Merge branch 'master' into 'release_v3.0'

version v3.1.0

See merge request !31

observation_sim/mock_objects/SpecDisperser/disperse_c/disperse.pyx

@@ -20,7 +20,30 @@ import cython
 cdef extern from "math.h":
     double sqrt(double x)
     double exp(double x)
+
+
+def check_nan2D(np.ndarray[FTYPE_t, ndim=2] arr):
+    cdef int i, j
+    cdef int nrows = arr.shape[0]
+    cdef int ncols = arr.shape[1]
     
+    # 遍历数组的每个元素并检查是否存在 NaN
+    for i in range(nrows):
+        for j in range(ncols):
+            if np.isnan(arr[i, j]) | np.isinf(arr[i, j]):
+                return True
+    return False
+
+def check_nan1d(np.ndarray[DTYPE_t, ndim=1] arr):
+    cdef int i
+    cdef int n = arr.shape[0]
+
+    # 遍历数组的每个元素并检查是否存在 NaN
+    for i in range(n):
+        if np.isnan(arr[i]) | np.isinf(arr[i]):
+            return True
+    return False
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.embedsignature(True)
@@ -53,6 +76,18 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
 
     nk = len(idxl)
     nl = len(full)
+    
+
+    #if check_nan2D(flam):
+    #    print("DEBUG: disperse, input Array 'flam' contains NaN.")
+    #if check_nan1d(ysens):
+    #    print("DEBUG: disperse, input Array 'ysens' contains NaN.")
+    #if check_nan1d(yfrac):
+    #    print("DEBUG: disperse, input Array 'yfrac' contains NaN.")
+    #if check_nan1d(full):
+    #    print("DEBUG: disperse, input Array 'full' contains NaN before processing.")
+
+
 
     if (flat is not None):
         nlamb = len(wlambda)
@@ -95,14 +130,15 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
 
     else:
         for i in range(0-x0[1], x0[1]):
-            if (x0[1]+i < 0) | (x0[1]+i >= shd[1]):
+            x_pos = x0[1]+i
+            if (x_pos < 0) | (x_pos >= shd[1]):
                 continue
 
             for j in range(0-x0[0], x0[0]):
-                if (x0[0]+j < 0) | (x0[0]+j >= shd[0]):
+                y_pos = x0[0]+j
+                if (y_pos < 0) | (y_pos >= shd[0]):
                     continue
-
-                fl_ij = flam[x0[0]+j, x0[1]+i] #/1.e-17
+                fl_ij = flam[y_pos, x_pos] #/1.e-17
                 if (fl_ij == 0):
                     continue
 
@@ -110,10 +146,13 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
                     k1 = idxl[k]+j*shg[1]+i
                     if (k1 >= 0) & (k1 < nl):
                         full[k1] += ysens[k]*fl_ij*(1-yfrac[k])
-
                     k2 = idxl[k]+(j+1)*shg[1]+i
                     if (k2 >= 0) & (k2 < nl):
                         full[k2] += ysens[k]*fl_ij*yfrac[k]
+  
+        #if (check_nan1d(full)):
+        #    print("DEBUG: disperse, output Array 'full' contains NaN after processing.+++++++++++++++++++++++++++")
+
 
     return True
 

observation_sim/mock_objects/__init__.py

@@ -5,3 +5,4 @@ from .Quasar import Quasar
 from .Star import Star
 from .Stamp import Stamp
 from .FlatLED import FlatLED
+from .ExtinctionMW import ExtinctionMW

observation_sim/psf/PSFGauss.py

@@ -17,7 +17,7 @@ class PSFGauss(PSFModel):
             self.fwhm = self.fwhmGauss(r=psfRa)
             self.sigGauss = psfRa  # 80% light radius
         self.sigSpin = sigSpin
-        self.psf = galsim.Gaussian(flux=1.0, fwhm=fwhm)
+        self.psf = galsim.Gaussian(flux=1.0, fwhm=self.fwhm)
 
     def perfGauss(self, r, sig):
         """
@@ -122,4 +122,4 @@ class PSFGauss(PSFModel):
 
         # return ell, beta, qr
         PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
-        return self.psf.shear(PSFshear), PSFshear
+        return self.psf.shear(PSFshear), PSFshear
\ No newline at end of file

observation_sim/psf/PSFInterpSLS.py

@@ -20,8 +20,10 @@ import os
 from astropy.io import fits
 
 from astropy.modeling.models import Gaussian2D
-from scipy import signal
-
+from scipy import signal, interpolate
+import datetime
+import gc
+# from jax import numpy as jnp
 
