[ { "path": ".gitignore", "content": "# ignore files generated by beast\nbeast/__pycache__\nbeast/_beast.so\nbeast/beast.py\nbeast/beast_wrap.cxx\nbeast/beast_wrap.o\n\n# ignore test executables\ntests/gmon.out\ntests/test\n" }, { "path": "Dockerfile", "content": "\nFROM ubuntu:20.04\n\n# deal with timezones (change to what suits your use case)\nENV TZ=America/New_York\n\nENV DEBIAN_FRONTEND=noninteractive\n\nWORKDIR home\n\n# need all of this to work (was missing some packages)\nRUN apt-get update \\\n&& apt-get -y install software-properties-common \\\n&& add-apt-repository ppa:deadsnakes/ppa \\\n&& apt-get install -y python3-scipy libopencv-dev python3-opencv \\\n swig python3-systemd git astrometry.net \\\n python3-astropy python3-pkgconfig \\\n python3-dev libpython3.7-dev libpython3.8-dev \\\n build-essential python3.6 libpython3.6-dev \\\n python3-pandas \\\n graphviz \\\n bc \\\n netcat \\\n tzdata \\\n&& rm -rf /var/lib/apt/lists/* \n\nADD http://data.astrometry.net/4100/index-4112.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4113.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4114.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4115.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4116.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4117.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4118.fits /usr/share/astrometry/\nADD http://data.astrometry.net/4100/index-4119.fits /usr/share/astrometry/\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2018 UB Nanosatellite Laboratory\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "# openstartracker\nA fast, robust, open source startracker based on a new class of baysian startracker algorithms\n\nFeatures:\n\n* Fast lost in space identification\n* Image to image matching\n* Collect and store size, shape and color information of unknown objects\n* Tracks unknown objects between images\n* Programable python frontend / reusable C++ backend (BEAST-2) with no external dependencies \n* Uses astrometry.net for calibration (check if your camera is good enough by uploading your star images to nova.astrometry.net)\n* Supports python 2 and 3 (see bottom)\n\n### Basic setup:\n\n\n##### From a fresh ubuntu linux install\n```\nsudo apt-get install python3-scipy libopencv-dev python3-opencv swig python3-systemd\n```\n\nAdditional packages needed for calibration and unit testing:\n~~~~\nsudo apt-get install git astrometry.net python3-astropy\n\ncd /usr/share/astrometry\n\nDownload fits files corresponding to your camera fov size (see astrometry.net for details\nsudo wget http://data.astrometry.net/4100/index-4112.fits\nsudo wget http://data.astrometry.net/4100/index-4113.fits\nsudo wget http://data.astrometry.net/4100/index-4114.fits\nsudo wget http://data.astrometry.net/4100/index-4115.fits\nsudo wget http://data.astrometry.net/4100/index-4116.fits\nsudo wget http://data.astrometry.net/4100/index-4117.fits\nsudo wget http://data.astrometry.net/4100/index-4118.fits\nsudo wget http://data.astrometry.net/4100/index-4119.fits\n\ngit clone https://github.com/UBNanosatLab/openstartracker.git\n\ncd openstartracker/tests\n./unit_test.sh -crei science_cam_may8_0.05sec_gain40\n\n~~~~\n\n#### Using Docker\nThe included Dockerfile allows the build and test environment to be configured in a reproducible way with all the dependencies.\nAccording to their website,\n> Docker is an open platform for developing, shipping, and running applications. Docker enables you to separate your applications from your infrastructure so you can deliver software quickly.\n\nIn practice, it's a nice way to ensure you always have the same dependencies installed, independent of the rest of your system.\n\n**Note:** the Docker environment here is best used for development purposed. When running openstartracker on a flight computer, it is most\nlikely better to run the program directly, without the Docker container. Docker is used here just to create a standard, reproducible dev and testing environment.\n\nTo use the Docker environment to run openstartracker,\n\n0. Install [Docker](https://docs.docker.com/get-docker/).\n1. Run `source setup.sh`. This will execute a `docker build ...` command to create the Docker environment from the Dockerfile, copy\nin all the code config files from this repo, \nand create a command alias called `dstart` that can be run to start the Docker environment and access the pre-built Docker environment.\n - This may take awhile to run the first time as the Docker image is built, but will run much more quickly in subsequent builds\n2. Run `dstart` to start and enter the Docker environment.\n3. Proceed with the following instructions.\n\n**Important note:** Any code and file changes should be made **outside** the Docker environment -- consider it a 'run only' space.\nAny time you make a change to the code in this repo, you'll want to re-run the `source setup.sh` command so that your changes are\nincluded in the Docker build.\n\n### Run on example imagery:\nYou can use the included `science_cam_may8_0.05sec_gain40` images to test out the calibration process:\n\n```\n# if using docker, run `source setup.sh` and then `dstart` to enter the Docker shell\ncd tests/\n./unit_test.sh -crei science_cam_may8_0.05sec_gain40/\n```\n\nThis command will **c**alibrate your image sensor, **r** regenerate the test data, run an **E**SA test, and finally run the **i**mage test where images are fed to the calibrated star tracker program to produce an attitude fix.\n\nThe usage message for `unit_test.py` is here:\n```\n./unit_test.sh -h\nUsage: ./unit_test.sh [options] testdir [cmd]\n\n\t-c\tCalibrate based on images in testdir/samples/\n\t-r\tRegenerate ESA test\n\t-e\tRun ESA test\n\t-i\tRun image test\n```\n\n### To calibrate a new camera:\n1. Create directories for you camera's imagery:\n~~~~\ncd openstartracker/tests/\nmkdir yourcamera\nmkdir yourcamera/samples\nmkdir yourcamera/calibration_data\n~~~~\n2. Add 3-10 star images of different parts of the sky taken with your camera to `tests/yourcamera/samples`\n3. Edit `APERTURE` and `EXPOSURE_TIME` in `calibrate.py` (you want to take images with the lowest exposure time that consistently solves)\n4. (if using docker, run `dstart` to enter the Docker environment)\n5. Run \n ```\n cd tests/\n ./unit_test.sh -crei yourcamera\n ```\n to calibrate and test\n\nThe ESA test should have a score of >70. If its worse than this, play around with exposure time (50ms is a good starting point)\n\n##### Reference frames used:\n\nFor RA,DEC,Ori, openstartracker uses the same convention as astrometry.net, where RA and DEC are in the same frame as the star positions specified in the hipparcos catalogue (updated to the current year). Orientation is degrees east of north (ie orientation 0 means that up and down aligns with north-south)\n\nNOTE: RA, DEC & Ori are provided for debugging purposes only. For spacecraft attitude determination, the star reference vectors should be used, either directly, or converted to a quaternion via one of the many algorithms that have been developed for this purpose (see: https://en.wikipedia.org/wiki/Wahba%27s_problem)\n" }, { "path": "beast/Makefile", "content": "CC = g++\nPYTHONHEADERS=/usr/include/python3.10\n#PYTHONHEADERS=/usr/include/python2.7\n\nall: $(OBJ)\n\tswig -python -c++ beast.i\n\t$(CC) -g -std=c++11 -Ofast -fPIC -c beast_wrap.cxx -o beast_wrap.o -lstdc++ -I$(PYTHONHEADERS)\n\t$(CC) -g -shared -fPIC beast_wrap.o -o _beast.so\n\t\nclean:\n\trm beast.so beast.o\tbeast.py beast.pyc beast_wrap.cxx beast_wrap.o\n" }, { "path": "beast/beast.h", "content": "#ifndef BEAST_H\n#define BEAST_H\n\n#include \"constellations.h\"\n#include \n\nstruct match_result {\n//TODO: private:\n\tconstellation_pair match;\n\t\n\t//eci to body (body=R*eci)\n\tfloat R11,R12,R13;\n\tfloat R21,R22,R23;\n\tfloat R31,R32,R33;\nprivate:\n\tstar_fov *img_mask;\n\tint *map; /* Usage: map[imgstar]=dbstar */\n\tsize_t map_size;\n\tconstellation* db_const;\n\tconstellation_db *db,*img;\npublic:\n\t\n\t/**\n\t* @brief TODO\n\t* @param db_\n\t* @param img_\n\t* @param img_mask_\n\t*/\n\tmatch_result(constellation_db *db_, constellation_db *img_, star_fov *img_mask_) {\n\t\tDBG_MATCH_RESULT_COUNT++;\n\t\tDBG_PRINT(\"DBG_MATCH_RESULT_COUNT++ %d\\n\",DBG_MATCH_RESULT_COUNT);\n\t\tdb=db_;\n\t\timg=img_;\n\t\timg_mask=img_mask_;\n\t\tmap_size=img->stars->size();\n\t\tmap=(int *)malloc(sizeof(map[0])*map_size);\n\t\tmatch.totalscore=-FLT_MAX;\n\t\t\n\t}\n\t~match_result() {\n\t\tDBG_MATCH_RESULT_COUNT--;\n\t\tDBG_PRINT(\"DBG_MATCH_RESULT_COUNT-- %d\\n\",DBG_MATCH_RESULT_COUNT);\n\t\tfree(map);\n\t}\n\tsize_t size() {return map_size;};\n\t/**\n\t* @brief TODO\n\t* @param db_const_\n\t* @param img_const_\n\t*/\n\tvoid init(constellation &db_const_, constellation &img_const_) {\n\t\tdb_const=&db_const_;\n\t\t\n\t\tmatch.img_s1=img_const_.s1;\n\t\tmatch.img_s2=img_const_.s2;\n\t\tmatch.db_s1=db_const_.s1;\n\t\tmatch.db_s2=db_const_.s2;\n\t}\n\t\n\t/**\n\t* @brief TODO\n\t* @param c\n\t*/\n\tvoid copy_over(match_result *c) {\n\t\tassert(c->db==db);\n\t\tassert(c->img==img);\n\t\tassert(c->img_mask==img_mask);\n\t\t\n\t\tc->match=match;\n\t\tc->db_const=db_const;\n\t\t\n\t\tc->R11=R11,c->R12=R12,c->R13=R13;\n\t\tc->R21=R21,c->R22=R22,c->R23=R23;\n\t\tc->R31=R31,c->R32=R32,c->R33=R33;\n\t\t\n\t\tmemcpy(c->map, map, sizeof(map[0])*map_size);\n\t}\n\t/**\n\t* @brief TODO\n\t* @param m\n\t* @return \n\t*/\n\tint related(constellation_pair &m) {\n\t\t\n\t\tif (match.totalscore==-FLT_MAX || m.totalscore==-FLT_MAX) return 0;\n\t\treturn (map[m.img_s1]==m.db_s1 && map[m.img_s2]==m.db_s2)?1:0;\n\t}\n\t/**\n\t* @brief TODO\n\t*/\n\tvoid search() {if (db->results->is_kdsorted()) db->results->kdsearch(R11,R21,R31,MAXFOV/2,THRESH_FACTOR*IMAGE_VARIANCE);}\n\t/**\n\t* @brief TODO\n\t*/\n\tvoid clear_search() {if (db->results->is_kdsorted()) db->results->clear_kdresults();}\n\t/**\n\t* @brief TODO\n\t*/\n\tvoid compute_score() {\n\t\t//TODO: figure out where 2*map_size came from\n\t\tmatch.totalscore=log(1.0/(IMG_X*IMG_Y))*(2*map_size);\n\t\tfloat* scores=(float *)malloc(sizeof(float)*map_size);\n\t\tfor (size_t i=0;iresults->r_size();i++) {\n\t\t\tstar *s=&(db->results->map[db->results->kdresults[i]]);\n\t\t\tint o=s->star_idx;\n\t\t\tfloat x=s->x*R11+s->y*R21+s->z*R31;\n\t\t\tfloat y=s->x*R12+s->y*R22+s->z*R32;\n\t\t\tfloat z=s->x*R13+s->y*R23+s->z*R33;\n\t\t\tfloat px=y/(x*PIXX_TANGENT);\n\t\t\tfloat py=z/(x*PIXY_TANGENT);\n\t\t\t\n\t\t\tint n=img_mask->get_id(px,py);\n\t\t\tif (n>=0) {\n\t\t\t\tfloat score = img_mask->get_score(n,px,py);\n\t\t\t\tif (score>scores[n]){/* only match the closest star */\n\t\t\t\t\tmap[n]=o;\n\t\t\t\t\tscores[n]=score;\n\t\t\t\t}\n\t\t\t}\n\t\t}\t\t\n\t\tfor(size_t n=0;nstars->copy();\n\t\ts->max_variance=db->stars->max_variance;\n\t\tfor(size_t n=0;nget_star(img->stars->get_star(n)->star_idx)[0]=db->stars->get_star(map[n])[0];\n\t\t\t} else {\n\t\t\t\ts->get_star(img->stars->get_star(n)->star_idx)->id=-1;\n\t\t\t}\n\t\t}\n\t\treturn s;\n\t}\n\n\t/**\n\t* @brief weighted_triad results\n\t* see https://en.wikipedia.org/wiki/Triad_method \n\t* and http://nghiaho.com/?page_id=846\n\t* \n\t* when compiled, this section contains roughly 430 floating point operations\n\t* according to https://www.karlrupp.net/2016/02/gemm-and-stream-results-on-intel-edison\n\t* we can perform >250 MFLOPS with doubles, and >500 MFLOPS with floats\n\t*/\n\tvoid weighted_triad() {\n\t\tstar *db_s1=db->stars->get_star(match.db_s1);\n\t\tstar *db_s2=db->stars->get_star(match.db_s2);\n\t\tstar *img_s1=img->stars->get_star(match.img_s1);\n\t\tstar *img_s2=img->stars->get_star(match.img_s2);\n\t\t\n\t\t/* v=A*w */\n\t\tfloat wa1=db_s1->x,wa2=db_s1->y,wa3=db_s1->z;\n\t\tfloat wb1=db_s2->x,wb2=db_s2->y,wb3=db_s2->z;\n\t\tfloat va1=img_s1->x,va2=img_s1->y,va3=img_s1->z;\n\t\tfloat vb1=img_s2->x,vb2=img_s2->y,vb3=img_s2->z;\n\t\tfloat wc1=wa2*wb3 - wa3*wb2;\n\t\tfloat wc2=wa3*wb1 - wa1*wb3;\n\t\tfloat wc3=wa1*wb2 - wa2*wb1;\n\t\tfloat wcnorm=sqrt(wc1*wc1+wc2*wc2+wc3*wc3);\n\t\twc1/=wcnorm;\n\t\twc2/=wcnorm;\n\t\twc3/=wcnorm;\n\n\t\tfloat vc1=va2*vb3 - va3*vb2;\n\t\tfloat vc2=va3*vb1 - va1*vb3;\n\t\tfloat vc3=va1*vb2 - va2*vb1;\n\t\tfloat vcnorm=sqrt(vc1*vc1+vc2*vc2+vc3*vc3);\n\t\tvc1/=vcnorm;\n\t\tvc2/=vcnorm;\n\t\tvc3/=vcnorm;\n\t\t\n\t\tfloat vaXvc1=va2*vc3 - va3*vc2;\n\t\tfloat vaXvc2=va3*vc1 - va1*vc3;\n\t\tfloat vaXvc3=va1*vc2 - va2*vc1;\n\n\t\tfloat waXwc1=wa2*wc3 - wa3*wc2;\n\t\tfloat waXwc2=wa3*wc1 - wa1*wc3;\n\t\tfloat waXwc3=wa1*wc2 - wa2*wc1;\n\t\t\n\t\t/* some of these are unused */\n\t\t\n\t\tfloat A11=va1*wa1 + vaXvc1*waXwc1 + vc1*wc1;\n\t\t/* float A12=va1*wa2 + vaXvc1*waXwc2 + vc1*wc2; */\n\t\t/* float A13=va1*wa3 + vaXvc1*waXwc3 + vc1*wc3; */\n\t\tfloat A21=va2*wa1 + vaXvc2*waXwc1 + vc2*wc1;\n\t\t/* float A22=va2*wa2 + vaXvc2*waXwc2 + vc2*wc2; */\n\t\t/* float A23=va2*wa3 + vaXvc2*waXwc3 + vc2*wc3; */\n\t\tfloat A31=va3*wa1 + vaXvc3*waXwc1 + vc3*wc1;\n\t\tfloat A32=va3*wa2 + vaXvc3*waXwc2 + vc3*wc2;\n\t\tfloat A33=va3*wa3 + vaXvc3*waXwc3 + vc3*wc3;\n\t\t\n\t\twc1=-wc1;\n\t\twc2=-wc2;\n\t\twc3=-wc3;\n\t\t\n\t\tvc1=-vc1;\n\t\tvc2=-vc2;\n\t\tvc3=-vc3;\n\t\tfloat vbXvc1=vb2*vc3 - vb3*vc2;\n\t\tfloat vbXvc2=vb3*vc1 - vb1*vc3;\n\t\tfloat vbXvc3=vb1*vc2 - vb2*vc1;\n\t\t\n\t\tfloat wbXwc1=wb2*wc3 - wb3*wc2;\n\t\tfloat wbXwc2=wb3*wc1 - wb1*wc3;\n\t\tfloat wbXwc3=wb1*wc2 - wb2*wc1;\n\t\t\n\t\t/* some of these are unused */\n\t\tfloat B11=vb1*wb1 + vbXvc1*wbXwc1 + vc1*wc1;\n\t\t/* float B12=vb1*wb2 + vbXvc1*wbXwc2 + vc1*wc2; */\n\t\t/* float B13=vb1*wb3 + vbXvc1*wbXwc3 + vc1*wc3; */\n\t\tfloat B21=vb2*wb1 + vbXvc2*wbXwc1 + vc2*wc1;\n\t\t/* float B22=vb2*wb2 + vbXvc2*wbXwc2 + vc2*wc2; */\n\t\t/* float B23=vb2*wb3 + vbXvc2*wbXwc3 + vc2*wc3; */\n\t\tfloat B31=vb3*wb1 + vbXvc3*wbXwc1 + vc3*wc1;\n\t\tfloat B32=vb3*wb2 + vbXvc3*wbXwc2 + vc3*wc2;\n\t\tfloat B33=vb3*wb3 + vbXvc3*wbXwc3 + vc3*wc3;\n\t\t\n\t\t/* use weights based on magnitude */\n\t\t/* weighted triad */\n\t\tfloat weightA=1.0/(db_s1->sigma_sq+img_s1->sigma_sq);\n\t\tfloat weightB=1.0/(db_s2->sigma_sq+img_s2->sigma_sq);\n\t\t\n\t\tfloat sumAB=weightA+weightB;\n\t\tweightA/=sumAB;\n\t\tweightB/=sumAB;\n\t\t\n\t\tfloat cz,sz,mz;\n\t\tfloat cy,sy,my;\n\t\tfloat cx,sx,mx;\n\t\t\n\t\tcz=weightA*A11+weightB*B11;\n\t\tsz=weightA*A21+weightB*B21;\n\t\tmz=sqrt(cz*cz+sz*sz);\n\t\tcz=cz/mz;\n\t\tsz=sz/mz;\n\t\t\n\t\tcy=weightA*sqrt(A32*A32+A33*A33)+weightB*sqrt(B32*B32+B33*B33);\n\t\tsy=-weightA*A31-weightB*B31;\n\t\tmy=sqrt(cy*cy+sy*sy);\n\t\tcy=cy/my;\n\t\tsy=sy/my;\n\t\t\n\t\tcx=weightA*A33+weightB*B33;\n\t\tsx=weightA*A32+weightB*B32;\n\t\tmx=sqrt(cx*cx+sx*sx);\n\t\tcx=cx/mx;\n\t\tsx=sx/mx;\n\t\t\n\t\tR11=cy*cz;\n\t\tR21=cz*sx*sy - cx*sz;\n\t\tR31=sx*sz + cx*cz*sy;\n\t\t\n\t\tR12=cy*sz;\n\t\tR22=cx*cz + sx*sy*sz;\n\t\tR32=cx*sy*sz - cz*sx;\n\t\t\n\t\tR13=-sy;\n\t\tR23=cy*sx;\n\t\tR33=cx*cy;\n\t}\n\tvoid DBG_(const char *s) {\n\t\tDBG_PRINT(\"%s\\n\",s);\n\t\tDBG_PRINT(\"%f\\t%f\\t%f\\n\", R11,R12,R13);\n\t\tDBG_PRINT(\"%f\\t%f\\t%f\\n\", R21,R22,R23);\n\t\tDBG_PRINT(\"%f\\t%f\\t%f\\n\", R31,R32,R33);\n\t\t\n\t\tdb->DBG_(\"DB\");\n\t\timg->DBG_(\"IMG\");\n\t\tDBG_PRINT(\"map_size=%lu\\n\", map_size);\n\t\tfor (size_t i=0; istars->size()<3||img->stars->size()<3) return;\n\t\timg_mask = new star_fov(img->stars,db->stars->max_variance);\n\t\t\n\t\t//find stars\n\t\tmatch_result *m=new match_result(db, img, img_mask);\n\t\twinner=new match_result(db, img, img_mask);\n\t\tfor (size_t n=0;nmap_size;n++) {\n\t\t\tconstellation lb=img->map[n];\n\t\t\tconstellation ub=img->map[n];\n\t\t\tlb.p-=POS_ERR_SIGMA*PIXSCALE*sqrt(img->stars->get_star(lb.s1)->sigma_sq+img->stars->get_star(lb.s2)->sigma_sq+2*db->stars->max_variance);\n\t\t\tub.p+=POS_ERR_SIGMA*PIXSCALE*sqrt(img->stars->get_star(ub.s1)->sigma_sq+img->stars->get_star(ub.s2)->sigma_sq+2*db->stars->max_variance);\n\t\t\tconstellation *lower=std::lower_bound (db->map, db->map+db->map_size, lb,constellation_lt_p);\t\n\t\t\tconstellation *upper=std::upper_bound (db->map, db->map+db->map_size, ub,constellation_lt_p);\n\t\t\t//rewind upper & do sanity checks\n\t\t\tif (db->map>=upper--) continue;\n\t\t\tif (db->map+db->map_size<=lower) continue;\n\t\t\tif (lower->idx<=upper->idx) {\n\t\t\t\tc_pairs=(struct constellation_pair*)realloc(c_pairs,sizeof(struct constellation_pair)*(c_pairs_size+(upper->idx-lower->idx+1)*2));\n\t\t\t}\n\t\t\tfor (int o=lower->idx;o<=upper->idx;o++) {\n\t\t\t\tm->init(db->map[o],img->map[n]);\n\t\t\t\tm->weighted_triad();\n\t\t\t\tm->search();\n\n\t\t\t\t#define ADD_SCORE\\\n\t\t\t\t\tm->compute_score();\\\n\t\t\t\t\tif (m->match.totalscore>winner->match.totalscore) {\\\n\t\t\t\t\t\tif (winner->match.totalscore!=-FLT_MAX) c_pairs[c_pairs_size++]=winner->match;\\\n\t\t\t\t\t\tm->copy_over(winner);\\\n\t\t\t\t\t} else c_pairs[c_pairs_size++]=m->match;\n\n\t\t\t\tADD_SCORE\n\t\t\t\t/* try both orderings of stars */\n\t\t\t\tm->match.flip();\n\t\t\t\tm->weighted_triad();\n\t\t\t\tADD_SCORE\n\t\t\t\tm->clear_search();\n\t\t\t}\n\t\t}\n\t\tdelete m;\n\t\t\n\t\t//calculate map\n\t\tif (winner->match.totalscore!=-FLT_MAX) { //Did we even match?\n\t\t\t/**\n\t\t\t * normalize the posterior to find the probability that the best match is the correct one\n\t\t\t * this relies on three assumptions:\n\t\t\t * 1. The sample point is approximately the maximum likelihood\n\t\t\t * 2. The probability distribution around the sample points are all the same\n\t\t\t * 3. the probability outside of the range od sampled points is approximately zero\n\t\t\t */\n\t\t\t//calculate p_match\n\t\t\tp_match=1.0;\n\t\t\tfor (size_t idx=0; idxrelated(c_pairs[idx])){\n\t\t\t\t\tp_match+=exp(c_pairs[idx].totalscore-winner->match.totalscore);\n\t\t\t\t}\n\t\t\t}\n\t\t\tp_match=1.0/p_match;\n\t\t}\n\t}\n\t\n\t~db_match() {\n\t\tDBG_DB_MATCH_COUNT--;\n\t\tDBG_PRINT(\"DBG_DB_MATCH_COUNT-- %d\\n\",DBG_DB_MATCH_COUNT);\n\t\tdelete winner;\n\t\tdelete img_mask;\n\t\tfree(c_pairs);\n\t}\n};\n#endif\n" }, { "path": "beast/beast.i", "content": "%module beast\n%{\n#include \"config.h\"\n#include \"stars.h\"\n#include \"constellations.h\"\n#include \"beast.h\"\n%}\n//newobject gives python control of these objects\n%newobject star_db::copy;\n%newobject star_db::copy_n_brightest;\n%newobject star_db::search;\n%newobject star_db::operator-;\n%newobject star_db::operator&;\n%newobject star_query::from_kdmask;\n%newobject star_query::from_kdresults;\n%newobject match_result::from_match;\n%include \"config.h\"\n%include \"stars.h\"\n%include \"constellations.h\"\n%include \"beast.h\"\n" }, { "path": "beast/config.h", "content": "#ifndef CONFIG_H\n#define CONFIG_H\n\n#include \n#include //EXIT_FAILURE\n#include \n#include \n\n#define PI\t\t 3.14159265358979323846 /* pi */\n#define TWOPI\t\t6.28318530717958647693\n\nint DBG_ENABLE;\n#define DBG_PRINT(format,args...) if (DBG_ENABLE==1) fprintf(stderr,format, ## args);\n\n\nint DBG_STAR_DB_COUNT,DBG_CONSTELLATION_DB_COUNT,DBG_DB_MATCH_COUNT;\nint DBG_MATCH_RESULT_COUNT,DBG_STAR_FOV_COUNT,DBG_STAR_QUERY_COUNT;\n\n//TODO: config class (allows different configs for different cameras)\nint IMG_X,IMG_Y,MAX_FALSE_STARS,DB_REDUNDANCY,REQUIRED_STARS;\nfloat PIXSCALE,DOUBLE_STAR_PX;\nfloat BASE_FLUX,IMAGE_VARIANCE,THRESH_FACTOR,POS_VARIANCE,POS_ERR_SIGMA;\nfloat MAXFOV,MINFOV,MATCH_VALUE,PIXX_TANGENT,PIXY_TANGENT;\n\nint KDBUCKET_SIZE;\n\nint ENV_VARS_SIZE;\nchar** ENV_VARS; //keep track of these so that valgrind is happy\n\n/**\n* @brief Loads global variables from calibration data\n* @param filename calibration file generated by TODO:calibrate.sh? unit_test.sh\n*/\nvoid load_config(const char *filename) {\n\tDBG_STAR_DB_COUNT=DBG_CONSTELLATION_DB_COUNT=DBG_DB_MATCH_COUNT=0;\n\tDBG_MATCH_RESULT_COUNT=DBG_STAR_FOV_COUNT=DBG_STAR_QUERY_COUNT=0;\n\tDBG_ENABLE=0;\n\tENV_VARS_SIZE=0;\n\tENV_VARS=NULL;\n\t//move calibration.txt to constellation_db\n\t/* load config */\n\t\n\tFILE *stream = fopen(filename, \"r\");\n\tif (stream == NULL) exit(EXIT_FAILURE);\n\t//FILE *stream = fopen(filename, \"r\");\n\tssize_t read;\n\tchar *line = NULL;\n\tsize_t len = 0;\n\twhile ((read = getline(&line, &len, stream)) != -1) {\n\t\tENV_VARS=(char**)realloc(ENV_VARS,(ENV_VARS_SIZE+1)*sizeof(ENV_VARS[0]));\n\t\tENV_VARS[ENV_VARS_SIZE]=(char *)malloc(sizeof(char) * len);\n\t\tputenv(strcpy(ENV_VARS[ENV_VARS_SIZE++],line));\n\t}\n\tfree(line);\n\tfclose(stream);\n\n\tIMG_X=atoi(getenv(\"IMG_X\"));\n\tIMG_Y=atoi(getenv(\"IMG_Y\"));\n\tPIXSCALE=atof(getenv(\"PIXSCALE\"));\n\tPOS_ERR_SIGMA=atof(getenv(\"POS_ERR_SIGMA\"));\n\tPOS_VARIANCE=atof(getenv(\"POS_VARIANCE\"));/* sigma_r^2 */\n\tIMAGE_VARIANCE=atof(getenv(\"IMAGE_VARIANCE\"));/* lambda */\n\tTHRESH_FACTOR=atof(getenv(\"THRESH_FACTOR\"));/* lambda */\n\tDOUBLE_STAR_PX=atof(getenv(\"DOUBLE_STAR_PX\"));\n\tMAX_FALSE_STARS=atoi(getenv(\"MAX_FALSE_STARS\"));/* >10 is slow */\n\tDB_REDUNDANCY=atoi(getenv(\"DB_REDUNDANCY\"));\n\tREQUIRED_STARS=atoi(getenv(\"REQUIRED_STARS\"));\n\tBASE_FLUX=atof(getenv(\"BASE_FLUX\"));\n\t\n\tMAXFOV=PIXSCALE*sqrt(IMG_X*IMG_X+IMG_Y*IMG_Y);\n\tMINFOV=PIXSCALE*IMG_Y;\n\tMATCH_VALUE=4*log(1.0/(IMG_X*IMG_Y))+log(2*PI);/* base */\n\tPIXX_TANGENT=2*tan((IMG_X*PIXSCALE/3600)*PI/(180*2))/IMG_X;\n\tPIXY_TANGENT=2*tan((IMG_Y*PIXSCALE/3600)*PI/(180*2))/IMG_Y;\n\tKDBUCKET_SIZE=((IMG_X*PIXSCALE/3600)*(IMG_Y*PIXSCALE/3600)*3.5);\n}\n#endif\n" }, { "path": "beast/constellations.h", "content": "#ifndef CONSTELLATIONS_H\n#define CONSTELLATIONS_H\n\n#include \"stars.h\"\n#include \n\nstruct constellation {\n\tfloat p;\n\tsize_t s1;\n\tsize_t s2;\n\tint idx;\n\tvoid DBG_(const char *s) {\n\t\tDBG_PRINT(\"%s\\t\",s);\n\t\tDBG_PRINT(\"p=%f \",p);\n\t\tDBG_PRINT(\"s1=%lu \",s1);\n\t\tDBG_PRINT(\"s2=%lu \",s2);\n\t\tDBG_PRINT(\"idx=%d\\n\",idx);\n\t}\n};\n\nstruct constellation_pair {\n\n//TODO: private:\n\tfloat totalscore;\n\tint db_s1,db_s2;\n\tint img_s1,img_s2;\n\npublic:\n/**\n* @brief TODO\n*/\n\tvoid flip() {\n\t\tint t=img_s1;\n\t\timg_s1=img_s2;\n\t\timg_s2=t;\n\t}\n\tvoid DBG_(const char *s) {\n\t\tDBG_PRINT(\"%s\\t\",s);\n\t\tDBG_PRINT(\"totalscore=%f \",totalscore);\n\t\tDBG_PRINT(\"db_s1=%d \",db_s1);\n\t\tDBG_PRINT(\"db_s2=%d \",db_s2);\n\t\tDBG_PRINT(\"img_s1=%d \",img_s1);\n\t\tDBG_PRINT(\"img_s2=%d\\n\",img_s2);\n\t}\n};\n\nstruct constellation_lt {\n\tbool operator() (const constellation &c1, const constellation &c2) {\n\t\tif (c1.p!