예제 #1
0
        static void Main(string[] args)
        {
            Console.Beep();
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("*******************************************************");
            Console.WriteLine("*                    RTT-150 CES                      *");
            Console.WriteLine("*            SPECTRA REDUCTION PIPELINE               *");
            Console.WriteLine("*******************************************************");
            Console.ForegroundColor = ConsoleColor.White;

            //InitFile.ReadInitFile("INIT.dat");
            Init.Initialize("INIT.dat");
            if (Init.ErrorString != "")
            {
                Console.WriteLine("Error in INIT file!");
                Console.WriteLine(Init.ErrorString);
                goto STOP;
            }

            string dir_main = (string)Init.Value("DIR_MAIN");

            if (!Directory.Exists(dir_main))
            {
                try
                {
                    Directory.CreateDirectory(dir_main);
                }
                catch
                {
                    Console.WriteLine("Cannot create main directory.\r\n");
                    goto STOP;
                }
            }

            string[] bias_files = null, flat_files = null, thar_files = null, obj_files = null;
            Console.WriteLine("Searching for Bias, Flat, ThAr and Object files...");
            try
            {
                bias_files = ImageSelector.GetFilesByHDU(
                    (string)Init.Value("DIR_BIAS"), (string)Init.Value("FMASK_BIAS"), "imagetyp", "*");
                flat_files = ImageSelector.GetFilesByHDU(
                    (string)Init.Value("DIR_FLAT"), (string)Init.Value("FMASK_FLAT"), "imagetyp", "*");
                thar_files = ImageSelector.GetFilesByHDU(
                    (string)Init.Value("DIR_CLBR"), (string)Init.Value("FMASK_CLBR"), "imagetyp", "*");
                obj_files = ImageSelector.GetFilesByHDU(
                    (string)Init.Value("DIR_OBJ"), (string)Init.Value("FMASK_OBJ"), "imagetyp", "*");
            }
            catch
            {
                Console.WriteLine("Error in files searching...");
                goto STOP;
            }

            Console.WriteLine("Bias files number: {0}", bias_files.Length);
            Console.WriteLine("Flat files number: {0}", flat_files.Length);
            Console.WriteLine("ThAr files number: {0}", thar_files.Length);
            Console.WriteLine("Object files number: {0}", obj_files.Length);

            if (bias_files.Length == 0 || flat_files.Length == 0 ||
                thar_files.Length == 0 || obj_files.Length == 0)
            {
                Console.WriteLine("Some necessary files were not found...");
                goto STOP;
            }

            Image[] biases  = new Image[bias_files.Length];
            Image[] flats   = new Image[flat_files.Length];
            Image[] thars   = new Image[thar_files.Length];
            Image[] objects = new Image[obj_files.Length];

            Console.WriteLine("Loading Bias images...");
            for (int i = 0; i < bias_files.Length; i++)
            {
                Console.WriteLine("->" + bias_files[i]);
                biases[i] = new Image();
                biases[i].LoadImage(bias_files[i]);
            }
            Console.WriteLine("Bias images averaging...");
            Image bias_aver = ImageCombinator.Median(biases);

            for (int i = 0; i < bias_files.Length; i++)
            {
                biases[i] = null;
            }

            Console.WriteLine("Loading Flat images...");
            for (int i = 0; i < flat_files.Length; i++)
            {
                Console.WriteLine("->" + flat_files[i]);
                flats[i] = new Image();
                flats[i].LoadImage(flat_files[i]);
            }
            Console.WriteLine("Flat images averaging...");
            Image flat_aver = ImageCombinator.Median(flats);

            for (int i = 0; i < flat_files.Length; i++)
            {
                flats[i] = null;
            }

            Console.WriteLine("Loading Th-Ar images...");
            for (int i = 0; i < thar_files.Length; i++)
            {
                Console.WriteLine("->" + thar_files[i]);
                thars[i] = new Image();
                thars[i].LoadImage(thar_files[i]);
            }
            Console.WriteLine("ThAr images averaging...");
            Image Thar_aver = ImageCombinator.Median(thars);

            for (int i = 0; i < thar_files.Length; i++)
            {
                thars[i] = null;
            }

