update codestyle-PEP8

observation_sim/instruments/chip/effects.py

@@ -3,7 +3,8 @@ from matplotlib.pyplot import flag
 import numpy as np
 from numpy.core.fromnumeric import mean, size
 from numpy.random import Generator, PCG64
-import math,copy
+import math
+import copy
 from numba import jit
 from astropy import stats
 
@@ -50,11 +51,11 @@ def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=
     NPixBad = int(NPix*fraction)
     NPixHot = int(NPix*fraction*HotFraction)
     NPixDead = NPixBad-NPixHot
-    
-    NPix_y,NPix_x = GSImage.array.shape
+
+    NPix_y, NPix_x = GSImage.array.shape
     mean = np.mean(GSImage.array)
     rgp = Generator(PCG64(int(seed)))
-    yxposfrac = rgp.random((NPixBad,2))
+    yxposfrac = rgp.random((NPixBad, 2))
     YPositHot = np.array(NPix_y*yxposfrac[0:NPixHot, 0]).astype(np.int32)
     XPositHot = np.array(NPix_x*yxposfrac[0:NPixHot, 1]).astype(np.int32)
     YPositDead = np.array(NPix_y*yxposfrac[NPixHot:, 0]).astype(np.int32)
@@ -108,7 +109,7 @@ def AddBiasNonUniform16(GSImage, bias_level=500, nsecy=2, nsecx=8, seed=202102,
     if int(bias_level) == 0:
         BiasLevel = np.zeros((nsecy, nsecx))
     elif bias_level > 0:
-        BiasLevel = Random16.reshape((nsecy,nsecx)) + bias_level
+        BiasLevel = Random16.reshape((nsecy, nsecx)) + bias_level
     if logger is not None:
         msg = str(" Biases of 16 channels: " + str(BiasLevel))
         logger.info(msg)
@@ -129,7 +130,7 @@ def MakeBiasNcomb(npix_x, npix_y, bias_level=500, ncombine=1, read_noise=5, gain
     BiasSngImg0 = galsim.Image(npix_x, npix_y, init_value=0)
     BiasSngImg = AddBiasNonUniform16(BiasSngImg0,
                                      bias_level=bias_level,
-                                     nsecy = 2, nsecx=8,
+                                     nsecy=2, nsecx=8,
                                      seed=int(seed),
                                      logger=logger)
     BiasCombImg = BiasSngImg*ncombine
@@ -198,7 +199,7 @@ def MakeFlatSmooth(GSBounds, seed):
     xmin, xmax, ymin, ymax = GSBounds.getXMin(), GSBounds.getXMax(), GSBounds.getYMin(), GSBounds.getYMax()
     Flty, Fltx = np.mgrid[ymin:(ymax+1), xmin:(xmax+1)]
     rg = Generator(PCG64(int(seed)))
-    p1, p2, bg=rg.poisson(1000, 3)
+    p1, p2, bg = rg.poisson(1000, 3)
     Fltz = 0.6*1e-7*(a1 * (Fltx-p1) ** 2 + a2 * (Flty-p2) ** 2 - a3*Fltx - a4*Flty) + bg*20
     FlatImg = galsim.ImageF(Fltz)
     return FlatImg
@@ -245,7 +246,7 @@ def MakeDarkNcomb(npix_x, npix_y, overscan=500, bias_level=500, seed_bias=202102
         DarkCombImg = AddBiasNonUniform16(
             DarkCombImg,
             bias_level=bias_level,
-            nsecy = 2, nsecx=8,
+            nsecy=2, nsecx=8,
             seed=int(seed_bias),
             logger=logger)
     if ncombine == 1:
@@ -272,7 +273,7 @@ def NonLinearity(GSImage, beta1=5E-7, beta2=0):
     return GSImage
 
 
-#Saturation & Bleeding Start#
+# Saturation & Bleeding Start#
 def BleedingTrail(aa, yy):
     if aa < 0.2:
         aa = 0.2
@@ -280,7 +281,7 @@ def BleedingTrail(aa, yy):
         pass
     try:
         fy = 0.5*(math.exp(math.log(yy+1)**3/aa)+np.exp(-1*math.log(yy+1)**3/aa))
-        faa= 0.5*(math.e+1/math.e)                
+        faa = 0.5*(math.e+1/math.e)
         trail_frac = 1-0.1*(fy-1)/(faa-1)
     except Exception as e:
         print(e)
@@ -306,14 +307,14 @@ def MakeTrail(imgarr, satuyxtuple, charge, fullwell=9e4, direction='up', trailcu
         if direction == 'up':
             if imgarr[yi-1, xi] >= fullwell:
                 imgarr[yi, xi] = fullwell
-                yi-=1
+                yi -= 1
                 continue
         elif direction == 'down':
             if imgarr[yi+1, xi] >= fullwell:
                 imgarr[yi, xi] = fullwell
                 yi += 1
                 continue
-        if aa<=1:
+        if aa <= 1:
             while imgarr[yi, xi] >= fullwell:
                 imgarr[yi, xi] = fullwell
                 if direction == 'up':
@@ -359,9 +360,9 @@ def MakeTrail(imgarr, satuyxtuple, charge, fullwell=9e4, direction='up', trailcu
 
 def ChargeFlow(imgarr, fullwell=9E4):
     size_y, size_x = imgarr.shape
-    satupos_y, satupos_x = np.where(imgarr>fullwell)
+    satupos_y, satupos_x = np.where(imgarr > fullwell)
 
-    if satupos_y.shape[0]==0:
+    if satupos_y.shape[0] == 0:
         # make no change for the image array
         return imgarr
     elif satupos_y.shape[0]/imgarr.size > 0.5:
@@ -383,16 +384,16 @@ def ChargeFlow(imgarr, fullwell=9E4):
 
         try:
             # Charge Clump moves up
-            if yi>=0 and yi<imgarr.shape[0]:
+            if yi >= 0 and yi < imgarr.shape[0]:
                 imgarr = MakeTrail(imgarr, (yi, xi), chargeup, fullwell=9e4, direction='up', trailcutfrac=0.9)
                 # Charge Clump moves down
                 imgarr = MakeTrail(imgarr, (yi, xi), chargedn, fullwell=9e4, direction='down', trailcutfrac=0.9)
         except Exception as e:
-            print(e,'@pix ',(yi+1, xi+1))
+            print(e, '@pix ', (yi+1, xi+1))
             return imgarr
-        
     return imgarr
 
+
 def SaturBloom(GSImage, nsect_x=1, nsect_y=1, fullwell=9e4):
     """
     To simulate digital detector's saturation and blooming effect. The blooming is along the read-out direction, perpendicular to the charge transfer direction. Charge clumpy overflows the pixel well will flow to two oposite directions with nearly same charges.
@@ -424,7 +425,7 @@ def SaturBloom(GSImage, nsect_x=1, nsect_y=1, fullwell=9e4):
 def readout16(GSImage, rowi=0, coli=0, overscan_value=0):
     # readout image as 16 outputs of sub-images plus prescan & overscan.
     # assuming image width and height sizes are both even.
-    # assuming image has 2 columns and 8 rows of output channels. 
+    # assuming image has 2 columns and 8 rows of output channels.
     # 00  01
     # 10  11
     # 20  21
@@ -453,7 +454,7 @@ def readout16(GSImage, rowi=0, coli=0, overscan_value=0):
         subbounds = galsim.BoundsI(npix_x/2+1, npix_x,  npix_y/8*rowi+1, npix_y/8*(rowi+1))
         subbounds = subbounds.shift(galsim.PositionI(GSImage.bounds.getXMin()-1, GSImage.bounds.getYMin()-1))
         subimg = GSImage[subbounds]
-        OutputSubimg.array[16 :int(npix_y/8)+16, 8:int(npix_x/2)+8] = subimg.array
+        OutputSubimg.array[16:int(npix_y/8)+16, 8:int(npix_x/2)+8] = subimg.array
     else:
         print("\n\033[31mError: "+"Wrong rowi or coli assignment. Permitted: 0<=rowi<=7, 0<=coli<=1."+"\033[0m\n")
         return OutputSubimg
@@ -469,19 +470,19 @@ def CTE_Effect(GSImage, threshold=27, direction='column'):
     imgarr = GSImage.array
     if direction == 'column':
         imgarr[0:size_sect_y, :] = CTEModelColRow(imgarr[0:size_sect_y, :], trail_direction='down', direction='column', threshold=threshold)
-        imgarr[size_sect_y:size_y, :] = CTEModelColRow(imgarr[size_sect_y:size_y,:], trail_direction='up', direction='column', threshold=threshold)
+        imgarr[size_sect_y:size_y, :] = CTEModelColRow(imgarr[size_sect_y:size_y, :], trail_direction='up', direction='column', threshold=threshold)
     elif direction == 'row':
-        imgarr[:,0:size_sect_x] = CTEModelColRow(imgarr[:,0:size_sect_x], trail_direction='right', direction='row', threshold=threshold)
-        imgarr[:,size_sect_x:size_x] = CTEModelColRow(imgarr[:,size_sect_x:size_x], trail_direction='left', direction='row', threshold=threshold)
+        imgarr[:, 0:size_sect_x] = CTEModelColRow(imgarr[:, 0:size_sect_x], trail_direction='right', direction='row', threshold=threshold)
+        imgarr[:, size_sect_x:size_x] = CTEModelColRow(imgarr[:, size_sect_x:size_x], trail_direction='left', direction='row', threshold=threshold)
     return GSImage
 
 
 @jit()
-def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27):
+def CTEModelColRow(img, trail_direction='up', direction='column', threshold=27):
 
-    #total trail flux vs (pixel flux)^1/2 is approximately linear
-    #total trail flux = trail_a * (pixel flux)^1/2 + trail_b
-    #trail pixel flux = pow(0.5,x)/0.5, normalize to 1
+    # total trail flux vs (pixel flux)^1/2 is approximately linear
+    # total trail flux = trail_a * (pixel flux)^1/2 + trail_b
+    # trail pixel flux = pow(0.5,x)/0.5, normalize to 1
     trail_a = 5.651803799619966
     trail_b = -2.671933068990729
 
@@ -491,7 +492,7 @@ def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27
     idx = np.where(img < threshold)
     if len(idx[0]) == 0:
         pass
-    elif len(idx[0])>0:
+    elif len(idx[0]) > 0:
         n_img[idx] = img[idx]
 
     yidx, xidx = np.where(img >= threshold)
@@ -505,13 +506,13 @@ def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27
             # trail_f=5E-5*f**1.5
             xy_num = 10
             all_trail = np.zeros(xy_num)
-            
+
             xy_upstr = np.arange(1, xy_num, 1)
 
             # all_trail_pix = np.sum(pow(0.5,xy_upstr)/0.5)
             all_trail_pix = 0
             for m in xy_upstr:
-                a1 = 12.97059491  
+                a1 = 12.97059491
                 b1 = 0.54286652
                 c1 = 0.69093105
                 a2 = 2.77298856
@@ -530,12 +531,8 @@ def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27
 
                 all_trail_pix += t_pow
                 all_trail[m] = t_pow
-
-
             trail_pix_eff = trail_f/all_trail_pix
-
             all_trail = trail_pix_eff*all_trail
-
             all_trail[0] = f - trail_f
             
             for m in np.arange(0, xy_num, 1):
@@ -559,10 +556,8 @@ def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27
     return n_img
 
 
-
-#---------- For Cosmic-Ray Simulation ------------
-#---------- Zhang Xin ----------------------------
-
+# ---------- For Cosmic-Ray Simulation ------------
+# ---------- Zhang Xin ----------------------------
 def getYValue(collection, x):
     index = 0;
     if (collection.shape[1] == 2):