 LOG_DEBUG = False  # ***#
 NPSF = 900  # ***# 30*30
@@ -433,7 +435,16 @@ class PSFInterpSLS(PSFModel):
         # PSF_int_trans[ids_szero] = 0
         # print(PSF_int_trans[ids_szero].shape[0],PSF_int_trans.shape)
         PSF_int_trans = PSF_int_trans/np.sum(PSF_int_trans)
+        ###DEBGU
+        ids_szero = PSF_int_trans<0
+        n01 = PSF_int_trans[ids_szero].shape[0]
+
+        n1 = np.sum(np.isinf(PSF_int_trans))
+        n2 = np.sum(np.isnan(PSF_int_trans))
+        if n1>0 or n2>0:
+            print("DEBUG: PSFInterpSLS, inf:%d, nan:%d, 0 num:%d"%(n1, n2, n01))
 
+        ####
         # from astropy.io import fits
         # fits.writeto(str(bandNo) + '_' + g_order+ '_psf_o.fits', PSF_int_trans)
 
@@ -479,6 +490,215 @@ class PSFInterpSLS(PSFModel):
 
         return PSF_int_trans, PSF_int
 
+    def get_PSF_AND_convolve_withsubImg(self, chip, cutImg=None, pos_img_local=[1000, 1000], bandNo=1, g_order='A', grating_split_pos=3685):
+        """
+        Get the PSF at a given image position
+
+        Parameters:
+            chip: A 'Chip' object representing the chip we want to extract PSF from.
+            pos_img: A 'galsim.Position' object representing the image position.
+            bandpass: A 'galsim.Bandpass' object representing the wavelength range.
+            pixSize: The pixels size of psf matrix
+            findNeighMode: 'treeFind' or 'hoclistFind'
+        Returns:
+            PSF: A 'galsim.GSObject'.
+        """
+        order_IDs = {'A': '1', 'B': '0', 'C': '0', 'D': '0', 'E': '0'}
+        contam_order_sigma = {'C': 0.28032344707964174,
+                              'D': 0.39900182912061344, 'E': 1.1988309797685412}  # arcsec
+        x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
+        y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
+        # print(pos_img.x - x_start)
+        # pos_img_x = pos_img_local[0] + x_start
+        # pos_img_y = pos_img_local[1] + y_start
+        # pos_img = galsim.PositionD(pos_img_x, pos_img_y)
+        # centerPos_local = cutImg.ncol/2.
+        if pos_img_local[0] < grating_split_pos:
+            psf_data = self.grating1_data
+        else:
+            psf_data = self.grating2_data
+
+        grating_order = order_IDs[g_order]
+        # if grating_order in ['-2','-1','2']:
+        #     grating_order = '1'
+
+        # if grating_order in ['0', '1']:
+        psf_order = psf_data['order'+grating_order]
+        psf_order_b = psf_order['band'+str(bandNo)]
+        psf_b_dat = psf_order_b['band_data']
+        # pos_p = psf_b_dat[1].data
+        pos_p = psf_b_dat[1].data/chip.pix_size - np.array([y_start, x_start])
+
+        pc_coeff = psf_b_dat[2].data
+        pcs = psf_b_dat[0].data
+
+        npc = 10
+        m_size = int(pcs.shape[0]**0.5)
+        sumImg = np.sum(cutImg.array)
+        tmp_img = cutImg*0
+        for j in np.arange(npc):
+            X_ = np.hstack((pos_p[:,1].flatten()[:, None], pos_p[:,0].flatten()[:, None]),dtype=np.float32)
+            Z_ = (pc_coeff[j].astype(np.float32)).flatten()
+            # print(pc_coeff[j].shape[0], pos_p[:,1].shape[0], pos_p[:,0].shape[0])
+            cx_len = int(chip.npix_x)
+            cy_len = int(chip.npix_y)
+            n_x = np.arange(0, cx_len, 1, dtype = int)
+            n_y = np.arange(0, cy_len, 1, dtype = int)
+            M, N = np.meshgrid(n_x, n_y)
+            # t1=datetime.datetime.now()
+    #         U = interpolate.griddata(X_, Z_, (M[0:cy_len, 0:cx_len],N[0:cy_len, 0:cx_len]),
+    # method='nearest',fill_value=1.0)
+            b_img = galsim.Image(cx_len, cy_len)
+            b_img.setOrigin(0,0)
+            bounds = cutImg.bounds & b_img.bounds