            Console.WriteLine("Bias substraction from averanged Flat image...");
            Image flat_aver_db = flat_aver - bias_aver;

            Console.WriteLine("Bias substraction from averanged ThAr image...");
            Image Thar_aver_db = Thar_aver - bias_aver;

            Console.WriteLine("Replasing pixels values:\r\n negative -> 1 for flat;\r\n negative -> 0 for Th-Ar;");
            for (int i = 0; i < flat_aver_db.NAXIS1; i++)
            {
                for (int j = 0; j < flat_aver_db.NAXIS2; j++)
                {
                    if (flat_aver_db[i, j] <= 0)
                    {
                        flat_aver_db[i, j] = 1;
                    }
                    if (Thar_aver_db[i, j] < 0)
                    {
                        Thar_aver_db[i, j] = 0;
                    }
                }
            }

            Console.WriteLine("Search for order locations...");
            int polinim_degree_order = 3;

            Locator.Locate(ref flat_aver_db, polinim_degree_order);

            double[][] pos_ord = Locator.Ord_Pos;
            double[][] pos_min = Locator.Min_Pos;

            double[][] fluxes;

            Console.WriteLine("Extraction of the flat spectra...");
            Extractor.Extract(flat_aver_db, pos_ord, pos_min, -1);
            fluxes = Extractor.FluxDebiased;
            Saver.SaveOrderedDistribution(fluxes, dir_main + "\\Flat_Orders.dat");

            // Flat spectra normalization;
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("Flat spectra normalization...");
            Console.ForegroundColor = ConsoleColor.White;

            Normator.Norm(fluxes, 12, 0, 13);
            Saver.SaveOrderedDistribution(Normator.OrdersNorm, dir_main + "\\Flat_Orders_Norm.dat");
            Saver.SaveOrderedDistribution(Normator.FitCurves, dir_main + "\\Flat_Orders_Fit.dat");

            // Th-Ar spectra extraction;
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("Extraction of the Th-Ar spectrum...");
            Console.ForegroundColor = ConsoleColor.White;

            Extractor.Extract(Thar_aver_db, pos_ord, pos_min, -1);

            fluxes = Extractor.FluxDebiased;

            Saver.SaveOrderedDistribution(fluxes, dir_main + "\\thar_extacted.txt");
            Saver.SaveOrderedDistribution(Extractor.Background, dir_main + "\\thar_bkg.dat");
            Saver.SaveOrderedDistribution(Extractor.Flux, dir_main + "\\thar_flx_and_bkg.dat");
            Saver.SaveOrderedDistribution(Extractor.fluxes_min0, dir_main + "\\Back.0.dat");

            // Wavelength calibration;
            double[][] lambdas;
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("Wavelength calibration...");
            Console.ForegroundColor = ConsoleColor.White;

            EcheData.LoadDispCurves("CalibrationCurves.dat");

            //int oo = 9, ox = 5;
            //double cutLimit = 3, fluxLimit = 500;
            //int iterNum = 20;

            int    oo        = (int)Init.Value("WAVE_OO");
            int    ox        = (int)Init.Value("WAVE_OX");
            int    iterNum   = (int)Init.Value("WAVE_NINER");
            double cutLimit  = (double)Init.Value("WAVE_REJ");
            double fluxLimit = 500;

            Console.WriteLine("OO = {0}; OX = {1}; Reject = {2}; IterNum = {3}; FluxLimit = {4}",
                              oo, ox, cutLimit, iterNum, fluxLimit);
            WLCalibration.Calibrate(fluxes, oo, ox, cutLimit, iterNum, fluxLimit);
            lambdas = WLCalibration.Lambdas;
            Saver.SaveOrderedDistribution(lambdas, dir_main + "\\lambdas.dat");

            // processing object images;
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("Processing object spectra...");
            Console.ForegroundColor = ConsoleColor.White;

