Merge branch 'demos' of gitlab.mpcdf.mpg.de:ift/nifty-dev into demos

demos/getting_started_1.py

@@ -22,13 +22,13 @@ if __name__ == '__main__':
 
     # Choose problem geometry and masking
 
-    # # One dimensional regular grid
-    # position_space = ift.RGSpace([1024])
-    # mask = np.ones(position_space.shape)
+    # One dimensional regular grid
+    position_space = ift.RGSpace([1024])
+    mask = np.ones(position_space.shape)
 
-    # Two dimensional regular grid with chess mask
-    position_space = ift.RGSpace([128,128])
-    mask = make_chess_mask()
+    # # Two dimensional regular grid with chess mask
+    # position_space = ift.RGSpace([128,128])
+    # mask = make_chess_mask()
 
     # # Sphere with half of its locations randomly masked
     # position_space = ift.HPSpace(128)

demos/getting_started_2.py

@@ -19,10 +19,10 @@ def get_2D_exposure():
 
 if __name__ == '__main__':
     # ABOUT THIS CODE
-    np.random.seed(42)
+    np.random.seed(41)
 
     # Set up the position space of the signal
-
+    #
     # # One dimensional regular grid with uniform exposure
     # position_space = ift.RGSpace(1024)
     # exposure = np.ones(position_space.shape)
@@ -84,5 +84,6 @@ if __name__ == '__main__':
 
     # Plot results
     result_sky = sky.at(H.position).value
+    ##PLOTTING
 
 

demos/getting_started_3.py

@@ -3,6 +3,7 @@ from nifty5.library.los_response import LOSResponse
 from nifty5.library.amplitude_model import make_amplitude_model
 from nifty5.library.smooth_sky import make_correlated_field
 import numpy as np
+from scipy.io import loadmat
 
 
 def get_random_LOS(n_los):
@@ -11,9 +12,9 @@ def get_random_LOS(n_los):
 
     return starts, ends
 
-
 if __name__ == '__main__':
-    np.random.seed(41)
+    ### ABOUT THIS TUTORIAL
+    np.random.seed(42)
     position_space = ift.RGSpace([128,128])
 
     # Setting up an amplitude model
@@ -33,14 +34,13 @@ if __name__ == '__main__':
     correlated_field_h = Amp * xi
     correlated_field = ht(correlated_field_h)
     # # alternatively to the block above one can do:
-    # correlated_field, _ = make_correlated_field(position_space,A)
+    # correlated_field, _ = make_correlated_field(position_space, A)
 
     # apply some nonlinearity
     signal = ift.PointwisePositiveTanh(correlated_field)
     # Building the Line of Sight response
-    LOS_starts, LOS_ends = get_random_LOS(1000)
+    LOS_starts, LOS_ends = get_random_LOS(100)
     R = LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
-
     # build signal response model and model likelihood
     signal_response = R(signal)
     # specify noise
@@ -73,7 +73,7 @@ if __name__ == '__main__':
     ift.plot([ A.at(MOCK_POSITION).value], name='power.pdf')
 
     # number of samples used to estimate the KL
-    N_samples = 10
+    N_samples = 20
     for i in range(5):
         H = H.at(position)
         samples = [H.curvature.draw_sample(from_inverse=True) for _ in range(N_samples)]