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mpdaf
Commit 53e3e7fb authored by Simon Conseil's avatar Simon Conseil
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Merge branch 'combine' into 'master' 

Allow to correct zero offset in mpdaf_merging_sigma_clipping

See merge request !163
parents 6a6166f1 279f97a2
Pipeline #3149 passed with stage
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    Showing with 181 additions and 193 deletions
    lib/mpdaf/obj/cubelist.py
    View file @ 53e3e7fb
    ......@@ -40,6 +40,7 @@ from astropy import units as u
    from astropy.table import Table
    from astropy.utils.console import ProgressBar
    from ctypes import c_char_p
    from datetime import datetime
    from numpy import allclose, array_equal
    from .cube import Cube
    ......@@ -139,7 +140,7 @@ def _pycombine(self, nmax=2, nclip=5.0, var='propagate', nstop=2, nl=None,
    if self.flux_scales is None and self.flux_offsets is None:
    for l in range(nl):
    if l % 100 == 0:
    info('%d/%d', l, nl)
    info('%d/%d %s', l, nl, datetime.now())
    arr.fill(np.nan)
    for i, f in enumerate(data):
    x, y, x2, y2 = pos[i]
    ......@@ -158,17 +159,17 @@ def _pycombine(self, nmax=2, nclip=5.0, var='propagate', nstop=2, nl=None,
    scales = np.ones(self.nfiles)
    else:
    scales = np.asarray(self.flux_scales)
    self._logger.info('Using scales: %s', scales)
    self._logger.info('Using scales')
    if self.flux_offsets is None:
    offsets = np.zeros(self.nfiles)
    else:
    offsets = np.asarray(self.flux_offsets)
    self._logger.info('Using offsets: %s', offsets)
    self._logger.info('Using offsets')
    for l in range(nl):
    if l % 100 == 0:
    info('%d/%d', l, nl)
    info('%d/%d %s', l, nl, datetime.now())
    arr.fill(np.nan)
    for i, f in enumerate(data):
    x, y, x2, y2 = pos[i]
    ......@@ -335,6 +336,11 @@ class CubeList:
    str(c.wcs.wcs.wcs.crpix), str(c.wcs.wcs.wcs.crval))
    for c in self.cubes]
    t = Table(rows=rows, names=('filename', 'shape', 'crpix', 'crval'))
    if self.flux_scales is not None:
    t['scale'] = self.flux_scales
    if self.flux_offsets is not None:
    t['offset'] = self.flux_offsets
    for line in t.pformat():
    self._logger.info(line)
    ......@@ -492,12 +498,19 @@ class CubeList:
    files = files.encode('utf8')
    if self.flux_scales is None:
    scales = np.ones(self.nfiles)
    scale = np.ones(self.nfiles)
    else:
    scales = np.asarray(self.flux_scales)
    scale = np.asarray(self.flux_scales)
    self._logger.info('Using scales')
    if self.flux_offsets is None:
    offset = np.zeros(self.nfiles)
    else:
    offset = np.asarray(self.flux_offsets)
    self._logger.info('Using offsets')
    ctools.mpdaf_merging_sigma_clipping(
    c_char_p(files), data, vardata, expmap, scales, select_pix,
    c_char_p(files), data, vardata, expmap, scale, offset, select_pix,
    valid_pix, nmax, np.float64(nclip_low), np.float64(nclip_up),
    nstop, np.int32(var_mean), np.int32(mad))
    ......