            // observatory coordinates and altitude reading;
            IIDType obs_lat      = (IIDType)Init.Value("OBS_LAT");
            IIDType obs_long     = (IIDType)Init.Value("OBS_LONG");
            double  obs_alt      = (double)Init.Value("OBS_ALT");
            double  obs_lat_deg  = obs_lat.DH + obs_lat.MM / 60.0 + obs_lat.SS / 3600.0;
            double  obs_long_deg = obs_long.DH + obs_long.MM / 60.0 + obs_long.SS / 3600.0;
            int     time_zone    = (int)Init.Value("TIME_ZONE");
            string  time_type    = (string)Init.Value("TIME_TYPE");

            string ra_str          = (string)Init.Value("OBJ_RASTR");
            string de_str          = (string)Init.Value("OBJ_DESTR");
            bool   read_coord_fits = (bool)Init.Value("OBJ_INFITS");

            IIDType ra = null, de = null;
            double  ra_hh = 0, de_deg = 0;
            string  time_str = (string)Init.Value("TIME_STR");
            string  date_str = (string)Init.Value("DATE_STR");
            string  expo_str = (string)Init.Value("EXPO_STR");

            if (!read_coord_fits)
            {
                ra     = (IIDType)Init.Value("OBJ_RA");
                de     = (IIDType)Init.Value("OBJ_DEC");
                ra_hh  = ra.DH + ra.MM / 60.0 + ra.SS / 3600.0;
                de_deg = ra.DH + ra.MM / 60.0 + ra.SS / 3600.0;
            }

            StreamWriter output = new StreamWriter(dir_main + "\\output.dat");

            FITSHeaderReader fhr          = new FITSHeaderReader();
            Image            object_image = null;

            for (int i = 0; i < obj_files.Length; i++)
            {
                // Lading object image;
                Console.WriteLine("Loading Object image {0}", obj_files[i]);
                object_image = new Image();
                object_image.LoadImage(obj_files[i]);

                // Reading FITS-header data;
                if (read_coord_fits)
                {
                    ra     = fhr.ReadAsIID(obj_files[i], ra_str);
                    de     = fhr.ReadAsIID(obj_files[i], de_str);
                    ra_hh  = ra.DH + ra.MM / 60.0 + ra.SS / 3600.0;
                    de_deg = ra.DH + ra.MM / 60.0 + ra.SS / 3600.0;
                }

                IIDType time     = fhr.ReadAsIID(obj_files[i], time_str);
                IIDType date     = fhr.ReadAsIID(obj_files[i], date_str);
                double  exposure = fhr.ReadAsDouble(obj_files[i], expo_str);

                DateTime dt = new DateTime(date.DH, date.MM, (int)date.SS,
                                           time.DH, time.MM, (int)time.SS);
                if (time_type == "LT")
                {
                    dt.AddHours(-(double)time_zone);
                }
                dt.AddSeconds(0.5 * exposure);

                // Calculate JD and HJD;
                double jd = DateConvertors.JDCnv(dt.Year, dt.Month, dt.Day,
                                                 (double)dt.Hour + dt.Minute / 60.0 + dt.Second / 3600.0);
                double epoch = dt.Year + dt.Month / 12.0 + dt.Day / 365.0 +
                               dt.Hour / (24 * 365.0) + dt.Minute / (24 * 365.0 * 60.0);
                Precess.Correct(ref ra_hh, ref de_deg, 2000.0, epoch);
                double hjd = DateConvertors.helio_jd(jd, ra_hh, de_deg);

                // Calculate components of observer motion to the object direction;
                double vdiurnal = 0, vbar = 0, vhel = 0, corr = 0;
                HelCorr.DoCorrection(obs_long_deg, obs_lat_deg, obs_alt, 360 - ra_hh, de_deg, jd,
                                     ref vdiurnal, ref vbar, ref vhel);
                corr = vdiurnal + vbar + vhel;

                // Bias substraction;
                Console.WriteLine("Bias substraction...");
                object_image = object_image - bias_aver;
                for (int j = 0; j < object_image.NAXIS1; j++)
                {
                    for (int k = 0; k < object_image.NAXIS2; k++)
                    {
                        if (object_image[j, k] < 0)
                        {
                            object_image[j, k] = 0;
                        }
                    }
                }

