubsection{Analysis Pipelines} 198 199 We now consider the collection of IPP analysis pipelines. Depending 200 on the particular pipeline, they may be run on individual images, 201 collections of images, or on derived data products. Because of the 202 nature of the image data, many of the analysis pipelines can be run in 203 parallel because, for example, the analysis of a chip in one image 204 does not depend on the results from another chip. We define the 205 analysis pipelines to be the largest complete analysis task which may 206 be performed on a single data item. The data analysis tasks are 207 divided into three categories, and further subdivided as follows: 208 209 \begin{enumerate} 210 \item Science Image Pipelines 197 \subsubsection{Analysis Stages} 198 199 We now consider the collection of IPP analysis stages. We define an 200 analysis stage to be the largest complete task which may be performed 201 in serial without interation between parallel threads. 202 203 Depending on the particular analysis stage, it may process individual 204 images, collections of images, or on derived data products. Because 205 of the nature of the image data, many of the analysis stages can be 206 run in parallel because, for example, the analysis of a chip in one 207 image does not depend on the results from another chip. 208 209 The data analysis stages are divided into three categories as follows: 210 211 \begin{enumerate} 212 \item Science Image Analysis Stages 211 213 \begin{enumerate} 212 \item Phase 1: image processing preparation 213 \item Phase 2: image reduction 214 \item Phase 3: exposure analysis 215 \item Phase 4: image combination 214 \item Phase 1: image processing preparation --- estimates 215 first-order astrometric and photometric solutions required to 216 process each major frame. 217 \item Phase 2: image reduction --- produces calibrated chips from 218 raw chips. 219 \item Phase 3: exposure analysis --- processes an FPA to produce 220 unified and consistent backgrounds, photometry and astrometry for 221 the component chips. 222 \item Phase 4: image combination --- processes sky cells overlapped 223 by a major frame. 216 224 \end{enumerate} 217 \item Calibration Image Pipelines225 \item Calibration Image Analysis Stages 218 226 \begin{enumerate} 219 \item Calibration 1: basic master-detrend creation 220 \item Calibration 2: Sky-model/fringe-mode generation 221 \item Calibration 3: Flat-field correction image Creation 227 \item Calibration 1: Basic master-detrend creation --- combination 228 of simple detrend images. 229 \item Calibration 2: Sky-model/fringe-mode generation --- 230 combination of more-complicated detrend images. 231 \item Calibration 3: Flat-field correction image creation --- 232 analysis of photometry from multiple dithered FPAs. 222 233 \end{enumerate} 223 \item Reference Catalog Pipelines234 \item Reference Catalog Analysis Stages 224 235 \begin{enumerate} 225 \item Astrometry reference catalog generation 226 \item Photometry reference catalog generation 236 \item Astrometry reference catalog generation --- processing of the 237 astrometric data to determine and apply a consistent global 238 solution. 239 \item Photometry reference catalog generation --- processing of the 240 photometric data to determine and apply a consistent global 241 solution. 227 242 \end{enumerate} 228 243 \end{enumerate} 229 244 230 Figure~\ref{ pipelines} shows the flow of data between the various IPP231 software systems and the different analysis tasks, each managed by the232 controller. The thick lines represent the flow of pixel data, the245 Figure~\ref{system} shows the flow of data between the various IPP 246 software systems and the different analysis stages, each managed by 247 the controller. The thick lines represent the flow of pixel data, the 233 248 thin lines represent the flow of metadata and object data, and the 234 249 grey lines represent the flow of commands. The hatched systems … … 240 255 \begin{center} 241 256 \resizebox{8cm}{!}{\includegraphics{pics/pipelines}} 242 \caption{ \label{ pipelines} IPP System Overview. \tbd{Small part at257 \caption{ \label{system} IPP System Overview. \tbd{Small part at 243 258 top is missing.} } 244 259 \end{center} … … 252 267 the pre-reduction analysis of the raw science images. In addition, we 253 268 have specified distinct machines to maintain the object and metadata 254 databases. This last aspect is largely theoretical until we have269 databases. \tbd{This last aspect is largely theoretical until we have 255 270 defined the details of these databases; it may be more appropriate 256 271 depending on the eventual solutions to distribute these database 257 elements across the Detector and Static Sky subclusters. 272 elements across the Detector and Static Sky subclusters.} 258 273 259 274 \begin{figure} … … 274 289 frameworks. In particular, the modules can be tied together with a 275 290 simple framework (an `engine') or with detailed flow-control through 276 the use of a high-level language such as Perl, Python, or Tcl. For 277 the high-level functions in the operational system, the IPP will make 278 use of \tbd{Python} as the scripting language to tie the modules 279 together. 291 the use of a high-level language such as Perl, Python, or Tcl 292 employing the SWIG interfaces. For the high-level functions in the 293 operational system, the IPP will make use of \tbd{Python} as the 294 scripting language to provide the required flow-control to tie the 295 modules together. 280 296 281 297 This approach satisfies the requirement that complicated low-level … … 304 320 \subsubsection{Modules} 305 321 306 The IPP analysis tasks are broken down into modules which represent322 The IPP analysis stages are broken down into modules which represent 307 323 specific functional operations. The modules will be written in C 308 324 using the \PS{} Library functions and will be grouped into a \PS{} 309 325 Module Library. The modules will be provided with SWIG interfaces to 310 all public APIs for their use in processing stages. Examples of modules 311 are overscan subtraction and image combination. 326 all public APIs for their use in processing stages. Examples of 327 modules are overscan subtraction and image combination. Some modules 328 (e.g.\ find objects on an image) will be used by multiple stages. 312 329 313 330 \subsubsection{Stages} … … 334 351 external to the IPP, and for initiating the reduction appropriate for 335 352 images as they are received. An example of the scheduler 336 functionality is `` I've just received exposure number 1234; run phase337 1--4controllers on exposure 1234''.353 functionality is ``Retrieve exposure number 1234; run phase 1--4 354 controllers on exposure 1234''. 338 355 339 356 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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