    lib/mpdaf/obj/tests/test_cubelist.py
    View file @ 53e3e7fb
    ......@@ -61,15 +61,24 @@ class TestCubeList(unittest.TestCase):
    shape = (5, 4, 3)
    ncubes = 3
    cubevals = [0, 1, 5]
    cubevals = np.array([0, 1, 5])
    scalelist = [0.5, 1, 1.5]
    offsetlist = [1.5, 2, 2.5]
    @classmethod
    def setUpClass(cls):
    cls.tmpdir = tempfile.mkdtemp()
    print('\n>>> Create cubes in', cls.tmpdir)
    cls.cubenames = []
    cls.arr = np.arange(np.prod(cls.shape)).reshape(cls.shape)
    cls.scaledcube = np.mean([
    (cls.arr * val + cls.offsetlist[k]) * cls.scalelist[k]
    for k, val in enumerate(cls.cubevals)], axis=0)
    cls.combined_cube = np.mean([(cls.arr * i) for i in cls.cubevals],
    axis=0)
    for i in cls.cubevals:
    cube = generate_cube(data=i, shape=cls.shape)
    cube = generate_cube(data=cls.arr * i, shape=cls.shape)
    cube.primary_header['CUBEIDX'] = i
    cube.primary_header['OBJECT'] = 'OBJECT %d' % i
    cube.primary_header['EXPTIME'] = 100
    ......@@ -92,10 +101,10 @@ class TestCubeList(unittest.TestCase):
    def test_get_item(self):
    clist = CubeList(self.cubenames)
    assert_array_equal(clist[0, 2, 2], self.cubevals)
    assert_array_equal(np.array([a.data for a in clist[0, :, :]])[:, 0, 0],
    assert_array_equal(clist[0, 0, 1], self.cubevals)
    assert_array_equal(np.array([a.data for a in clist[0, :, :]])[:, 0, 1],
    self.cubevals)
    assert_array_equal(np.array([a.data for a in clist[0]])[:, 0, 0],
    assert_array_equal(np.array([a.data for a in clist[0]])[:, 0, 1],
    self.cubevals)
    def test_checks(self):
    ......@@ -114,26 +123,22 @@ class TestCubeList(unittest.TestCase):
    @pytest.mark.skipif(not HAS_CFITSIO, reason="requires cfitsio")
    def test_median(self):
    clist = CubeList(self.cubenames)
    combined_cube = np.ones(self.shape)
    cube, expmap, stat_pix = clist.median(header={'FOO': 'BAR'})
    self.assert_header(cube)
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.arr)
    assert_array_equal(expmap.data, self.expmap)
    @pytest.mark.skipif(not HAS_FITSIO, reason="requires fitsio")
    def test_pymedian(self):
    clist = CubeList(self.cubenames)
    combined_cube = np.ones(self.shape)
    cube, expmap, stat_pix = clist.pymedian(header={'FOO': 'BAR'})
    self.assert_header(cube)
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.arr)
    assert_array_equal(expmap.data, self.expmap)
    @pytest.mark.skipif(not HAS_CFITSIO, reason="requires cfitsio")
    def test_combine(self):
    clist = CubeList(self.cubenames)
    combined_cube = np.full(self.shape, 2, dtype=float)
    cube, expmap, stat_pix = clist.combine(header={'FOO': 'BAR'})
    cube2, expmap2, stat_pix2 = clist.combine(header={'FOO': 'BAR'},
    mad=True)
    ......@@ -141,17 +146,15 @@ class TestCubeList(unittest.TestCase):
    assert_array_equal(expmap.data, expmap2.data)
    self.assert_header(cube)
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.combined_cube)
    assert_array_equal(expmap.data, self.expmap)
    cube = clist.combine(nclip=(5., 5.), var='stat_mean')[0]
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.combined_cube)
    @pytest.mark.skipif(not HAS_FITSIO, reason="requires fitsio")
    def test_pycombine(self):
    clist = CubeList(self.cubenames)
    combined_cube = np.full(self.shape, 2, dtype=float)
    cube, expmap, stat_pix, rejmap = clist.pycombine(header={'FOO': 'BAR'})
    cube2, expmap2, stat_pix2, rejmap2 = clist.pycombine(
    header={'FOO': 'BAR'}, mad=True)
    ......@@ -159,43 +162,33 @@ class TestCubeList(unittest.TestCase):
    assert_array_equal(expmap.data, expmap2.data)