                // Image optimization;
                Smooth.DoSmooth(ref object_image);

                // Create directory for extracted spectra;
                string dirName  = obj_files[i].Substring(0, obj_files[i].IndexOf(".fit"));
                int    first    = obj_files[i].LastIndexOf("\\");
                int    last     = obj_files[i].IndexOf(".fit");
                string fileName = dirName + "\\" + obj_files[i].Substring(first, last - first);
                Directory.CreateDirectory(dirName);

                // save object data;
                output.WriteLine("{0}\t{1}\t{2}\t{3}" +
                                 "\t{4:0.0000000}\t{5:0.0000000}\t{6:0.000}\t{7:0.000}\t{8:0.000}\t{9:0.000}",
                                 obj_files[i].Substring(first + 1), ra.ToString(), de.ToString(),
                                 dt.ToString(), jd, hjd, vdiurnal, vbar, vhel, corr);
                output.Flush();

                Console.WriteLine("Extraction spectra from {0} ", obj_files[i]);
                Extractor.Extract(object_image, pos_ord, pos_min, -1);
                fluxes = Extractor.FluxDebiased;
                double[][] backgr = Extractor.Background;
                Saver.SaveOrderedDistribution(backgr, fileName + "_bkg.dat");
                Saver.SaveOrderedDistribution(fluxes, fileName + "_x.dat");
                for (int k = 0; k < fluxes.Length; k++)
                {
                    for (int j = 0; j < fluxes[0].Length; j++)
                    {
                        fluxes[k][j] = fluxes[k][j] / Normator.OrdersNorm[k][j];
                    }
                }
                Saver.SaveOrderedDistribution(fluxes, fileName + "_x_n.dat");


                for (int k = 0; k < fluxes.Length; k++)
                {
                    StreamWriter sw = new StreamWriter(fileName +
                                                       string.Format("_{0:000}", k) + ".dat");
                    for (int j = 0; j < fluxes[k].Length; j++)
                    {
                        sw.WriteLine(
                            string.Format("{0:0000.0000}\t{1:0.00000E000}",
                                          lambdas[k][j], fluxes[k][j]).Replace(",", "."));
                    }
                    sw.Close();
                }
            }

            output.Close();

STOP:
            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("End of the pipeline. Press any key to exit...");
            Console.Beep();
            Console.ReadKey();
        }
예제 #2
0
        public static void Locate(ref Image im, int polynom_degree)
        {
            double[] order_centers = new double[2];
            int      wing          = 2;
            int      middle        = im.NAXIS2 / 2;
            Image    slice         = new Image(im.NAXIS1, 1 + 2 * wing);

            for (int i = 0; i < slice.NAXIS1; i++)
            {
                for (int j = 0; j < slice.NAXIS2; j++)
                {
                    slice[i, j] = im[i, middle - wing + j];
                }
            }

            double[] aver_column = new double[im.NAXIS1];

            for (int i = 0; i < aver_column.Length; i++)
            {
                aver_column[i] = Statistics.Median(slice.GetRow(i));
            }

            StreamWriter sw = new StreamWriter((string)Init.Value("DIR_MAIN") + "\\Slice.txt");

            for (int i = 0; i < aver_column.Length - 1; i++)
            {
                sw.WriteLine("{0}\t{1}", i, aver_column[i].ToString());
            }
            sw.Close();

            // Search for orders locations in cetral slit;

            double[] xmax_0       = new double[5000];
            int      orders_count = 0;

            int dist = 5; // for ZTSh

            EcheData.LoadOrdMidPos("OrdPos.dat");

            double[] xmax = new double[EcheData.ord_mid_pos.Length];
            for (int i = 0; i < xmax.Length; i++)
            {
                xmax[i] = (double)EcheData.ord_mid_pos[i];
            }

            for (int i = 0; i < xmax.Length; i++)
            {
                int x1 = (int)xmax[i] - dist;
                int x2 = (int)xmax[i] + dist;
                if (x1 < 0)
                {
                    x1 = 0;
                }
                if (x2 > aver_column.Length - 1)
                {
                    x2 = aver_column.Length - 1;
                }
                int    xm  = x1;
                double max = aver_column[x1];
                for (int j = x1 + 1; j <= x2; j++)
                {
                    if (aver_column[j] > max)
                    {
                        max = aver_column[j];
                        xm  = j;
                    }
                }
                xmax[i] = xm;
            }

            orders_count = xmax.Length;