=c2.p) return c1.pcopy();\n\t\t\tresults=new star_query(stars);\n\t\t\tresults->sort();\n\t\t\tmap=NULL;\n\t\t\tint ns=stars->size();/* number of stars to check */\n\t\t\tif (ns>stars_per_fov) ns=stars_per_fov;//MAX_FALSE_STARS+2\n\n\t\t\tmap_size=ns*(ns-1)/2;\n\t\t\tmap=(constellation*)malloc(map_size*sizeof(map[0]));\n\t\t\t\n\t\t\tint idx=0;\n\t\t\tfor (int j=1;jmap[i].dist_arcsec(results->map[j]);\n\t\t\t\tmap[idx].s1=results->map[i].star_idx;\n\t\t\t\tmap[idx].s2=results->map[j].star_idx;\n\t\t\t}\n\t\t\tstd::sort(map, map+map_size,constellation_lt_p);\n\t\t\twhile (--idx>=0) map[idx].idx=idx;\n\t\t} else {\n\t\t\tstars=s->copy();\n\t\t\tresults=new star_query(stars);\n\t\t\tresults->kdmask_uniform_density(stars_per_fov);//2+DB_REDUNDANCY\n\t\t\tstd::set c_set;\n\t\t\tfor (size_t i=0;imap_size;i++) if (results->get_kdmask(i)==0) {\n\t\t\t\tresults->kdsearch(results->map[i].x,results->map[i].y,results->map[i].z,MAXFOV,THRESH_FACTOR*IMAGE_VARIANCE);\n\t\t\t\tconstellation c;\n\t\t\t\tfor (size_t j=0;jr_size();j++) if (i!=results->kdresults[j] && results->map[i].flux>=results->map[results->kdresults[j]].flux){\n\t\t\t\t\tc.p=results->map[i].dist_arcsec(results->map[results->kdresults[j]]);\n\t\t\t\t\tc.s1=results->map[i].star_idx;\n\t\t\t\t\tc.s2=results->map[results->kdresults[j]].star_idx;\n\t\t\t\t\tc_set.insert(c);\n\t\t\t\t}\n\t\t\t\tresults->clear_kdresults();\n\t\t\t}\n\t\t\tresults->reset_kdmask();\n\t\t\t//preallocate map\n\t\t\tmap_size=c_set.size();\n\t\t\tmap=(constellation*)malloc(map_size*sizeof(map[0]));\n\t\t\tstd::set::iterator it = c_set.begin();\n\t\t\tfor (size_t idx=0; idxDBG_(\"STARS\");\n\t\tresults->DBG_(\"RESULTS\");\n\t\tfor (size_t i=0; i\n\n/**\n * multidimensional hash function based on morton codes\n * \n * the magic numbers were generated by doc/gen_interleave.py\n *\n * If we ever need to implement decoding, useful info can be found here:\n * https://fgiesen.wordpress.com/2009/12/13/decoding-morton-codes/\n * (basically just do everything backwards)\n * \n */\nstruct kdhash_2f {\n\t#define DIAM 0xffffffff\n\t#define R_IN (DIAM/2)\n\t#define R_OUT (R_IN+1)\n\t#define INTERLEAVE(X)\\\n\t\tif (X > DIAM) X=DIAM;\\\n\t\tif (X < 0) X=0;\\\n\t\tX = (X | X << 16) & 0xffff0000ffff;\\\n\t\tX = (X | X << 8) & 0xff00ff00ff00ff;\\\n\t\tX = (X | X << 4) & 0xf0f0f0f0f0f0f0f;\\\n\t\tX = (X | X << 2) & 0x3333333333333333;\\\n\t\tX = (X | X << 1) & 0x5555555555555555;\n\t\n\t/**\n\t* @brief hash function which interleaves values so that nearby coordinates will have nearby hashes\n\t* this approach also also allows you to truncate the hash when less precision is needed\n\t*/\n\tstatic inline uint64_t hash(const float x0,const float x1){\n\t\tint64_t h0=R_OUT*(x0+1.0);\n\t\tint64_t h1=R_OUT*(x1+1.0);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\treturn h1<<1|h0;\n\t}\n\t\n\t/**\n\t* @brief returns returns the number of bits we need to truncate in order to search a specified range\n\t*/\n\tstatic inline uint8_t bin_size(const float r) {\n\t\tint64_t h = R_OUT*r;\n\t\tif (h > R_IN) h=R_IN;\n\t\tif (h < 0) h=0;\n\t\tuint8_t sz=0;\n\t\tfor (;h;h>>=1) sz+=1;\n\t\treturn sz;\n\t}\n\t/**\n\t* @brief returns a mask which can be used to clamp the hash to a specified precision\n\t* @param r mask a range of this size, rounded up to the nearest power of two\n\t*/\n\tstatic inline uint64_t mask(const float r) {\n\t\tuint64_t mask=-1;\n\t\treturn mask<<2*bin_size(r);\n\t}\n\t\n\t/**\n\t* @brief As before, but allows independent ranges for the different parameters \n\t*/\n\tstatic inline uint64_t mask(const float r0,const float r1){\n\t\tint64_t h0=R_OUT>>bin_size(r0);\n\t\tint64_t h1=R_OUT>>bin_size(r1);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\treturn h1<<1|h0;\n\t}\n\t#undef INTERLEAVE\n\t#undef R_OUT\n\t#undef R_IN\n\t#undef DIAM\n};\n\nstruct kdhash_3f {\n\t#define DIAM 0x1fffff\n\t#define R_IN (DIAM/2)\n\t#define R_OUT (R_IN+1)\n\t#define INTERLEAVE(X)\\\n\t\tif (X > DIAM) X=DIAM;\\\n\t\tif (X < 0) X=0;\\\n\t\tX = (X | X <<32) & 0x001f00000000ffff;\\\n\t\tX = (X | X <<16) & 0x001f0000ff0000ff;\\\n\t\tX = (X | X << 8) & 0x100f00f00f00f00f;\\\n\t\tX = (X | X << 4) & 0x10c30c30c30c30c3;\\\n\t\tX = (X | X << 2) & 0x1249249249249249;\n\t///See kdhash_2f::hash\n\tstatic inline uint64_t hash(const float x0,const float x1,const float x2){\n\t\tint64_t h0=R_OUT*(x0+1.0);\n\t\tint64_t h1=R_OUT*(x1+1.0);\n\t\tint64_t h2=R_OUT*(x2+1.0);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\tINTERLEAVE(h2)\n\t\treturn h2<<2|h1<<1|h0;\n\t}\n\t///See kdhash_2f::bin_size\n\tstatic inline uint8_t bin_size(const float radians) {\n\t\tint64_t h = R_OUT*radians;\n\t\tif (h > R_IN) h=R_IN;\n\t\tif (h < 0) h=0;\n\t\tuint8_t sz=0;\n\t\tfor (;h;h>>=1) sz+=1;\n\t\treturn sz;\n\t}\n\t///See kdhash_2f::mask\n\tstatic inline uint64_t mask(const float radians) {\n\t\tuint64_t mask=-1;\n\t\treturn mask<<3*bin_size(radians);\n\t}\n\t\n\t///See kdhash_2f::mask\n\tstatic inline uint64_t mask(const float r0,const float r1,const float r2){\n\t\tint64_t h0=R_OUT>>bin_size(r0);\n\t\tint64_t h1=R_OUT>>bin_size(r1);\n\t\tint64_t h2=R_OUT>>bin_size(r2);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\tINTERLEAVE(h2)\n\t\treturn h2<<2|h1<<1|h0;\n\t}\n\t#undef INTERLEAVE\n\t#undef R_OUT\n\t#undef R_IN\n\t#undef DIAM\n};\n\nstruct kdhash_4f {\n\t#define DIAM 0xfffff\n\t#define R_IN (DIAM/2)\n\t#define R_OUT (R_IN+1)\n\t#define INTERLEAVE(X)\\\n\t\tif (X > DIAM) X=DIAM;\\\n\t\tif (X < 0) X=0;\\\n\t\tX = (X | X << 32) & 0xf800000007ff;\\\n\t\tX = (X | X << 16) & 0xf80007c0003f;\\\n\t\tX = (X | X << 8) & 0xc0380700c03807;\\\n\t\tX = (X | X << 4) & 0x843084308430843;\\\n\t\tX = (X | X << 2) & 0x909090909090909;\\\n\t\tX = (X | X << 1) & 0x1111111111111111;\n\n\t///See kdhash_2f::hash\n\tstatic inline uint64_t hash(const float x0,const float x1,const float x2,const float x3){\n\t\tint64_t h0=R_OUT*(x0+1.0);\n\t\tint64_t h1=R_OUT*(x1+1.0);\n\t\tint64_t h2=R_OUT*(x2+1.0);\n\t\tint64_t h3=R_OUT*(x3+1.0);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\tINTERLEAVE(h2)\n\t\tINTERLEAVE(h3)\n\t\treturn h3<<3|h2<<2|h1<<1|h0;\n\t}\n\t///See kdhash_2f::bin_size\n\tstatic inline uint8_t bin_size(const float radians) {\n\t\tint64_t h = R_OUT*radians;\n\t\tif (h > R_IN) h=R_IN;\n\t\tif (h < 0) h=0;\n\t\tuint8_t sz=0;\n\t\tfor (;h;h>>=1) sz+=1;\n\t\treturn sz;\n\t}\n\t///See kdhash_2f::mask\n\tstatic inline uint64_t mask(const float radians) {\n\t\tuint64_t mask=-1;\n\t\treturn mask<<4*bin_size(radians);\n\t}\n\t///See kdhash_2f::mask\n\tstatic inline uint64_t mask(const float r0,const float r1,const float r2,const float r3){\n\t\tint64_t h0=R_OUT>>bin_size(r0);\n\t\tint64_t h1=R_OUT>>bin_size(r1);\n\t\tint64_t h2=R_OUT>>bin_size(r2);\n\t\tint64_t h3=R_OUT>>bin_size(r3);\n\t\tINTERLEAVE(h0)\n\t\tINTERLEAVE(h1)\n\t\tINTERLEAVE(h2)\n\t\tINTERLEAVE(h3)\n\t\treturn h0<<3|h1<<2|h2<<1|h3;\n\t}\n\t#undef INTERLEAVE\n\t#undef R_OUT\n\t#undef R_IN\n\t#undef DIAM\n};\n#endif\n" }, { "path": "beast/stars.h", "content": "#ifndef STARS_H\n#define STARS_H\n\n#include \"config.h\"\n#include \"kdhash.h\"\n\n#include //assert()\n#include //INT_MAX\n//stl for days\n#include //sort, nth_element\n#include //sort, nth_element\n#include \n#include \n#include \n#include \n#include \n#include // std::next\n\nstruct star {\n\tfloat x;\n\tfloat y;\n\tfloat z;\n\tfloat flux;\n\t/**user defined id (ie hipparcos id, -1)*/\n\tint id;\n\tfloat px;\n\tfloat py;\n\tint unreliable;\n\t/** how many stars were inserted before this one? */\n\tint star_idx;\n\tfloat sigma_sq;\n\tsize_t hash_val;\n\t/**\n\t* @brief add star from catalog \n\t*\n\t* @param x ECI 'coming out of image'\n\t* @param y ECI \n\t* @param z ECI z\n\t* @param flux Pixel brightness\n\t* @param id User defined id\n\t*/\n\tstar(){};\n\tstar(const float x_, const float y_, const float z_, const float flux_, const int id_) {\n\t\tx=x_;\n\t\ty=y_;\n\t\tz=z_;\n\t\tflux=flux_;\n\t\tid=id_;\n\t\t\n\t\tpx=y/(x*PIXX_TANGENT);\n\t\tpy=z/(x*PIXY_TANGENT);\n\n\t\tunreliable=0;\n\t\tstar_idx=-1;\n\t\tsigma_sq=POS_VARIANCE;\n\t\thash_val=kdhash_3f::hash(x,y,z);\n\t}\n\t\n\t/**\n\t* @brief add star from image\n\t*\n\t* @param px Pixel x minus camera center\n\t* @param py Pixel y minus camera center\n\t* @param flux Pixel brightness\n\t* @param id User defined id\n\t*/\n\tstar(const float px_, const float py_, const float flux_, const int id_) {\n\t\tpx=px_;\n\t\tpy=py_;\n\t\tflux=flux_;\n\t\tid=id_;\n\n\t\tfloat j=(PIXX_TANGENT*px); /* j=(y/x) */\n\t\tfloat k=(PIXY_TANGENT*py); /* k=z/x */\n\t\tx=1./sqrt(j*j+k*k+1);\n\t\ty=j*x;\n\t\tz=k*x;\n\n\t\tunreliable=0;\n\t\tstar_idx=-1;\n\t\tsigma_sq=IMAGE_VARIANCE/flux;\n\t\thash_val=kdhash_3f::hash(x,y,z);\n\t}\n\n\t#define OP operator==\n\tbool OP(const star& s) const {return hash_val==s.hash_val;}\n\t#undef OP\n\t\n\t/**\n\t* @brief numerically stable method to calculate distance between stars\n\t* @param s Star\n\t* @return Angular seperation in arcsec\n\t*/\n\tfloat dist_arcsec(const star& s) const {\n\t\tfloat a=x*s.y - s.x*y;\n\t\tfloat b=x*s.z - s.x*z;\n\t\tfloat c=y*s.z - s.y*z;\n\t\treturn (3600*180.0/PI)*asin(sqrt(a*a+b*b+c*c));\n\t}\n\t/**\n\t* @brief Print debug info\n\t* @param s Label\n\t*/\n\tvoid DBG_(const char *s) {\n\t\tDBG_PRINT(\"%s\\t\",s);\n\t\tDBG_PRINT(\"x=%f \", x);\n\t\tDBG_PRINT(\"y=%f \", y);\n\t\tDBG_PRINT(\"z=%f \", z);\n\t\tDBG_PRINT(\"flux=%f \", flux);\n\t\tDBG_PRINT(\"star_idx=%d \", star_idx);\n\t\tDBG_PRINT(\"id=%d \", id);\n\t\tDBG_PRINT(\"unreliable=%d \", unreliable);\n\t\tDBG_PRINT(\"sigma_sq=%f \", sigma_sq);\n\t\tDBG_PRINT(\"px=%f \", px);\n\t\tDBG_PRINT(\"py=%f\\n\", py);\n\t}\n};\n\nbool star_gt_x(const star &s1, const star &s2) {return s1.x > s2.x;}\nbool star_gt_y(const star &s1, const star &s2) {return s1.y > s2.y;}\nbool star_gt_z(const star &s1, const star &s2) {return s1.z > s2.z;}\nbool star_gt_flux(const star &s1, const star &s2) {return s1.flux > s2.flux;}\nbool star_lt_x(const star &s1, const star &s2) {return s1.x < s2.x;}\nbool star_lt_y(const star &s1, const star &s2) {return s1.y < s2.y;}\nbool star_lt_z(const star &s1, const star &s2) {return s1.z < s2.z;}\nbool star_lt_flux(const star &s1, const star &s2) {return s1.flux < s2.flux;}\n\nstruct star_db {\nprivate:\n\tstd::unordered_map hash_map;\n\tstd::set hash_set;\n\tstd::vector star_idx_vector;\n\tstd::multimap flux_map;\n\tsize_t sz;\n\n\t/**\n\t * @brief Transcribe a portion of the db between first and last\n\t * \n\t * @param first an iterator to a map. key can be anything, but value must be a star hash \n\t * @param last copy up to but not including the element pointed to by this iterator\n\t */\n\ttemplate star_db* copy(T first,T last) {\n\t\tstar_db* s = new star_db;\n\t\tfor (;first!=last;first++) (*s)+=hash_map[*first];\n\t\treturn s;\n\t}\npublic:\n\t//TODO\n\tfloat max_variance;\n\tstar_db() {\n\t\tDBG_STAR_DB_COUNT++;\n\t\tDBG_PRINT(\"DBG_STAR_DB_COUNT++ %d\\n\",DBG_STAR_DB_COUNT);\n\t\tmax_variance=0.0;\n\t\tsz=0;\n\t}\n\t~star_db() {\n\t\tDBG_STAR_DB_COUNT--;\n\t\tDBG_PRINT(\"DBG_STAR_DB_COUNT-- %d\\n\",DBG_STAR_DB_COUNT);\n\t}\n\tsize_t size() {return sz;}\n\t///Philosophically inspired by python sets\n\t#define OP operator+=\n\tstar_db* OP(const star& s) { return *this+=&s;}\n\tstar_db* OP(const star* s) {\n\t\tif (count(s)==0) {\n\t\t\tif (max_variancesigma_sq) max_variance=s->sigma_sq;\n\t\t\tstar temp=s[0];\n\t\t\ttemp.star_idx=size();\n\t\t\thash_map.emplace(temp.hash_val,temp);\n\t\t\thash_set.insert(temp.hash_val);\n\t\t\tflux_map.emplace(temp.flux,temp.hash_val);\n\t\t\tstar_idx_vector.push_back(temp.hash_val);\n\t\t\tsz++;\n\t\t}\n\t\treturn this;\n\t}\n\t#undef OP\n\t//TODO - faster to use insert?\n\tstar_db* OP(star_db* s) {\n\t\tfor (size_t i=0;isize();i++) (*this)+=s->get_star(i);\n\t\treturn this;\n\t}\n\t#define OP operator-\n\tstar_db* OP(const star_db* s) {\n\t\tstar_db* r = new star_db;\n\t\tfor (auto it = star_idx_vector.cbegin(); it != star_idx_vector.cend(); ++it) {\n\t\t\tif (s->hash_map.count(*it)==0) *r+=hash_map.at(*it);\n\t\t}\n\t\treturn r;\n\t}\n\t#undef OP\n\t#define OP operator&\n\tstar_db* OP(const star_db* s) {\n\t\tstar_db* r = new star_db;\n\t\tfor (auto it = star_idx_vector.cbegin(); it != star_idx_vector.cend(); ++it) {\n\t\t\tif (s->hash_map.count(*it)>0) *r+=hash_map.at(*it);\n\t\t}\n\t\treturn r;\n\t}\n\t#undef OP\n\t/**\n\t * @brief returns stars in the order they were added\n\t * \n\t * @param idx the index of the star \n\t */\n\tstar* get_star_by_hash(const size_t hash) {return &(hash_map.at(hash));}\n\t/**\n\t * @brief returns stars in the order they were added\n\t * \n\t * @param idx the index of the star \n\t */\n\tstar* get_star(const int idx) {return size()>0?get_star_by_hash(star_idx_vector[idx]):NULL;}\n\t/**\n\t* @brief make a copy of the star db\n\t*/\n\tstar_db* copy() {return copy(star_idx_vector.cbegin(),star_idx_vector.cend());}\n\t/**\n\t* @brief make a copy of the n brightest elements in the star db\n\t*/\n\tstar_db* copy_n_brightest(const size_t n) {\n\t\t//return copy(flux_map.crbegin(),std::next(flux_map.crbegin(),std::min(n,size())));\n\t\tstar_db* s = new star_db;\n\t\tauto first=flux_map.crbegin();\n\t\tauto last=std::next(flux_map.crbegin(),std::min(n,size()));\n\t\tfor (;first!=last;first++) (*s)+=get_star_by_hash(first->second);\n\t\treturn s;\n\t}\n\t//std::array* n_brightest(T &hs,n) {\n\t//\t\n\t//}\n\t/**\n\t* @brief return stars in the bounding volume around the specified star\n\t* @param r minimum radius of the bounding volume (arcseconds).\n\t* \n\t*/\n\t//TODO: group together by hash_lb,ub, n_brightest_search (maybe return a list?) \n\ttemplate void search(T &hs, const float x,const float y,const float z, float r, const float min_flux) {\n\t\tr=r/3600.0;\n\t\tr=r*PI/180.0;\n\t\tr=2*fabs(sin(r/2.0));\n\t\tsize_t mask=kdhash_3f::mask(r);\n\t\tfor (int8_t dx=-1;dx<=1;dx++) for (int8_t dy=-1;dy<=1;dy++) for (int8_t dz=-1;dz<=1;dz++) {\n\t\t\tsize_t h=kdhash_3f::hash(x+dx*r,y+dy*r,z+dz*r);\n\t\t\tauto first = hash_set.lower_bound(h&mask);\n\t\t\tauto last = hash_set.upper_bound(h|(~mask));\n\t\t\tfor (;first!=last;first++) {\n\t\t\t\tstar *s=get_star_by_hash(*first);\n\t\t\t\tfloat dist_x=s->x-x;\n\t\t\t\tfloat dist_y=s->y-y;\n\t\t\t\tfloat dist_z=s->z-z;\n\t\t\t\tif (dist_x*dist_x+dist_y*dist_y+dist_z*dist_z<=r*r) {\n\t\t\t\t\tif (min_flux <= s->flux) hs.insert(*first);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\n\t/**\n\t* @brief Load stars from hip_main.dat\n\t*\n\t* @param catalog path to hip_main.dat\n\t* @param year Update star positions to the specified year\n\t*/\n\tvoid load_catalog(const char* catalog, const float year) {\n\t\tFILE *stream = fopen(catalog, \"r\");\n\t\tif (stream == NULL) exit(EXIT_FAILURE);\n\t\tmax_variance=POS_VARIANCE;\n\t\t\n\t\tssize_t read;\n\t\tchar *line = NULL;\n\t\tsize_t len = 0;\n\t\t\n\t\tchar* hip_record[78];\n\t\twhile ((read = getline(&line, &len, stream)) != -1){\n\t\t\t\tfloat yeardiff=year-1991.25;\n\t\t\t\thip_record[0]=strtok(line,\"|\");\n\t\t\t\tfor (size_t j=1;jhash_val);}\n\tsize_t count(star_db* s) {\n\t\tsize_t n=0;\n\t\tfor (size_t i=0;isize();i++) n+=count(s->get_star(i));\n\t\treturn n;\n\t}\n\t\n\n\tvoid DBG_(const char *s) {\n\t\tDBG_PRINT(\"%s\\n\",s);\n\t\tDBG_PRINT(\"star_db at %lu contains %lu elements\\n\",(size_t)this,size());\n\t\tDBG_PRINT(\"max_variance=%f\\n\",max_variance);\n\t\tfor (size_t i=0; iDBG_(\"star\");\n\t\t}\n\t}\n};\n\nstruct star_fov {\nprivate:\n\tint *mask;\n\tstar_db *stars;\n\tint *collision;\n\tint collision_size;\n\tfloat db_max_variance;\n\tfloat *s_px;\n\tfloat *s_py;\n\t/**\n\t* @brief Get the id of the best match to the specified coordinates \n\t* Used to resolve collisions where the coordinates falls into the region of overlap between two stars\n\t* Adds a bit of complexity in exchange for being able to break ties at\n\t* the subpixel level, which can sometimes make a difference \n\t* \n\t* @param id - the id from the image map\n\t* Any id <-1 is interpreted as an index in the collision buffer (starts at -2)\n\t*\n\t* @param px Pixel x minus camera center\n\t* @param py Pixel y minus camera center\n\t*\n\t* @return The id of whichever star is the best match to the coordinates in question \n\t*/\n\tint resolve_id(int id,const float px,const float py) {\n\t\tif (id>=-1) return id;\n\t\t//Any id <-1 is interpreted as an index in the collision buffer (starts at -2)\n\t\tid=-id;\n\t\tint id1=resolve_id(collision[id-2],px,py);\n\t\tint id2=resolve_id(collision[id-1],px,py);\n\t\treturn (get_score(id1,px,py)>get_score(id2,px,py))?id1:id2;\n\t}\npublic:\n\t\n\t/**\n\t* @brief TODO\n\t*\n\t* @param id\n\t* @param px\n\t* @param py\n\t*\n\t* @return \n\t*/\n\tfloat get_score(const int id,const float px,const float py) {\n\t\tfloat sigma_sq,maxdist_sq;\n\t\tsigma_sq=stars->max_variance+db_max_variance;\n\t\tmaxdist_sq=-sigma_sq*(log(sigma_sq)+MATCH_VALUE);\n\t\t\n\t\tfloat dx=px-s_px[id];\n\t\tfloat dy=py-s_py[id];\n\t\tif (dx<-0.5) dx+=1.0;/* use whichever corner of the pixel gives the best score */\n\t\tif (dy<-0.5) dy+=1.0;\n\t\treturn (maxdist_sq-(dx*dx+dy*dy))/(2*sigma_sq);\n\t}\n\t\t\t\n\t/**\n\t* @brief TODO\n\t*\n\t* @param id\n\t* @param px\n\t* @param py\n\t* @param sigma_sq\n\t* @param maxdist_sq\n\t*\n\t* @return \n\t*/\n\tfloat get_score(const int id,const float px,const float py,const float sigma_sq,const float maxdist_sq) {\n\t\tfloat dx=px-s_px[id];\n\t\tfloat dy=py-s_py[id];\n\t\tif (dx<-0.5) dx+=1.0;/* use whichever corner of the pixel gives the best score */\n\t\tif (dy<-0.5) dy+=1.0;\n\t\treturn (maxdist_sq-(dx*dx+dy*dy))/(2*sigma_sq);\n\t}\n\t\n\tint get_id(float px, float py) {\n\t\tint nx=(int)(px+IMG_X/2.0f);\n\t\tif (nx==-1) nx++;\n\t\telse if (nx==IMG_X) nx--;\n\t\n\t\tint ny=(int)(py+IMG_Y/2.0f);\n\t\tif (ny==-1) ny++;\n\t\telse if (ny==IMG_Y) ny--;\n\t\tint id=-1;\n\t\tif (nx>=0&&nx=0&&nysize()*sizeof(s_px[0]));\n\t\ts_py=(float*)malloc(stars->size()*sizeof(s_py[0]));\n\t\tmask=(int*)malloc(IMG_X*IMG_Y*sizeof(mask[0]));\n\t\tmemset(mask, -1, IMG_X*IMG_Y*sizeof(mask[0]));\n\t\t/* generate image mask */\n\t\tfor (size_t id=0;idsize();id++){\n\t\t\t/* assume the dimmest possible star since we dont know the brightness of the other image */\n\t\t\tfloat sigma_sq,maxdist_sq;\n\t\t\tsigma_sq=stars->max_variance+db_max_variance;\n\t\t\tmaxdist_sq=-sigma_sq*(log(sigma_sq)+MATCH_VALUE);\n\t\t\tfloat maxdist=sqrt(maxdist_sq);\n\t\t\ts_px[id]=stars->get_star(id)->px;\n\t\t\ts_py[id]=stars->get_star(id)->py;\n\t\t\t\n\t\t\tint xmin=s_px[id]-maxdist-1;\n\t\t\tint xmax=s_px[id]+maxdist+1;\n\t\t\tint ymin=s_py[id]-maxdist-1;\n\t\t\tint ymax=s_py[id]+maxdist+1;\n\t\t\t\n\t\t\tif(xmax>IMG_X/2) xmax=IMG_X/2;\n\t\t\tif(xmin<-IMG_X/2)xmin=-IMG_X/2;\n\t\t\tif(ymax>IMG_Y/2) ymax=IMG_Y/2;\n\t\t\tif(ymin<-IMG_Y/2)ymin=-IMG_Y/2;\n\t\t\tfor(int i=xmin;i0) {\n\t\t\t\t\t/* has this pixel already been assigned to a different star? */\n\t\t\t\t\tint id2=mask[x+y*IMG_X];\n\t\t\t\t\tif (id2!=-1){\n\t\t\t\t\t\tcollision_size+=2;\n\t\t\t\t\t\tmask[x+y*IMG_X]=-collision_size;\n\t\t\t\t\t\tcollision=(int*)realloc(collision,collision_size*sizeof(collision[0]));\n\t\t\t\t\t\tcollision[collision_size-2]=id;\n\t\t\t\t\t\tcollision[collision_size-1]=id2;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tmask[x+y*IMG_X]=id;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\t~star_fov() {\n\t\tDBG_STAR_FOV_COUNT--;\n\t\tDBG_PRINT(\"DBG_STAR_FOV_COUNT-- %d\\n\",DBG_STAR_FOV_COUNT);\n\t\tfree(s_px);\n\t\tfree(s_py);\n\t\tfree(mask);\n\t\tfree(collision);\n\t}\n};\n\nstruct star_query {\n\tstar *map;\n\tsize_t map_size;\n\tsize_t *kdresults;\nprivate:\n\tuint8_t kdsorted;\n\t\n\tsize_t kdresults_size;\n\tint8_t *kdmask;\n\tsize_t kdresults_maxsize;\n\tstar_db *stars;\n\t\t \n\t /** You may be looking at the most compact kd-tree in existence\n\t * It does not use pointers or indexes or leaf nodes or any extra memory\n\t * Instead the list is kdsorted in place using std::nth_element()\n\t * which is standard c++ implementation of quickselect.