+            if bounds.area() == 0:
+                continue
+
+            # ys = cutImg.ymin
+            # if ys < 0: 
+            #     ys = 0
+            # ye = cutImg.ymin+cutImg.nrow
+            # if ye >= cy_len-1: 
+            #     ye = cy_len-1
+            # if ye - ys <=0:
+            #     continue
+            # xs = cutImg.xmin
+            # if xs < 0: 
+            #     xs = 0
+            # xe = cutImg.xmin+cutImg.ncol
+            # if xe >= cx_len-1: 
+            #     xe = cx_len-1
+            # if xe - xs <=0:
+            #     continue
+            ys = bounds.ymin
+            ye = bounds.ymax+1
+            xs = bounds.xmin
+            xe = bounds.xmax+1
+            U = interpolate.griddata(X_, Z_, (M[ys:ye, xs:xe],N[ys:ye, xs:xe]),
+    method='nearest',fill_value=1.0)
+            # t2=datetime.datetime.now()
+            
+            # print("time interpolate:", t2-t1)
+
+            # if U.shape != cutImg.array.shape:
+            #     print('DEBUG:SHAPE',cutImg.ncol,cutImg.nrow,cutImg.xmin, cutImg.ymin)
+            #     continue
+            img_tmp = cutImg
+            img_tmp[bounds] = img_tmp[bounds]*U
+            psf = pcs[:, j].reshape(m_size, m_size)
+            tmp_img = tmp_img + signal.fftconvolve(img_tmp.array, psf, mode='same', axes=None)
+
+            # t3=datetime.datetime.now()
+            # print("time convole:", t3-t2)
+            del U
+            del img_tmp
+        if np.sum(tmp_img.array)==0:
+            tmp_img = cutImg
+        else:
+            tmp_img = tmp_img/np.sum(tmp_img.array)*sumImg
+        return tmp_img
+
+
+    def convolveFullImgWithPCAPSF(self, chip, folding_threshold=5.e-3):
+        keys_L1= chip_utils.getChipSLSGratingID(chip.chipID)
+        # keys_L2 = ['order-2','order-1','order0','order1','order2']
+        keys_L2 = ['order0','order1']
+        keys_L3 = ['w1','w2','w3','w4']
+
+        npca = 10
+
+        x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
+        y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
+
+        for i,gt in enumerate(keys_L1):
+            psfCo = self.grating1_data
+            if i > 0:
+                psfCo = self.grating2_data
+            for od in keys_L2:
+                psfCo_L2 = psfCo['order1']
+                if od in ['order-2','order-1','order0','order2']:
+                    psfCo_L2 = psfCo['order0']
+                for w in keys_L3:
+                    img = chip.img_stack[gt][od][w]
+                    pcs = psfCo_L2['band'+w[1]]['band_data'][0].data
+                    pos_p = psfCo_L2['band'+w[1]]['band_data'][1].data/chip.pix_size - np.array([y_start, x_start])
+                    pc_coeff = psfCo_L2['band'+w[1]]['band_data'][2].data
+                    # print("DEBUG-----------",np.max(pos_p[:,1]),np.min(pos_p[:,1]), np.max(pos_p[:,0]),np.min(pos_p[:,0]))
+                    sum_img = np.sum(img.array)
+                    
+                    
+                    # coeff_mat = np.zeros([npca, chip.npix_y, chip.npix_x])
+                    # for m in np.arange(chip.npix_y):
+                    #     for n in np.arange(chip.npix_x):
+                    #         px = n
+                    #         py = m
+                            
+                    #         dist2 = (pos_p[:, 1] - px)*(pos_p[:, 1] - px) + (pos_p[:, 0] - py)*(pos_p[:, 0] - py)
+                    #         temp_sort_dist = np.zeros([dist2.shape[0], 2])
+                    #         temp_sort_dist[:, 0] = np.arange(0, dist2.shape[0], 1)
+                    #         temp_sort_dist[:, 1] = dist2
+                    #         # print(temp_sort_dist)