    self.assert_header(cube)
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.combined_cube)
    assert_array_equal(expmap.data, self.expmap)
    cube = clist.pycombine(nclip=(5., 5.), var='stat_mean')[0]
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.combined_cube)
    @pytest.mark.skipif(not HAS_CFITSIO, reason="requires cfitsio")
    def test_combine_scale(self):
    clist = CubeList(self.cubenames, scalelist=[2.] * self.ncubes)
    combined_cube = np.full(self.shape, 2 * 2, dtype=float)
    clist = CubeList(self.cubenames, scalelist=self.scalelist,
    offsetlist=self.offsetlist)
    cube, expmap, stat_pix = clist.combine(header={'FOO': 'BAR'})
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.scaledcube)
    @pytest.mark.skipif(not HAS_FITSIO, reason="requires fitsio")
    def test_pycombine_scale(self):
    clist = CubeList(self.cubenames, scalelist=[2.] * self.ncubes)
    combined_cube = np.full(self.shape, 2 * 2, dtype=float)
    clist = CubeList(self.cubenames, scalelist=self.scalelist,
    offsetlist=self.offsetlist)
    cube2, expmap2, _, _ = clist.pycombine(header={'FOO': 'BAR'})
    assert_array_equal(cube2.data, combined_cube)
    clist = CubeList(self.cubenames, scalelist=[2.] * self.ncubes,
    offsetlist=[0.5] * self.ncubes)
    combined_cube = np.full(self.shape, 5, dtype=float)
    cube2, expmap2, _, _ = clist.pycombine(header={'FOO': 'BAR'})
    assert_array_equal(cube2.data, combined_cube)
    assert_array_equal(cube2.data, self.scaledcube)
    @pytest.mark.skipif(not HAS_FITSIO, reason="requires fitsio")
    def test_mosaic_combine(self):
    clist = CubeMosaic(self.cubenames, self.cubenames[0])
    combined_cube = np.full(self.shape, 2, dtype=float)
    cube, expmap, stat_pix, rejmap = clist.pycombine(header={'FOO': 'BAR'})
    self.assert_header(cube)
    assert_array_equal(cube.data, combined_cube)
    assert_array_equal(cube.data, self.combined_cube)
    assert_array_equal(expmap.data, self.expmap)
    cube2, expmap2, _, _ = clist.pycombine(header={'FOO': 'BAR'}, mad=True)
    ......
    lib/mpdaf/tools/ctools.py
    View file @ 53e3e7fb
    ......@@ -81,6 +81,7 @@ else:
    array_1d_double, # double* var
    array_1d_int, # int* expmap
    array_1d_double, # double* scale
    array_1d_double, # double* offset
    array_1d_int, # int* selected_pix
    array_1d_int, # int* valid_pix
    ctypes.c_int, # int nmax
    ......
    src/merging.c
    View file @ 53e3e7fb
    ......@@ -63,15 +63,15 @@ int get_max_threads(int nfiles, int typ_var) {
    exit(EXIT_FAILURE);
    }
    int num_nthreads = limit.rlim_cur/nfiles * 0.9;
    int num_nthreads = limit.rlim_cur / nfiles * 0.9;
    if (1000/nfiles < num_nthreads) {
    //limit of cfitsio
    num_nthreads = 1000/nfiles;
    num_nthreads = 1000 / nfiles;
    }
    printf("num_nthreads: %d\n", num_nthreads);
    if (typ_var==0) {
    num_nthreads = num_nthreads/2;
    num_nthreads = num_nthreads / 2;
    }
    int nthreads=1;
    ......@@ -136,13 +136,30 @@ int compute_loop_limits(long naxes, int* limits) {
    return EXIT_SUCCESS;
    }
    void report_progress(time_t *ref, long firstpix[], int limits[], float value) {
    time_t now;
    struct tm *info;
    char buffer[80];
    time(&now);
    if ((value >= 0) || ((now - *ref) > 60)) {
    *ref = now;
    info = localtime(&now);
    strftime(buffer, 80, "%x - %I:%M%p", info);
    if (value < 0) {
    value = firstpix[2] * 100.0 / (limits[1] - limits[0]);
    }
    printf("%s %3.1f%%\n", buffer, value);
    fflush(stdout);
    }
    }
    int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    {