\n\t * \n\t * References:\n\t * Numerical Recipies (ISBN: 9780521884075)\n\t * https://en.wikipedia.org/wiki/Quickselect\n\t * https://stackoverflow.com/questions/17021379/balancing-kd-tree-which-approach-is-more-efficient\n\t */\n\t\n\t#define KDSORT_NEXT(A,B)\\\n\t\tint mid=(min+max)/2;\\\n\t\tif (min+1KDBUCKET_SIZE) B(min,mid);\\\n\t\t\telse std::sort(map+min, map+mid,star_gt_flux);\\\n\t\t\tif (max-(mid+1)>KDBUCKET_SIZE) B(mid+1,max);\\\n\t\t\telse std::sort(map+(mid+1), map+max,star_gt_flux);\\\n\t\t}\n\tvoid kdsort_x(const int min, const int max) {KDSORT_NEXT(star_lt_x,kdsort_y)}\n\tvoid kdsort_y(const int min, const int max) {KDSORT_NEXT(star_lt_y,kdsort_z)}\n\tvoid kdsort_z(const int min, const int max) {KDSORT_NEXT(star_lt_z,kdsort_x)}\n\t#undef KDSORT_NEXT\npublic:\n\t/**\n\t* @brief TODO\n\t* @param s\n\t*/\n\tstar_query(star_db *s) {\n\t\tDBG_STAR_QUERY_COUNT++;\n\t\tDBG_PRINT(\"DBG_STAR_QUERY_COUNT++ %d\\n\",DBG_STAR_QUERY_COUNT);\n\t\tstars=s;\n\t\tmap_size=stars->size();\n\t\tkdsorted=0;\n\t\tkdresults_size=map_size;\n\t\tkdresults_maxsize=INT_MAX;\n\n\t\tkdmask=(int8_t*)malloc((map_size+1)*sizeof(kdmask[0]));\n\t\tkdresults=(size_t*)malloc((map_size+1)*sizeof(kdresults[0]));\n\t\tmap=(star*)malloc(map_size*sizeof(map[0]));\n\t\tfor (size_t i=0;iget_star(i)[0];\n\t\t\tkdresults[i]=i;\n\t\t}\n\t\treset_kdmask();\n\t}\n\t~star_query() {\n\t\tDBG_STAR_QUERY_COUNT--;\n\t\tDBG_PRINT(\"DBG_STAR_QUERY_COUNT-- %d\\n\",DBG_STAR_QUERY_COUNT);\n\t\tfree(map);\n\t\tfree(kdresults);\n\t\tfree(kdmask);\n\t}\n\tuint8_t is_kdsorted() {return kdsorted;}\n\t/**\n\t* @brief kdsort the list in question.\n\t*/\n\tvoid kdsort() {\n\t\tif (kdsorted==0) {\n\t\t\tkdsort_x(0,map_size);\n\t\t\tkdsorted=1;\n\t\t}\n\t}\n\tvoid sort() {\n\t\tstd::sort(map, map+map_size,star_gt_flux);\n\t\tkdsorted=0;\n\t}\n\tsize_t r_size() {return kdresults_size;}\n\tint8_t get_kdmask(size_t i) {return kdmask[i];}\n\t/**\n\t* @brief Clears kdmask, but does not reset kdresults. Slow.\n\t*/\n\tvoid reset_kdmask() {\n\t\tmemset(kdmask,0,sizeof(kdmask[0])*stars->size());\n\t}\n\t\n\t/**\n\t* @brief Rewind kdresults, and set the corresponding kdmask to zero\n\t*/\n\tvoid clear_kdresults() {\n\t\twhile (kdresults_size>0) {\n\t\t\tkdresults_size--;\n\t\t\tkdmask[kdresults[kdresults_size]]=0;\n\t\t}\n\t}\n\n\t/**\n\t* @brief TODO\n\t*\n\t* @param idx\n\t* @param x\n\t* @param y\n\t* @param z\n\t* @param r\n\t* @param min_flux\n\t*/\n\tvoid kdcheck(const int idx, float x, float y, float z, const float r, const float min_flux){\n\t\tx-=map[idx].x;\n\t\ty-=map[idx].y;\n\t\tz-=map[idx].z;\n\t\tif (x-r <= 0 && 0 <= x+r &&\n\t\t\ty-r <= 0 && 0 <= y+r &&\n\t\t\tz-r <= 0 && 0 <= z+r &&\n\t\t\tmin_flux <= map[idx].flux &&\n\t\t\tkdmask[idx] == 0 &&\n\t\t\tx*x+y*y+z*z<=r*r) {\n\t\t\tkdmask[idx]=1;\n\t\t\t/* Insertion sort into list from brightest to dimmest.\n\t\t\t * \n\t\t\t * Note: Different sorting algorithms can result in different\n\t\t\t * stars being selected in the case where there are two candidates of\n\t\t\t * equal brightness for the last star. This has no effect on match quality\n\t\t\t */\n\t\t\t \n\t\t\tint n=kdresults_size++;\n\t\t\t\n\t\t\tfloat sm_flux=map[idx].flux;\n\t\t\tfor (;n>0&&sm_flux>map[kdresults[n-1]].flux;n--) kdresults[n]=kdresults[n-1];\n\t\t\tkdresults[n]=idx;\n\t\t\t//if we go over the maximum, bump the dimmest star from the results\n\t\t\tif (kdresults_size>kdresults_maxsize) {\n\t\t\t\tkdresults_size=kdresults_maxsize;\n\t\t\t\tkdmask[kdresults[kdresults_size]]=0;\n\t\t\t}\n\t\t}\n\t}\n\t\n\t/**\n\t* @brief\t\tsearch map for points within r pixels of x,y,z \n\t* @details\t\tput all results found into kdresults (sorted by brightness), mask via kdmask\n\t* @param x\t\tcos(deg2rad*RA)*cos(deg2rad*DEC)\n\t* @param y\t\tsin(deg2rad*RA)*cos(deg2rad*DEC)\n\t* @param z\t\tsin(deg2rad*DEC)\n\t* @param r\t\tsearch distance (pixels)\n\t* @param min_flux\tminimum pixel brightness\n\t* @param min\t\tstart of bounding box\n\t* @param max\t\tend of bounding box\n\t* @param dim\t\tstart dimension\n\t*/\n\tvoid kdsearch(const float x, const float y, const float z, const float r, const float min_flux, const int min, const int max, const int dim) {\n\t\tkdsort();\n\t\tfloat r_deg=r/3600.0;\n\t\tfloat r_rad=r_deg*PI/180.0;\n\t\tif (dim==0) kdsearch_x(x, y, z, 2*fabs(sin(r_rad/2.0)), min_flux,min,max);\n\t\telse if (dim==1) kdsearch_y(x, y, z, 2*fabs(sin(r_rad/2.0)), min_flux,min,max);\n\t\telse if (dim==2) kdsearch_z(x, y, z, 2*fabs(sin(r_rad/2.0)), min_flux,min,max);\n\t}\n\tvoid kdsearch(float x, float y, float z, float r, float min_flux) {\n\t\tkdsearch(x, y, z, r, min_flux,0,stars->size(),0);\n\t}\n\t//use seperate functions for each diminsion so that the compiler can unroll the recursion\n\t#define KDSEARCH_NEXT(A,B,C,D)\\\n\t\tint mid=(min+max)/2;\\\n\t\tif (minKDBUCKET_SIZE) D(x,y,z,r,min_flux,min,mid);\\\n\t\t\telse for (int i=min;iKDBUCKET_SIZE) D(x,y,z,r,min_flux,mid+1,max);\\\n\t\t\telse for (int i=mid+1;isize();i++) {\n\t\t\tint8_t lastmask=kdmask[i];\n\t\t\tkdsearch(map[i].x,map[i].y,map[i].z,DOUBLE_STAR_PX*PIXSCALE,THRESH_FACTOR*IMAGE_VARIANCE);\n\t\t\t//TODO: this seems to remove more stars than it should\n\t\t\t//if (kdresults_size>1||lastmask || map[i].flux0) {\n\t\t\tif (kdresults_size>1||lastmask || map[i].flux uniform_set;\n\t\tint kdresults_maxsize_old=kdresults_maxsize;\n\t\tkdresults_maxsize=min_stars_per_fov;\n\t\tfor (size_t i=0;isize();i++) if (kdmask[i]==0) {\n\t\t\tkdsearch(map[i].x,map[i].y,map[i].z,MINFOV/2,THRESH_FACTOR*IMAGE_VARIANCE);\n\t\t\tfor (size_t j=0;jsize();i++) kdmask[i]=1;\n\t\tstd::unordered_set::iterator it = uniform_set.begin();\n\t\tfor (size_t i=0; imax_variance=stars->max_variance;\n\t\tfor (size_t i=0;isize();i++){\n\t\t\tif (kdmask[i]==0) *rd+=map[i];\n\t\t}\n\t\treturn rd;\n\t}\n\t/**\n\t* @brief TODO\n\t* @return \n\t*/\n\tstar_db* from_kdresults() {\n\t\tstar_db* rd=new star_db;\n\t\trd->max_variance=stars->max_variance;\n\t\tfor (size_t i=0;i0){\n\t\t\tint i=0;\n\t\t\tDBG_PRINT(\"kdmask[%d]=%d\\n\",i,kdmask[i]);\n\t\t\tDBG_PRINT(\"kdresults[%d]=%lu\\n\",i,kdresults[i]);\n\t\t\tmap[kdresults[i]].DBG_(\"STARS\");\n\t\t\tDBG_PRINT(\".\\n.\\n\");\n\t\t\ti=kdresults_size-1;\n\t\t\tDBG_PRINT(\"kdmask[%d]=%d\\n\",i,kdmask[i]);\n\t\t\tDBG_PRINT(\"kdresults[%d]=%lu\\n\",i,kdresults[i]);\n\t\t\tmap[kdresults[i]].DBG_(\"STARS\");\n\t\t}\n\t}\n};\n#endif\n" }, { "path": "build_production.sh", "content": "#!/bin/sh\n[ \"$1\" == \"\" ] && exit\ncd beast\n./go&&\ncd ..&&\nmkdir startracker-production&&\ncp -r beast/ startracker-production/&&\ncp tests/$1/calibration.txt startracker-production&&\ncp tests/$1/median_image.png startracker-production&&\ncp hip_main.dat startracker-production&&\ncp startracker.py startracker-production/&&\ntouch startracker-production.tgz&&\nrm startracker-production.tgz &&\nrsync -av startracker-production/ root@192.168.100.213:~/startracker-production/ &&\nrm -rf startracker-production\n\n" }, { "path": "doc/gen_interleave.py", "content": "from __future__ import print_function\nimport random\n\n#This script is based on \n#https://stackoverflow.com/questions/1024754/how-to-compute-a-3d-morton-number-interleave-the-bits-of-3-ints\n\ndef prettyBinString(x,d=32,steps=4,sep=\".\",emptyChar=\"0\"):\n b = bin(x)[2:]\n zeros = d - len(b)\n\n\n if zeros <= 0: \n zeros = 0\n k = steps - (len(b) % steps)\n else:\n k = steps - (d % steps)\n\n s = \"\"\n #print(\"zeros\" , zeros)\n #print(\"k\" , k)\n for i in list(range(zeros)): \n #print(\"k:\",k)\n if(k%steps==0 and i!= 0):\n s+=sep\n s += emptyChar\n k+=1\n\n for i in list(range(len(b))):\n if( (k%steps==0 and i!=0 and zeros == 0) or (k%steps==0 and zeros != 0) ):\n s+=sep\n s += b[i]\n k+=1\n return s \n\ndef binStr(x): return prettyBinString(x,32,4,\" \",\"0\")\n\n\ndef computeBitMaskPatternAndCode(numberOfBits, numberOfEmptyBits):\n bitDistances=[ i*numberOfEmptyBits for i in list(range(numberOfBits)) ]\n print(\"Bit Distances: \" + str(bitDistances))\n bitDistancesB = [bin(dist)[2:] for dist in bitDistances]\n print(\"Bit Distances (binary): \" + str(bitDistancesB))\n moveBits=[]\n \n maxLength = len(max(bitDistancesB, key=len))\n abort = False\n for i in list(range(maxLength)):\n moveBits.append([])\n for idx,bits in enumerate(bitDistancesB):\n if not len(bits) - 1 < i:\n if(bits[len(bits)-i-1] == \"1\"):\n moveBits[i].append(idx)\n\n for i in list(range(len(moveBits))):\n print(\"Shifting bits by \" + str(2**i) + \"\\t for bits idx: \" + str(moveBits[i]))\n\n bitPositions = list(range(numberOfBits));\n print(\"BitPositions: \" + str(bitPositions))\n maskOld = (1 << numberOfBits) -1\n\n codeString = \"x &= \" + hex(maskOld) + \"\\n\"\n for idx in list(range(len(moveBits)-1, -1, -1)):\n if len(moveBits[idx]):\n shifted = 0\n for bitIdxToMove in moveBits[idx]:\n shifted |= 1<\n#include \n#include \n#include \n#include \n#include \n#include \n\n#define BUFFER_SIZE 1024\n#define MAX_STARS 50\n\n/* Coordinates of a star in both camera and ECI frames, and its weight */\ntypedef struct {\n double cam_x;\n double cam_y;\n double cam_z;\n double eci_x;\n double eci_y;\n double eci_z;\n double weight;\n} star_t;\n\n/* sendImageCommand:\n * This function establishes a TCP connection to a server, sends an image command,\n * and receives a response from the server.\n *\n * Input arguments:\n * - server_ip: IP address of the server.\n * - server_port: Port number of the server.\n * - command: Image command to send to the server.\n * - output_buffer: Starer to the output buffer where the response will be stored.\n * - max_output_size: Maximum size of the output buffer to prevent buffer overflow.\n *\n * Output arguments:\n * - output_buffer: The response from the server will be stored in this buffer.\n *\n * Return value:\n * - If the function succeeds, it returns the number of bytes received and stored in the output buffer.\n * - If an error occurs, it returns -1.\n */\nint sendImageCommand(const char* server_ip, int server_port, const char* command, char* output_buffer, int max_output_size) {\n int sockfd;\n struct sockaddr_in server_addr;\n\n // Create socket\n if ((sockfd = socket(AF_INET, SOCK_STREAM, 0)) == -1) {\n perror(\"Socket creation failed\");\n return -1;\n }\n\n // Fill server information\n server_addr.sin_family = AF_INET;\n server_addr.sin_port = htons(server_port);\n server_addr.sin_addr.s_addr = inet_addr(server_ip);\n\n // Connect to the server\n if (connect(sockfd, (struct sockaddr *)&server_addr, sizeof(server_addr)) != 0) {\n perror(\"Connection to server failed\");\n close(sockfd);\n return -1;\n }\n\n // Send the image command to the server\n if (send(sockfd, command, strlen(command), 0) == -1) {\n perror(\"Send failed\");\n close(sockfd);\n return -1;\n }\n\n // Receive response from the server\n int bytes_received = recv(sockfd, output_buffer, max_output_size - 1, 0);\n if (bytes_received == -1) {\n perror(\"Receive failed\");\n close(sockfd);\n return -1;\n }\n output_buffer[bytes_received] = '\\0'; // Null-terminate the received data\n\n // Close the socket\n close(sockfd);\n\n // Return the number of bytes received\n return bytes_received;\n}\n\n/* parse_ost_string:\n * Parses a openstartracker string into an array of star structures.\n *\n * Input arguments:\n * - ost_str: The openstartracker string to parse.\n * Example (2 stars): \"1.0,2.0,3.0,4.0,5.0,6.0,7.0 2.0,3.0,4.0,5.0,6.0,7.0,8.0\"\n * - max_num_stars: The maximum number of stars to parse.\n * If the openstartracker string contains more stars than max_num_stars, only the first max_num_stars will be parsed.\n * \n * Output arguments:\n * - stars: The parsed stars will be stored in this array.\n * The array must be pre-allocated by the caller.\n * The array must have at least max_num_stars elements.\n * If the openstartracker string contains fewer stars than max_num_stars, the remaining elements will be left unchanged.\n *\n * Return value:\n * - The number of stars successfully parsed from the openstartracker string.\n */\nint parse_ost_string(const char *ost_str, int max_num_stars, star_t *stars) {\n char *ptr = (char *)ost_str;\n int i = 0;\n for (; i < max_num_stars; i++) { \n // Parse the openstartracker string into a star structure\n int n = sscanf(ptr, \"%lf,%lf,%lf,%lf,%lf,%lf,%lf\", &stars[i].cam_x, &stars[i].cam_y, &stars[i].cam_z, &stars[i].eci_x, &stars[i].eci_y, &stars[i].eci_z, &stars[i].weight);\n if (n != 7) {\n break;\n }\n // Move the pointer to the next star\n ptr = strchr(ptr, ' ');\n if (ptr == NULL) {\n break;\n }\n // Skip the space character\n ptr++;\n }\n return i;\n}\n\nstar_t stars[MAX_STARS];\nint main() {\n const char *server_ip = \"127.0.0.1\"; // IP address of the server\n int server_port = 8010; // Port number the server is listening on\n int max_num_stars = MAX_STARS; // Maximum number of stars to parse\n\n // Send the image command to the server\n char output_buffer[BUFFER_SIZE];\n int bytes_received = sendImageCommand(server_ip, server_port, \"rgb.solve_image('science_cam_may8_0.05sec_gain40/samples/img0.png')\", output_buffer, BUFFER_SIZE);\n if (bytes_received == -1) {\n return 1;\n }\n\n // Parse the openstartracker string into an array of star structures\n int num_stars = parse_ost_string(output_buffer, max_num_stars, stars);\n if (num_stars == 0) {\n printf(\"No stars found\\n\");\n return 1;\n }\n\n // Print the stars\n for (int i = 0; i < num_stars; i++) {\n printf(\"Star %d: cam=(%f, %f, %f), eci=(%f, %f, %f), weight=%f\\n\", i, stars[i].cam_x, stars[i].cam_y, stars[i].cam_z, stars[i].eci_x, stars[i].eci_y, stars[i].eci_z, stars[i].weight);\n }\n\n return 0;\n}\n" }, { "path": "doc/viz_q.m", "content": "%Extract the X, Y and Z coordinates\n%M=vertcat(sqrt(.5)*[1 0 0;-1 0 0;0 1 0;0 -1 0;0 0 1;0 0 -1],sqrt(1/3)*[1 1 0;1 -1 0;-1 1 0;-1 -1 0;1 0 1;1 0 -1;-1 0 1;-1 0 -1;0 1 1;0 1 -1;0 -1 1;0 -1 -1],[.5 .5 .5;.5 .5 -.5;.5 -.5 .5;.5 -.5 -.5;-.5 .5 .5;-.5 .5 -.5;-.5 -.5 .5;-.5 -.5 -.5]);\nsz=100;\n[x,y,z]=sphere(sz);\nM=[x(:),y(:),z(:)];\nM=normr(horzcat(M,max(abs(M),[],2)));\n%trisurf(delaunay(M(:,1:3)),M(:,1),M(:,2),M(:,3))\n%surf(reshape(x(:),[sz+1,sz+1]),reshape(y(:),[sz+1,sz+1]),reshape(z(:),[sz+1,sz+1]))\nsurf(reshape(M(:,1),[sz+1,sz+1]),reshape(M(:,2),[sz+1,sz+1]),reshape(M(:,3),[sz+1,sz+1]))\npbaspect([1 1 1])\n" }, { "path": "setup.sh", "content": "#!/bin/bash\n\n# requires docker to be installed\n\n# build the Dockerfile to include the current director and have internet access\ndocker build --network=host -t startracker1 .\n\n# directory of this setup.sh script\nDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\n\n# Make an alias to start the docker environment\n# in interactive mode\n# in the proper directory\nalias dstart='docker run -it -v '$DIR'/:/home startracker1'\n" }, { "path": "starsToStruc.py", "content": "import ctypes\nimport socket \n\nfrom io import StringIO, BytesIO\n\nclass stars_to_struct():\n fields = [(\"stars\", ctypes.c_int),\n (\"declination\", ctypes.c_float),\n (\"right_ascension\", ctypes.c_float),\n (\"body_x\", ctypes.c_float*\"stars\"),\n (\"body_y\", ctypes.c_float*\"stars\"),\n (\"body_z\", ctypes.c_float*\"stars\"),\n (\"ref_x\", ctypes.c_float*\"stars\"),\n (\"ref_y\", ctypes.c_float*\"stars\"),\n (\"ref_z\", ctypes.c_float*\"stars\"),\n (\"uncertainty\", ctypes.c_float*\"stars\")]\n \n def _init_(self):\n super(stars_to_struct, self)._init_()\n self.stars = 0\n self.declination = 0.0\n self.right_ascension = 0.0\n self.body_x = [0.0]*self.stars\n self.body_y = [0.0]*self.stars\n self.body_z = [0.0]*self.stars\n self.ref_x = [0.0]*self.stars\n self.ref_y = [0.0]*self.stars\n self.ref_z = [0.0]*self.stars\n self.uncertainty = [0.0]*self.stars\n \n def load_image(self, stars, declination, right_ascension, body_x, body_y, body_z, ref_x, ref_y, ref_z, uncertainty): \n self.stars = stars\n self.declination = declination\n self.right_ascension = right_ascension\n self.body_x = body_x\n self.body_y = body_y\n self.body_z = body_z\n self.ref_x = ref_x\n self.ref_y = ref_y\n self.ref_z = ref_z\n self.uncertainty = uncertainty\n" }, { "path": "startracker.py", "content": "from __future__ import print_function\nfrom time import time\nimport sys, traceback\nimport socket,select, os, gc\nimport cv2\nimport numpy as np\nimport numpy.linalg as LA\nfrom io\timport StringIO,BytesIO\nimport fcntl\nimport beast\nfrom systemd import daemon\n\nP_MATCH_THRESH=0.99\nSIMULATE=0\nif 'WATCHDOG_USEC' not in os.environ:\n\tos.environ['WATCHDOG_USEC']=\"30000000\"\n\ndef trace(frame, event, arg):\n\tprint(\"%s, %s:%d\" % (event, frame.f_code.co_filename, frame.f_lineno), file=sys.stderr)\n\treturn trace\n\n#sys.settrace(trace)\n\nCONFIGFILE=sys.argv[1]\nYEAR=float(sys.argv[2])\n\n#set up server before we do anything else\nserver=socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\ntry: \n\tserver.bind(('127.0.0.1', 8010))\nexcept:\n\tprint(\"server socket already open: try terminal command: sudo kill $(sudo lsof -t -i:8010)\")\n\texit()\n\nserver.listen(5)\nserver.setblocking(0)\n\nprint (\"Loading config\" )\nprint (CONFIGFILE)\nbeast.load_config(CONFIGFILE)\nprint (\"Loading hip_main.dat\" )\nS_DB=beast.star_db()\nS_DB.load_catalog(\"hip_main.dat\",YEAR)\nprint (\"Filtering stars\" )\nSQ_RESULTS=beast.star_query(S_DB)\nSQ_RESULTS.kdmask_filter_catalog()\nSQ_RESULTS.kdmask_uniform_density(beast.cvar.REQUIRED_STARS)\nS_FILTERED=SQ_RESULTS.from_kdmask()\nprint (\"Generating DB\" )\nC_DB=beast.constellation_db(S_FILTERED,2+beast.cvar.DB_REDUNDANCY,0)\nprint (\"Ready\")\ndef a2q(A):\n\tq4=0.5*np.sqrt(1+np.trace(A));\n\t\n\tq1=1/(4*q4)*(A[1,2]-A[2,1]);\n\tq2=1/(4*q4)*(A[2,0]-A[0,2]);\n\tq3=1/(4*q4)*(A[0,1]-A[1,0]);\n\t\n\treturn np.array([q1,q2,q3,q4])\n\ndef q2a(q):\n\tq=q/LA.norm(q)\n\treturn np.array([[q[0]**2-q[1]**2-q[2]**2+q[3]**2,2*(q[0]*q[1]+q[2]*q[3]),2*(q[0]*q[2]-q[1]*q[3])],[2*(q[0]*q[1]-q[2]*q[3]),-q[0]**2+q[1]**2-q[2]**2+q[3]**2,2*(q[1]*q[2]+q[0]*q[3])],[2*(q[0]*q[2]+q[1]*q[3]),2*(q[1]*q[2]-q[0]*q[3]),-q[0]**2-q[1]**2+q[2]**2+q[3]**2]])\n\n#A=prev_body2ECI\n#B=curr_body2ECI\n#t1=prev_updatetime\n#t2=curr_updatetime\n#t3=present_time\ndef extrapolate_matrix(A,B,t1,t2,t3):\n\t# Calculate error angles between A and B via small angle approximation\n\t# of MRPs.\n\tR=np.dot(B,np.transpose(A))\n\tdq=a2q(R)\n\tdp=np.array([dq[0],dq[1],dq[2]])/(1 + dq[3])\n\tanglesAB=4*dp\n\n\t# Extrapolate to new error angles between B and C.\n\tanglesBC=anglesAB/(t2-t1)*(t3-t2)\n\n\t# Convert to a quaternion via small angle approximation, then get C.