+                    #         dits2_sortlist = sorted(temp_sort_dist, key=lambda x: x[1])
+                    #         # print(dits2_sortlist)
+                    #         nearest4p = np.zeros([4, 3])
+                    #         pc_coeff_4p = np.zeros([npca, 4])
+
+                    #         for i in np.arange(4):
+                    #             smaller_ids = int(dits2_sortlist[i][0])
+                    #             nearest4p[i, 0] = pos_p[smaller_ids, 1]
+                    #             nearest4p[i, 1] = pos_p[smaller_ids, 0]
+                    #             # print(pos_p[smaller_ids, 1],pos_p[smaller_ids, 0])
+                    #             nearest4p[i, 2] = dits2_sortlist[i][1]
+                    #             pc_coeff_4p[:, i] = pc_coeff[npca, smaller_ids]
+                    #         # idw_dist = 1/(np.sqrt((px-nearest4p[:, 0]) * (px-nearest4p[:, 0]) + (
+                    #         #     py-nearest4p[:, 1]) * (py-nearest4p[:, 1])))
+                    #         idw_dist = 1/(np.sqrt(nearest4p[:, 2]))
+
+                    #         coeff_int = np.zeros(npca)
+                    #         for i in np.arange(4):
+                    #             coeff_int = coeff_int + pc_coeff_4p[:, i]*idw_dist[i]
+                    #         coeff_mat[:, m, n] = coeff_int
+
+                    m_size = int(pcs.shape[0]**0.5)
+                    tmp_img = np.zeros_like(img.array,dtype=np.float32)
+                    for j in np.arange(npca):
+                        print(gt, od, w, j)
+                        X_ = np.hstack((pos_p[:,1].flatten()[:, None], pos_p[:,0].flatten()[:, None]),dtype=np.float32)
+                        Z_ = (pc_coeff[j].astype(np.float32)).flatten()
+                        # print(pc_coeff[j].shape[0], pos_p[:,1].shape[0], pos_p[:,0].shape[0])
+                        sub_size = 4
+                        cx_len = int(chip.npix_x/sub_size)
+                        cy_len = int(chip.npix_y/sub_size)
+                        n_x = np.arange(0, chip.npix_x, sub_size, dtype = int)
+                        n_y = np.arange(0, chip.npix_y, sub_size, dtype = int)
+
+                        M, N = np.meshgrid(n_x, n_y)
+                        t1=datetime.datetime.now()
+                #         U = interpolate.griddata(X_, Z_, (M[0:cy_len, 0:cx_len],N[0:cy_len, 0:cx_len]),
+                # method='nearest',fill_value=1.0)
+                        U1 = interpolate.griddata(X_, Z_, (M, N),
+                method='nearest',fill_value=1.0)
+                        U = np.zeros_like(chip.img.array, dtype=np.float32)
+                        for mi in np.arange(cy_len):
+                            for mj in np.arange(cx_len):
+                                U[mi*sub_size:(mi+1)*sub_size, mj*sub_size:(mj+1)*sub_size]=U1[mi,mj]
+                        t2=datetime.datetime.now()
+                        
+                        print("time interpolate:", t2-t1)
+
+                        img_tmp = img.array*U
+                        psf = pcs[:, j].reshape(m_size, m_size)
+                        tmp_img = tmp_img + signal.fftconvolve(img_tmp, psf, mode='same', axes=None)
+
+                        t3=datetime.datetime.now()
+                        print("time convole:", t3-t2)
+                        del U
+                        del U1
+                        
+                    chip.img = chip.img + tmp_img*sum_img/np.sum(tmp_img)
+                    del tmp_img
+                    gc.collect()
+
         # pixSize = np.rad2deg(self.pixsize*1e-3/28)*3600  #set psf pixsize
         #
         # # assert self.iccd == int(chip.getChipLabel(chipID=chip.chipID)), 'ERROR: self.iccd != chip.chipID'

