synthetic data with bg + sqrt noise

Examples/fit/gisas2d/fake.py

@@ -24,13 +24,11 @@ def fake_data():
     # Compute model distribution
     simulation = model.get_simulation(params)
     simulation.runSimulation()
-    data = simulation.result().array()
+    theory = simulation.result().array()
 
     # Draw noisy data
     np.random.seed(0)
-    noise_factor = 0.1
-    noisy = np.random.normal(data, noise_factor*np.sqrt(data))
-    noisy[noisy < 0.1] = 0.1
+    data = np.random.normal(theory, np.sqrt(theory)) + np.random.poisson(3.57, theory.shape)
 
     # Save to numpy
     np.savetxt("gisas-model1.txt.gz", data)

Examples/fit/gisas2d/gisas_fit2d.py

@@ -47,7 +47,7 @@ def get_simulation(params):
     """
     Returns a GISAXS simulation with beam and detector defined
     """
-    intensity = 1e8
+    intensity = 1e6
     beam = ba.Beam(intensity, 0.1*nm, ba.Direction(0.2*deg, 0))
     det = ba.SphericalDetector(100, -1*deg, 1*deg, 100, 0, 2*deg)
     sample = get_sample(params)