\n\t#C=np.dot(q2a(np.array([0.5*anglesBC[0],0.5*anglesBC[1],0.5*anglesBC[2],1])),B)\n\tC=q2a(np.array([0.5*anglesBC[0],0.5*anglesBC[1],0.5*anglesBC[2],1]))\n\n\treturn (C,(1000000.0)*anglesAB/(t2-t1))\n\n#Note: SWIG's policy is to garbage collect objects created with\n#constructors, but not objects created by returning from a function\n\ndef wahba(A, B, weight=[]):\n\t\"\"\"\n\tTakes in two matrices of points and finds the attitude matrix needed to\n\ttransform one onto the other\n\n\tInput:\n\t\tA: nx3 matrix - x,y,z in body frame\n\t\tB: nx3 matrix - x,y,z in eci\n\t\tNote: the \"n\" dimension of both matrices must match\n\tOutput:\n\t\tattitude_matrix: returned as a numpy matrix\n\t\"\"\"\n\tassert len(A) == len(B)\n\tif (len(weight) == 0):\n\t\tweight=np.array([1]*len(A))\n\t# dot is matrix multiplication for array\n\tH =\tnp.dot(np.transpose(A)*weight,B)\n\n\t#calculate attitude matrix\n\t#from http://malcolmdshuster.com/FC_MarkleyMortari_Girdwood_1999_AAS.pdf\n\tU, S, Vt = LA.svd(H)\n\tflip=LA.det(U)*LA.det(Vt)\n\n\t#S=np.diag([1,1,flip]); U=np.dot(U,S)\n\tU[:,2]*=flip\n\n\tbody2ECI = np.dot(U,Vt)\n\treturn body2ECI\n\ndef print_ori(body2ECI):\n\t#DEC=np.degrees(np.arcsin(body2ECI[2,0]))\n\t##rotation about the z axis (-180 to +180)\n\t#RA=np.degrees(np.arctan2(body2ECI[1,0],body2ECI[0,0]))\n\t##rotation about the camera axis (-180 to +180)\n\t#ORIENTATION=np.degrees(-np.arctan2(body2ECI[1,2],body2ECI[2,2]))\n\tDEC=np.degrees(np.arcsin(body2ECI[0,2]))\n\tRA=np.degrees(np.arctan2(body2ECI[0,1],body2ECI[0,0]))\n\tORIENTATION=np.degrees(-np.arctan2(body2ECI[1,2],body2ECI[2,2]))\n\tif ORIENTATION>180:\n\t\tORIENTATION=ORIENTATION-360\n\n\t#rotation about the y axis (-90 to +90)\n\tprint (\"DEC=\"+str(DEC), file=sys.stderr)\n\t#rotation about the z axis (-180 to +180)\n\tprint (\"RA=\"+str(RA), file=sys.stderr)\n\t#rotation about the camera axis (-180 to +180)\n\tprint (\"ORIENTATION=\"+str(ORIENTATION), file=sys.stderr)\n\nclass star_image:\n\tdef __init__(self, imagefile,median_image):\n\t\tb_conf=[time(),beast.cvar.PIXSCALE,beast.cvar.BASE_FLUX]\n\t\tself.img_stars = beast.star_db()\n\t\tself.img_data = []\n\t\tself.match=None\n\t\tself.db_stars=None\n\t\tself.match_from_lm=None\n\t\tself.db_stars_from_lm=None\n\t\t\n\t\t#Placeholders so that these don't get garbage collected by SWIG\n\t\tself.fov_db=None\n\t\tself.const_from_lm=None\n\t\t\n\t\t#TODO: improve memory efficiency\n\t\tif \"://\" in imagefile:\n\t\t\timport urllib\n\t\t\timg=cv2.imdecode(np.asarray(bytearray(urllib.urlopen(imagefile).read()), dtype=\"uint8\"), cv2.IMREAD_COLOR)\n\t\telse:\n\t\t\timg=cv2.imread(imagefile)\n\t\tif img is None:\n\t\t\tprint (\"Invalid image, using blank dummy image\", file=sys.stderr)\n\t\t\timg=median_image\n\t\t\t\n\t\timg=np.clip(img.astype(np.int16)-median_image,a_min=0,a_max=255).astype(np.uint8)\n\t\timg_grey = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n\t\t\n\t\t#removes areas of the image that don't meet our brightness threshold\n\t\tret,thresh = cv2.threshold(img_grey,beast.cvar.THRESH_FACTOR*beast.cvar.IMAGE_VARIANCE,255,cv2.THRESH_BINARY)\n\t\tcontours,heirachy = cv2.findContours(thresh,1,2);\n\n\t\tfor c in contours:\n\t\t\tM = cv2.moments(c)\n\t\t\tif M['m00']>0:\n\t\t\t\t#this is how the x and y position are defined by cv2\n\t\t\t\tcx = M['m10']/M['m00']\n\t\t\t\tcy = M['m01']/M['m00']\n\t\t\t\t#see https://alyssaq.github.io/2015/computing-the-axes-or-orientation-of-a-blob/\n\t\t\t\t#for how to convert these into eigenvectors/values\n\t\t\t\tu20 = M[\"m20\"]/M[\"m00\"] - cx**2\n\t\t\t\tu02 = M[\"m02\"]/M[\"m00\"] - cy**2\n\t\t\t\tu11 = M[\"m11\"]/M[\"m00\"] - cx*cy\n\t\t\t\t#the center pixel is used as the approximation of the brightest pixel\n\t\t\t\tself.img_stars+=beast.star(cx-beast.cvar.IMG_X/2.0,(cy-beast.cvar.IMG_Y/2.0),float(cv2.getRectSubPix(img_grey,(1,1),(cx,cy))[0,0]),-1)\n\t\t\t\tself.img_data.append(b_conf+[cx,cy,u20,u02,u11]+cv2.getRectSubPix(img,(1,1),(cx,cy))[0,0].tolist())\n\t\t\n\t\n\tdef match_near(self,x,y,z,r):\n\t\tSQ_RESULTS.kdsearch(x,y,z,r,beast.cvar.THRESH_FACTOR*beast.cvar.IMAGE_VARIANCE)\n\t\t#estimate density for constellation generation\n\t\tC_DB.results.kdsearch(x,y,z,r,beast.cvar.THRESH_FACTOR*beast.cvar.IMAGE_VARIANCE)\n\t\tfov_stars=SQ_RESULTS.from_kdresults()#REE\n\t\tself.fov_db = beast.constellation_db(fov_stars,C_DB.results.r_size(),1)\n\t\tC_DB.results.clear_kdresults()\n\t\tSQ_RESULTS.clear_kdresults()\n\t\t\n\t\timg_const=beast.constellation_db(self.img_stars,beast.cvar.MAX_FALSE_STARS+2,1)\n\t\tnear = beast.db_match(self.fov_db,img_const)\n\t\tif near.p_match>P_MATCH_THRESH:\n\t\t\tself.match = near\n\t\t\tself.db_stars = near.winner.from_match()\n\t\t\n\tdef match_lis(self):\n\t\t#for the first pass, we only want to use the brightest MAX_FALSE_STARS+REQUIRED_STARS\n\t\timg_stars_n_brightest = self.img_stars.copy_n_brightest(beast.cvar.MAX_FALSE_STARS+beast.cvar.REQUIRED_STARS)\n\t\timg_const_n_brightest = beast.constellation_db(img_stars_n_brightest,beast.cvar.MAX_FALSE_STARS+2,1)\n\t\tlis=beast.db_match(C_DB,img_const_n_brightest)\n\t\t#TODO: uncomment once p_match is fixed\n\t\t#if lis.p_match>P_MATCH_THRESH:\n\t\tif lis.p_match>P_MATCH_THRESH and lis.winner.size()>=beast.cvar.REQUIRED_STARS:\n\t\t\tx=lis.winner.R11\n\t\t\ty=lis.winner.R21\n\t\t\tz=lis.winner.R31\n\t\t\tself.match_near(x,y,z,beast.cvar.MAXFOV/2)\n\t\t\t#self.match = lis\n\t\t\t#self.db_stars = lis.winner.from_match()\n\t\t\t\n\tdef match_rel(self,last_match):\n\t\t#make copy of stars from lastmatch\n\t\timg_stars_from_lm=last_match.img_stars.copy()\n\t\tw=last_match.match.winner\n\t\t#convert the stars to ECI\n\t\tfor i in range(img_stars_from_lm.size()):\n\t\t\ts=img_stars_from_lm.get_star(i)\n\t\t\tx=s.x*w.R11+s.y*w.R12+s.z*w.R13\n\t\t\ty=s.x*w.R21+s.y*w.R22+s.z*w.R23\n\t\t\tz=s.x*w.R31+s.y*w.R32+s.z*w.R33\n\t\t\ts.x=x\n\t\t\ts.y=y\n\t\t\ts.z=z\n\t\t#create constellation from last match\n\t\tself.const_from_lm=beast.constellation_db(img_stars_from_lm,beast.cvar.MAX_FALSE_STARS+2,1)\n\t\t#match between last and current\n\t\timg_const=beast.constellation_db(self.img_stars,beast.cvar.MAX_FALSE_STARS+2,1)\n\t\trel=beast.db_match(self.const_from_lm,img_const)\n\t\tif rel.p_match>P_MATCH_THRESH:\n\t\t\tself.match_from_lm = rel\n\t\t\tself.db_stars_from_lm = rel.winner.from_match()\n\t\t\t\t\n\tdef print_match(self,bodyCorrection=None,angrate_string=\"\"):\n\t\tif bodyCorrection is None:\n\t\t\tbodyCorrection=np.eye(3)\n\t\tif self.match is not None:\n\t\t\tself.match.winner.print_ori()\n\t\tdb=self.db_stars\n\t\tim=self.img_stars\n\t\tif db is None:\n\t\t\tif self.db_stars_from_lm is None:\n\t\t\t\t#neither relative nor absolute matching could be used\n\t\t\t\tprint(\"\")\n\t\t\t\treturn\n\t\t\telse:\n\t\t\t\tdb=self.db_stars_from_lm\n\t\tassert(db.size()==im.size())\n\t\tstar_out=[]\n\t\tfor i in range(db.size()):\n\t\t\ts_im=im.get_star(i)\n\t\t\ts_db=db.get_star(i)\n\t\t\tif (s_db.id>=0):\n\t\t\t\tweight=1.0/(s_db.sigma_sq+s_im.sigma_sq)\n\t\t\t\ttemp=np.dot(bodyCorrection, np.array([[s_im.x],[s_im.y],[s_im.z]]))\n\t\t\t\tstar_out.append(str(temp[0,0])+','+str(temp[1,0])+','+str(temp[2,0])+','+str(s_db.x)+','+str(s_db.y)+','+str(s_db.z)+','+str(weight))\n\t\tprint (\"stars\",len(star_out), file=sys.stderr)\n\t\tprint (\"ang_rate: \"+angrate_string, file=sys.stderr)\n\t\tprint (\" \".join(star_out)+\" \"+angrate_string)\n\nNONSTARS={}\nNONSTAR_NEXT_ID=0\nNONSTAR_DATAFILENAME=\"/dev/null\"\n#NONSTAR_DATAFILENAME=\"data\"+str(time())+\".txt\"\nNONSTAR_DATAFILE=open(NONSTAR_DATAFILENAME,\"w\")\nclass nonstar:\n\tdef __init__(self,current_image,i,source):\n\t\tglobal NONSTARS,NONSTAR_NEXT_ID,NONSTAR_DATAFILENAME,NONSTAR_DATAFILE\n\t\tself.id=NONSTAR_NEXT_ID\n\t\tNONSTARS[self.id]=self\n\t\tcurrent_image.img_stars.get_star(i).id=self.id\n\t\tNONSTAR_NEXT_ID+=1\n\t\tself.data=[]\n\t\tself.add_data(current_image,i,source)\n\t\t\n\tdef add_data(self,current_image,i,source):\n\t\ts_im=current_image.img_stars.get_star(i)\n\t\ts_db_x=0.0\n\t\ts_db_y=0.0\n\t\ts_db_z=0.0\n\t\tw=None\n\t\tif (current_image.match != None and current_image.match.p_match>P_MATCH_THRESH):\n\t\t\tw=current_image.match.winner\n\t\telif (current_image.match != None and current_image.match.p_match>P_MATCH_THRESH):\n\t\t\tw=current_image.match_from_lm.winner\n\t\tif w != None:\n\t\t\t#convert the stars to ECI\n\t\t\ts_db_x=s_im.x*w.R11+s_im.y*w.R12+s_im.z*w.R13\n\t\t\ts_db_y=s_im.x*w.R21+s_im.y*w.R22+s_im.z*w.R23\n\t\t\ts_db_z=s_im.x*w.R31+s_im.y*w.R32+s_im.z*w.R33\n\t\tself.data.append([source,s_im.x,s_im.y,s_im.z,s_db_x,s_db_y,s_db_z]+current_image.img_data[i])\n\tdef write_data(self,fd):\n\t\tif sys.version_info[0]>2:\n\t\t\tos.write(fd,bytes(str(self.id)+\" \" +str(len(self.data))+\"\\n\",encoding='UTF-8'))\n\t\telse:\n\t\t\tos.write(fd,str(self.id)+\" \" +str(len(self.data))+\"\\n\")\n\t\tfor i in self.data:\n\t\t\ts=[str(j) for j in i]\n\t\tif sys.version_info[0]>2:\n\t\t\tos.write(fd,bytes(\" \".join(s)+\"\\n\",encoding='UTF-8'))\n\t\telse:\n\t\t\tos.write(fd,\" \".join(s)+\"\\n\")\n\tdef __del__(self):\n\t\tself.write_data(NONSTAR_DATAFILE.fileno())\n\ndef flush_nonstars():\n\tglobal NONSTARS,NONSTAR_NEXT_ID,NONSTAR_DATAFILENAME,NONSTAR_DATAFILE\n\tNONSTARS={}\n\tNONSTAR_NEXT_ID=0\n\tgc.collect()\n\tNONSTAR_DATAFILE.close()\n\tNONSTAR_DATAFILENAME=\"data\"+str(time())+\".txt\"\n\tNONSTAR_DATAFILE=open(NONSTAR_DATAFILENAME,\"w\")\n\t\ndef update_nonstars(current_image,source):\n\tglobal NONSTARS,NONSTAR_NEXT_ID\n\tnonstars_next={}\n\tim=current_image.img_stars\n\tdb=current_image.db_stars\n\tdb_lm=current_image.db_stars_from_lm\n\tif (db!=None):\n\t\tassert(db.size()==im.size())\n\tif (db_lm!=None):\n\t\tassert(db_lm.size()==im.size())\n\t\tfor i in range(im.size()):\n\t\t\tim.get_star(i).id=db_lm.get_star(i).id\n\tfor i in range(im.size()):\n\t\ts_im=im.get_star(i)\n\t\t#is this a star? if so remove from nonstars\n\t\tif (db != None and db.get_star(i).id>=0):\n\t\t\tif (s_im.id in NONSTARS):\n\t\t\t\tdel NONSTARS[s_im.id]\n\t\t\ts_im.id=-1\n\t\t#if it's already there, add the latest mesurement\n\t\telif (s_im.id in NONSTARS):\n\t\t\tNONSTARS[s_im.id].add_data(current_image,i,source)\n\t\t\tnonstars_next[s_im.id]=NONSTARS[s_im.id]\n\t\t#otherwise add a new nonstar\n\t\telse:\n\t\t\tns=nonstar(current_image,i,source)\n\t\t\tnonstars_next[ns.id]=ns\n\tNONSTARS=nonstars_next\n\t\n\t#wrap around to prevent integer overflow\n\tif (NONSTAR_NEXT_ID>2**30):\n\t\tflush_nonstars()\n\ndef winner_attitude(w):\n\t#w=self.last_match.match.winner\n\teci2body=np.array([[1,0,0],[0,1,0],[0,0,1]],dtype=float)\n\teci2body[0,0]=w.R11\n\teci2body[0,1]=w.R12\n\teci2body[0,2]=w.R13\n\teci2body[1,0]=w.R21\n\teci2body[1,1]=w.R22\n\teci2body[1,2]=w.R23\n\teci2body[2,0]=w.R31\n\teci2body[2,1]=w.R32\n\teci2body[2,2]=w.R33\n\treturn np.transpose(eci2body)\n\nclass star_camera:\n\tdef __init__(self, median_file,source=\"RGB\"):\n\t\tself.source=source\n\t\tself.current_image=None\n\t\tself.last_match=None\n\t\tself.median_image=cv2.imread(median_file)\n\t\n\tdef solve_image(self,imagefile,lis=1,quiet=0):\n\t\tstarttime=time()\n\t\tif (SIMULATE==1 and quiet==0):\n\t\t\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\t\ts.connect((\"jeb\",7011))\n\t\t\tdata = s.recv(2048)\n\t\t\ts.close()\n\t\tprint(\"Time1: \"+str(time() - starttime), file=sys.stderr)\n\t\tself.current_image=star_image(imagefile,self.median_image)\n\t\tprint(\"Time2: \"+str(time() - starttime), file=sys.stderr)\n\t\tif (lis==1):\n\t\t\tself.current_image.match_lis()\n\t\tprint(\"Time3: \"+str(time() - starttime), file=sys.stderr)\n\t\tif self.last_match is not None:\n\t\t\tself.current_image.match_rel(self.last_match)\n\t\tif (quiet==0):\n\t\t\tif (SIMULATE==1): \n\t\t\t\tprint (data.rstrip(\"\\n\").rstrip(\"\\r\"))\n\t\t\telse:\n\t\t\t\tself.current_image.print_match()\n\t\t\tprint(\"Time4: \"+str(time() - starttime), file=sys.stderr)\n\t\t\t\n\t\tupdate_nonstars(self.current_image,self.source)\n\t\tprint(\"Time5: \"+str(time() - starttime), file=sys.stderr)\n\t\tif self.current_image.match is not None:\n\t\t\tself.last_match=self.current_image\n\t\telse:\n\t\t\tself.last_match=None\n\t\tprint(\"Time6: \"+str(time() - starttime), file=sys.stderr)\n\t\t\n\tdef extrapolate_image(self,imagefile1,imagefile2,time1,time2):\n\t\t#self.solve_image(imagefile2,lis=1,quiet=0)\n\t\tself.solve_image(imagefile1,lis=1,quiet=1)\n\t\tprint(1, file=sys.stderr)\n\t\tif (self.last_match is None):\n\t\t\tprint(2, file=sys.stderr)\n\t\t\tprint (\"\")\n\t\t\treturn\n\t\ta1=winner_attitude(self.last_match.match.winner)\n\t\tself.solve_image(imagefile2,lis=1,quiet=1)\n\t\tprint(3, file=sys.stderr)\n\t\tif (self.last_match is None):\n\t\t\tprint(4, file=sys.stderr)\n\t\t\tprint (\"\")\n\t\t\treturn\n\t\ta2=winner_attitude(self.last_match.match.winner)\n\t\tprint(a1,a2,LA.svd(a1)[1],LA.svd(a1)[1], file=sys.stderr)\n\t\ta,angrate=extrapolate_matrix(a1,a2,time1,time2,time()*1e6)\n\t\tprint(a,LA.svd(a)[1], file=sys.stderr)\n\t\t\n\t\tself.last_match.print_match(a,\",\".join([str(i) for i in angrate.tolist()]))\n\t\t\n#dummy for now\n#TODO: add science data from IR cam\nclass science_camera:\n\tdef __init__(self, median_file,source=\"IR\"):\n\t\tself.source=source\n\t\tself.current_image=None\n\t\tself.last_match=None\n\t\tself.median_image=cv2.imread(median_file)\n\tdef solve_image(self,imagefile):\n\t\tif sys.version_info[0]>2:\n\t\t\tos.write(1,bytes(os.path.abspath(NONSTAR_DATAFILENAME),encoding='UTF-8'))\n\t\telse:\n\t\t\tos.write(1,os.path.abspath(NONSTAR_DATAFILENAME))\n\nrgb=star_camera(sys.argv[3])\nir=science_camera(sys.argv[3])\n\nCONNECTIONS = {}\nclass connection:\n\t\"\"\"Tracks activity on a file descriptor and allows TCP read/writes\"\"\"\n\tdef __init__(self, conn, epoll):\n\t\t\"\"\"\n\t\tCreate connection to track file descriptor activity\n\t\t@note: Adds C{fd . self} to C{CONNECTIONS}\n\t\t@param conn: Any file object with the fileno() method\n\t\t@param epoll: File descriptor edge polling object\n\t\t\"\"\"\n\t\tself.conn=conn\n\t\tself.fd = self.conn.fileno()\n\t\tepoll.register(self.fd, select.EPOLLIN)\n\t\tself.epoll = epoll\n\t\tCONNECTIONS[self.fd] = self\n\n\tdef read(self):\n\t\t\"\"\"\n\t\tComplete non-blocking read on file descriptor\n\t\tof an arbitrary amount of data\n\t\t@return: Entire read string\n\t\t@rtype: C{string}\n\t\t\"\"\"\n\t\t# need nonblocking for read\n\t\tfl = fcntl.fcntl(self.fd, fcntl.F_GETFL)\n\t\tfcntl.fcntl(self.fd, fcntl.F_SETFL, fl | os.O_NONBLOCK)\n\t\tdata = b''\n\t\ttry:\n\t\t\twhile True:\n\t\t\t\tlastlen=len(data)\n\t\t\t\tdata += os.read(self.fd, 1024)\n\t\t\t\tif len(data)==lastlen:\n\t\t\t\t\tbreak\n\t\texcept OSError as e:\n\t\t\t# error 11 means we have no more data to read\n\t\t\tif e.errno == 11:\n\t\t\t\tpass\n\t\t\telif e.errno == 104:\n\t\t\t\tprint(\"WARNING: ABNORMAL DISCONNECT\", file=sys.stderr)\n\t\t\telse:\n\t\t\t\traise\n\t\treturn data\n\n\tdef write(self, data):\n\t\t\"\"\"\n\t\tBlocking read on file descriptor\n\t\t@param data: ASCII data to write\n\t\t@type data: C{string}\n\t\t\"\"\"\n\t\tif len(data) == 0: return\n\t\tif self.fd==0: #stdin\n\t\t\tif sys.version_info[0]>2:\n\t\t\t\tos.write(1, bytes(data,encoding='UTF-8'))\n\t\t\telse:\n\t\t\t\tos.write(1, data)\n\t\t\treturn\n\t\t# need blocking IO for writing\n\t\tfl = fcntl.fcntl(self.fd, fcntl.F_GETFL)\n\t\tfcntl.fcntl(self.fd, fcntl.F_SETFL, fl & ~os.O_NONBLOCK)\n\t\tif sys.version_info[0]>2:\n\t\t\tos.write(self.fd, bytes(data,encoding='UTF-8'))\n\t\telse:\n\t\t\tos.write(self.fd, data)\n\n\tdef close(self):\n\t\t\"\"\"Close connection with file descriptor\"\"\"\n\t\tself.epoll.unregister(self.fd)\n\t\tself.conn.close()\n\t\tif CONNECTIONS[self.fd]:\n\t\t\tdel CONNECTIONS[self.fd]\n\nepoll = select.epoll()\nepoll.register(server.fileno(), select.EPOLLIN)\n\ntry:\n\tconnection(sys.stdin,epoll)\nexcept IOError:\n\tpass\n\ndaemon.notify(\"WATCHDOG=1\")\nlastPing = time()\nwhile True:\n\t#systemd watchdog\n\tevents = epoll.poll(float(os.environ['WATCHDOG_USEC'])/2.0e6 - (time() - lastPing))\n\tif len(events) == 0 or time() >= (lastPing + float(os.environ['WATCHDOG_USEC'])/2.0e6):\n\t\tdaemon.notify(\"WATCHDOG=1\")\n\t\tlastPing = time()\n\t#events = epoll.poll()\n\tfor fd, event_type in events:\n\t\t# Activity on the master socket means a new connection.\n\t\tif fd == server.fileno():\n\t\t\tconn, addr = server.accept()\n\t\t\tconnection(conn, epoll)\n\t\telif fd in CONNECTIONS:\n\t\t\tw = CONNECTIONS[fd]\n\t\t\tdata = w.read()\n\t\t\tprint(data.decode(encoding='UTF-8'), file=sys.stderr)\n\t\t\tif len(data) > 0:\n\t\t\t\tif sys.version_info[0]>2:\n\t\t\t\t\tstdout_redir = StringIO()\n\t\t\t\telse:\n\t\t\t\t\tstdout_redir = BytesIO()\n\t\t\t\tstdout_old = sys.stdout\n\t\t\t\tsys.stdout = stdout_redir\n\t\t\t\ttry:\n\t\t\t\t\texec(data)\n\t\t\t\texcept SystemExit:\n\t\t\t\t\tw.close()\n\t\t\t\t\traise\n\t\t\t\texcept:\n\t\t\t\t\ttraceback.print_exc(file=sys.stdout)\n\t\t\t\tsys.stdout = stdout_old\n\t\t\t\tdata_out = stdout_redir.getvalue()\n\t\t\t\tprint(data_out, file=sys.stderr)\n\t\t\t\tw.write(data_out)\n\t\t\telse:\n\t\t\t\tw.close()\n\n" }, { "path": "tests/Makefile", "content": "CC = g++\n\nall: $(OBJ)\n\t$(CC) -std=c++11 -Wall -Ofast -g -pg test.c -o test -lm\n\n" }, { "path": "tests/calibrate.py", "content": "from __future__ import print_function\nfrom os import listdir,system,environ\nfrom os.path import isfile, join\nimport cv2\nimport numpy as np\nimport math\nfrom scipy.stats import poisson\nimport sys\nfrom astropy.io import fits\nfrom astropy import wcs\nfrom scipy import spatial\n\n## Environment variables:\ntry: EXPOSURE_TIME = float(environ['EXPOSURE_TIME'])\nexcept KeyError: EXPOSURE_TIME = 0.05 # s\ntry: APERTURE = float(environ['APERTURE'])\nexcept KeyError: APERTURE = 60.7 # mm\ntry: DOUBLE_STAR_PX = float(environ['DOUBLE_STAR_PX'])\nexcept KeyError: DOUBLE_STAR_PX = 3.5 #pixels of seperation needed to distinguish stars from each other\ntry: POS_ERR_SIGMA = float(environ['POS_ERR_SIGMA'])\nexcept KeyError: POS_ERR_SIGMA = 2 #Check all constellations which fall inside these bounds\n### Note: Increasing this can actualy reduce the probability of finding a match\n### as the true match has to stand out against a larger crowd \n\n\n### NOTE: all of the following options multiply runtime by (N+2)^2\ntry: MAX_FALSE_STARS = int(environ['MAX_FALSE_STARS'])\nexcept KeyError: MAX_FALSE_STARS = 2 #maximum number of objects that can be brighter than the two brightest stars \ntry: DB_REDUNDANCY = int(environ['DB_REDUNDANCY'])\nexcept KeyError: DB_REDUNDANCY = 1 #of the brightest DB_REDUNDANCY+2 stars, we need at least 2\ntry: REQUIRED_STARS = int(environ['REQUIRED_STARS'])\nexcept KeyError: REQUIRED_STARS = 5 #How many stars should we try to match?\n### For ultrawide fov this may be set to 3 for faster matching\n### For ultranarrow fov, it may be necessary to set this to 5 (Also send me an email and we'll talk)\n### TODO: figure out how big \"ultrawide\" and \"ultranarrow\" are\n\ndef angles2xyz(ra,dec):\n\tx=np.cos(np.radians(ra))*np.cos(np.radians(dec))\n\ty=np.sin(np.radians(ra))*np.cos(np.radians(dec))\n\tz=np.sin(np.radians(dec))\n\treturn list((x,y,z))\n\n#load our star catalog, converting from id,ra,dec to x,y,z,id\ndef getstardb(year=1991.25,filename=\"hip_main.dat\"):\n\tyeardiff=year-1991.25\n\tstardb={}\n\tstarfile = open(filename)\n\tfor line in starfile.readlines():\n\t\tfields=line.split('|')\n\t\ttry:\n\t\t\tHIP_ID=int(fields[1]);\n\t\t\tMAG=float(fields[5]);\n\t\t\tDEC=yeardiff*float(fields[13])/3600000.0 + float(fields[9]);\n\t\t\tcosdec=np.cos(np.pi*DEC/180.0);\n\t\t\tRA=yeardiff*float(fields[12])/(cosdec*3600000.0) + float(fields[8]);\n\t\t\tX=np.cos(np.pi*RA/180.0)*cosdec;\n\t\t\tY=np.sin(np.pi*RA/180.0)*cosdec;\n\t\t\tZ=np.sin(np.pi*DEC/180.0);\n\t\texcept ValueError:\n\t\t\tcontinue\n\t\ttry:\n\t\t\tf6=int(fields[6])\n\t\texcept ValueError:\n\t\t\tf6=0\n\t\tif (int(fields[29])==0 or int(fields[29])==1) and f6!=3:\n\t\t\tUNRELIABLE=0\n\t\telse:\n\t\t\tUNRELIABLE=1\n\t\tstardb[HIP_ID]=[HIP_ID,MAG,DEC,RA,X,Y,Z,UNRELIABLE]\n\treturn stardb\n\n\ndef basename(filename):\n\tif \".\" in filename:\n\t\tfilename=\".\".join(filename.split(\".\")[0:-1])\n\treturn filename\n\n#only do this part if we were run as a python script\nif __name__ == '__main__':\n\tsamplepath=sys.argv[1]+\"/samples\"\n\timage_names = [ f for f in listdir(samplepath) if isfile(join(samplepath,f)) ]\n\tnum_images=len(image_names)\n\t#NOTE: if you get NoneType error, delete non-png files\n\timages = np.asarray([cv2.imread( join(samplepath,image_names[n]) ).astype(float) for n in range(0, num_images)])\n\tmedian_image=np.median(images,axis=0)\n\tcv2.imwrite(sys.argv[1]+\"/median_image.png\",median_image)\n\tsystem(\"md5sum \"+samplepath+\"/* >\"+sys.argv[1]+\"/checksum.txt\")\n\tif system(\"diff -q \"+sys.argv[1]+\"/checksum.txt \"+sys.argv[1]+\"/calibration_data/checksum.txt\")!=0:\n\t\tprint (\"Clearing old calibration data:\")\n\t\tsystem(\"rm -rfv \"+sys.argv[1]+\"/calibration_data/* \")\n\t\n\tsystem(\"mv \"+sys.argv[1]+\"/checksum.txt \"+sys.argv[1]+\"/calibration_data/checksum.txt\")\n\t\t\n\tstardb=getstardb()\n\t\n\tastrometry_results={}\n\t#filter the background image for astrometry - more important for starfield generator\n\tfor n in range(0, num_images):\n\t\timages[n]-=median_image\n\t\timage_name=sys.argv[1]+\"/calibration_data/\"+basename(image_names[n])+\".png\"\n\t\timg=np.clip(images[n],a_min=0,a_max=255).astype(np.uint8)\n\t\tcv2.imwrite(image_name,img)\n\t\tsolve_cmd=\"solve-field --skip-solved --cpulimit 60 \"+image_name\n\t\t#solve_cmd=\"solve-field --skip-solved --cpulimit 60 -v \"+image_name #verbose\n\t\tprint (solve_cmd)\n\t\tsystem(solve_cmd)\n\t\tif isfile(basename(image_name)+'.wcs'):\n\t\t\tprint ('wcsinfo '+basename(image_name)+'.wcs | tr [:lower:] [:upper:] | tr \" \" \"=\" | grep \"=[0-9.-]*$\" > '+basename(image_name)+'.solved')\n\t\t\tsystem('wcsinfo '+basename(image_name)+'.wcs | tr [:lower:] [:upper:] | tr \" \" \"=\" | grep \"=[0-9.-]*$\" > '+basename(image_name)+'.solved')\n\t\t\thdulist=fits.open(basename(image_name)+\".corr\")\n\t\t\tastrometry_results[image_names[n]]=np.array([[i['flux'],i['field_x'],i['field_y'],i['index_x'],i['index_y']]+angles2xyz(i['index_ra'],i['index_dec']) for i in hdulist[1].data])\n\t\t\n\t\n\t#Use only values below the median for variance calculation.