observation_sim/sim_steps/__init__.py

 import os
 
+
 class SimSteps:
-    def __init__(self, overall_config, chip_output, all_filters):
+    def __init__(self, overall_config, chip_output, all_filters, ra_offset=0., dec_offset=0.):
         self.overall_config = overall_config
         self.chip_output = chip_output
         self.all_filters = all_filters
+        self.ra_offset = ra_offset
+        self.dec_offset = dec_offset
 
     from .prepare_headers import prepare_headers, updateHeaderInfo
     from .add_sky_background import add_sky_background_sci, add_sky_flat_calibration, add_sky_background
@@ -15,6 +18,7 @@ class SimSteps:
     from .readout_output import add_prescan_overscan, add_readout_noise, apply_gain, quantization_and_output
     from .add_LED_flat import add_LED_Flat
 
+
 SIM_STEP_TYPES = {
     "scie_obs": "add_objects",
     "sky_background": "add_sky_background",
@@ -31,6 +35,6 @@ SIM_STEP_TYPES = {
     "readout_noise": "add_readout_noise",
     "gain": "apply_gain",
     "quantization_and_output": "quantization_and_output",
-    "led_calib_model":"add_LED_Flat",
-    "sky_flatField":"add_sky_flat_calibration",
-}
+    "led_calib_model": "add_LED_Flat",
+    "sky_flatField": "add_sky_flat_calibration",
+}
\ No newline at end of file

observation_sim/sim_steps/add_objects.py

@@ -145,7 +145,7 @@ def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
 
         # Get position of object on the focal plane
         pos_img, _, _, _, fd_shear = obj.getPosImg_Offset_WCS(
-            img=chip.img, fdmodel=fd_model, chip=chip, verbose=False, chip_wcs=chip_wcs, img_header=self.h_ext)
+            img=chip.img, fdmodel=fd_model, chip=chip, verbose=False, chip_wcs=chip_wcs, img_header=self.h_ext, ra_offset=self.ra_offset, dec_offset=self.dec_offset)
 
         # [TODO] For now, only consider objects which their centers (after field distortion) are projected within the focal plane
         # Otherwise they will be considered missed objects
@@ -194,7 +194,8 @@ def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
 
             if isUpdated == 1:
                 # TODO: add up stats
-                self.chip_output.cat_add_obj(obj, pos_img, pos_shear)
+                self.chip_output.cat_add_obj(
+                    obj, pos_img, pos_shear, ra_offset=self.ra_offset, dec_offset=self.dec_offset)
                 pass
             elif isUpdated == 0:
                 missed_obj += 1
@@ -216,7 +217,27 @@ def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
         # Unload SED:
         obj.unload_SED()
         del obj
-        gc.collect()
+        # gc.collect()
+
+    if chip.survey_type == "spectroscopic" and not self.overall_config["run_option"]["out_cat_only"] and chip.slsPSFOptim:
+        # from observation_sim.instruments.chip import chip_utils as chip_utils
+        # gn = chip_utils.getChipSLSGratingID(chip.chipID)[0]
+        # img1 = np.zeros([2,chip.img.array.shape[0],chip.img.array.shape[1]])
+
+        # for id1 in np.arange(2):
+        #     gn = chip_utils.getChipSLSGratingID(chip.chipID)[id1]
+        #     img_i = 0
+        #     for id2 in ['0','1']:
+        #         o_n = "order"+id2
+        #         for id3 in ['1','2','3','4']:
+        #             w_n = "w"+id3
+        #             img1[img_i] = img1[img_i] + chip.img_stack[gn][o_n][w_n].array
+        #         img_i = img_i + 1
+        # from astropy.io import fits
+        # fits.writeto('order0.fits',img1[0],overwrite=True)
+        # fits.writeto('order1.fits',img1[1],overwrite=True)
+
+        psf_model.convolveFullImgWithPCAPSF(chip)
     del psf_model
     gc.collect()
 

observation_sim/sim_steps/add_pattern_noise.py

@@ -27,7 +27,7 @@ def add_poisson_and_dark(self, chip, filt, tel, pointing, catalog, obs_param):
                                              InputDark=None)
     else:
         chip.img, _ = chip_utils.add_poisson(img=chip.img,
-                                             chip=self,
+                                             chip=chip,
                                              exptime=exptime,
                                              poisson_noise=chip.poisson_noise,
                                              dark_noise=0.)
@@ -81,5 +81,5 @@ def add_bias(self, chip, filt, tel, pointing, catalog, obs_param):
                                                nsecx=chip.nsecx,
                                                seed=self.overall_config["random_seeds"]["seed_biasNonUniform"]+chip.chipID)
     elif obs_param["bias_16channel"] == False:
-        chip.img += self.bias_level
+        chip.img += chip.bias_level
     return chip, filt, tel, pointing

observation_sim/sim_steps/readout_output.py

@@ -44,6 +44,7 @@ def apply_gain(self, chip, filt, tel, pointing, catalog, obs_param):
                                                                     seed=self.overall_config["random_seeds"]["seed_gainNonUniform"]+chip.chipID)
     elif obs_param["gain_16channel"] == False:
         chip.img /= chip.gain
+        chip.gain_channel = np.ones(chip.nsecy*chip.nsecx)*chip.gain
     return chip, filt, tel, pointing
 
 
@@ -86,10 +87,10 @@ def quantization_and_output(self, chip, filt, tel, pointing, catalog, obs_param)
     fname = os.path.join(self.chip_output.subdir,
                          self.h_prim['FILENAME'] + '.fits')
 
