add pre/over-scan & format output

ObservationSim/Instrument/Chip/Chip.py

@@ -358,7 +358,11 @@ class Chip(FocalPlane):
         #     img.addNoise(dark_noise)
 
         # Add dark current & Poisson noise
+        InputDark = False
         if config["ins_effects"]["add_dark"] == True:
+            if InputDark:
+                img = chip_utils.add_inputdark(img=img, chip=self, exptime=exptime)
+            else:
                 img, _ = chip_utils.add_poisson(img=img, chip=self, exptime=exptime, poisson_noise=poisson_noise)
         else:
             img, _ = chip_utils.add_poisson(img=img, chip=self, exptime=exptime, poisson_noise=poisson_noise, dark_noise=0.)
@@ -391,13 +395,23 @@ class Chip(FocalPlane):
             chip_utils.log_info(msg="  Apply CTE Effect", logger=self.logger)
             img = effects.CTE_Effect(GSImage=img, threshold=27)
 
+        ### prescan & overscan
+        if config["ins_effects"]["add_prescan"] == True:
+            img = chip_utils.AddPreScan(GSImage=img, pre1=27, pre2=4, over1=71, over2=80)
+        ### 1*16 output
+        if config["ins_effects"]["format_output"] == True:
+            img = chip_utils.formatOutput(GSImage=img)
+            self.nsecy = 1
+            self.nsecx = 16
+                    
+
         # Add Bias level
         if config["ins_effects"]["add_bias"] == True:
             chip_utils.log_info(msg="  Adding Bias level and 16-channel non-uniformity", logger=self.logger)
             if config["ins_effects"]["bias_16channel"] == True:
                 img = effects.AddBiasNonUniform16(img, 
                     bias_level=float(self.bias_level), 
-                    nsecy = 2, nsecx=8, 
+                    nsecy = self.nsecy, nsecx=self.nsecx, 
                     seed=SeedBiasNonuni+self.chipID,
                     logger=self.logger)
             elif config["ins_effects"]["bias_16channel"] == False:
@@ -415,7 +429,7 @@ class Chip(FocalPlane):
         if config["ins_effects"]["gain_16channel"] == True:
             img, self.gain_channel = effects.ApplyGainNonUniform16(
                 img, gain=self.gain, 
-                nsecy = 2, nsecx=8, 
+                nsecy = self.nsecy, nsecx=self.nsecx, 
                 seed=SeedGainNonuni+self.chipID,
                 logger=self.logger)
         elif config["ins_effects"]["gain_16channel"] == False:

ObservationSim/Instrument/Chip/ChipUtils.py

@@ -171,10 +171,11 @@ def add_poisson(img, chip, exptime=150., seed=0, sky_level=0., poisson_noise=Non
 def add_brighter_fatter(img):
     #Inital dynamic lib
     try:
-        with pkg_resources.files('ObservationSim.Instrument.Chip.lib_bf').joinpath("libmoduleBF.so") as lib_path:
+        with pkg_resources.files('ObservationSim.Instrument.Chip.libBF').joinpath("libmoduleBF.so") as lib_path:
+            print('--1', lib_path)
             lib_bf = ctypes.CDLL(lib_path)
     except AttributeError:
-        with pkg_resources.path('ObservationSim.Instrument.Chip.lib_bf', "libmoduleBF.so") as lib_path:
+        with pkg_resources.path('ObservationSim.Instrument.Chip.libBF', "libmoduleBF.so") as lib_path:
             lib_bf = ctypes.CDLL(lib_path)
     lib_bf.addEffects.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_float), ctypes.POINTER(ctypes.c_float), ctypes.c_int]
     