    char* filenames[MAX_FILES];
    char buffer[80], begin[80];
    int nfiles=0;
    time_t now;
    struct tm *info;
    time(&now);
    // read input files list
    nfiles = split_files_list(input, filenames);
    ......@@ -152,7 +169,7 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    omp_set_num_threads(num_nthreads); // Set number of threads to use
    // create threads
    #pragma omp parallel shared(filenames, nfiles, data, expmap, valid_pix, buffer, begin)
    #pragma omp parallel shared(filenames, nfiles, data, expmap, valid_pix)
    {
    #endif
    ......@@ -169,6 +186,7 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    {
    printf("Read fits files\n");
    printf("naxes %zu %zu %zu\n", naxes[0], naxes[1], naxes[2]);
    report_progress(&now, NULL, NULL, 0);
    }
    // read other files and compare that the shape is the same
    ......@@ -191,8 +209,7 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    int *indx;
    double *pix[MAX_FILES_PER_THREAD], *wdata;
    long npixels = naxes[0];
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    pix[i] = (double *) malloc(npixels * sizeof(double));
    if (pix[i] == NULL) {
    printf("Memory allocation error\n");
    ......@@ -203,25 +220,19 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    wdata = (double *) malloc(nfiles * sizeof(double));
    indx = (int *) malloc(nfiles * sizeof(int));
    for (firstpix[2] = limits[0]; firstpix[2] <= limits[1]; firstpix[2]++)
    {
    for (firstpix[1] = 1; firstpix[1] <= naxes[1]; firstpix[1]++)
    {
    for (firstpix[2] = limits[0]; firstpix[2] <= limits[1]; firstpix[2]++) {
    for (firstpix[1] = 1; firstpix[1] <= naxes[1]; firstpix[1]++) {
    int index0 = (firstpix[1]-1)*naxes[0] + (firstpix[2]-1)*naxes[0]*naxes[1];
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    if (fits_read_pix(fdata[i], TDOUBLE, firstpix, npixels, NULL, pix[i],
    NULL, &status))
    break;
    }
    for(ii=0; ii< npixels; ii++)
    {
    for(ii=0; ii< npixels; ii++) {
    n = 0;
    for (i=0; i<nfiles; i++)
    {
    if (!isnan(pix[i][ii]))
    {
    for (i=0; i<nfiles; i++) {
    if (!isnan(pix[i][ii])) {
    wdata[n] = pix[i][ii];
    indx[n] = n;
    n = n + 1;
    ......@@ -229,59 +240,45 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    }
    }
    int index = ii + index0;
    if (n==0)
    {
    if (n==0) {
    data[index] = NAN; //mean value
    expmap[index] = 0; //exp map
    }
    else if (n==1)
    {
    } else if (n==1) {
    data[index] = wdata[0]; //mean value
    expmap[index] = 1; //exp map
    }
    else
    {
    } else {
    data[index] = mpdaf_median(wdata,n,indx);
    expmap[index] = n;
    }
    }
    }
    #pragma omp master
    {
    time(&now);
    info = localtime(&now);
    strftime(buffer,80,"%x - %I:%M%p", info);
    if(strcmp(buffer,begin) != 0)
    if (firstpix[2] % 100 == 0) {
    #pragma omp master
    {
    printf("%s %3.1f%%\n", buffer, (firstpix[2]-limits[0])*100.0/(limits[1]-limits[0]));
    fflush(stdout);
    strcpy(begin, buffer);
    report_progress(&now, firstpix, limits, -1);
    }
    }
    }
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    #pragma omp atomic
    valid_pix[i] += valid[i];
    }
    free(wdata);
    free(indx);
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    free(pix[i]);
    fits_close_file(fdata[i], &status);
    }
    if (status)
    {
    if (status) {
    fits_report_error(stderr, status);
    exit(EXIT_FAILURE);
    }
    #ifdef _OPENMP
    }
    #endif
    printf("%s 100%%\n", buffer);
    fflush(stdout);
    report_progress(&now, NULL, NULL, 100);