-    f_name_size = 68
-    if (len(self.h_prim['FILENAME']) > f_name_size):
-        self.updateHeaderInfo(header_flag='prim', keys=['FILENAME'], values=[
-                              self.h_prim['FILENAME'][0:f_name_size]])
+    # f_name_size = 68
+    # if (len(self.h_prim['FILENAME']) > f_name_size):
+    #     self.updateHeaderInfo(header_flag='prim', keys=['FILENAME'], values=[
+    #                           self.h_prim['FILENAME'][0:f_name_size]])
 
     hdu1 = fits.PrimaryHDU(header=self.h_prim)
 

setup.py

@@ -76,7 +76,7 @@ with open("requirements.txt", "r") as f:
     ]
 
 setup(name='csst_msc_sim',
-      version='3.0.0',
+      version='3.1.0',
       packages=find_packages(),
       # install_requires=[
       #     # 'numpy>=1.18.5',

submit_jobs.slurm

 #!/bin/bash
 
 #SBATCH -J CSSTSim
-#SBATCH -N 1
+#SBATCH -N 2
 #SBATCH --ntasks-per-node=6
 #SBATCH -p debug
-#SBATCH --mem=60G
+#SBATCH --mem=96G
 
 module load mpi/hpcx/2.4.1/gcc-7.3.1
 date
@@ -12,4 +12,4 @@ date
 #限定单节点任务数
 srun hostname -s | sort -n | awk -F"-" '{print $2}' | uniq > pnodes
 
-mpirun -mca pml ucx -x  UCX_NET_DEVICES=mlx5_0:1 -machinefile pnodes -np 6 --map-by node python3 /public/home/fangyuedong/project/csst_msc_sim/run_sim.py  --config_file config_overall.yaml --catalog C9_Catalog -c /public/home/fangyuedong/project/csst_msc_sim/config
+mpirun -mca pml ucx -x  UCX_NET_DEVICES=mlx5_0:1 -machinefile pnodes -np 12 --map-by node python3 /public/home/fangyuedong/project/csst_msc_sim/run_sim.py  --config_file config_overall.yaml --catalog C9_Catalog -c /public/home/fangyuedong/project/csst_msc_sim/config
\ No newline at end of file

tools/get_pointing.py

@@ -63,10 +63,15 @@ class Chip(object):
             ycen = self.cen_pix_y
         if pix_scale == None:
             pix_scale = self.pix_scale
-        dudx =  -np.cos(img_rot.rad) * pix_scale
-        dudy =  -np.sin(img_rot.rad) * pix_scale
-        dvdx =  -np.sin(img_rot.rad) * pix_scale
-        dvdy =  +np.cos(img_rot.rad) * pix_scale
+        # dudx =  -np.cos(img_rot.rad) * pix_scale
+        # dudy =  -np.sin(img_rot.rad) * pix_scale
+        # dvdx =  -np.sin(img_rot.rad) * pix_scale
+        # dvdy =  +np.cos(img_rot.rad) * pix_scale
+
+        dudx = -np.cos(img_rot.rad) * pix_scale
+        dudy = +np.sin(img_rot.rad) * pix_scale
+        dvdx = -np.sin(img_rot.rad) * pix_scale
+        dvdy = -np.cos(img_rot.rad) * pix_scale
         
         # dudx =  +np.sin(img_rot.rad) * pix_scale
         # dudy =  +np.cos(img_rot.rad) * pix_scale
@@ -139,12 +144,11 @@ def getobsPA(ra, dec):
     angle = math.acos(np.dot(l1l2cross,pdl2cross)/(np.linalg.norm(l1l2cross)*np.linalg.norm(pdl2cross)))
 
     angle = (angle)/math.pi*180
-
-    # if (ra>90 and ra< 270):
-    #     angle=-angle
+    angle = angle + 90
+    if (ra<90 or ra> 270):
+        angle=-angle
     return angle
 