\n\t#This is equivalent to calculating variance after having filtered out\n\t#stars and background light\n\tTHRESH_FACTOR=5\n\tIMAGE_VARIANCE=np.ma.average(images**2,weights=images<0)\n\t\n\tbestimage=\"\"\n\tmaxstars=0\n\t#for stars over 5*IMAGE_VARIANCE, find the corresponding star in the db\n\tsd = np.array(list(stardb.values()), dtype = object)\t# had to explicitly convert to list for python3\n\t\n\tstar_kd = spatial.cKDTree(sd[:,4:7])\n\tfor i in astrometry_results:\n\t\tastrometry_results[i]=astrometry_results[i][astrometry_results[i][:,0]>IMAGE_VARIANCE*THRESH_FACTOR]\n\t\tastrometry_results[i]=np.hstack((sd[star_kd.query(astrometry_results[i][:,5:8])[1]],astrometry_results[i]))\n\t\tif len(astrometry_results[i])>maxstars:\n\t\t\tbestimage=i\n\t\t\tmaxstars=len(astrometry_results[i])\n\tastrometry_results_all=np.vstack(list(astrometry_results.values()))\n\t# Expicitly convert to a float array to prevent numpy error\n\tastrometry_results_all = astrometry_results_all.astype('float')\n\t\n\t#find the dimmest star\n\tdimmest_match = astrometry_results_all[np.argmax(astrometry_results_all[:,1]),:]\n\n\tBASE_FLUX=dimmest_match[8]/pow(10.0,-dimmest_match[1]/2.5)\n\tprint (\"BASE_FLUX: \",BASE_FLUX) \n\t\n\tdb_img_dist=np.linalg.norm(astrometry_results_all[:,9:11]-astrometry_results_all[:,11:13],axis=1)\n\tdb_img_dist=db_img_dist-IMAGE_VARIANCE/(astrometry_results_all[:,8])\n\t\n\tPOS_VARIANCE=np.mean(db_img_dist)\n\t\n\t# execfile went away in python3\n\t#https://stackoverflow.com/questions/6357361/alternative-to-execfile-in-python-3\n\tfilename = sys.argv[1]+\"/calibration_data/\"+basename(bestimage)+\".solved\"\n\texec(compile(open(filename, \"rb\").read(), filename, 'exec'))\n\t\n\tf_calib=open(sys.argv[1]+\"/calibration.txt\", 'w')\n\tf_calib.write(\"IMG_X=\"+str(IMAGEW)+\"\\n\")\n\tf_calib.write(\"IMG_Y=\"+str(IMAGEH)+\"\\n\")\n\tf_calib.write(\"PIXSCALE=\"+str(PIXSCALE)+\"\\n\")\n\tf_calib.write(\"DB_REDUNDANCY=\"+str(DB_REDUNDANCY)+\"\\n\")\n\tf_calib.write(\"DOUBLE_STAR_PX=\"+str(DOUBLE_STAR_PX)+\"\\n\")\n\tf_calib.write(\"REQUIRED_STARS=\"+str(REQUIRED_STARS)+\"\\n\")\n\tf_calib.write(\"MAX_FALSE_STARS=\"+str(MAX_FALSE_STARS)+\"\\n\")\n\tf_calib.write(\"BASE_FLUX=\"+str(BASE_FLUX)+\"\\n\")\n\tf_calib.write(\"THRESH_FACTOR=\"+str(THRESH_FACTOR)+\"\\n\")\n\tf_calib.write(\"IMAGE_VARIANCE=\"+str(IMAGE_VARIANCE)+\"\\n\")\n\tf_calib.write(\"POS_ERR_SIGMA=\"+str(POS_ERR_SIGMA)+\"\\n\")\n\tf_calib.write(\"POS_VARIANCE=\"+str(POS_VARIANCE)+\"\\n\")\n\tf_calib.write(\"APERTURE=\"+str(APERTURE)+\"\\n\")\n\tf_calib.write(\"EXPOSURE_TIME=\"+str(EXPOSURE_TIME)+\"\\n\")\n\tf_calib.close()\n\t\n\tprint (\"Calibration finished\")\n\tprint (\"calibration.txt and median_image.png are in \"+sys.argv[1]+\"\\n\")\n\tsystem(\"cat \"+sys.argv[1]+\"/calibration.txt\")\n" }, { "path": "tests/science_cam_may8_0.05sec_gain40/calibration.txt", "content": "IMG_X=1392\nIMG_Y=1040\nPIXSCALE=15.7768356032\nDB_REDUNDANCY=1\nDOUBLE_STAR_PX=3.5\nREQUIRED_STARS=5\nMAX_FALSE_STARS=2\nBASE_FLUX=254758.120205\nTHRESH_FACTOR=5\nIMAGE_VARIANCE=2.12414468046\nPOS_ERR_SIGMA=2\nPOS_VARIANCE=0.666903299486\nAPERTURE=60.7\nEXPOSURE_TIME=0.05\n" }, { "path": "tests/science_cam_may8_0.05sec_gain40/calibration_data/.gitignore", "content": "# ignore all the calibration outputs in this directory, but don't ignore this .gitignore file\n*\n!.gitignore\n" }, { "path": "tests/science_cam_may8_0.05sec_gain40/input.csv", "content": "1332.66135414,1005.65868251,9.27684107053,668.988734129,830.457341566,9.76312036221,1086.81114937,464.955892187,7.77428272825,252.52804707,586.92908643,8.96669902205,810.549694541,909.672468917,6.67570087357,1222.77253018,588.922220972,9.09333834234,47.8054763038,215.68989355,9.68856752687,155.37293176,102.037964161,9.93286761468,1211.85862788,631.498265359,8.42413992351,1158.19421457,16.883473647,9.03784428344,550.527074851,351.967375445,7.04454402364,1035.70951011,179.052375687,7.80281486578,745.421840851,723.192195778,9.64978076066,893.575154082,493.966146616,10.2691241853,341.183745219,134.211038454,6.8262450064,663.47598836,993.625426313,8.88604694037,878.931937752,59.0887557823,8.12825542144,530.899387009,68.3497597776,8.69925204243,48.6301861272,750.818020798,7.17102229746,538.77769969,843.165215628,8.07385539306,1126.80197661,96.6815209717,7.23839628871,1195.0039573,766.553198746,7.83002695359,778.442991882,734.239442645,8.98892455436,389.854681168,813.250786801,10.3819620783,1199.18962075,445.460883651,8.49054356324,1244.33324977,843.263615312,7.74220053988,1133.03379914,312.108485129,7.460106584,428.860210364,49.1137933311,8.66706978222,442.610329145,706.495818936,6.68162303057,553.288210904,1031.29588468,9.87530276662,745.018908822,227.616401557,7.85192332412,706.064549624,124.445431798,8.99762316643,907.35834934,161.573987156,7.55659177796,1292.24658496,567.709089909,9.17607559759,708.811923457,434.435344299,8.23507548301,55.6824121956,32.1080370985,9.30940180884,1367.26130863,798.527335616,8.60603196482,8.52994280141,892.559738307,9.00410617275,936.706605963,309.36602258,8.45638566575,919.732674913,118.59412702,8.09732282652,993.125128963,701.543990499,8.74720398814,1142.41539859,357.888915335,7.40120262257,380.318257941,610.652920347,7.65963419734,1214.63298937,124.799484879,9.89266690486,894.814636584,958.227777159,10.8916802947,981.540497619,424.321568881,7.86877067496,1088.07474413,924.876826698,8.8945156091,293.058423834,505.163872888,7.21827209799,653.743191279,325.206887944,8.08294207819,1169.83563685,293.762541805,7.97295714855,351.360974628,385.404218102,6.26380735588,1383.46986196,47.6180909771,8.81337708816,353.476290031,884.74272247,6.50625132605,1188.4572659,241.499550703,7.3433412911,349.171686796,1.78688525006,10.7863770211,697.75369712,468.903583642,10.0073128336,969.106478415,435.247992142,8.25199979098,445.282887464,997.604679554,9.52032407196,579.741471062,537.566133275,9.33044815785,289.890174343,185.965372827,8.42171109675,34.3416764664,607.321210309,8.1663780163,839.778639893,449.639379723,6.93819352732,894.517304047,490.783485168,7.63970126336,674.088357472,7.21357647729,7.9113477377,271.245537763,901.728787463,7.13004025655,130.122980674,538.981134709,7.41641308146,174.795081003,788.044874365,7.88548103111,519.64363743,626.800745885,8.78715385698,443.989655145,1033.80565811,7.35381529356,195.290766237,248.52452148,8.40085734041,1331.03316444,363.58614873,7.21789962467,1365.10303272,855.313927946,6.53462122133,193.482075012,1028.52089922,8.88493282993,531.926375169,131.656314787,9.90691242008,304.876302472,552.086823904,8.42745685035,664.128687263,107.7798142,8.50176239177,693.957041092,831.70783991,8.91916380322,1274.7570241,1032.08049656,8.82080400554,1003.66946637,746.246100436,8.60818660145,233.186497325,602.615915382,7.99336494193,624.714860064,650.464351469,8.92562889079,109.998625488,308.140080994,7.48643300472,71.2039075457,258.994652725,10.0484150283,526.667680804,279.191507679,9.57594469155,901.747099641,572.744624963,8.4175551312,835.04885274,837.436077076,8.01422621988,4.15643690466,273.16175585,8.11250596628,191.800809728,332.896753484,8.28901225384,1053.39040359,732.559469306,9.79655811435,183.705755281,754.160515257,8.03360767957,1242.77487761,975.301822611,6.9085260576,313.59644267,674.072634771,7.75830397039,552.397778382,485.832875076,9.44546945441\n944.69742609,72.8980624104,7.39656102292,262.303232843,614.845511191,8.4788528588,21.4038946892,262.489453583,6.5663163383,7.25423398866,24.6277721103,9.21068910086,963.844181491,181.75000974,8.55608972161,399.835769215,452.856157083,8.17919160447,422.996464844,561.40856483,7.16151654833,1265.47626687,176.390152153,8.1502239686,172.751544984,456.771289528,7.60967962467,928.336312796,248.042606949,5.90627340893,334.960110822,775.65934916,7.90969285114,71.747194989,106.618080266,7.86405732352,738.506702891,657.432299132,8.08316055767,680.148330544,779.890353534,6.06728448844,1257.20727154,625.416686358,7.26017268027,364.746484888,803.231061485,10.2208641996,445.262244817,499.240820148,8.13533723207,703.237221803,954.861612723,8.83432766743,1324.64848395,801.074312784,7.38839502415,1312.41157759,853.791276665,7.64448309998,773.918573911,710.17249481,7.13424781959,844.112269164,84.0919228077,7.25433438754,823.172648943,269.211439929,7.5015409017,739.377744113,514.199518749,6.95195660116,640.881301089,731.496519739,9.2722867377,226.134036557,788.123390348,8.03200271261,742.826627301,793.604506117,8.85416366108,64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16528,16032,15264,16389,15711,16214,15619,15427,16262,15461,16045,16515,15436,15728,16421,16569,15830,15158,15599,16076,15501,15603,15302,16058,16240,15800,15872,16023,15624,15457,15732,15284,15956,-1,16698,15948,15743,15914,15779,15524,16651,15583,16334,16311,16472,15523,15174,16066\n51188,51019,50566,50489,49772,50987,49950,50525,49855,50697,49404,50510,50247,49784,50718,-1,50168,50948,49769,49488,50311,51198,49599,50393,49428,49418,50721,50406,49665,49513,51259,49916,51333,51530,51330,50956,50529,51112,50234,50631,51227,51228,49384,49911,49670,50708,50163,50492,49797,51175,49943,50497,51238,50356,49793,50723,50442,49891,51375,50349,50888,51129,51349,50592,51474,49645,49845\n35718,37081,35055,36565,35810,36791,36976,36206,35300,35251,35583,35060,36821,35543,37134,35547,36687,35529,35737,36260,35173,35556,35107,35634,36150,36969,35343,35749,36447,36004,35259,36162,35299,35231,37104,36394,35600,35669,36563,35843,-1,37088,35340,35306,36055,36880,37349,36080,36541,37220,35881,37142,36137,35705,36131,36388,36235,36572,35629,35602,35271,35792,36526,36071,36613,35896,35649\n79917,79724,79368,79782,79914,79115,80111,80078,81119,80420,79483,79177,80325,79811,79441,80879,80641,79154,79726,80175,80917,80500,79985,79219,78709,80117,79930,79889,79772,79225,80118,78994,79413,80077,79445,79683,78760,80435,80646,80601,80092,80026,79999,79305,79193,80681,80318,80665,79327,80189,79533,80967,79894,78787,79365,-1,79686,78671,80985,80370,79639,80471,79846,80476,80523\n108435,104458,103628,104953,104105,100966,105694,109039,103602,105618,103219,106339,-1,107459,101750,106244,104736,104982,103342,103908,104968,102787,104149,-1,101863,102270,107828,107236,108676,104213,104414,103754,106048,104609,104957,106583,105526,100504,101634,101161,106024,104667,104147,100646,106748,101824,100670,109596,107700,106503,101732,103170,105557,104964\n104878,106011,105661,105756,105349,105218,106375,104622,105767,104618,105152,104947,105059,106031,105899,105469,105155,105588,104629,105993,105819,106161,104626,105549,105066,105203,106095,105046,106015,104872,105212,104813,104930,104893,104911,105409,106325,105800,104616,105797,106236,105040,105534,105750,120250,105729,104740,105563,105359,105926,-1,105302,105985\n114188,114412,116963,116387,113696,115769,115620,114270,116056,113647,114560,115295,115192,116263,116407,116444,115510,114646,116284,114039,115181,116270,113287,114716,115890,115580,114217,113460,115380,116084,114333,115848,114142,113563,114019,114198,114571,116640,113885,112839,114810,115754,114613,113890,114150,114899,114790,114072,115612,114851,115744,115930,113735,113330,115087,115035,114901,114996,115073,115506,115895,113863,113900,114583,114982,115233,115636,-1,114091,113351,114658,114151,114728,115462,116734,115157,114086,115429,114216,116390,116099,114961,114731\n69085,70284,70446,69862,70048,70449,70900,69624,69573,71224,69662,69616,70254,69518,69805,70844,70585,-1,71381,71172,70633,71104,70066,70625,69824,71056,69620,69704,69690,71165,70198,71235,71109,-1,70710,70139,70841,70617,70420,70428,71611,71618,69919,70999,71491,70848,70447,69641,70910,69040,71432,70881,70435,69951,70151,69121,70347,71499,69732,69405,71008,69527,69521,70393\n77886,78153,78344,77260,77245,76814,78363,76870,77402,77299,76791,78016,76696,78012,77791,77284,77501,78281,77681,76764,78537,77222,77252,78048,78050,78248,76773,78768,77551,77091,78222,77598,-1,78741,78077,77207,77655,78276,78235,78448,77508,-1,77950,78028,77153,77767,76938,78102,77053,76844,78214,77180,77046,78334,76802,77583,77488,78657,77624\n67741,68806,68936,68452,-1,69052,68626,69289,68592,69282,68810,69572,69044,69414,69409,69152,68469,68440,-1,68789,68899,69651,67931,68639,69242,67991,69352,68380,68458,69328,69402,68192,68949,69747,68636,68755,69531,68221,68348,68997,68739,68738,68050,68661,68716,68524,68595,68297,68794,69058\n113757,115265,115235,115010,115497,113886,115178,113929,114934,113539,114453,115049,115593,114615,114047,115676,114206,114022,115168,114656,114177,113878,114973,114748,114360,113590,115102,115176,115843,-1,115055,114601,-1,115442,114319,115794,114139,114765,113615,114434,114896,114842,114176,115535,115897,114634,115571,115314,115318,115659,115113,114464,114746,114455,114331,115532,114487,114466,115519,115501,113935,115231,114191,115150,113838,114400,114318,113959,115846,114935,114447,114635,114115,115300,113817,114867\n110662,110933,110356,109603,110275,109972,109249,109153,110431,111346,111457,110309,111071,110362,110964,110807,110310,110056,109767,110704,110988,108476,109124,109358,111633,110441,110925,110836,110025,109701,109669,111795,109252,108887,108607,109225,108879,110414,109894,109841,111543,109759,110154,110266,111317,110142,111088,110314,109679,110090,111854,110020,109599,110713,109857,109363,111972,110923,110893,110500,109492,109572,110991,110967,110647,111094,110327,109867,109606,110525,109425,109130,111243,111060,110459,109426,109617,109200,110648,111179,108921,112026,110021,110934,109497,111362,110417,110824,109876,108689,110497,108889,110026,109065,109926,109373,111423,110561,109096,108794,108977,109424,109713,110294,110230,110504,109213\n19907,20218,20179,19707,21119,20407,20747,-1,20628,20658,20233,19590,20603,20787,20742,21050,21264,20598,20932,19778,20469,20676,20307,20158,21172,19933,20344,20263,20401,20882,20429,20471,19814,20666,20029,20358,20360,20503,20723,19996,19598,20409,19511,20675,20793,20265,20431,20797,20671,20874,20809,20476,19871,20137,19849,21076,20240,20602,20271\n46833,45837,47950,45754,46734,46396,47042,46931,47199,47873,46617,47190,46305,46521,46363,47451,46657,46263,46494,46081,45626,45958,47020,46430,45903,46438,45995,47530,47872,46102,47564,45945,45868,47562,47439,45759,47647,-1,46616,47448,47346,47067,46966,47167,46562,46379,-1,46440,47036,46245,46934,46065,45707,47766,45745,45857,46231,46257,46001,47246,45957,47210,47434,47782,47769,46688,47144,45679,46536,46714,47108,47678,47208,46937,46626,46738\n104785,105060,105491,104686,104774,105235,104232,105107,105939,105225,104272,104641,104946,104395,105389,105120,104294,104201,105258,104157,104271,104897,105574,105575,104985,104658,105204,105705,104783,104720,105424,105301,104995,104938,104557,104380,105734,104898,104300,104394,105051,105019,104684,104560,103947,104904,-1,104910,104869,104939,105329,105236,104657,105641,105035,105931,105970,105115,105667\n92559,92466,92770,92904,92960,92684,93093,92144,93381,92716,92494,94053,93078,93551,93323,92546,91925,92884,92426,93743,92914,92878,92200,92117,93245,92091,93768,93836,92023,92118,93865,93190,93063,93717,93958,92524,92320,93614,93342,92628,92501,92955,92963,93716,94066,93195,92746,93554,93935,93429,93317,92349,93526,92634,92781,93135\n108162,110895,112781,107665,-1,106406,105514,111876,111504,110256,107693,111049,108045,105426,106881,113169,108260,105876,110070,107833,104796,108799,110078,110158,110100,109848,112092,104453,114859,113346,103372,108449,105316,108801,106057,108900,108970,108768,106051,105073,107782,107089,108615,107681,110852,106424,111153,110329,114550,110282,109773,108377,112405,112189,111793,104206,103520,106846,106861,108759,114258,107999,114640,111486,114281,110800,114993,115028,105720\n46910,45510,45989,46311,45678,46304,45454,46501,47274,47233,47211,47375,47189,47148,46232,46137,46149,46419,47504,46662,45827,45894,45699,46999,46100,45648,46349,46272,47196,45579,45886,45597,46155,46745,45917,45456,46635,46357,45747,46866,45777,47138,46896,46986,45540,46570,46545,46104,-1,47262,46900,46397,46561,45826\n4368,3843,3825,4656,4525,4723,4855,4326,3893,4272,4716,4073,3898,4539,3834,4530,3872,5030,3699,4753,4417,4072,5158,4259,5180,3741,3845,4833,4020,3607,4702,4586,4606,4063,4234,4106,4817,3672,4662,4739,3822,4104,4022,4164,4233,4815,4361,4691,4158,4195,5068,3850,4925,4700,3858,3794,4420,4730,3567,-1,3606,4659,5051,4410,5056,5201,4093,5143,4442,4091,4376,4251\n44210,43315,44554,43992,44185,44936,44859,43939,44190,44580,44407,44427,44320,44277,44639,43776,43805,43746,44311,43288,43309,43868,43578,44633,44754,44182,43565,44254,44468,44284,44151,43896,44807,44793,44237,45030,44328,44578,43651,43859,44059,44249,44933,43608,45078,43039,44974,43818,44134,44212\n114001,114159,115330,114032,114692,114824,114753,112853,114138,112965,115205,114776,113957,114659,113201,113794,113815,113575,114789,114472,113638,113657,113761,115221,114655,115020,115506,113777,113701,113016,115580,115418,113564,113882,113922,115139,114301,114362,114292,114199,-1,114590,113219,113850,113077,114950,114614,114167,114585,114626,113643,114874,113434,113439,114064,113596,115084,115328,114709,113704,113604,113004,114248,114598,114869,113352,114179,114736,115510,114272,113799,113629,113607,114610,113955\n8549,8691,8050,8013,7440,8264,8940,7870,7759,7836,7716,8710,7959,8509,7916,8411,8486,7782,8542,8007,7877,8197,8268,7373,8095,8944,7555,-1,8022,7274,-1,7796,8874,8880,8497,8645,9237,7862,8217,7417,9094,8564,7578,8360,8446,7872,8174,8759,8390,7485,7692,8572,8361,7567,7980,8296,7726,7611,8729,8304,8923,8636,8683,8220,7776\n20838,21258,21025,21249,21370,21400,20797,21239,21003,21296,20212,20882,20849,21375,19938,19804,20358,20829,20523,20598,21719,20757,20675,20787,21245,21536,20913,20603,21377,21493,19777,20265,20240,19871,20723,20912,20589,20809,20409,20236,21119,20310,20893,19707,21156,21382,21297,20666,20401,19717,-1,19882\n4922,3455,4845,3990,4078,3554,4494,4982,4346,4333,3909,4031,3369,4657,4066,4854,5164,4842,5296,3725,4074,3610,3630,3496,3995,3386,4371,4071,3670,3734,4101,4335,3770,4211,5121,4788,4736,4257,3783,4587,4009,4488,4342,5197,4044,5154,4276,4375,3975,4909,3861,4385,4824,4146,4403,4380,-1\n26299,27179,26718,26196,27312,26852,26291,26162,26640,26730,26993,25997,26419,27040,27376,27071,27092,-1,25730,26708,25755,25492,26574,26351,27180,26845,27017,25845,26177,25764,26854,27039,26863,26606,27088,26650,25925,26875,27456,26566,26832,27016,25512,26601,27613,26619,26192,26754,26969,26759,26712,26225,27562,27336,25948,25969,27172,26330,26396,27605,26208,26094,25500,26374,25789,25871,26206,25763,27447,26332,25988,26711,26134,26781,26783,27063,26946,26536,25418,27110,27077,26658,25422,26571\n73015,72951,71749,71846,72214,72538,72582,71418,72932,73304,72199,72807,73018,73065,72393,72318,72458,73302,72620,73282,71760,72495,73160,73158,73530,72175,72085,72123,72440,71897,71644,72007,72124,72469,72219,72369,72968,71839,71925,73034,72892,72794,73380,71485,72711,72927,72945,72923,-1,72730,73144\n117529,117858,118320,117847,117900,224,117866,118013,117656,239,223,117774,117298,117474,117877,433,118054,117169,117359,118137,118268,118185,117526,117683,117459,118242,117787,117467,117804,117475,117463,118104,429,117674,118235,117179,117818,117704,118030,117914,117805,117473,117953,118035,117927,117530,117776,117607,448,31,117933,117367,368,117781,117498,129\n40795,40226,41685,40378,39729,40671,41624,40960,40603,39985,41159,39958,39940,-1,40240,40676,40619,39614,40140,41046,40209,41187,41682,40278,40311,40103,41269,40852,40484,41821,39792,40782,41248,41292,41086,40815,40715,39939,41124,40524,41482,41283,40401,40716,40472,40891,41172,39233,40192,41423\n70765,71934,72080,72217,71245,71621,72645,72310,71114,71380,70801,70415,71071,72525,71473,71792,70864,70644,71816,70889,71654,71850,71689,-1,70961,72172,70261,71619,71295,71242,70932,71614,71407,70482,70518,70598,72501,70513,72202,71098,72093,70382,71730,70681,70608,70991,71776,71992,72148\n97859,98158,98370,98433,98241,98991,99575,99111,98079,99223,99796,97745,98119,97938,98823,98710,99048,99771,99215,98862,98492,98758,99010,99056,98199,99578,98840,98442,98526,98515,99477,98455,97942,99158,98626,99577,98641,98314,99234,99410,98777,99045,98508,98538,99367,98690,98377,99295,98124,99055,98342,98250,98826,99282,98805,98292,97964,99054,98907,98366,99686,98201,98705,99541,99250,99113,97958,99843,98487,98721,99198,98578,98965,98400\n42824,42871,41960,41770,41337,43024,43091,41359,40968,41367,42744,43368,42364,42306,41931,41262,42465,43213,41502,-1,43634,42730,41530,42686,42696,41554,41610,43419,42583,42405,-1,41614,42983,41975,42475,41845,43440,42230,43155,41945,41996,42417,41935,42580,42864,41669,42386,41310,41149,43113,41286,41912,42623,42616,41820,42223,41552,42798,41888,43685,42856,41798,41691,43279,42006,42445,43437,41519,41454,42090\n26236,24788,23766,24495,26195,25438,24443,25356,26297,26996,24525,26806,27346,26498,27832,25907,27498,24653,26968,26371,27472,26663,26988,25620,27888,25868,25109,26485,26814,23982,27370,27199,26358,24726,25754,27046,25414,28293,27990,23958,26890,27541,27028,27030,26829,25094,24313,24914,26011,25328,25150,24794,24479,27167,25379,27188,26700,25393,25748,24658,25769,25991\n12108,11780,11072,11359,10932,12424,11566,12133,10870,12499,11306,12810,12399,11586,12658,11820,11994,11671,12837,11761,12425,12167,11212,12317,11600,11594,12313,11051,11287,11683,12250,12164,11041,12360,11914,12110,10909,12913,12797,12141,-1,-1,11622,11549,12220,11567,11778,12466,11986,12507,12521,11644,11356,12576,11750,11073,11392,11204,12594,12105,11552,12217\n5095,4747,4308,5485,5644,5296,5237,5301,5217,5504,5197,5799,5164,5047,5384,5443,4914,4648,4372,4474,5590,5620,4982,5281,4909,6048,5220,4777,5666,5524,4948,5933,4592,5918,4796,5760,5754,5356,5154,5121,6150,5326,5294\n42197,40093,41376,41768,42475,42451,40711,41270,40393,40411,40245,42352,42833,41830,40236,42341,41472,41272,41943,42590,42237,42744,42314,41998,40729,40319,40573,41514,42250,40898,41082,42093,41382,40887,41974,40928,42238,41713,41056,40698,40936,41914,40681,41225,40241,40788,41406,41782,40447,40224,40507,41224,41411,40823,41592,40128,40488,41630,40946,41263,41655,42498,40403,42111,42525,42231,41141,42596\n13964,13883,-1,12534,12574,13249,12809,12660,14110,13995,12978,13236,13916,12749,13292,13724,12539,13445,13527,13520,-1,12374,13857,14006,13970,14235,12650,12736,13843,13781,13032,13919,12981,12945,12595,12267,13168,13406,13402,13933,13383,13655,13636,13740,12774,14151,13051,13064,14050,12760,12849,12341,12444,13182,12390,13630,12279,12882,13660,13421\n104647,105034,103189,104410,104763,104941,