use more generic names where applicable

demos/getting_started_3.py

 import nifty5 as ift
-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):
@@ -19,7 +15,7 @@ if __name__ == '__main__':
     position_space = ift.RGSpace([128, 128])
 
     # Setting up an amplitude model
-    A, amplitude_internals = make_amplitude_model(
+    A, amplitude_internals = ift.library.make_amplitude_model(
         position_space, 16, 1, 10, -4., 1, 0., 1.)
 
     # Building the model for a correlated signal
@@ -35,14 +31,15 @@ if __name__ == '__main__':
     Amp = power_distributor(A)
     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)
+    # alternatively to the block above one can do:
+    # correlated_field,_ = ift.library.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(100)
-    R = LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
+    R = ift.library.LOSResponse(position_space, starts=LOS_starts,
+                                ends=LOS_ends)
     # build signal response model and model likelihood
     signal_response = R(signal)
     # specify noise

nifty5/library/__init__.py

@@ -6,4 +6,4 @@ from .point_sources import PointSources
 from .poissonian_energy import PoissonianEnergy
 from .wiener_filter_curvature import WienerFilterCurvature
 from .wiener_filter_energy import WienerFilterEnergy
-from .smooth_sky import make_correlated_field, make_mf_correlated_field
+from .correlated_fields import make_correlated_field, make_mf_correlated_field

nifty5/library/smooth_sky.py → nifty5/library/correlated_fields.py

@@ -62,6 +62,6 @@ def make_mf_correlated_field(s_space_spatial, s_space_energy,
 
     position = MultiField({'xi': Field.from_random('normal', h_space)})
     xi = Variable(position)['xi']
-    logsky_h = A*xi
-    logsky = ht(logsky_h)
-    return PointwiseExponential(logsky)
+    correlated_field_h = A*xi
+    correlated_field = ht(correlated_field_h)
+    return PointwiseExponential(correlated_field)

nifty5/library/poissonian_energy.py

@@ -26,7 +26,7 @@ from ..sugar import log, makeOp
 class PoissonianEnergy(Energy):
     def __init__(self, lamb, d):
         """
-        lamb: Sky model object
+        lamb: Model object
 
         value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit
         covariance

nifty5/models/constant.py

@@ -19,7 +19,7 @@ from .model import Model
 
 
 class Constant(Model):
-    """A sky model with a constant (multi-)field as value.
+    """A model with a constant (multi-)field as value.
 
     Parameters
     ----------