-
 # @jit()
 def getSelectPointingList(center = [60,-40], radius = 2):
     points = np.loadtxt('sky.dat')
@@ -261,7 +265,7 @@ def findPointingbyChipID(chipID = 8, ra = 60., dec = -40.):
 
 
 if __name__ == "__main__":
-    tchip, tra, tdec = 8, 60., -40.
+    tchip, tra, tdec = 13, 60., -40.
     pointing = findPointingbyChipID(chipID=tchip, ra=tra, dec=tdec)
     print("[ra_center, dec_center, image_rot]: ", pointing)
 

tools/get_pointing_accuracy.py

+
+from pylab import *
+import math, sys, numpy as np
+import astropy.coordinates as coord
+from astropy.coordinates import SkyCoord
+from astropy import wcs, units as u
+from observation_sim.config.header import ImageHeader
+from observation_sim.instruments import Telescope, Chip, FilterParam, Filter, FocalPlane
+
+
+def transRaDec2D(ra, dec):
+    x1 = np.cos(dec / 57.2957795) * np.cos(ra / 57.2957795)
+    y1 = np.cos(dec / 57.2957795) * np.sin(ra / 57.2957795)
+    z1 = np.sin(dec / 57.2957795)
+    return np.array([x1, y1, z1])
+
+def ecl2radec(lon_ecl, lat_ecl):
+    ## convert from ecliptic coordinates to equatorial coordinates
+    c_ecl = SkyCoord(
+        lon=lon_ecl * u.degree, lat=lat_ecl * u.degree, frame="barycentrictrueecliptic"
+    )
+    c_eq = c_ecl.transform_to("icrs")
+    ra_eq, dec_eq = c_eq.ra.degree, c_eq.dec.degree
+    return ra_eq, dec_eq
+
+
+def radec2ecl(ra, dec):
+    ## convert from equatorial coordinates to ecliptic coordinates
+    c_eq = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
+    c_ecl = c_eq.transform_to("barycentrictrueecliptic")
+    lon_ecl, lat_ecl = c_ecl.lon.degree, c_ecl.lat.degree
+    return lon_ecl, lat_ecl
+
+def cal_FoVcenter_1P_equatorial(ra_FieldCenter, dec_FieldCenter, chipID = 1, pa = -23.5):
+
+    ### [ra_FieldCenter, dec_FieldCenter] is the center ra, dec of calibration fileds, such as: NEP, NGC 6397, etc.
+    ### [ra_ChipCenter, dec_ChipCenter] is the center ra, dec of the Chip center.
+    ### [ra_PointCenter, dec_PointCenter] is the telescope pointing center.
+    ## Calculate PA angle
+    chip = Chip(chipID)
+
+    h_ext = ImageHeader.generateExtensionHeader(
+        chip=chip,
+        xlen=chip.npix_x,
+        ylen=chip.npix_y,
+        ra=(ra_FieldCenter * u.degree).value, 
+        dec=(dec_FieldCenter * u.degree).value,
+        pa=pa,
+        gain=chip.gain,
+        readout=chip.read_noise,
+        dark=chip.dark_noise,
+        saturation=90000,
+        pixel_scale=chip.pix_scale,
+        pixel_size=chip.pix_size,
+        xcen=chip.x_cen,
+        ycen=chip.y_cen,
+        )
+
+    # assume that the telescope point to the sky center; then abtain the chip center under this situation; then calculate the differenc between the assumed telescope center and chip center; then add this difference to the sky center
+
+    wcs_h = wcs.WCS(h_ext)
+    pixs = np.array([[4608, 4616]])
+    world_point = wcs_h.wcs_pix2world(pixs, 0)
+    ra_ChipCenter, dec_ChipCenter = world_point[0][0], world_point[0][1]
+
+    d_ra = ra_FieldCenter - ra_ChipCenter
+    d_dec = dec_FieldCenter - dec_ChipCenter
+
+    ra_PointCenter = ra_FieldCenter + d_ra
+    dec_PointCenter = dec_FieldCenter + d_dec
+
+    lon_ecl_PointCenter, lat_ecl_PointCenter = radec2ecl(
+        ra_PointCenter, dec_PointCenter
+    )
+
+    return ra_PointCenter, dec_PointCenter, lon_ecl_PointCenter, lat_ecl_PointCenter
+
+def cal_FoVcenter_1P_ecliptic(lon_ecl_FieldCenter, lat_ecl_FieldCenter, chipID = 1, pa = -23.5):
+
+    ### [ra_FieldCenter, dec_FieldCenter] is the center ra, dec of calibration fileds, such as: NEP, NGC 6397, etc.
+    ### [ra_ChipCenter, dec_ChipCenter] is the center ra, dec of the Chip center.