104749,103641,103413,105009,104215,104593,104754,103012,103580,104214,104844,104689,103163,104579,104527,103108,104002,103528,103799,103352,104133,104548,104547,103216,103295,103782,103793,103098,103824,104190,103749,104862,104233,103535,103540,103255,103277,104354,103603,103822,104541,104050,104561,103269,103624,103003,103404,103410,104588,104566,104334,103051,103963,104242,104801,103596,104741,103452,104217,103706,104347,104678,104614,103084,104814,104765,103873,104159,104699,103442,103137,103126,104599,104550,103894,103061,104104,103502,104852,102953,104257,103532,103272,104552,103299,104877,104042,103802,104273,103526,104694,104158,103041,104008,103655,104074,103239,104417\n17237,15675,18366,15404,15673,17131,15986,15798,15950,17811,15790,17718,16819,16052,15363,17384,18372,17782,18100,16047,17807,17261,16196,16447,17427,15632,16575,16612,15863,15976,18167,18295,15979,15903,15666,17713,16642,-1,18160,15531,17334,16625,17341,17869,17991,17104,17800,17476,15444,16455,16088,18212,15388,16605,18309,17963,16965,16986,18207,16244,17905,16820,16403,15982,18282,17634,16316,15717,17772,16118,18604,17033,16880,18103,18653,16394,15892,18215,15610,17675,16149,16871,16210,15556,16001,16973,17231,18370,17833,16351,17359,17357,18453,16962,16361,17197,15679,17827,17877,15814\n46098,45431,44794,45112,46122,45822,44620,46157,45148,44714,44962,46769,44559,44519,45017,46663,45795,45403,45196,45412,44301,46321,46282,44897,46583,46025,45438,46022,45539,46749,46581,45053,46051,45618,46757,45835,44720,46766,44898,44882,44335,46652,46243,-1,46083,44347,45258,45860,45688,44866,46125,46735,45006,46289,45883,45329,46131,45661,45870,46391,45702,46393,44388,46042,45334,45536,45325,45572\n25614,26291,25969,25984,26342,-1,26351,25304,25948,25871,25361,26536,26192,26606,26619,25158,25428,26034,25801,25192,25078,25997,25252,24776,25141,26550,26326,26611,26374,25112,24647,25925,26206,26570,26162,26299,25237,25763,24735,26601,25450,25764,26094,26650,25001,26658,25845,25500,25157,25794,25169,25492,25512,26227,25484,25422,25159,25290,-1,24878,25988,25711,24984,25789,25418,25160,25494,25730,26419,26712,25175,25696,26718,25590,25291\n57683,58514,56855,58992,58001,57876,58555,58147,57156,57025,57487,57140,58357,58080,58617,59194,57431,57413,59024,57846,58980,56731,59371,60054,58355,58256,57364,57558,59228,56665,58770,57450,57556,56854,59470,57720,59538,57272,59454,58496,59305,56375,58870,58501,57377,57023,57131,57774,59370,57493,59543,57854,59312,58927,58318,59548,57203,59073,58258,57873,57406,58694,59372,59007,58784,58108,57483,57141,59203\n72235,73319,72372,73560,72802,72448,73739,73379,73687,72104,73082,73030,73505,73107,72075,72959,71890,72800,71952,73650,73195,72761,73049,72957,72279,72248,73779,72940,72984,72755,73485,73331,73487,72630,72712,73221,72350,73387,72427,72459,73672,73774,72240,72536,73267,72948,71935,72977,72857,73538,72215,72439,72767,72276,72559,73250,72140,72155,72485,73140,73559,-1,72196,72926,73448,72985,72797,71893,73511,72639,72584,73341,71977,72600,72484,72432,72705,73398,72185,73145,73123,72038,73174,73484,72257,73108,72149,73335,73266,72010,72241\n111050,111041,112842,112360,112175,111828,111922,112402,111135,112345,111268,112628,111032,110571,110754,111408,112513,111919,111605,111380,113076,111559,112590,111869,111990,112089,113141,112921,110918,110710,111872,111279,112952,110487,111033,111599,110603,110716,111074,111567,112837,112775,111438,112144,111351,112476,112227,111864,-1,112055,112173,111203,111814,110840,112140,112840,111307,111378,111721,110788,111796,112768,111734,111627,111227,111217,112249,112539,112609,110757,110858,112224,111202,111636,111842\n2198,980,1409,1093,1987,2268,437,1233,1456,391,1125,297,885,474,1400,1337,1639,117896,2271,1372,1671,1257,566,2414,11,826,767,117798,812,428,118212,817,967,1916,1352,63,727,119,1075,1711,2103,1008,173,1043,1229,118214,1289,591,984,473,-1,1650,841,1026,539,1475,118218,808,195,1152,982,747,118024,1593,1642,1603,1478,1676,485,1826,404,424,988,889,559,1972,1370,1011,1496,1255,1009,1090,972,1343,905,176,1302\n91789,93843,91863,92479,92366,92419,91636,93301,92851,94117,91790,92853,92832,92944,93960,92228,93185,92243,93828,92591,93409,93926,93512,92129,92863,93634,92554,91869,91868,93305,92216,93166,94058,93348,93433,93397,92581,92827,93214,92986,93777,92911,92809,92420,93024,91473,92550,93442,93436,93386,93770,92190,93043,92396,92281,92703,92398,91635,93798,93776,-1,93720,91945,92178,93396,92768,92338,92405,92875,93712,93111,92632,92439,92261,93032\n28715,-1,30741,29617,28506,29760,28698,28502,30115,28644,30316,29451,29815,30226,30182,28725,28851,28997,29478,29835,30013,29283,30270,29308,29987,30411,28423,30309,29302,30573,29072,30751,28729,29301,29173,29581,-1,28445,28919,29309,30029,30273,28974,28844,30168,30222,30253,30250,29534,28994,29116,29911,28890,28861,28575,28206,30056,28908,29776,28626,29230,29242,30540,29688,28429,28683,28477,29878,29008,28448,30748,30403,28929\n40386,40292,42329,42384,44418,40618,41119,40030,40456,43022,43700,43930,45169,38765,39745,42210,42690,42541,43351,41580,39589,40105,41003,42407,40108,42855,40817,43277,42463,39204,42425,40725,43927,44219,42841,39899,39853,42369,40555,43201,-1,43747,41740,41273,41985,41478,42206,42858,44192,45212,40834,42532,42388,41254,41217,40502,40540,41907,39041,44480,42727,39770,43629,38634,41079,40179,42168,40436,40705,41304,41916,40511,42644,39802,39045,40173,38877,41604,-1,41631,41140,44473,40391,40728,41000,41070,41837,41093,42649,43449,41451,42915,42342,44699,43598,44106\n41740,44737,41217,44626,46407,43351,42858,44221,42342,43617,46719,44418,45275,44473,44939,43449,43598,46679,43022,45757,43700,42384,46452,44219,44106,44255,42425,45940,43823,45582,42649,43930,42644,44480,-1,42388,41985,46720,41916,45135,41451,44192,43201,45036,43958,42532,46940,42210,43747,41329,44445,46358,43604,43277,41604,45620,45847,44599,46573,45583,42915,43996,45622,43974,44218,45435,46741,43629,45799,45315,43927,44786,42407,41281,42841,46682,44699,45585,42855,46760,45212,42206,44247,45238,45651,44226,42727,44298,43221,41907,42690,44025,42369,46040,45976,46849\n51945,52707,51249,52257,521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045,16515,15436,15728,16421,16569,15830,15158,15599,16076,15501,15603,15302,16058,16240,15800,15872,16023,15624,15457,15732,15284,15956,-1,16698,15948,15743,15914,15779,15524,16651,15583,16334,16311,16472,15523,15174,16066\n51188,51019,50566,50489,49772,50987,49950,50525,49855,50697,49404,50510,50247,49784,50718,-1,50168,50948,49769,49488,50311,51198,49599,50393,49428,49418,50721,50406,49665,49513,51259,49916,51333,51530,51330,50956,50529,51112,50234,50631,51227,51228,49384,49911,49670,50708,50163,50492,49797,51175,49943,50497,51238,50356,49793,50723,50442,49891,51375,50349,50888,51129,51349,50592,51474,49645,49845\n35718,37081,35055,36565,35810,36791,36976,36206,35300,35251,35583,35060,36821,35543,37134,35547,36687,35529,35737,36260,35173,35556,35107,35634,36150,36969,35343,35749,36447,36004,35259,36162,35299,35231,37104,-1,35600,35669,36563,35843,-1,37088,35340,35306,36055,36880,37349,36080,36541,37220,35881,37142,36137,35705,36131,36388,36235,36572,35629,35602,35271,35792,36526,36071,36613,35895,35649\n79917,79724,79368,79782,79914,79115,80111,80078,81119,80420,79483,79177,80325,79811,79441,80879,80641,79154,79726,80175,80917,80500,79985,79219,78709,80117,79930,79889,79772,79225,80118,78994,79413,80077,79445,79683,78760,80435,80646,80601,80092,80026,79999,79305,79193,80681,80318,80665,79327,80189,79533,80967,79894,78787,79365,-1,79686,78671,80985,80370,79639,80471,79846,80476,80523\n108435,104458,103628,104953,104105,100966,105694,109039,103602,105618,103219,106339,-1,107459,101750,106244,104736,104982,103342,103908,104968,102787,104149,-1,101863,102270,107828,107236,108676,104213,104414,103754,106048,104609,104957,106583,105526,100504,101634,101161,106024,104667,104147,100646,106748,101824,100670,109596,107700,106503,101732,103170,105557,104964\n104878,106011,105661,105756,105349,105218,106375,104622,105767,104618,105152,104947,105059,106031,105899,105469,105155,105588,104629,105993,105819,106161,104626,105549,105066,105203,106095,105046,106015,104872,105212,104813,-1,104893,104911,105409,106325,105800,104616,105797,106236,105040,105534,105750,120250,105729,104740,105563,105359,105926,-1,105302,105985\n114188,114412,116963,116387,113696,115769,115620,114270,116056,113647,114560,115295,115192,116263,116407,116444,115506,114646,116284,114039,115181,116270,113287,-1,115890,115580,114217,113460,115380,116084,114333,115848,114142,113563,114019,114198,114571,116640,113885,112839,114810,115754,114613,113890,114150,114899,114790,114072,115612,114851,115744,115930,113735,113330,115087,115035,114901,114996,115073,-1,115895,113863,113900,114583,114982,115233,115636,-1,114091,113351,114658,114151,114728,115462,116734,115157,114086,115429,114216,116390,116099,114961,114731\n69085,70284,70446,69862,70048,70449,70900,69624,-1,71224,69662,69616,70254,69518,69805,70844,70585,-1,71381,71172,70633,71104,70066,70625,69824,71056,69620,69704,69690,71165,70198,71235,71109,-1,70710,70139,70841,70617,70420,70428,71611,71618,69919,70999,71491,70848,70447,69641,70910,69040,71432,70881,70435,69951,70151,69121,70347,71499,69732,69405,71008,69527,69521,70393\n77886,78153,78344,77260,77245,76814,78363,76870,77402,77299,76791,78016,76696,78012,77791,77284,77501,78281,77681,76764,78537,77222,77252,78048,78050,78248,76773,78768,77551,77091,78222,77598,-1,78741,78077,77207,77655,78276,78235,78448,77508,-1,77950,78028,77153,77767,76938,78102,77053,76844,78214,77180,77046,78334,76802,77583,77488,78657,77624\n67741,68806,68936,68452,-1,69052,68626,69289,-1,69282,68810,69572,69044,69414,69409,69152,68469,68440,-1,68789,68899,69651,67931,68639,69242,67991,69352,68380,68458,69328,69402,68192,68949,69747,68636,68755,69531,68221,68348,68997,68739,68738,68050,68661,68716,68524,68595,68297,68794,69058\n113757,115265,115235,115010,115497,113886,115178,113929,114934,113539,114453,115049,115593,114615,114047,115676,114206,114022,115168,114656,114177,113878,114973,114748,114360,-1,115102,115176,115843,-1,115055,114601,-1,115442,114319,115794,114139,114765,113615,114434,114896,114842,-1,115535,115897,114634,115571,115314,115318,115659,115113,114464,114746,114455,114331,115532,114487,114466,115519,115501,113935,115231,114191,115150,113838,114400,114318,113959,-1,114935,114447,114635,114115,115300,113817,114867\n110662,110933,110356,109603,110275,109972,109249,109153,110431,111346,111457,110309,111071,110362,-1,110807,110310,110056,109767,110704,110988,108476,109124,109358,111633,110441,110925,110836,110025,109701,109669,111795,109252,108887,108607,109225,108879,110414,109894,109841,111543,109759,110154,110266,111317,110142,111088,110314,109679,110090,111854,110020,109599,110713,109857,109363,111972,110923,110893,110500,109492,109572,110991,110967,110647,-1,110327,109867,109606,110525,109425,109130,111243,111060,110459,109426,109617,109200,110648,111179,108921,112026,110021,110934,109497,111362,110417,110824,109876,108689,110497,108889,110026,109065,109926,109373,111423,110561,109096,108794,108977,109424,109713,110294,110230,110504,109213\n19907,20218,20179,19707,21119,20407,20747,-1,20628,20658,20233,19590,20603,20787,20742,21050,21264,20598,20932,19778,20469,20676,20307,20158,21172,19933,20344,20263,20401,20882,20429,20471,19814,20666,20029,20358,20360,20503,20723,19996,19598,20409,19511,20675,20793,20265,20431,20797,20671,20874,20809,20476,19871,20137,19849,21076,20240,20602,20271\n46833,45837,47950,45754,46734,46396,47042,46931,47199,47873,46617,47190,46305,46521,46363,47451,46657,46263,46494,46081,45626,45958,47020,46430,45903,46438,45995,47530,47872,46102,47564,45945,45868,47562,47439,45759,47647,-1,46616,47448,47346,47067,46966,47167,46562,46379,-1,46440,47036,46245,46934,46065,45707,47766,45745,45857,46231,46257,46001,47246,45957,47210,47434,47782,47769,46688,47144,45679,46536,-1,47108,47678,47208,46937,46626,46738\n104785,105060,105491,104686,104774,105235,104232,105107,105939,105225,104272,104641,104946,104395,105389,105120,104294,104201,105258,104157,104271,104897,105574,105575,104985,104658,105204,105705,104783,104720,105424,105301,104995,104938,104557,104380,105734,104898,104300,104394,105051,105019,104684,104560,103947,104904,-1,104910,104869,104939,105329,105236,104657,105641,105035,105931,105970,105115,105667\n92559,92466,92770,92904,92960,92684,93093,92144,93381,92716,92494,94053,93078,93551,93323,92546,91925,92884,92426,93743,92914,92878,92200,92117,93245,92091,93768,93836,92023,-1,93865,93190,-1,93717,93958,92524,92320,93614,93342,92628,92501,92955,92963,93716,94066,93195,92746,93554,93935,93429,93317,92349,93526,92634,92781,93135\n108162,110895,112781,107665,-1,106406,105514,111876,111504,110256,107693,111049,108045,105426,106881,113169,108260,105876,110070,107833,104796,108799,110078,110158,110100,109848,112092,104453,114859,113346,103372,108449,105316,108801,106057,-1,108970,-1,106051,105073,107782,107089,108615,107681,110852,106424,111153,110329,114550,110282,109773,108377,112405,112189,111793,104206,103520,106846,106861,108759,114258,107999,114640,111486,114281,110800,114993,115028,105720\n46910,45510,45989,46311,45678,46304,45454,46501,47274,47233,47211,47375,47189,47148,46232,46137,46149,46419,47504,46662,45827,45894,45699,46999,46100,45648,46349,46272,47196,45579,45886,45597,46155,46745,45917,45456,46635,46357,45747,46866,45777,47138,46896,46986,45540,46570,46545,46104,-1,47262,46900,46397,46561,45826\n4368,3845,3825,4656,4525,4723,4855,4326,3893,4272,4716,4073,3898,4539,3834,4530,3872,5030,3699,4753,4417,4072,5158,4259,5180,3741,-1,4833,4020,3607,4702,4586,4606,4063,4234,4106,4817,3672,4662,4739,3822,4104,4022,4164,4233,4815,4361,4691,4158,4195,5068,3850,4925,4700,3858,3794,4420,4730,3567,-1,3606,4659,5051,4410,5056,5201,4093,5143,4442,4091,4376,4251\n-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1\n114001,114159,115330,114032,114692,114824,114753,112853,114138,112965,115205,114776,113957,114659,-1,113794,113815,113575,114789,114472,113638,113657,113761,115221,114655,115020,115506,113777,113701,113016,115580,115418,113564,113882,113922,115139,114301,114362,114292,114199,-1,114590,113219,113850,113077,114950,114614,114167,114585,114626,113643,114874,113434,113439,114064,113596,115084,115328,114709,113704,113604,113004,114248,114598,114869,113352,114179,-1,115510,114272,113799,113629,113607,114610,113955\n8549,-1,8050,8013,7440,8264,8940,7870,7759,7836,7716,8710,7959,8509,7916,8411,8486,7782,8542,8007,7877,8197,8268,7373,8095,8944,7555,-1,8022,7274,-1,7796,8874,8880,8497,8645,9237,7862,8217,7417,9094,8564,7578,8360,8446,7872,8174,8759,8390,7485,7692,8572,8361,7567,7980,8296,7726,7611,8729,8304,8923,8636,8683,8220,-1\n20838,21258,21025,21249,21370,21400,20797,21239,21003,21296,20212,20882,20849,21375,19938,19804,20358,20829,20523,20598,21719,20757,20675,20787,21245,21536,20913,20603,21377,21493,19777,20265,20240,19871,20723,20912,20589,20809,20409,20236,21119,20310,20893,19707,21156,21382,21297,20666,20401,19717,-1,19882\n4922,3455,4845,3990,4078,3554,4494,4982,4346,4333,3909,4031,3369,4657,4066,4854,5164,4842,5296,3725,4074,3610,3630,3496,3995,3386,4371,4071,3670,3734,4101,4335,3770,4211,5121,4788,4736,4257,3783,4587,4009,4488,4342,5197,4044,5154,4276,4375,3975,4909,3861,4385,4824,4146,4403,4380,-1\n26299,27179,26718,26196,27312,26852,26291,26162,26640,26730,26993,25997,26419,27040,27376,27071,27092,-1,25730,26708,25755,25492,26574,26351,27180,26845,27017,25845,26177,25764,26854,27039,26863,26606,27088,26650,25925,26875,27456,26566,26832,27016,25512,26601,27613,26619,26192,26754,26969,26759,26712,26225,27562,27336,25948,25969,27172,26330,26396,27605,26208,26094,25500,26374,25789,25871,26206,25763,27447,26332,25988,26711,26134,26781,26783,27063,26946,26536,25418,27110,27077,26658,25422,26571\n73015,72951,71749,71846,72214,72538,72582,-1,72932,73304,72199,72807,73018,73065,72393,72318,72458,73302,72620,73282,71760,72495,73160,73158,73530,72175,72085,72123,72440,71897,71644,72007,72124,72469,72219,72369,72968,71839,71925,73034,72892,72794,73380,71485,72711,72927,72945,72923,-1,72730,73144\n117529,117858,118320,117847,117900,224,117866,118013,117656,239,223,117774,117298,117474,117877,433,118054,117169,117359,118137,118268,118185,117526,117683,-1,118242,117787,117467,117804,117475,117463,118104,429,117674,118235,117179,117818,117704,118030,117914,117805,117473,117953,118035,117927,117530,117776,117607,448,31,117933,117367,368,117781,117498,129\n40795,40226,41685,40378,39729,40671,41624,40960,40603,39985,41159,39958,39940,-1,40240,40676,40619,39614,-1,41046,40209,41187,41682,40278,40311,40103,41269,40852,40484,41821,39792,40782,41248,41292,41086,40815,40715,39939,41124,-1,41482,41283,40401,40716,40472,40891,41172,39233,40192,41423\n70765,71934,72080,72217,71245,71621,72645,72310,71114,71380,70801,70415,71071,72525,71473,71792,70864,70644,71816,70889,71654,71850,71689,-1,70961,72172,70261,71619,71295,71242,70932,71614,71407,70482,70518,70598,72501,70513,72202,71098,72093,70382,71730,70681,70608,70991,71776,71992,72148\n97859,98158,98370,98433,98241,98991,99575,99111,98079,99223,99796,97745,98119,97938,98823,98710,-1,99771,99215,98862,-1,98758,99010,99056,98199,-1,98840,98442,98526,98515,99477,98455,97942,99158,98626,99578,98641,98314,99234,99410,98777,99048,98508,98538,99367,98690,98377,99295,98124,99055,98342,98250,98826,99282,98805,98292,97964,99054,98907,98366,99686,98201,98705,99541,99250,99113,97958,99843,98487,98721,99198,98578,98965,98400\n42824,42871,41960,41770,41337,43024,43091,41359,40968,41367,42744,43368,42364,42306,41931,41262,42465,43213,41502,-1,43634,42730,41530,42686,42696,41554,41610,43419,42583,42405,-1,41614,42983,41975,42475,41845,43440,42230,43155,41945,41996,42417,41935,42580,-1,41669,42386,41310,41149,43113,41286,41912,42623,42616,41820,42223,41552,42798,41888,43685,42856,41798,41691,43279,42006,42445,43437,41519,41454,42090\n26236,24788,23766,24495,26195,25438,24443,25356,26297,26996,24525,26806,27346,26498,27832,25907,27498,24653,26968,26371,27472,26663,26988,25620,27888,25868,25109,26485,26814,23982,27370,27199,26358,24726,25754,27046,25414,28293,27990,23958,26890,27541,27028,27030,26829,25094,24313,24914,26011,25328,25150,24794,24479,27167,25379,27188,26700,25393,25748,24658,25769,25991\n12108,11780,11072,11359,10932,12424,11566,12133,10870,12499,11306,12810,12399,11586,12658,11820,11994,11671,12837,-1,12425,12167,11212,12317,11600,11594,12313,11051,11287,11683,12250,12164,11041,12360,11914,12110,10909,12913,12797,12141,-1,-1,11622,11549,12220,11567,11778,12466,11986,12507,12521,11644,11356,12576,11750,11073,11392,11204,12594,12105,11552,12217\n-1,4747,4308,5485,5644,5296,5237,5301,5217,5504,5197,5799,5164,5047,5384,-1,4914,4648,4372,4474,5590,5620,4982,5281,4909,6048,5220,4777,5666,5524,4948,5933,4592,5918,4796,5760,5754,5356,5154,5121,6150,5326,5294\n42197,40093,41376,41768,42475,42451,40711,41270,40393,40411,40245,42352,42833,41830,40236,42341,41472,41272,41943,42590,42237,42744,42314,41998,40729,40319,40573,41514,42250,40898,41082,42093,41382,40887,41974,40928,42238,41713,41056,40698,40936,41914,40681,41225,40241,40788,41406,41782,40447,40224,40507,41224,41411,40823,41592,40128,40488,41630,40946,41263,41655,42498,40403,42111,42525,-1,41141,42596\n13964,13883,-1,12534,12574,13249,12809,12660,14110,13995,12978,13236,13916,-1,13292,13724,12539,13445,13527,13520,-1,12374,13857,14006,13970,14235,12650,12736,13843,13781,13032,13919,12981,12945,12595,12267,13168,13406,13402,13933,13383,13655,13636,13740,12774,14151,13051,13064,14050,12760,12849,12341,12444,13182,12390,13630,12279,12882,13660,13421\n104647,105034,103189,104410,104763,104941,104749,103641,103413,105009,104215,104593,104754,103012,103580,104214,104844,104689,103163,104579,104527,103108,104002,103528,103799,103352,104133,104548,104547,103216,103295,103782,103793,103098,103824,104190,103749,104862,104233,103535,103540,103255,103277,104354,103603,103822,104541,104050,104561,103269,103624,103003,103404,103410,104588,104566,104334,103051,103963,104242,104801,103596,104741,103452,104217,103706,104347,104678,104614,103084,104814,104765,103873,104159,104699,103442,103137,103126,104599,104550,103894,103061,104104,103502,104852,102953,104257,103532,103272,104552,103299,104877,104042,103802,104273,103526,104694,104158,103041,104008,103655,104074,103239,104417\n17237,15675,18366,15404,15673,17131,15986,15798,15950,17811,15790,17718,16819,16052,15363,17384,18372,17782,18100,16047,17807,17261,16196,16447,17427,15632,16575,16612,15863,15976,18167,18295,15979,15903,15666,17713,16642,-1,18160,15531,17334,16625,17341,17869,17991,17104,17800,17476,15444,16455,16088,18212,15388,16605,18309,17963,16965,16986,18207,16244,17905,16820,16403,15982,18282,17634,16316,15717,17772,16118,18603,17033,16880,18103,18653,16394,15892,18215,15610,17675,16149,16871,16210,15556,16001,16973,17231,18370,17833,16351,17359,17357,18453,16962,16361,17197,15679,17827,17877,15814\n46098,45431,44794,45112,46122,45822,44620,46157,45148,44714,44962,46769,44559,44519,45017,46663,45795,45403,45196,45412,44301,46321,46282,44897,46583,46025,45438,46022,45539,46749,46581,45053,46051,45618,46757,45835,44720,