            // Orders Tracing;

            pos_ord = new double[orders_count][];
            for (int i = 0; i < pos_ord.Length; i++)
            {
                pos_ord[i] = new double[im.NAXIS2];
            }

            for (int i = 0; i < pos_ord.Length; i++)
            {
                pos_ord[i][middle] = xmax[i];
            }

            int nwin = 8;
            int o1 = 0, o2 = 0;

            for (int i = middle - 1; i >= 0; i--)
            {
                for (int n = o1; n < orders_count + o2; n++)
                {
                    int    n1  = (int)pos_ord[n][i + 1] - nwin;
                    int    n2  = (int)pos_ord[n][i + 1] + nwin;
                    double max = 0;
                    for (int k = n1; k <= n2; k++)
                    {
                        if (im[k, i] > max)
                        {
                            pos_ord[n][i] = k;
                            max           = im[k, i];
                        }
                    }
                }
            }

            for (int i = middle + 1; i < im.NAXIS2; i++)
            {
                for (int n = o1; n < orders_count + o2; n++)
                {
                    int    n1  = (int)pos_ord[n][i - 1] - nwin;
                    int    n2  = (int)pos_ord[n][i - 1] + nwin;
                    double max = 0;
                    for (int k = n1; k <= n2; k++)
                    {
                        if (im[k, i] > max)
                        {
                            pos_ord[n][i] = k;
                            max           = im[k, i];
                        }
                    }
                }
            }

            pos_min = new double[pos_ord.Length + 1][];
            for (int i = 0; i < pos_min.Length; i++)
            {
                pos_min[i] = new double[im.NAXIS2];
            }

            for (int i = 0; i < im.NAXIS2; i++)
            {
                for (int n = 1; n < pos_min.Length - 1; n++)
                {
                    int    n1  = (int)pos_ord[n - 1][i];
                    int    n2  = (int)pos_ord[n][i];
                    double min = double.MaxValue;
                    for (int k = n1; k <= n2; k++)
                    {
                        if (im[k, i] < min)
                        {
                            pos_min[n][i] = k;
                            min           = im[k, i];
                        }
                    }
                }
            }


            for (int i = 0; i < im.NAXIS2; i++)
            {
                pos_min[0][i] = pos_min[1][i] - (pos_min[2][i] - pos_min[1][i]);
                if (pos_min[0][i] < 0)
                {
                    pos_min[0][i] = 0;
                }
            }


            for (int i = 0; i < im.NAXIS2; i++)
            {
                pos_min[pos_min.Length - 1][i] = pos_min[pos_min.Length - 2][i] +
                                                 (pos_min[pos_min.Length - 2][i] - pos_min[pos_min.Length - 3][i]);
                if (pos_min[pos_min.Length - 1][i] > aver_column.Length - 1)
                {
                    pos_min[pos_min.Length - 1][i] = aver_column.Length - 1;
                }
            }


            Saver.SaveOrderedDistribution(pos_min, (string)Init.Value("DIR_MAIN") + "\\trace_min.txt");

            Saver.SaveColumn(xmax, (string)Init.Value("DIR_MAIN") + "\\orders_ident.dat");

            double[] pixels_x = new double[im.NAXIS2];
            for (int i = 0; i < pixels_x.Length; i++)
            {
                pixels_x[i] = i;
            }

            GravImprove(im, ref pos_min, ref pos_ord);
            Saver.SaveOrderedDistribution(pos_ord, (string)Init.Value("DIR_MAIN") + "\\trace.txt");

            for (int i = 0; i < pos_ord.Length; i++)
            {
                ImproveTraces(pixels_x, ref pos_ord[i], polynom_degree);
            }

            for (int i = 0; i < pos_min.Length; i++)
            {
                ImproveTraces(pixels_x, ref pos_min[i], polynom_degree);
            }