+    ### [ra_PointCenter, dec_PointCenter] is the telescope pointing center.
+
+    ra_FieldCenter, dec_FieldCenter = ecl2radec(
+        lon_ecl_FieldCenter, lat_ecl_FieldCenter
+    )
+
+    ## Calculate PA angle
+    chip = Chip(chipID)
+
+    h_ext = ImageHeader.generateExtensionHeader(
+        chip=chip,
+        xlen=chip.npix_x,
+        ylen=chip.npix_y,
+        ra=(ra_FieldCenter * u.degree).value, 
+        dec=(dec_FieldCenter * u.degree).value,
+        pa=pa,
+        gain=chip.gain,
+        readout=chip.read_noise,
+        dark=chip.dark_noise,
+        saturation=90000,
+        pixel_scale=chip.pix_scale,
+        pixel_size=chip.pix_size,
+        xcen=chip.x_cen,
+        ycen=chip.y_cen,
+        )
+
+    # assume that the telescope point to the sky center; then abtain the chip center under this situation; then calculate the differenc between the assumed telescope center and chip center; then add this difference to the sky center
+
+    wcs_h = wcs.WCS(h_ext)
+    pixs = np.array([[4608, 4616]])
+    world_point = wcs_h.wcs_pix2world(pixs, 0)
+    ra_ChipCenter, dec_ChipCenter = world_point[0][0], world_point[0][1]
+
+    d_ra = ra_FieldCenter - ra_ChipCenter
+    d_dec = dec_FieldCenter - dec_ChipCenter
+
+    ra_PointCenter = ra_FieldCenter + d_ra
+    dec_PointCenter = dec_FieldCenter + d_dec
+
+    lon_ecl_PointCenter, lat_ecl_PointCenter = radec2ecl(
+        ra_PointCenter, dec_PointCenter
+    )
+
+    return ra_PointCenter, dec_PointCenter, lon_ecl_PointCenter, lat_ecl_PointCenter
+
+def getChipCenterRaDec(chipID = 1, p_ra = 60., p_dec = -40.):
+    chip = Chip(chipID)
+
+    h_ext = ImageHeader.generateExtensionHeader(
+        chip=chip,
+        xlen=chip.npix_x,
+        ylen=chip.npix_y,
+        ra=(p_ra * u.degree).value, 
+        dec=(p_dec * u.degree).value,
+        pa=pa,
+        gain=chip.gain,
+        readout=chip.read_noise,
+        dark=chip.dark_noise,
+        saturation=90000,
+        pixel_scale=chip.pix_scale,
+        pixel_size=chip.pix_size,
+        xcen=chip.x_cen,
+        ycen=chip.y_cen,
+        )
+    wcs_h = wcs.WCS(h_ext)
+    pixs = np.array([[4608, 4616]])
+    world_point = wcs_h.wcs_pix2world(pixs, 0)
+    RA_chip, Dec_chip = world_point[0][0], world_point[0][1]
+    return RA_chip, Dec_chip
+
+if __name__ == '__main__':
+    ra_input, dec_input = 270.00000, 66.56000  # NEP
+    pa = 23.5
+    # chipid = 2
+
+    for chipid in np.arange(1,31,1):
+        ra, dec, lon_ecl, lat_ecl = cal_FoVcenter_1P_equatorial(
+                    ra_input, dec_input,chipID=chipid, pa=pa)
+
+        print("chip id is %d, chip center [ra,dec] is [%f, %f], pointing center calculated [ra,dec] is [%f, %f]"%(chipid, ra_input, dec_input, ra, dec))
+        #for check the result
+        # testRA, testDec = getChipCenterRaDec(chipID = chipid, p_ra = ra, p_dec = dec)
+        # print(ra_input-testRA, dec_input-testDec)
+

tools/target_location_check.py

@@ -119,10 +119,15 @@ def getTanWCS(ra, dec, img_rot, pix_scale=0.074):
     """
     xcen, ycen = 0, 0
     img_rot = img_rot * galsim.degrees
+    # dudx = -np.cos(img_rot.rad) * pix_scale
+    # dudy = -np.sin(img_rot.rad) * pix_scale
+    # dvdx = -np.sin(img_rot.rad) * pix_scale
+    # dvdy = +np.cos(img_rot.rad) * pix_scale
+
     dudx = -np.cos(img_rot.rad) * pix_scale
-    dudy = -np.sin(img_rot.rad) * pix_scale
+    dudy = +np.sin(img_rot.rad) * pix_scale
     dvdx = -np.sin(img_rot.rad) * pix_scale
-    dvdy = +np.cos(img_rot.rad) * pix_scale
+    dvdy = -np.cos(img_rot.rad) * pix_scale
 
     moscen = galsim.PositionD(x=xcen, y=ycen)
     sky_center = galsim.CelestialCoord(