    return EXIT_SUCCESS;
    }
    ......@@ -289,14 +286,28 @@ int mpdaf_merging_median(char* input, double* data, int* expmap, int* valid_pix)
    // var=0: 'propagate'
    // var=1: 'stat_mean'
    // var=2: 'stat_one'
    int mpdaf_merging_sigma_clipping(char* input, double* data, double* var, int* expmap, double* scale, int* selected_pix, int* valid_pix, int nmax, double nclip_low, double nclip_up, int nstop, int typ_var, int mad)
    int mpdaf_merging_sigma_clipping(
    char* input,
    double* data,
    double* var,
    int* expmap,
    double* scale,
    double*offset,
    int* selected_pix,
    int* valid_pix,
    int nmax,
    double nclip_low,
    double nclip_up,
    int nstop,
    int typ_var,
    int mad
    )
    {
    char* filenames[MAX_FILES];
    char buffer[80], begin[80];
    int nfiles=0;
    time_t now;
    struct tm *info;
    time(&now);
    printf("merging cube using mean with sigma clipping\n");
    printf("nmax = %d\n", nmax);
    ......@@ -312,7 +323,7 @@ int mpdaf_merging_sigma_clipping(char* input, double* data, double* var, int* ex
    omp_set_num_threads(num_nthreads); // Set number of threads to use
    // create threads
    #pragma omp parallel shared(filenames, nfiles, data, var, expmap, scale, valid_pix, buffer, begin, nmax, nclip_low, nclip_up, nstop, selected_pix, typ_var, mad)
    #pragma omp parallel shared(filenames, nfiles, data, var, expmap, scale, valid_pix, nmax, nclip_low, nclip_up, nstop, selected_pix, typ_var, mad)
    {
    #endif
    ......@@ -329,6 +340,7 @@ int mpdaf_merging_sigma_clipping(char* input, double* data, double* var, int* ex
    {
    printf("Read fits files\n");
    printf("naxes %zu %zu %zu\n", naxes[0], naxes[1], naxes[2]);
    report_progress(&now, NULL, NULL, 0);
    }
    // read other files and compare that the shape is the same
    ......@@ -363,7 +375,7 @@ int mpdaf_merging_sigma_clipping(char* input, double* data, double* var, int* ex
    double *pix[MAX_FILES_PER_THREAD], *pixvar[MAX_FILES_PER_THREAD], *wdata, *wvar=NULL;
    int *indx, *files_id;
    double x[3];
    long npixels = naxes[0];
    long npixels = naxes[0] * naxes[1];
    for (i=0; i<nfiles; i++)
    {
    pix[i] = (double *) malloc(npixels * sizeof(double));
    ......@@ -392,145 +404,114 @@ int mpdaf_merging_sigma_clipping(char* input, double* data, double* var, int* ex
    for (firstpix[2] = limits[0]; firstpix[2] <= limits[1]; firstpix[2]++)
    {
    for (firstpix[1] = 1; firstpix[1] <= naxes[1]; firstpix[1]++)
    {
    int index0 = (firstpix[1]-1)*naxes[0] + (firstpix[2]-1)*naxes[0]*naxes[1];
    int index0 = (firstpix[2] - 1) * npixels;
    for (i=0; i<nfiles; i++)
    {
    if (fits_read_pix(fdata[i], TDOUBLE, firstpix, npixels, NULL, pix[i],
    NULL, &status))
    // read data values for the current plane, and optionally stat
    for (i=0; i<nfiles; i++) {
    if (fits_read_pix(fdata[i], TDOUBLE, firstpix, npixels,
    NULL, pix[i], NULL, &status))
    break;
    }
    if (typ_var==0) {
    for (i=0; i<nfiles; i++) {
    if (fits_read_pix(fvar[i], TDOUBLE, firstpix, npixels,
    NULL, pixvar[i], NULL, &status))
    break;
    }
    if (typ_var==0)
    {
    for (i=0; i<nfiles; i++)
    {
    if (fits_read_pix(fvar[i], TDOUBLE, firstpix, npixels, NULL, pixvar[i],
    NULL, &status))
    break;
    }
    }
    for(ii=0; ii< npixels; ii++)
    {
    n = 0;
    for (i=0; i<nfiles; i++)
    {
    if (!isnan(pix[i][ii]))
    {
    wdata[n] = pix[i][ii]*scale[i];
    files_id[n] = i;
    indx[n] = n;
    if (typ_var==0)
    {
    wvar[n] = pixvar[i][ii]*scale[i]*scale[i];
    }
    n = n + 1;
