update

csst_ifs_sim/csst_ifs_sim.py

@@ -2505,11 +2505,8 @@ class IFSsimulator():
         # zone 2
         self.image_r[1536:3072, 3072:6144] += self.information['exptime'] * \
             self.information['dark4_r']
-
-
 # #############################################################################
 
-
     def applyPoissonNoise(self):
 
         """
@@ -3251,7 +3248,6 @@ class IFSsimulator():
         self.image_b = np.rint(self.image_b).astype(int)
         self.log.info('Maximum and total values of the image are %i and %i, respectively' % (np.max(self.image_b),
                                                                                              np.sum(self.image_b)))
-
         # avoid negative numbers in case bias level was not added
         self.image_r[self.image_r < 0.0] = 0.
         # cut of the values larger than max
@@ -3261,62 +3257,46 @@ class IFSsimulator():
         self.log.info('Maximum and total values of the image are %i and %i, respectively' % (np.max(self.image_r),
                                                                                              np.sum(self.image_r)))
 # ############################################################################
-
     # def applyImageShift(self):
     #     """
-
     #     Returns
     #     -------
     #     None.
-
     #     """
-
     #     np.random.seed(9*self.simnumber)
     #     ud = np.random.random()   # Choose a random rotation
     #     dx = 2 * (ud-0.5) * self.information['shiftmax']
-
     #     np.random.seed(99*self.simnumber)
     #     ud = np.random.random()   # Choose a random rotation
     #     dy = 2 * (ud-0.5) * self.information['shiftmax']
-
     #     self.image_b = ndimage.shift(self.image_b.copy(), [
     #                                  dy+self.information['shift_b_y'], dx+self.information['shift_b_x']], order=0, mode='nearest')
     #     self.image_r = ndimage.shift(self.image_r.copy(), [
     #                                  dy+self.information['shift_r_y'], dx+self.information['shift_r_x']], order=0, mode='nearest')
-
     #     self.log.info('Applied image shifting to g r i channels.')
     #     self.information['ra'] = dx*self.information['pixel_size']
     #     self.information['dec'] = dy*self.information['pixel_size']
-
-
 ##############################################################################
-
     # def applyImageRotate(self):
     #     """
-
     #     Returns
     #     -------
     #     None.
-
     #     """
     #     np.random.seed(10*self.simnumber)
     #     ud = np.random.random()   # Choose a random rotation
     #     angle = 2 * (ud-0.5) * self.information['tel_rotmax']
-
     #     inputimg = self.image_b.copy()
     #     # here we choose reshape=False, the rotated image will
     #     rotimg = ndimage.rotate(
     #         inputimg, angle+self.information['rotate_b'], order=1, reshape=False)
     #     self.image_b = rotimg
-
     #     inputimg = self.image_r.copy()
     #     # here we choose reshape=False, the rotated image will
     #     rotimg = ndimage.rotate(
     #         inputimg, angle+self.information['rotate_r'], order=1, reshape=False)
     #     self.image_r = rotimg
-
     #     self.information['Tel_rot'] = angle
-
     #     self.log.info(
     #         'Applied telescope rotation with angle (in degree)= %f.', angle)
 ###############################################################################
@@ -3326,7 +3306,6 @@ class IFSsimulator():
         Returns
         -------
         None.
-
         """
         imgb = self.image_b.copy()
         prescan = int(self.information['prescan'])