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();
}
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");
}
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;
}
}
}