46766,44898,44882,44335,46652,46243,-1,46083,44347,45258,45860,45688,44866,46125,46735,45006,46289,45883,45329,46131,45661,45870,46391,45702,46393,44388,46042,45334,45536,45325,45572\n25614,26291,25969,25984,26342,-1,26351,25304,25948,25871,25361,26536,26192,26606,26619,25158,25428,26034,25801,25192,25078,25997,25252,24776,25141,26550,26326,26611,26374,25112,24647,25925,26206,26570,26162,26299,25237,-1,24735,26601,25450,25764,26094,26650,25001,26658,25845,25500,25157,25794,25169,25492,25512,26227,25484,25422,-1,25290,-1,24878,25988,25711,24984,25789,25418,25160,25494,25730,26419,26712,25175,25696,26718,25590,25291\n57683,58514,56855,58992,58001,57876,58555,58147,57156,57025,57487,57140,58357,58080,58617,59194,57431,57413,59024,57846,58980,56731,59371,60054,58355,58256,57364,57558,59228,56665,58770,57450,57556,56854,59470,57720,59538,57272,59454,58496,59305,56375,-1,58501,57377,57023,57131,57774,59370,57493,59543,57854,59312,58927,58318,59548,57203,59073,58258,57873,57406,58694,59372,59007,58784,58108,57483,57141,59203\n72235,73319,72372,73560,72802,72448,73739,73379,73687,72104,73082,73030,73505,73107,72075,72959,71890,72800,71952,73650,73195,72761,73049,72957,72279,72248,73779,72940,72984,72755,73485,73331,73487,72630,72712,73221,72350,73387,72427,72459,73672,73774,72240,72536,73267,72948,71935,72977,72857,73538,72215,72439,72767,72276,72559,73250,72140,72155,72485,73140,73559,-1,72196,72926,73448,72985,72797,71893,73511,72639,72584,73341,71977,72600,72484,72432,72705,73398,72185,73145,73123,72038,73174,73484,72257,73108,72149,73335,73266,72010,72241\n111050,111041,112842,112360,112175,111828,111922,112402,111135,112345,111268,112628,111032,110571,110754,111408,112513,111919,111605,111380,113076,111559,112590,111869,111990,112089,113141,112921,110918,110710,111872,111279,112952,110487,111033,111599,110603,110716,111074,111567,112837,112775,111438,112144,111351,112476,112227,111864,-1,112055,112173,111203,111814,110840,112140,112840,111307,111378,111721,110788,111796,112768,111734,111627,111227,111217,112249,112539,112609,110757,110858,112224,111202,111636,111842\n2198,980,1409,1093,1987,2268,437,1233,1456,391,1125,297,885,474,1400,1337,1639,117896,2271,1372,1671,1257,566,2414,11,826,767,117798,812,428,118212,817,967,1916,1352,63,727,119,1075,1711,2103,1008,173,1043,1229,118214,1289,591,984,473,-1,1650,841,1026,539,1475,118218,808,195,1152,982,747,118024,1593,1642,1603,1478,1676,485,1826,404,424,988,889,559,1972,1370,1011,1496,1255,1009,1090,972,1343,905,176,1302\n91789,93843,91863,92479,92366,92419,-1,93301,92851,94117,91790,92853,92832,92944,93960,92228,93185,92243,93828,92591,93409,93926,93512,92129,92863,93634,92554,91869,91868,93305,92216,93166,94058,93348,93433,93397,92581,92827,93214,92986,93777,92911,92809,92420,93024,91473,92550,93442,93436,93386,93770,92190,93043,92396,92281,92703,92398,91636,93798,93776,-1,93720,-1,92178,93396,92768,92338,92405,92875,93712,93111,92632,92439,92261,93032\n28715,-1,30741,29617,28506,29760,28698,28502,30115,28644,30316,29451,29815,30226,30182,28725,28851,28997,29478,29835,30013,29283,30270,29308,29987,30411,28423,30309,29302,30573,29072,30751,28729,29301,29173,29581,-1,28445,28919,29309,30029,30273,28974,28844,30168,30222,30253,30250,29534,28994,29116,29911,28890,28861,28575,28206,30056,28908,29776,28626,29230,29242,30540,29688,28429,28683,28477,29878,29008,28448,30748,30403,28929\n40386,40292,42329,42384,44418,40618,41119,40030,40456,43022,43700,43930,45169,38765,39745,42210,42690,-1,43351,41580,39589,40105,41003,42407,40108,42855,40817,43277,42463,39204,42425,40725,43927,44219,42841,39899,39853,42369,40555,43201,-1,43747,41740,41273,41985,41478,42206,42858,44192,45212,40834,42532,42388,41254,41217,40502,40540,41907,39041,44480,42727,39770,43629,38634,41070,40179,42168,40436,40705,41304,41916,40511,42644,39802,39045,40173,38877,41604,-1,41631,41140,44473,40391,40728,41000,-1,41837,41093,42649,43449,41451,42915,42342,44699,43598,44106\n41740,44737,41217,44626,46407,43351,42858,44221,42342,43617,46719,44418,45275,44473,44939,43449,43598,46679,43022,45757,43700,42384,46452,44219,44106,44255,42425,45940,43823,45582,42649,43930,42644,44480,-1,42388,41985,46720,41916,45135,41451,44192,43201,45036,43958,42532,46940,42210,43747,-1,44445,46358,43604,43277,41604,45620,-1,44599,46573,45583,42915,43996,-1,43974,44218,45435,46741,43629,45799,45315,43927,44786,42407,41281,42841,46682,44699,45585,42855,46760,45212,42206,44247,45238,45651,44226,42727,44298,43221,41907,42690,44025,42369,46040,45976,46849\n51945,52707,-1,52257,52187,51532,52123,51213,50950,51626,51624,51757,52198,51120,50770,-1,51304,51571,51582,51239,52056,51105,52350,52625,51725,52373,51696,52528,50792,50999,52532,51922,51168,51802,51179,51316,51036,51787,52387,52554,-1,51360,-1,51184,51898,51008,51892,51520,51786,-1,51351,52604,51975,52315,50990\n54682,55295,54698,55887,55758,55056,56084,55326,55174,54922,54960,55830,55454,54329,54838,54466,54420,55505,55639,55802,55049,-1,55409,55002,54149,55700,55480,56161,54771,55596,55091,55381,55248,55487,55183,56245,55541,54843,55636,56179,55553,55375,55269,54406,55670,54820,56016,54914,55173,55570,55414,55152,55813,55228,55313,55812,55294,54707,54924,54749,55363\n77827,76942,78548,77591,78410,77695,79082,78489,77272,77970,79011,78274,79123,78894,78646,77178,78667,77062,77936,78461,77714,77550,79294,77084,78942,77674,77850,77652,78403,77161,78519,77790,77447,78542,77303,78180,77438,79255,78474,77370,79126,77496,78745,79423,76740,78492,77253,79157,78911,79241,76893,79164,79272,77738,78427,78327,79428,78892,78856,77484\n48013,48074,49040,49345,49383,47818,49554,49224,50295,50012,47524,48041,49150,49968,49189,50165,47245,49437,50035,47531,48171,-1,48673,48871,49003,48391,47532,47633,48243,49330,49063,49162,48548,48317,47605,48931,48423,49251,47635,49415,50039,47264,49174,49825,48954,48732,49253,-1,50060,49535,-1,49560,50280,48917,47568,49727,49789,49055,48393,48887,48397,48239,49179,48682,48485,47305,48986,49908,50097,47366,48242,47331\n74023,73118,71400,73483,71786,72673,70922,73785,70237,70897,71492,73728,70168,74147,70940,70098,73625,74110,72556,73229,71858,70035,71437,73067,74011,70213,72967,71355,71831,72853,73756,70052,74181,-1,73520,71281,73948,72864,70565,74290,74044,74415,71678,71780,72713,71960,73572,70773,73322,71551,72000,70546,72542,70917,70215,72690,70856,70673,73950,74398,70436,71563,71124,71908,70686,71677,72989,72366,72754,72131,70870,71847,70459,72816,73127,70423,71681,72242,71645,71475,73817,70832,70925,73609,71050,72633,73584,70074,72437,72777,72001,71264,70203,70821,70911,72103,72649,70301,73755,70080,73247,74163,73091,71622,73776,71307,72773,72431,73129,72264,72995,71093,72657,73343,72367,72379,73017,71337,72206,71217,72305,71683,72390,72232,70808,72851,72500,73347\n72629,71377,73010,71591,72574,72889,72618,71394,72154,72309,71628,72298,72776,72014,72739,71778,72339,72990,72015,72449,71507,72122,71434,71829,72931,72473,72049,71817,-1,71820,72275,72265,72648,71879,72771,72002,71842,71869,-1,72226,72697,71868,72243,72069,71422,72740,72094,72644,72631,71785,72212,72614,71903,72091,72635,72521,72663,72766,-1\n83338,83785,83641,84063,82528,83229,84079,85030,83387,83618,84069,85993,83268,85260,82367,86093,83028,85049,85068,82698,84662,84724,83323,84077,83502,82173,84311,85258,84197,83983,85751,84241,83380,85529,84148,84288,83794,84229,82672,84777,83749,83081,83312,83205,84457,-1,83894,84157,84760,84268,82531,84534,85520,84447,84834,84257,85142,83702,83269,84339,84394,-1,85267,84547,84619,83661,85577,83250,85461,84111,85198,84406,85038,85010,86061,85421,85725,83429,82991,85743,84030,84686,-1,84839,85164,85050,84338,84232,83535,85835,85897,84755,84231,84208,83960,84844,83249,85145,85214\n15525,15625,16985,15822,16593,17068,16538,16627,15894,16117,16399,16221,16991,16359,17049,17031,15777,16571,17058,17281,15564,17300,16838,16273,16268,16314,16924,16095,17133,16469,15546,16369,17084,16572,15558,15683,16497,16568,15508,17235,16831,-1,17121,16007,16241,16532,16304,16788,-1,17426,16748,17413,15906,15536,15823,16865,15975,16393,15951,-1,17232\n986,1871,2534,988,1593,1591,2229,2396,525,2305,714,2420,1086,1434,1490,751,1125,1164,2108,1496,1634,2934,1233,2059,2004,1818,1879,1575,625,2089,1474,1445,712,1093,2258,926,1594,2225,1905,1951,2053,1014,1152,2553,2681,1642,2476,828,808,2153,668,1409,1867,2434,2742,683,2455,626,1011,737,1641,508,1400,1833,1302,1475,1256,1501,1261,2668,1122\n23739,26296,23175,25416,26758,25334,24969,22361,21764,25607,19145,19619,22160,26877,22093,24125,24760,23803,26486,21558,22621,23318,20405,19667,19697,18980,26097,21212,18913,27225,26362,21970,26115,23885,19429,22769,22011,26878,22703,25022,27209,25162,17990,22561,20928,21662,20225,19766,23943,18510,19845,21165,20173,26790,21116,25653,26662,26103,20416,24544,22495,22152,20584,17510,20330,24036,20746,26673,25714,19841,-1,25110,19390,20249,21859,-1,17810,19017,24440,26888,22188\n118212,498,1603,1400,566,1478,11,679,1642,1896,474,1337,1352,967,118272,1090,497,885,113,118308,817,175,826,119,1639,1009,297,767,1409,118214,1496,428,1255,1008,424,404,972,1247,1346,1434,173,1818,1043,1604,118282,323,1075,984,591,118264,980,727,473,1474,889,118216,1157,1593,747,1711,1826,485,841,1343,195,229,1671,118162,106,1026,2034,1372,259,559,1289,1125,118218,1456,1370,63,982,1618,995,437,1229,905\n74172,73953,73433,73636,73238,73670,74209,73613,74050,73416,74299,73867,74392,74007,74234,73822,73827,74455,73370,73097,74728,74493,73995,73074,74232,74568,73753,74412,73904,73751,73032,74235,73766,73724,73945,74600,73517,73514,74593,-1,73692,73161,73606,74005,73880,73290,74391,74010,73720,74401,72947,73428,73400,72784,74367\n60157,59711,60565,59467,60486,59939,59316,60135,59275,59171,60623,60011,59728,60106,59694,60474,58604,59615,59623,58532,58515,58679,58451,58745,59195,59000,58186,58629,59163,59447,59677,59993,58626,59465,59336,-1,59217,60207,58587,58596,59053,58653,59124,60117,59791,59914,58900,58236,59522,58861,59619,59089,58593\n19151,19759,19570,19352,19869,18802,18860,18592,18529,20552,21303,20059,19726,20051,20566,18445,21312,-1,20931,19025,20696,18878,20976,19783,21692,20153,18536,21096,20805,18303,19848,19880,19327,19277,20030,19086,19020,21183,20428,19579,19904,20277,19353,20543,18229,21406,18656,19344,21048,19921,18969,19045,18889,21149,20101\n67044,68187,67249,-1,68134,66892,67768,67900,-1,-1,68560,67158,68497,67804,67011,68261,67662,67800,68479,67509,66464,69038,67178,-1,67294,66848,68369,67375,68585,67039,67392,67790,67985,67883,69274,67634,-1,67778,67106,68828,68700,67194,68536,67529,68982,68252,69379,69131,69447,68420,67752,67338,68912,69350,69142,68032,67874,67518,67581,68590,-1,67410,67880,67147,68403,68012\n100881,101734,101947,101249,101608,100798,101649,101120,101999,101123,100623,100800,100345,101221,102061,100931,101751,101606,101040,100817,102026,101906,100477,102289,100337,100154,101988,100556,101199,101706,101740,100948,100445,100650,100862,100014,100703,101011,101088,100342,101555,101357,101492,101036,101680,100730,101468,101277,100855,100471,101507,101293,100975,100785,101244,100463,99892,101190,101027,101331,101157,101923,101111,101409,101330,101378\n109107,109328,110630,-1,109359,106803,110072,109976,108538,110914,110306,108610,108976,107584,107678,107853,110939,108310,110259,108534,107614,109485,107285,108020,107774,108631,108590,110257,108195,109233,107854,107922,108093,110088,109726,108575,108464,109274,109094,110592,110117,109749,108944,109214,109547,-1,107104,111286,107773,110227,109260,109628,109203,108778,108216,107496,108429,108340,107561,107947,106995,109512,109925,108148,107783,106836,109266,108493,110260,110083,109491,106509,109699,110102,110838,108463,108528,110567,108445,108247,107490\n83434,83701,83745,82832,83340,82569,83403,83846,82652,82876,83214,84485,82747,82852,83693,83159,82430,83922,83036,83516,83101,83155,82911,84442,82786,82453,84377,84735,82514,84440,84087,82709,83845,84150,82565,83379,83709,83025,82936,84290,82819,83879,82545,84668,84425,83858,83880,82279,84104,83607,-1,83009,83789,83132,83235,84694,83266,82551,82869,83848,83104,83100,83598,82489,83583,83561,83959,83886,83527,83418,84638,83646,84332,82561,82767,84151,83014,82943,83457,83281,84238,83843,83559,83410,83491,82625,83124,83499\n51910,54216,53600,52989,53972,52692,52136,54674,52684,53173,52294,54251,53008,53041,54552,52439,52700,53587,54250,52724,54040,54786,53380,54407,54389,53910,54662,54793,54457,52041,53517,52529,54314,52375,53584,52755,51771,53261,53155,53706,51220,53119,53351,52040,54328,53510,53680,53042,51697,53551,52976,52251,53025,52057,52413,54501,53748,53036,53358,53304,53061,54419,51630,54016,52826,52309,51695,53157,53504,52480,53974,53890\n47704,46687,46764,46911,-1,46464,47665,47325,47515,46398,46568,46462,46160,46457,46885,46771,46036,47142,46774,46637,47342,45717,47151,46400,45909,46837,46261,46627,47143,47234,47219,46713,46454,46994,47336,46681,46303,46069,46862,47022,45990,46532,46197,47136,46761,46106,46431,46416,47012,47748,47435,46189,46486,47072,47384,45912,47880,-1,46854,46812,47091,47508,46634,45740,46053,47462\n3471,1516,1824,1828,2931,1800,1766,2218,2028,2803,2747,2321,1880,-1,1476,-1,1029,1883,3459,-1,2348,2462,2262,3207,2256,2802,2446,-1,1801,1624,2848,2888,1705,1430,3068,2376,2029,1693,2472,2849,2282,3547,2020,2383,2711,2435,2616,1977,1955,3653,3195,1723,3332,3243,3422,1165,3154,2230,2217,2835,1426,2065,2107,3048,2993,2757,1571\n67560,67105,66998,68663,67079,68094,67500,67318,66983,67115,-1,67185,67963,68165,67379,67104,68508,67291,68281,67404,67038,67939,66860,68498,66989,67898,67283,67770,68156,67233,66788,67481,67186,67674,67180,67545,67660,67860,68074,66921,67805,67779,68353,68228,67705,66662,68594,-1,67862,68137,67430,67461,67246,67377,67827,67329,68251,67598,66661,67586,67416,67943,68052,67312\n3910,4776,3398,4396,4455,3105,3468,3117,3142,4026,3629,3453,4347,3967,4201,3913,4401,3257,4631,4052,3088,3464,3733,4145,3637,3715,4248,3628,4218,4603,3592,4502,3180,4363,4343,4663,3826,3182,4148,4225,3695,3729,3735,4649,3788,3188,3731,3202,3608,3506,3219,4282,3514,3321,4236,4577,3586,4274,4217,3320,3177,3631,-1,4508,4265,4682\n29975,30943,29390,30857,31036,29696,29776,30618,30182,30101,29866,30091,30880,30839,29846,30855,31233,30200,30331,29570,29902,29955,30306,30738,30142,29628,30917,29967,30346,30304,-1,31219,30747,30273,30348,29742,30349,30510,30644,29749,31230,31031,30388,30779,30984,30827,29826,30188,30697,30518,30309,30622,30020,30823,30835,29554,30137,31389,30934,30452,30724,29675,-1,29790,30525,-1,29668,31001,29793,30354,30305,30035,31129,30396,30639,30115,30893,29510\n48584,49090,48516,48962,49388,-1,49141,49369,49591,48943,48845,-1,49776,48747,49500,48964,49856,49337,48906,49339,49099,49521,48479,49796,50142,48327,49982,49294,49543,49654,48096,49610,48722,48693,49780,48757,48879,49725,48884,48505,48912,49533,48937,48979,49216,49309,49810,48856,49469,49446\n9387,8389,8280,8660,8621,9384,9336,9251,9120,7809,8405,7822,8932,9209,8694,7886,9004,10161,8732,8368,9806,8481,9468,8059,7739,9296,9208,8431,9015,8699,9179,8343,9524,9606,9812,8820,8854,7574,7941,8641,8514,8722,8534,7627,8527,8369,9130,8023,9381,9443,-1,8715,8697,7742,7938,8739,8202,8002,8681,8162,9106\n40305,38437,37046,39265,39747,38822,39807,37828,38117,38449,39160,37178,39878,38865,38560,38604,38800,39426,38780,40006,39995,39230,40067,39444,37449,39363,40185,40262,40905,39871,38684,36958,39285,37898,38333,36999,37431,37840,40743,39555,38911,39639,37406,38219,36916,39518,-1,39348,39572,40094,38136,38354,40614,39301,38320,38086,37973,39643,39207,40992,40086,-1,37501,39944,37860,40510,39539,40497,38840,39055,40040,38325\n94506,94750,94261,95408,96004,94575,94558,95416,95827,94548,94663,95519,95735,95380,95619,95782,-1,95662,95003,95695,94329,95420,95472,96089,95456,94176,94958,95102,95226,95252,94184,94777,95106,95004,95905,94678,95348,94938,94425,94752,95262,95006,95356,94441,94926,94583,94730,94202,94367,95120,-1,-1,95358,95849,94705,95164,95203,95054,95182,95278,-1,95941,95804,95747,95509,94552,94914,94537,94642,95838,94661,94272\n43788,45142,44030,44325,-1,44306,43837,44584,45008,44257,44119,45099,44931,44476,43415,44893,43774,44372,43397,44532,44179,44316,44379,44594,44293,43724,44124,45156,43726,44890,43924,44622,44820,44575,45398,45203,44696,43648,44470,44063,43521,45041,43907,-1,44585,45368,45161,44564,44450,44854,43810,45047,44040,45267,43741,44173,43857,44951,44038\n" }, { "path": "tests/score.py", "content": "from __future__ import print_function\nimport sys\nstars=open(\"hip_main.dat\",\"r\").readlines()\nresult=open(sys.argv[1],\"r\").readlines()\nresult_real=open(sys.argv[2],\"r\").readlines()\nassert len(result)==len(result_real)\n\nstardict={}\nfor i in range(0,len(stars)):\n\tstardict[int(stars[i].split(\"|\")[1])]=i\n\nscores=[]\nfor i in range(0,len(result)):\n\tthisscore=[]\n\tt=0\n\tc=0\n\tw=0\n\ta=result[i].split(\",\")\n\tb=result_real[i].split(\",\")\n\tassert len(a)==len(b)\n\tfor j in range(0,len(a)):\n\t\tif int(a[j])!=-1:\n\t\t\tt+=1\n\t\t\tthisscore+=[stardict[int(a[j])]]\n\t\tif int(b[j])!=-1:\n\t\t\tif int(a[j])==int(b[j]):\n\t\t\t\tc+=1\n\t\t\telse:\n\t\t\t\tw+=1\n\tthisscore=[max((c-2*w)/float(max(t,1)),-1)]+thisscore\n\tif t>0:\n\t\tscores+=[[1.0]+thisscore]\n\telse:\n\t\tscores+=[[0.0]+thisscore]\n\na=0\nb=0\t\t\nfor i in range(0,len(scores)):\n\tif scores[i][1]\n# \n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limitation the rights to use, copy, modify, merge, publish,\n# distribute, distribute with modifications, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n# IN NO EVENT SHALL THE ABOVE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\n# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR\n# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR\n# THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n# \n# Except as contained in this notice, the name(s) of the above copyright\n# holders shall not be used in advertising or otherwise to promote the\n# sale, use or other dealings in this Software without prior written\n# authorization.\n\n# Imports\n# -------\n\nfrom __future__ import print_function\nimport pandas as pd\nimport numpy as np\nimport math\nimport sys\n\nBASE_PHOTONS = 19100 # photoelectrons per mm^2 and second of a magnitude 0 G2 star\n \n\n# Geometric Transformations\n# -------------------------\n\ndef angles_to_vector(azimuth, altitude):\n \"\"\"Transforms azimuth altitude representation to unit-length vectors.\"\"\"\n caz = np.cos(azimuth)\n saz = np.sin(azimuth)\n\n cal = np.cos(altitude)\n sal = np.sin(altitude)\n\n x = caz * cal\n y = saz * cal\n z = sal\n\n return np.array([x, y, z]).transpose()\n\ndef vector_to_angles(vectors):\n \"\"\"Transforms unit-length vectors to the azimuth altitude representation.\"\"\"\n x, y, z = split_vectors(vectors)\n\n az = np.arctan2(y, x)\n alt = np.arcsin(z)\n\n return np.array([az, alt])\n\ndef split_vectors(vectors):\n \"\"\"Splits vectors into their x, y and z components.\"\"\"\n return vectors[..., 0], vectors[..., 1], vectors[..., 2]\n\ndef randomVectors(num):\n \"\"\"Generates `num` random three dimensional unit-length vectors.\"\"\"\n rands = np.random.rand(num, 2)\n\n ca = np.cos(2. * np.pi * rands[:, 0])\n sa = np.sin(2. * np.pi * rands[:, 0])\n\n z = rands[:, 1] * 2 - 1\n r = np.sqrt(1 - z * z)\n\n return np.vstack([r * ca, r * sa, z]).transpose()\n\ndef random_matrix():\n \"\"\"Generates a random 3x3 orientation matrix.\n Based on James Arvo's algorithm from Graphics Gems III, pages 117-120\"\"\"\n rands = np.random.rand(3)\n\n ca = np.cos(2. * np.pi * rands[0])\n sa = np.sin(2. * np.pi * rands[0])\n\n R = ([[ca, sa, 0],\n [-sa, ca, 0],\n [0, 0, 1.]])\n\n cb = np.cos(2 * np.pi * rands[1])\n sb = np.sin(2 * np.pi * rands[1])\n\n sc = np.sqrt(rands[2])\n mc = np.sqrt(1 - rands[2])\n\n v = np.array([[cb * sc, sb * sc, mc]])\n\n H = np.eye(3) - 2 * np.dot(v.transpose(), v)\n\n matrix = -np.dot(H, R)\n\n return matrix\n\ndef add_vector_noise(base_vectors, stddev):\n \"\"\"Adds Gaussian noise to a list of vectors.\"\"\"\n num = len(base_vectors)\n\n v_r = randomVectors(num)\n\n v_r = np.cross(base_vectors, v_r)\n\n v_r /= np.linalg.norm(v_r, axis=1).reshape((-1, 1))\n\n v_r = base_vectors + stddev * (np.random.randn(num, 1) * v_r)\n\n return v_r / np.linalg.norm(v_r, axis=1).reshape((-1, 1))\n\n# Camera\n# ------\n\nclass Camera(object):\n def __init__(self, resolution, pixel_ar = 1, principal_point = (0.5, 0.5)):\n self.resolution = resolution\n self.pixel_ar = pixel_ar\n self.principal_point = principal_point\n\n def from_angles(self, azimuth, altitude):\n \"\"\"Transforms camera-relative azimuth altitude information to pixel coordinates.\"\"\"\n res_x, res_y = self.resolution\n pp_x, pp_y = self.principal_point\n\n ar = self.pixel_ar * res_x / res_y\n\n theta = np.pi / 2 - altitude\n\n # x-axis flip: note that we look in the positive z-axis direction,\n # so in a right-handed coordinate system the x-axis goes to the left,\n # but we want it to go to the right, so we flip the x-axis\n # the y-axis still goes up\n # this equals transforming the azimuth as follows:\n alpha = np.pi - azimuth\n\n r = self.project(theta)\n x = ( np.cos(alpha) * r + pp_x) * res_x\n y = (ar * np.sin(alpha) * r + pp_y) * res_y\n\n return np.array((y, x)).transpose()\n\n def to_angles(self, pos):\n \"\"\"Transforms pixel coordinates to camera-relative azimuth altitude information.