@@ -194,3 +195,86 @@ def add_brighter_fatter(img):
     img.array[:, :] = np.reshape(arr_imc, [nx, ny])
     del arr_ima, arr_imc
     return img
+
+def add_inputdark(img, chip, exptime):
+    fname = "/share/home/weichengliang/CSST_git/test_new_sim/csst-simulation/ObservationSim/Instrument/data/dark/dark_1000s_example_0.fits"
+    hdu = fits.open(fname)
+    #ny, nx = img.array.shape
+    #inputdark = np.zeros([ny, nx])
+    img.array[:, :] += hdu[0].data/hdu[0].header['exptime']*exptime
+    hdu.close()
+    del inputdark
+    return img
+
+def AddPreScan(GSImage, pre1=27, pre2=4, over1=71, over2=80, nsecy = 2, nsecx=8):
+    img= GSImage.array
+    ny, nx = img.shape
+    dx = int(nx/nsecx)
+    dy = int(ny/nsecy)
+
+    imgt=np.zeros([int(nsecy*nsecx), int(ny/nsecy+pre2+over2), int(nx/nsecx+pre1+over1)])
+    for iy in range(nsecy):
+        for ix in range(nsecx):
+            if iy % 2 == 0:
+                tx = ix
+            else:
+                tx = (nsecx-1)-ix
+            ty = iy 
+            chunkidx = int(tx+ty*nsecx) #chunk1-[1,2,3,4], chunk2-[5,6,7,8], chunk3-[9,10,11,12], chunk4-[13,14,15,16]
+
+            imgtemp = np.zeros([int(ny/nsecy+pre2+over2), int(nx/nsecx+pre1+over1)])
+            if int(chunkidx/4) == 0:
+                imgtemp[pre2:-over2, pre1:-over1] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+                imgt[chunkidx, :, :] = imgtemp
+            if int(chunkidx/4) == 1:
+                imgtemp[pre2:-over2, over1:-pre1] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+                imgt[chunkidx, :, :] = imgtemp #[:, ::-1]
+            if int(chunkidx/4) == 2:
+                imgtemp[over2:-pre2, over1:-pre1] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+                imgt[chunkidx, :, :] = imgtemp #[::-1, ::-1]
+            if int(chunkidx/4) == 3:
+                imgtemp[over2:-pre2, pre1:-over1] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+                imgt[chunkidx, :, :] = imgtemp #[::-1, :]
+
+    imgtx1 = np.hstack(imgt[:nsecx:,       :, :])
+    imgtx2 = np.hstack(imgt[:(nsecx-1):-1, :, :])
+
+    newimg = galsim.Image(int(nx+(pre1+over1)*nsecx), int(ny+(pre2+over2)*nsecy), init_value=0)
+    newimg.array[:, :] = np.concatenate([imgtx1, imgtx2])
+
+    newimg.wcs = GSImage.wcs
+    return newimg
+
+def formatOutput(GSImage, nsecy = 2, nsecx=8):
+    img = GSImage.array
+    ny, nx = img.shape
+    dx = int(nx/nsecx)
+    dy = int(ny/nsecy)
+    
+    imgt = np.zeros([int(nsecx*nsecy), dy, dx])
+    for iy in range(nsecy):
+        for ix in range(nsecx):
+            if iy % 2 == 0:
+                tx = ix
+            else:
+                tx = (nsecx-1)-ix
+            ty = iy
+            chunkidx = int(tx+ty*nsecx)
+            if int(chunkidx/4) == 0:
+                imgt[chunkidx, :, :] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+            if int(chunkidx/4) == 1:
+                imgt[chunkidx, :, :] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+            if int(chunkidx/4) == 2:
+                imgt[chunkidx, :, :] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+            if int(chunkidx/4) == 3:
+                imgt[chunkidx, :, :] = img[iy*dy:(iy+1)*dy, ix*dx:(ix+1)*dx]
+    
+    imgttx0 = np.hstack(imgt[ 0:4:,    :,    :])
+    imgttx1 = np.hstack(imgt[ 4:8:,    :, ::-1])
+    imgttx2 = np.hstack(imgt[8:12:, ::-1, ::-1])
+    imgttx3 = np.hstack(imgt[12:16:,::-1,    :])
+    
+    newimg = galsim.Image(int(dx*nsecx*nsecy), dy, init_value=0)
+    newimg.array[:, :] = np.hstack([imgttx0, imgttx1, imgttx2, imgttx3])
+    return newimg
+

config/config_50sqdeg.yaml

@@ -9,13 +9,13 @@
 # Base diretories and naming setup
 # Can add some of the command-line arguments here as well;
 # OK to pass either way or both, as long as they are consistent
-work_dir: "/share/home/fangyuedong/new_sim/workplace/"
+work_dir: "/share/home/weichengliang/CSST_git/test_new_sim/outputs/"
 data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
-run_name: "test_new_sim"
+run_name: "testRun0"
 
 # Whether to use MPI
 run_option:
-  use_mpi: NO
+  use_mpi: YES
   # NOTE: "n_threads" paramters is currently not used in the backend
   # simulation codes. It should be implemented later in the web frontend
   # in order to config the number of threads to request from NAOC cluster
@@ -85,7 +85,7 @@ obs_setting:
   # if you just want to run default pointing:
   # - pointing_dir: null
   # - pointing_file: null
-  pointing_dir: "/share/home/fangyuedong/50sqdeg_pointings/"
+  pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
   pointing_file: "pointing_50_combined.dat"
 
   # Number of calibration pointings
@@ -163,6 +163,7 @@ ins_effects:
   add_dark:       ON  # Whether to add dark noise
   add_readout:    ON  # Whether to add read-out (Gaussian) noise
   add_bias:       ON  # Whether to add bias-level to images
+  add_prescan:    OFF
   bias_16channel: ON  # Whether to add different biases for 16 channels
   gain_16channel: ON  # Whether to make different gains for 16 channels
   shutter_effect: ON  # Whether to add shutter effect
@@ -177,6 +178,7 @@ ins_effects:
   add_hotpixels:  ON  # Whether to add hot pixels
   add_deadpixels: ON  # Whether to add dead(dark) pixels
   bright_fatter:  ON  # Whether to simulate Brighter-Fatter (also diffusion) effect
+  format_output:  OFF
 
   # Values: 
   # default values have been defined individually for each chip in: