Loading observation_sim/instruments/chip/effects.py +10 −6 Original line number Diff line number Diff line Loading @@ -267,8 +267,12 @@ def PRNU_Img(xsize, ysize, sigma=0.01, seed=202101): return prnuimg def NonLinear_f(x, beta_1, beta_2): return x - beta_1 * x * x + beta_2 * x * x * x def NonLinearity(GSImage, beta1=5E-7, beta2=0): NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x # NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x GSImage.applyNonlinearity(NonLinear_f, beta1, beta2) return GSImage Loading Loading @@ -615,11 +619,11 @@ def defineEnergyForCR(cr_event_size, seed=12345): random.seed(seed) energys = np.zeros(cr_event_size) for i in np.arange(cr_event_size): energy_index = random.normalvariate(mean, sigma); energy_index = random.normalvariate(mean, sigma) energys[i] = pow(10, energy_index) return energys def convCR(CRmap=None, addPSF=None, sp_n=4): sh = CRmap.shape Loading Loading @@ -691,8 +695,8 @@ def produceCR_Map(xLen, yLen, exTime, cr_pixelRatio, gain, attachedSizes, seed=2 # --------------------------------- for i in np.arange(cr_event_size): xPos = round((xLen - 1)* np.random.random()); yPos = round((yLen - 1)* np.random.random()); xPos = round((xLen - 1) * np.random.random()) yPos = round((yLen - 1) * np.random.random()) cr_lens = int(cr_size[i]); if cr_lens == 0: continue Loading Loading
observation_sim/instruments/chip/effects.py +10 −6 Original line number Diff line number Diff line Loading @@ -267,8 +267,12 @@ def PRNU_Img(xsize, ysize, sigma=0.01, seed=202101): return prnuimg def NonLinear_f(x, beta_1, beta_2): return x - beta_1 * x * x + beta_2 * x * x * x def NonLinearity(GSImage, beta1=5E-7, beta2=0): NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x # NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x GSImage.applyNonlinearity(NonLinear_f, beta1, beta2) return GSImage Loading Loading @@ -615,11 +619,11 @@ def defineEnergyForCR(cr_event_size, seed=12345): random.seed(seed) energys = np.zeros(cr_event_size) for i in np.arange(cr_event_size): energy_index = random.normalvariate(mean, sigma); energy_index = random.normalvariate(mean, sigma) energys[i] = pow(10, energy_index) return energys def convCR(CRmap=None, addPSF=None, sp_n=4): sh = CRmap.shape Loading Loading @@ -691,8 +695,8 @@ def produceCR_Map(xLen, yLen, exTime, cr_pixelRatio, gain, attachedSizes, seed=2 # --------------------------------- for i in np.arange(cr_event_size): xPos = round((xLen - 1)* np.random.random()); yPos = round((yLen - 1)* np.random.random()); xPos = round((xLen - 1) * np.random.random()) yPos = round((yLen - 1) * np.random.random()) cr_lens = int(cr_size[i]); if cr_lens == 0: continue Loading