\"\"\"\n res_x, res_y = self.resolution\n pp_x, pp_y = self.principal_point\n\n ar = self.pixel_ar * res_x / res_y\n\n x = pos[:, 1]\n y = pos[:, 0]\n\n x = (x / res_x - pp_x)\n y = (y / res_y - pp_y) / ar\n\n az = np.pi - np.arctan2(y, x)\n\n r = np.sqrt(x ** 2 + y ** 2)\n theta = self.unproject(r)\n\n alt = np.pi / 2 - theta\n\n return az, alt\n\nclass EquidistantCamera(Camera):\n def __init__(self, f, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.f = f\n super(EquidistantCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n return theta * self.f\n\n def unproject(self, r):\n return r / self.f\n\nclass RectilinearCamera(Camera):\n def __init__(self, f, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.f = f\n super(RectilinearCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n result = np.tan(theta) * self.f\n result[theta > np.pi / 2.] = 1.e9\n return result\n\n def unproject(self, r):\n return np.arctan(r / self.f)\n\nclass OrthographicCamera(Camera):\n def __init__(self, f, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.f = f\n super(OrthographicCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n result = np.sin(theta) * self.f\n result[theta > np.pi / 2.] = 1.e9\n return result\n\n def unproject(self, r):\n return np.arcsin(r / self.f)\n\nclass EquisolidAngleCamera(Camera):\n def __init__(self, f, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.f = f\n super(EquisolidAngleCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n result = np.sin(theta / 2.) * 2. * self.f\n result[theta > np.pi] = 1.e9\n return result\n\n def unproject(self, r):\n return 2. * np.arcsin(r / (2. * self.f))\n\nclass StereographicCamera(Camera):\n def __init__(self, f, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.f = f\n super(StereographicCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n result = np.tan(theta / 2.) * 2. * self.f\n result[theta > np.pi] = 1.e9\n return result\n\n def unproject(self, r):\n return 2. * np.arctan(r / (2. * self.f))\n\nclass CubicCamera(Camera):\n def __init__(self, k1, k2, resolution, pixel_ar = 1., principal_point = (0.5, 0.5)):\n self.k1 = k1\n self.k2 = k2\n super(CubicCamera, self).__init__(resolution, pixel_ar, principal_point)\n\n def project(self, theta):\n return self.k1 * theta + self.k2 * theta ** 3\n\n def unproject(self, r):\n # 0 = a * theta ^ 3 + c * theta + d\n a = self.k2\n c = self.k1\n d = -r\n\n if a == 0:\n theta = r/c\n return theta\n\n delta_0 = -3. * a * c\n delta_1 = 27. * a * a * d\n\n if a < 0:\n C = ((delta_1 + np.sqrt(np.complex64(delta_1 ** 2 - 4. * delta_0 ** 3))) / 2.) ** (1. / 3.)\n\n theta = np.real(-1. / (3. * a) * (C + delta_0 / (C)))\n else:\n C = ((delta_1 + np.sqrt(delta_1 ** 2 - 4. * delta_0 ** 3)) / 2) ** (1. / 3.)\n\n theta = -1. / (3. * a) * (C + delta_0 / (C))\n\n\n return theta\n\n\n# Star Catalog\n# ------------\n\nclass StarCatalog:\n def __init__(self, filename='hip_main.dat'):\n if filename is not None:\n self.read(filename)\n self.preprocess()\n\n def preprocess(self):\n filter_index = np.logical_not(np.logical_or(np.isnan(self.catalog['RAdeg']), np.isnan(self.catalog['Vmag'])))\n\n self.catalog = self.catalog[filter_index]\n\n self.star_vectors = angles_to_vector(np.deg2rad(self.catalog['RAdeg']), np.deg2rad(self.catalog['DEdeg']))\n\n self.magnitudes = self.catalog['Vmag'].values\n\n # alternative magnitude\n #VT = self.catalog['VTmag']\n #BT = self.catalog['BTmag']\n ## http://www.aerith.net/astro/color_conversion.html\n ## http://ads.nao.ac.jp/cgi-bin/nph-bib_query?bibcode=2002AJ....124.1670M&db_key=AST&high=3d1846678a19297\n #self.magnitudes = VT + 0.00097 - 0.1334 * (BT - VT) + 0.05486 * (BT - VT) ** 2 - 0.01998 * (BT - VT) ** 3\n\n # randomly fill out missing magnitudes\n #magnitudes = self.catalog['Vmag'].values\n #nans = np.isnan(magnitudes)\n #magnitudes[nans] = np.random.choice(magnitudes[~nans], np.sum(nans))\n #self.catalog['Vmag'] = magnitudes\n\n def lookup_indices(self, indices):\n result = indices.copy()\n result[indices >= 0] = self.catalog['HIP'].iloc[indices[indices >= 0]]\n return result\n\n def read(self, filename='hip_main.dat'):\n \"\"\"Loads the Hipparchos star catalog.\"\"\"\n\n columns = [\n \"Catalog\",\n \"HIP\",\n \"Proxy\",\n \"RAhms\",\n \"DEdms\",\n \"Vmag\",\n \"VarFlag\",\n \"r_Vmag\",\n \"RAdeg\",\n \"DEdeg\",\n \"AstroRef\",\n \"Plx\",\n \"pmRA\",\n \"pmDE\",\n \"e_RAdeg\",\n \"e_DEdeg\",\n \"e_Plx\",\n \"e_pmRA\",\n \"e_pmDE\",\n \"DE:RA\",\n \"Plx:RA\",\n \"Plx:DE\",\n \"pmRA:RA\",\n \"pmRA:DE\",\n \"pmRA:Plx\",\n \"pmDE:RA\",\n \"pmDE:DE\",\n \"pmDE:Plx\",\n \"pmDE:pmRA\",\n \"F1\",\n \"F2\",\n \"---\",\n \"BTmag\",\n \"e_BTmag\",\n \"VTmag\",\n \"e_VTmag\",\n \"m_BTmag\",\n \"B-V\",\n \"e_B-V\",\n \"r_B-V\",\n \"V-I\",\n \"e_V-I\",\n \"r_V-I\",\n \"CombMag\",\n \"Hpmag\",\n \"e_Hpmag\",\n \"Hpscat\",\n \"o_Hpmag\",\n \"m_Hpmag\",\n \"Hpmax\",\n \"HPmin\",\n \"Period\",\n \"HvarType\",\n \"moreVar\",\n \"morePhoto\",\n \"CCDM\",\n \"n_CCDM\",\n \"Nsys\",\n \"Ncomp\",\n \"MultFlag\",\n \"Source\",\n \"Qual\",\n \"m_HIP\",\n \"theta\",\n \"rho\",\n \"e_rho\",\n \"dHp\",\n \"e_dHp\",\n \"Survey\",\n \"Chart\",\n \"Notes\",\n \"HD\",\n \"BD\",\n \"CoD\",\n \"CPD\",\n \"(V-I)red\",\n \"SpType\",\n \"r_SpType\",\n ]\n\n self.catalog = pd.read_csv(filename, sep='|', names=columns, skipinitialspace=True)\n\n# Star Detector\n# -----\n\nclass StarDetector:\n def __init__(self, sigma_psf, t_exp, aperture, base_flux):\n self.sigma_psf = sigma_psf\n self.t_exp = t_exp\n self.aperture = aperture\n self.base_flux = base_flux\n \n @staticmethod\n def norm_gaussian(sigma):\n return math.erf((2*sigma)**-0.5)**2/4.\n #photon_floor is the cutoff at which we can round the number of photons from a star down to zero\n def compute_flux(self, magnitude, add_noise=True,photon_floor=0.001):\n flux = self.base_flux * (10 ** (-magnitude / 2.5))\n\n if add_noise:\n flux = flux + np.random.normal(0, IMAGE_VARIANCE, len(flux))\n \n flux_per_photon = self.base_flux* self.t_exp * self.aperture ** 2 * np.pi/BASE_PHOTONS\n flux += flux_per_photon*np.random.normal(0, self.norm_gaussian(self.sigma_psf)*np.sqrt(np.clip(flux/flux_per_photon,photon_floor,None)), len(flux))\n return np.clip(flux,photon_floor*flux_per_photon,None)\n\n def compute_magnitude(self, flux):\n return -2.5 * np.log10(flux / self.base_flux)\n\n def compute_magnitude_threshold(self):\n threshold = THRESH_FACTOR*IMAGE_VARIANCE\n return self.compute_magnitude(threshold)\n\n def add_noise(self, magnitude):\n return self.compute_magnitude(self.compute_flux(magnitude))\n\n# Scene\n# -----\n\nclass Scene:\n def __init__(self, catalog, camera, detector, gaussian_noise_sigma=None, quantization_noise=None, magnitude_gaussian=None):\n self.catalog = catalog\n self.camera = camera\n self.detector = detector\n self.gaussian_noise_sigma = gaussian_noise_sigma\n self.quantization_noise = quantization_noise\n self.magnitude_gaussian = magnitude_gaussian\n self.orientation = None\n self.pos = None\n self.ids = None\n self.magnitude_threshold = detector.compute_magnitude_threshold()\n def compute(self, orientation=None):\n \"\"\"Generates a scene for the star tracker.\n If not orientation is given a random one is generated.\n Gaussian noise is applied to star positions if enabled.\"\"\"\n\n res_x, res_y = self.camera.resolution\n\n if orientation is None:\n orientation = random_matrix()\n\n self.orientation = orientation\n\n star_ids = np.arange(len(self.catalog.star_vectors))\n pos = np.dot(self.catalog.star_vectors, orientation.transpose())\n\n\n # instead vector noise\n if self.gaussian_noise_sigma:\n pos = add_vector_noise(pos, self.gaussian_noise_sigma)\n\n az, alt = vector_to_angles(pos)\n\n scene = self.camera.from_angles(az, alt)\n\n if self.quantization_noise:\n scene = np.int32(scene)\n\n selection = np.logical_and(np.logical_and(scene[:, 0] >= 0, scene[:, 0] < res_y), np.logical_and(scene[:, 1] >= 0, scene[:, 1] < res_x))\n\n scene = scene[selection]\n scene_ids = star_ids[selection]\n\n self.pos = scene\n self.magnitudes = self.catalog.magnitudes[scene_ids]\n self.ids = self.catalog.lookup_indices(scene_ids)\n\n def add_false_stars(self, false_stars):\n \"\"\"Adds randomly generated false stars to a scene.\"\"\"\n\n if isinstance(false_stars, int):\n res_x, res_y = self.camera.resolution\n\n false_star_pos = np.random.rand(false_stars, 2) * (res_y, res_x)\n else:\n false_star_pos = false_stars\n false_stars = len(false_stars)\n\n if self.quantization_noise:\n false_star_pos = np.int32(false_star_pos)\n\n self.pos = np.concatenate([self.pos, false_star_pos])\n self.magnitudes = np.concatenate([self.magnitudes, np.random.choice(self.catalog.magnitudes[self.catalog.magnitudes < self.magnitude_threshold], size=false_stars)])\n self.ids = np.concatenate([self.ids, -np.ones(false_stars, np.int32)])\n\n def copy(self, camera=None, orientation=None, copy_false_stars=True):\n\n false_stars = self.pos[self.ids == -1] if copy_false_stars else []\n\n if camera is None:\n camera = self.camera\n else:\n false_stars = camera.from_angles(*self.camera.to_angles(false_stars))\n\n if orientation is None:\n orientation = self.orientation\n\n scene = Scene(self.catalog, camera, self.detector, self.gaussian_noise_sigma, self.quantization_noise, self.magnitude_gaussian)\n scene.compute(orientation)\n\n scene.add_false_stars(false_stars)\n\n scene.scramble()\n\n scene.add_magnitude_noise(self.magnitude_gaussian)\n\n scene.filter_magnitudes()\n\n return scene\n\n def scramble(self):\n \"\"\"Scrambles the order of stars in a scene.\"\"\"\n\n scramble_index = np.random.permutation(range(len(self.ids))) #permutation returns ndarray\n \n if len(scramble_index) == 0:\n return None\n\n self.pos = self.pos[scramble_index, ...]\n self.magnitudes = self.magnitudes[scramble_index]\n self.ids = self.ids[scramble_index]\n\n def add_magnitude_noise(self, gaussian=None):\n #if catalog:\n # self.magnitudes[self.scene_ids >= 0] += np.random.normal(0, self.catalog.catalog['e_VTmag'])\n\n self.magnitudes = self.detector.add_noise(self.magnitudes)\n\n if gaussian is not None:\n self.magnitudes += np.random.normal(0, gaussian, size=len(self.magnitudes))\n\n def filter_magnitudes(self):\n filter_index = self.magnitudes <= self.magnitude_threshold\n self.pos = self.pos[filter_index]\n self.magnitudes = self.magnitudes[filter_index]\n self.ids = self.ids[filter_index]\n\n @staticmethod\n def random(catalog, camera, detector, min_true, max_true, min_false, max_false, min_stars=1, max_tries = 1000, gaussian_noise_sigma=None, quantization_noise=None, magnitude_gaussian=None):\n scene = Scene(catalog, camera, detector, gaussian_noise_sigma, quantization_noise, magnitude_gaussian)\n\n num_stars = 0\n tries = 0\n\n ok = False\n\n while not ok and tries < max_tries:\n scene.compute()\n\n scene.add_false_stars(np.random.randint(min_false, max_false + 1))\n\n scene.scramble()\n\n scene.add_magnitude_noise(magnitude_gaussian)\n\n scene.filter_magnitudes()\n\n num_stars = np.sum(scene.ids >= 0)\n\n ok = num_stars >= min_true and num_stars <= max_true and len(scene.ids) > min_stars\n\n tries += 1\n\n if tries == max_tries:\n return None\n\n return scene\n\n#only do this part if we were run as a python script\nif __name__ == '__main__':\n\tif len(sys.argv)!=4:\n\t\tsys.stdout.write(\"Usage: python simulator.py calibration.txt input.csv results.csv\\n\")\n\t\texit()\n\n\texec(open(sys.argv[1]).read())\n\n\tres_x = float(IMG_X) # pixels\n\tres_y = float(IMG_Y) # pixels\n\n\t# normalized focal length\n\tf = 0.5 / np.tan(np.deg2rad((IMG_X*PIXSCALE/3600)) / 2.)\n\t# pixel aspect ratio\n\tpixel_ar = 1.\n\n\t# normalized principal point\n\tppx = 0.5\n\tppy = 0.5\n\n\tgaussian_noise_sigma = (np.pi*PIXSCALE)*np.sqrt(POS_VARIANCE)/(180*3600.0) # rad\n\n\tcam = 0\n\n\tsigma_psf = 0.5*float(DOUBLE_STAR_PX) # pixel\n\n\n\tt_exp = float(EXPOSURE_TIME) # s\n\taperture = float(APERTURE) # mm\n\tbase_flux = float(BASE_FLUX)\n\n\tmagnitude_gaussian = 0.02 # mag\n\n\tmin_true = 0\n\tmax_true = 1000\n\tmin_false = 0\n\tmax_false = MAX_FALSE_STARS\n\n\n\tcatalog = StarCatalog()\n\tcameras = [\n\t\tRectilinearCamera, #no distortion\n\t\tEquidistantCamera, # the rest model barrel and pin-cushion distortion\n\t\tEquisolidAngleCamera,\n\t\tStereographicCamera,\n\t\tOrthographicCamera,\n\t]\n\n\tcamera = cameras[cam](f, (res_x, res_y), pixel_ar, (ppx, ppy))\n\tdetector = StarDetector(sigma_psf, t_exp, aperture, base_flux)\n\tnum_scenes = 100\n\tinputs = []\n\toutputs = []\n\n\tfor i in range(num_scenes):\n\t\tscene = Scene.random(catalog, camera, detector, min_true, max_true, min_false, max_false, gaussian_noise_sigma=gaussian_noise_sigma, magnitude_gaussian=magnitude_gaussian)\n\t\tinputs.append(np.hstack((scene.pos[::, ::-1], scene.magnitudes.reshape(-1, 1))).flatten())\n\t\toutputs.append(scene.ids)\n\n\tdef write_csv(filename, lines):\n\t\twith open(filename, 'w') as f:\n\t\t\tfor line in lines:\n\t\t\t\tf.write(','.join(str(value) for value in line) + '\\n')\n\n\twrite_csv(sys.argv[2], inputs)\n\twrite_csv(sys.argv[3], outputs)\n" }, { "path": "tests/test.c", "content": "/****************************************************************************\n * Copyright (c) 2016 Joerg H. Mueller *\n * *\n * Permission is hereby granted, free of charge, to any person obtaining a *\n * copy of this software and associated documentation files (the *\n * \"Software\"), to deal in the Software without restriction, including *\n * without limitation the rights to use, copy, modify, merge, publish, *\n * distribute, distribute with modifications, sublicense, and/or sell *\n * copies of the Software, and to permit persons to whom the Software is *\n * furnished to do so, subject to the following conditions: *\n * *\n * The above copyright notice and this permission notice shall be included *\n * in all copies or substantial portions of the Software. *\n * *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS *\n * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF *\n * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. *\n * IN NO EVENT SHALL THE ABOVE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, *\n * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR *\n * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR *\n * THE USE OR OTHER DEALINGS IN THE SOFTWARE. *\n * *\n * Except as contained in this notice, the name(s) of the above copyright *\n * holders shall not be used in advertising or otherwise to promote the *\n * sale, use or other dealings in this Software without prior written *\n * authorization. *\n ****************************************************************************/\n\n#include \"../beast/beast.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n#define MAX_STARS 1000\n#define VECTOR_LENGTH 3\n\n#define EDISON_SPEED_FACTOR 30\n\nstar_db *S_DB,*S_FILTERED;\nstar_query *SQ_RESULTS;\nconstellation_db *C_DB;\n\n/***\n * Determines the HIP numbers of stars in a scene.\n * \n * @param spikes The data of the spikes in the scene. x, y, and magnitude interleaved.\n * @param result The output array that should be filled with the HIP numbers.\n * @param length The number of spikes in the scene.\n */\nint IDNUM;\nvoid star_id(double spikes[], int result[], size_t length)\n{\t\n\tstar_db* img_stars=new star_db;\n\tfor(size_t i = 0; i < length; i++) {\n\t\t*img_stars+=star(spikes[3*i]-IMG_X/2.0,-(spikes[3*i+1]-IMG_Y/2.0),BASE_FLUX*powf(10.0,-spikes[3*i+2]/2.5),-1);\n\t\tresult[i] = -1;\n\t}\n\tstar_db* img_stars_n_brightest=img_stars->copy_n_brightest(MAX_FALSE_STARS+REQUIRED_STARS);\n\tconstellation_db * img_n_brightest=new constellation_db(img_stars_n_brightest,MAX_FALSE_STARS+2,1);\n\tdb_match* lis = new db_match(C_DB,img_n_brightest);\n\tif (lis->p_match>0.9) {\n\t\tfloat x=lis->winner->R11;\n\t\tfloat y=lis->winner->R21;\n\t\tfloat z=lis->winner->R31;\n\t\t\n\t\t//Tests relative matching, and fills in missing stars\n\t\t//and fill in missing stars\n\t\tSQ_RESULTS->kdsearch(x,y,z,MAXFOV/2,THRESH_FACTOR*IMAGE_VARIANCE);\n\t\tC_DB->results->kdsearch(x,y,z,MAXFOV/2,THRESH_FACTOR*IMAGE_VARIANCE);\n\t\tstar_db* near_stars=SQ_RESULTS->from_kdresults();\n\t\tconstellation_db* fov_db = new constellation_db(near_stars,C_DB->results->r_size(),1);\n\t\tdelete near_stars;\n\t\tC_DB->results->clear_kdresults();\n\t\tSQ_RESULTS->clear_kdresults();\n\t\t\n\t\tconstellation_db * img=new constellation_db(img_stars,MAX_FALSE_STARS+2,1);\n\t\tdb_match* fov_match = new db_match(fov_db,img);\n\t\tstar_db* db_stars=fov_match->winner->from_match();\n\t\tfor(size_t i = 0; i < length; i++) result[i] = db_stars->get_star(i)->id;\n\t\tdelete img;\n\t\tdelete db_stars;\n\t\tdelete fov_db;\n\t\tdelete fov_match;\n\t}\n\tdelete lis;\n\tdelete img_n_brightest;\n\tdelete img_stars;\n\tdelete img_stars_n_brightest;\n}\n\n\nint main(int argc, char* argv[])\n{\n\tif(argc < 4) {\n\t\tprintf(\"./test input.csv calibration.txt year\\n\");\n\t\tprintf(\"year >= 1991.25\");\n\t\treturn 0;\n\t}\n\t\n\tload_config(argv[2]);\n\tS_DB=new star_db;\n\tS_DB->load_catalog(\"hip_main.dat\",atof(argv[3]));\n\t\n\tSQ_RESULTS=new star_query(S_DB);\n\tSQ_RESULTS->kdmask_filter_catalog();\n\tSQ_RESULTS->kdmask_uniform_density(REQUIRED_STARS);\n\tS_FILTERED=SQ_RESULTS->from_kdmask();\n\tC_DB=new constellation_db(S_FILTERED,2+DB_REDUNDANCY,0);\n\tSQ_RESULTS->reset_kdmask();\n\t// read scenes and run star identification\n\tFILE* file = fopen(argv[1], \"r\");\n\n\tchar *line = NULL;\n\tsize_t len = 0;\n\tssize_t read;\n\n\tdouble* data = (double*)malloc(sizeof(double) * VECTOR_LENGTH * MAX_STARS);\n\tint* results = (int*)malloc(sizeof(int) * MAX_STARS);\n\n\tclock_t time_sum = 0;\n\tint run_times = 0;\n\tfor(IDNUM=0;(read = getline(&line, &len, file)) != -1;IDNUM++) {\n\t\tsize_t i = 0;\n\t\tfor(char* result = strtok(line, \",\"); result != NULL; i++) {\n\t\t\tdata[i] = atof(result);\n\t\t\tresult = strtok(NULL, \",\");\n\t\t}\n\t\tunsigned int length = i / VECTOR_LENGTH;\n\t\tclock_t start = clock();\n\t\tstar_id(data, results, length);\n\t\tclock_t end = clock();\n\t\ttime_sum += end - start;\n\t\tfor(i = 0; i < length; i++) printf(\"%d%c\", results[i], i == length - 1 ? '\\n' : ',');\n\t\trun_times++;\n\t\tfprintf(stderr,\"Time on edison: %f\\n\", ((float)time_sum*EDISON_SPEED_FACTOR) / (CLOCKS_PER_SEC*run_times));\n\t}\n\n\tfree(line);\n\tfree(results);\n\tfree(data);\n\tfclose(file);\n\n\treturn 0;\n}\n\n" }, { "path": "tests/unit_test.sh", "content": "#!/bin/bash\n\nCALIBRATE=0\nREGENERATE=0\nESA_TEST=0\nIMG_TEST=0\n\n#PYTHON=\"/usr/bin/python2.7\"\nPYTHON=\"/usr/bin/python3.10\"\n\nwhile getopts \":crei\" opt; do\n case $opt in\n c)\n\t CALIBRATE=1\n ;;\n r)\n\t REGENERATE=1\n ;;\n e)\n\t ESA_TEST=1\n ;;\n i)\n\t IMG_TEST=1\n ;;\n \\?)\n echo \"Usage: ./unit_test.sh [options] testdir [cmd]\"\n echo -e \"\"\n echo -e \"\\t-c\\tCalibrate based on images in testdir/samples/\"\n echo -e \"\\t-r\\tRegenerate ESA test\"\n echo -e \"\\t-e\\tRun ESA test\"\n echo -e \"\\t-i\\tRun image test\"\n echo -e \"\"\n echo -e \"Example cmd:\"\n\t echo -e \"\\tmassif-visualizer: valgrind --tool=massif\"\n\t echo -e \"\\tkcachegrind: valgrind --tool=cachegrind\"\n echo -e \"\"\n exit\n ;;\n esac\ndone\nshift \"$[$OPTIND-1]\"\n\npushd \"`dirname $0`\">/dev/null\n\nTESTDIR=\"$1\"\nif [ ! -d \"$TESTDIR\" ]; then\n\techo \"'$TESTDIR' is not a valid directory \"\n\texit\nfi\n\nshift\n\nKILLPID=\"\"\nif [[ $ESA_TEST == 1 ]]; then\n\tmake || exit\nfi\nif [[ $IMG_TEST == 1 ]]; then\n\tpushd ../beast >/dev/null\n\t./go || exit\n\tpopd>/dev/null\nfi\nif [[ $CALIBRATE == 1 ]]; then\n\techo \"Calibrating...\"\n\ttime $PYTHON calibrate.py $TESTDIR || exit\nfi\nif [[ $REGENERATE == 1 ]]; then\n\techo \"Regenerating...\"\n\ttime $PYTHON simulator.py $TESTDIR/calibration.txt $TESTDIR/input.csv $TESTDIR/result.csv || exit\nfi\n\nif [[ $ESA_TEST == 1 ]]; then\n\techo \"ESA test...\"\n\tmake &&\n\ttime $@ ./test $TESTDIR/input.csv $TESTDIR/calibration.txt 1991.25 | tee $TESTDIR/result_real.csv &&\n\tgprof test | gprof2dot -s | dot -Tpdf -o test.pdf &&\n\techo \"camera coverage simulation percent:\" &&\n\techo \"100-`diff --suppress-common-lines --speed-large-files -y $TESTDIR/result.csv $TESTDIR/result_real.csv | wc -l`/1\" | bc -l &&\n\t$PYTHON score.py $TESTDIR/result.csv $TESTDIR/result_real.csv \nfi\n\nif [[ $IMG_TEST == 1 ]]; then\n\t$@ $PYTHON startracker.py $TESTDIR/calibration.txt 1991.25 $TESTDIR/median_image.png &\n\tKILLPID=\"$!\"\n\tsleep 10\n\t#make sure we dont crash when given an image w/ no stars\n\techo \"rgb.solve_image('$TESTDIR/median_image.png')\" | nc -w1 127.0.0.1 8010\n\tsleep 0.5\n\tfor i in $TESTDIR/samples/*; do\n\t\techo \"rgb.solve_image('$i')\" | nc -w1 127.0.0.1 8010\n\t\tsleep 0.5\n\t\techo \"rgb.solve_image('$i')\" | nc -w1 127.0.0.1 8010\n\t\tsleep 0.5\n\tdone\n #sleep 0.5\n #echo 'exception test' | nc 127.0.0.1 8010\n sleep 0.5\n\techo 'quit()' | nc -w1 127.0.0.1 8010\nfi\nif [ \"$KILLPID\" != \"\" ] ; then \n\tkill $KILLPID\nfi\npopd>/dev/null\n\n" } ]