            Saver.SaveOrderedDistribution(pos_ord, (string)Init.Value("DIR_MAIN") + "\\orders_impoved.dat");
            Saver.SaveOrderedDistribution(pos_min, (string)Init.Value("DIR_MAIN") + "\\mins_improved.dat");

            for (int i = 0; i < xmax.Length; i++)
            {
                xmax[i] = pos_ord[i][middle];
            }

            Saver.SaveColumn(xmax, (string)Init.Value("DIR_MAIN") + "\\Orders.txt");
        }
예제 #3
0
        public static void Calibrate(double[][] fluxes, int oo, int ox, double cutLimit, int iterMax, double fluxLimit)
        {
            int orders_count = fluxes.Length;
            int pixels_count = fluxes[0].Length;

            double[] point_order   = new double[5000];
            double[] point_lambda  = new double[5000];
            double[] point_pixel   = new double[5000];
            int      points_number = 0;

            Console.WriteLine("Repers identification...");

            // repers identification cycle;
            Console.Write("Search in orders: ");
            for (int order = 0; order < orders_count; order++)
            {
                Console.Write("[{0}]", order);
                double   wbegin     = EcheData.GetWL(order, 0);
                double   wend       = EcheData.GetWL(order, pixels_count - 1);
                double[] cur_fluxes = new double[pixels_count];
                double   tmx        = 0;
                for (int i = 0; i < pixels_count; i++)
                {
                    cur_fluxes[i] = fluxes[order][i];
                    if (cur_fluxes[i] > tmx)
                    {
                        tmx = cur_fluxes[i];
                    }
                }
                for (int i = 0; i < pixels_count; i++)
                {
                    cur_fluxes[i] = cur_fluxes[i] / tmx;
                }

                int n_atlas = ThAr.Wls.Length;

                int      lines_count = 0;
                double[] lines_wavls = new double[n_atlas];
                for (int i = 0; i < n_atlas; i++)
                {
                    if (wbegin > wend)  // smaller pixels numbers corresponded largest wavelengths
                    {
                        if (ThAr.Wls[i] < wbegin)
                        {
                            if (ThAr.Wls[i] > wend)
                            {
                                lines_wavls[lines_count] = ThAr.Wls[i];
                                lines_count++;
                            }
                        }
                    }
                    else
                    {
                        if (ThAr.Wls[i] > wbegin)
                        {
                            if (ThAr.Wls[i] < wend)
                            {
                                lines_wavls[lines_count] = ThAr.Wls[i];
                                lines_count++;
                            }
                        }
                    }
                }

                Array.Resize(ref lines_wavls, lines_count);

                int swin  = 6;
                int shift = 5;

                for (int i = 0; i < lines_count; i++)
                {
                    double pix = FindPix(lines_wavls[i], order, 0, pixels_count - 1, 0.001);
                    int    k1  = (int)pix - swin + shift;
                    int    k2  = (int)pix + swin + shift;
                    if (k1 < 1)
                    {
                        k1 = 1;
                    }
                    if (k2 > pixels_count - 2)
                    {
                        k2 = pixels_count - 2;
                    }
                    double max   = 0;
                    int    j_max = 0;
                    for (int j = k1; j <= k2; j++)
                    {
                        if (cur_fluxes[j] > cur_fluxes[j - 1] && cur_fluxes[j] > cur_fluxes[j + 1])
                        {
                            if (cur_fluxes[j] > max)
                            {
                                max   = cur_fluxes[j];
                                j_max = j;
                            }
                        }
                    }
                    if (max * tmx > fluxLimit)
                    {
                        //Saver.SaveColumn(cur_fluxes, "CURR.txt");