    valid[i] = valid[i] + 1;
    }
    for(ii=0; ii< npixels; ii++) {
    n = 0;
    for (i=0; i<nfiles; i++) {
    if (!isnan(pix[i][ii])) {
    wdata[n] = (offset[i] + pix[i][ii]) * scale[i];
    files_id[n] = i;
    indx[n] = n;
    if (typ_var==0) {
    wvar[n] = pixvar[i][ii] * scale[i] * scale[i];
    }
    n += 1;
    valid[i] += 1;
    }
    int index = ii + index0;
    if (n==0)
    {
    data[index] = NAN; //mean value
    expmap[index] = 0; //exp map
    var[index] = NAN; //var
    }
    else if (n==1)
    {
    data[index] = wdata[0]; //mean value
    expmap[index] = 1; //exp map
    if (typ_var==0) //var
    var[index] = wvar[0];
    else
    var[index] = NAN;
    select[files_id[0]] += 1;
    }
    }
    int index = ii + index0;
    if (n==0) {
    data[index] = NAN; //mean value
    expmap[index] = 0; //exp map
    var[index] = NAN; //var
    } else if (n==1) {
    data[index] = wdata[0]; //mean value
    expmap[index] = 1; //exp map
    if (typ_var==0) //var
    var[index] = wvar[0];
    else
    {
    if (mad==1)
    {
    mpdaf_mean_madsigma_clip(wdata, n, x, nmax, nclip_low, nclip_up, nstop, indx);
    }
    else
    {
    mpdaf_mean_sigma_clip(wdata, n, x, nmax, nclip_low, nclip_up, nstop, indx);
    }
    data[index] = x[0];//mean value
    expmap[index] = x[2];//exp map
    if (typ_var==0)
    {
    var[index] = mpdaf_sum(wvar,x[2],indx)/x[2]/x[2];
    }
    else
    {
    if (x[2]>1)
    {
    var[index] = (x[1]*x[1]);//var
    if (typ_var==1)
    {
    var[index] /= (x[2]-1);
    }
    }
    else
    {
    var[index] = NAN;//var
    var[index] = NAN;
    select[files_id[0]] += 1;
    } else {
    if (mad==1) {
    mpdaf_mean_madsigma_clip(wdata, n, x, nmax, nclip_low,
    nclip_up, nstop, indx);
    } else {
    mpdaf_mean_sigma_clip(wdata, n, x, nmax, nclip_low,
    nclip_up, nstop, indx);
    }
    data[index] = x[0]; // mean value
    expmap[index] = x[2]; // exp map
    if (typ_var==0) { // var
    var[index] = mpdaf_sum(wvar, x[2], indx) / (x[2] * x[2]);
    } else {
    if (x[2]>1) {
    var[index] = (x[1] * x[1]);
    if (typ_var==1) {
    var[index] /= (x[2] - 1);
    }
    }
    for (i=0; i<x[2]; i++)
    {
    select[files_id[indx[i]]] += 1;
    } else {
    var[index] = NAN;
    }
    }
    for (i=0; i<x[2]; i++) {
    select[files_id[indx[i]]] += 1;
    }
    }
    }
    #pragma omp master
    {
    time(&now);
    info = localtime(&now);
    strftime(buffer,80,"%x - %I:%M%p", info);
    if(strcmp(buffer,begin) != 0)
    if (firstpix[2] % 100 == 0) {
    #pragma omp master
    {
    printf("%s %3.1f%%\n", buffer, firstpix[2]*100.0/(limits[1]-limits[0]));
    fflush(stdout);
    strcpy(begin, buffer);
    report_progress(&now, firstpix, limits, -1);
    }
    }
    }
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    #pragma omp atomic
    valid_pix[i] += valid[i];
    #pragma omp atomic
    selected_pix[i] += select[i];
    }
    free(wdata);
    free(indx);
    free(files_id);
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    free(pix[i]);
    fits_close_file(fdata[i], &status);
    }
    if (typ_var==0)
    {
    if (typ_var==0) {
    free(wvar);
    for (i=0; i<nfiles; i++)
    {
    for (i=0; i<nfiles; i++) {
    free(pixvar[i]);
    fits_close_file(fvar[i], &status);
    }
    }
    if (status)
    {
    if (status) {
    fits_report_error(stderr, status);
    exit(EXIT_FAILURE);
    }
    #ifdef _OPENMP
    }
    #endif
    printf("%s 100%%\n", buffer);
    fflush(stdout);
    report_progress(&now, NULL, NULL, 100);
    return EXIT_SUCCESS;
    }
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