Merge pull request lenstronomy#487 from sibirrer/main

Minor changes in documentation and re-naming

lenstronomy/LensModel/Profiles/nfw.py

@@ -13,9 +13,10 @@ class NFW(LensProfileBase):
     this class contains functions concerning the NFW profile
 
     relation are: R_200 = c * Rs
-    The definition of 'Rs' is in angular (arc second) units and the normalization is put in in regards to a deflection
+    The definition of 'Rs' is in angular (arc second) units and the normalization is put in with regard to a deflection
     angle at 'Rs' - 'alpha_Rs'. To convert a physical mass and concentration definition into those lensing quantities
-    for a specific redshift configuration and cosmological model, you can find routines in lenstronomy.Cosmo.lens_cosmo.py
+    for a specific redshift configuration and cosmological model, you can find routines in
+    lenstronomy.Cosmo.lens_cosmo.py
 
     Examples for converting angular to physical mass units
     ------------------------------------------------------
@@ -123,9 +124,10 @@ def hessian(self, x, y, Rs, alpha_Rs, center_x=0, center_y=0):
         f_xy = gamma2
         return f_xx, f_xy, f_xy, f_yy
 
-    def density(self, R, Rs, rho0):
+    @staticmethod
+    def density(R, Rs, rho0):
         """
-        three dimensional NFW profile
+        three-dimensional NFW profile
 
         :param R: radius of interest
         :type R: float/numpy array
@@ -152,16 +154,14 @@ def density_lens(self, r, Rs, alpha_Rs):
 
     def density_2d(self, x, y, Rs, rho0, center_x=0, center_y=0):
         """
-        projected two dimensional NFW profile (kappa)
+        projected two-dimensional NFW profile (kappa)
 
-        :param R: radius of interest
-        :type R: float/numpy array
+        :param x: x-coordinate
+        :param y: y-coordinate
         :param Rs: scale radius
         :type Rs: float
         :param rho0: density normalization (characteristic density)
         :type rho0: float
-        :param r200: radius of (sub)halo
-        :type r200: float>0
         :param center_x: x-centroid position
         :param center_y: y-centroid position
         :return: Epsilon(R) projected density at radius R
@@ -236,8 +236,6 @@ def nfwPot(self, R, Rs, rho0):
         :type Rs: float
         :param rho0: density normalization (characteristic density)
         :type rho0: float
-        :param r200: radius of (sub)halo
-        :type r200: float>0
         :return: Epsilon(R) projected density at radius R
         """
         x = R/Rs
@@ -255,10 +253,10 @@ def nfwAlpha(self, R, Rs, rho0, ax_x, ax_y):
         :type Rs: float
         :param rho0: density normalization (characteristic density)
         :type rho0: float
-        :param r200: radius of (sub)halo
-        :type r200: float>0
-        :param axis: projection to either x- or y-axis
-        :type axis: same as R
+        :param ax_x: projection to either x- or y-axis
+        :type ax_x: same as R
+        :param ax_y: projection to either x- or y-axis
+        :type ax_y: same as R
         :return: Epsilon(R) projected density at radius R
         """
         R = np.maximum(R, 0.00000001)
@@ -278,18 +276,18 @@ def nfwGamma(self, R, Rs, rho0, ax_x, ax_y):
         :type Rs: float
         :param rho0: density normalization (characteristic density)
         :type rho0: float
-        :param r200: radius of (sub)halo
-        :type r200: float>0
-        :param axis: projection to either x- or y-axis
-        :type axis: same as R
+        :param ax_x: projection to either x- or y-axis
+        :type ax_x: same as R
+        :param ax_y: projection to either x- or y-axis
+        :type ax_y: same as R
         :return: Epsilon(R) projected density at radius R
         """
         c = 0.000001
         R = np.maximum(R, c)
         x = R/Rs
         gx = self.g_(x)
         Fx = self.F_(x)
-        a = 2*rho0*Rs*(2*gx/x**2 - Fx)#/x #2*rho0*Rs*(2*gx/x**2 - Fx)*axis/x
+        a = 2*rho0*Rs*(2*gx/x**2 - Fx)  # /x #2*rho0*Rs*(2*gx/x**2 - Fx)*axis/x
         return a*(ax_y**2-ax_x**2)/R**2, -a*2*(ax_x*ax_y)/R**2
 
     def F_(self, X):

lenstronomy/Util/image_util.py

@@ -130,6 +130,8 @@ def re_size_array(x_in, y_in, input_values, x_out, y_out):
     :param y_out:
     :return:
     """
+    # from skimage.transform import resize
+    # resize(input_values)
     interp_2d = interpolate.interp2d(x_in, y_in, input_values, kind='linear')
     # interp_2d = scipy.interpolate.RectBivariateSpline(x_in, y_in, input_values, kx=1, ky=1)
     out_values = interp_2d.__call__(x_out, y_out)

lenstronomy/Util/numba_util.py

@@ -22,11 +22,13 @@
 if numba_enabled:
     try:
         import numba
+        from numba import extending
     except ImportError:
         numba_enabled = False
         numba = None
+        extending = None
 
-__all__ = ['jit', 'generated_jit', 'nan_to_num', 'nan_to_num_arr', 'nan_to_num_single']
+__all__ = ['jit', 'overload', 'nan_to_num', 'nan_to_num_arr', 'nan_to_num_single']
 
 
 def jit(nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model, inline='never'):
@@ -40,23 +42,28 @@ def wrapper(func):
     return wrapper
 
 
-def generated_jit(nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model):
+def overload(nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model):
     """
     Wrapper around numba.generated_jit. Allows you to redirect a function to another based on its type
      - see the Numba docs for more info
     """
     if numba_enabled:
+
         def wrapper(func):
+            # TODO change to overload, but currently breaks tests with nopython
             return numba.generated_jit(func, nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath,
                                        error_model=error_model)
+            # return extending.overload(func, jit_options={'nopython': nopython, 'cache': cache,
+            #                                             'parallel': parallel,
+            #                                             'fastmath': fastmath, 'error_model': error_model})
     else:
         def wrapper(func):
             return func
 
     return wrapper
 
 
-@generated_jit()
+@overload()
 def nan_to_num(x, posinf=1e10, neginf=-1e10, nan=0.):
     """
     Implements a Numba equivalent to np.nan_to_num (with copy=False!) array or scalar in Numba.

test/test_LensModel/test_Profiles/test_interpol.py

@@ -178,4 +178,4 @@ def test_shift(self):
 
 
 if __name__ == '__main__':
-    pytest.main("-k TestInterpol")
+    pytest.main()