                        k1 = j_max - 4;
                        k2 = j_max + 4;
                        if (k1 < 0)
                        {
                            k1 = 0;
                        }
                        if (k2 > pixels_count - 1)
                        {
                            k2 = pixels_count - 1;
                        }
                        double[] x = new double[k2 - k1 + 1];
                        double[] y = new double[k2 - k1 + 1];
                        int      l = 0;
                        for (int k = k1; k <= k2; k++)
                        {
                            x[l] = (double)k;
                            y[l] = cur_fluxes[k];
                            l++;
                        }
                        //double center = GravCenter(x, y);
                        double center = GravCenterSpline(x, y);

                        point_lambda[points_number] = lines_wavls[i];
                        point_order[points_number]  = order;
                        point_pixel[points_number]  = center;
                        points_number++;
                    }
                }
            }
            Console.Write("\r\n");
            Console.WriteLine("{0} repers were found.", points_number);

            Array.Resize(ref point_lambda, points_number);
            Array.Resize(ref point_order, points_number);
            Array.Resize(ref point_pixel, points_number);

            for (int i = 0; i < points_number; i++)
            {
                point_order[i]++;
            }
            for (int i = 0; i < points_number; i++)
            {
                point_order[i] = point_order[i] / orders_count;
            }
            for (int i = 0; i < points_number; i++)
            {
                point_pixel[i] = point_pixel[i] / pixels_count;
            }
            for (int i = 0; i < points_number; i++)
            {
                point_lambda[i] = point_lambda[i] / 7000;
            }

            double[] coeff = null;
            double   stderror;
            int      rejected_poins_number = 0;

            Console.Write("Iterations: ");
            for (int i = 0; i < iterMax; i++)
            {
                Console.Write("[{0}]", i + 1);
                coeff    = Fitting(point_order, point_pixel, point_lambda, oo, ox);
                stderror = 0;
                for (int j = 0; j < points_number; j++)
                {
                    stderror += Math.Pow(point_lambda[j] - Surface(coeff, point_order[j], point_pixel[j], oo, ox), 2);
                }
                stderror = Math.Sqrt(stderror / points_number);
                double diff;
                int    k = 0;
                for (int j = 0; j < points_number; j++)
                {
                    diff = Math.Abs(point_lambda[j] - Surface(coeff, point_order[j], point_pixel[j], oo, ox));
                    if (diff < cutLimit * stderror)
                    {
                        point_lambda[k] = point_lambda[j];
                        point_order[k]  = point_order[j];
                        point_pixel[k]  = point_pixel[j];
                        k++;
                    }
                    else
                    {
                        rejected_poins_number++;
                    }
                }
                points_number = k;
            }
            Console.WriteLine("\r\n{0} points were rejected.", rejected_poins_number);

            double[] diffs = new double[points_number];
            stderror = 0;
            for (int j = 0; j < points_number; j++)
            {
                diffs[j]  = point_lambda[j] * 7000 - 7000 * Surface(coeff, point_order[j], point_pixel[j], oo, ox);
                stderror += diffs[j] * diffs[j];
            }
            stderror = Math.Sqrt(stderror / points_number);
            Console.WriteLine("Std. Error: {0:0.000E00}", stderror);

            System.IO.StreamWriter sw = new System.IO.StreamWriter((string)Init.Value("DIR_MAIN") + "\\found_repers.dat");
            for (int i = 0; i < points_number; i++)
            {
                sw.WriteLine("{0}\t{1}\t{2}\t{3}", point_order[i] * orders_count - 1, point_pixel[i] * pixels_count, point_lambda[i] * 7000, diffs[i]);
            }
            sw.Close();

            Lambdas = new double[orders_count][];
            for (int i = 0; i < orders_count; i++)
            {
                Lambdas[i] = new double[pixels_count];
            }

            for (int order = 1; order <= orders_count; order++)
            {
                for (int p = 0; p < pixels_count; p++)
                {
                    int    k   = 0;
                    double sum = 0;
                    for (int i = 0; i <= oo; i++)
                    {
                        for (int j = 0; j <= ox; j++)
                        {
                            sum += coeff[k] * Math.Pow((double)order / orders_count, i) *
                                   Math.Pow((double)p / pixels_count, j);
                            k++;
                        }
                    }
                    Lambdas[order - 1][p] = sum * 7000;
                }
            }
        }