Merge pull request #1 from aymgal/master

Fix bug of NaN in lnprob

lenstronomy/LightModel/Profiles/fromfile.py

@@ -0,0 +1,50 @@
+__author__ = 'aymgal'
+
+import numpy as np
+import astropy.io.fits as pyfits
+
+import lenstronomy.Util.util as util
+
+
+
+class FromFile(object):
+    """
+    light profile loaded from a fits file
+
+    TODO : implement 'scale' and 'center_x' / 'center_y' parameters,
+    in order to shift and/or zoom on the image file
+    """
+    param_names = []
+    lower_limit_default = {}
+    upper_limit_default = {}
+
+
+    def __init__(self, file_path, data_class):
+        image = pyfits.open(file_path)[0].data
+        self.coords = data_class._coords
+        nx, ny = data_class.nx, data_class.ny
+        if image.shape != (nx, ny):
+            # need to resize the image
+            try:
+                import skimage
+            except ImportError:
+                self.image = image[:nx, :ny]
+            else:
+                # cleaner way to resize the image
+                self.image = skimage.transform.resize(image, (nx, ny))
+        else:
+            self.image = image
+
+
+    def function(self, x, y, center_x=0, center_y=0):
+        """
+
+        :param x:
+        :param y:
+        :return:
+        """
+        x_shift = x - center_x
+        y_shift = y - center_y
+        i_, j_ = self.coords.map_coord2pix(y_shift, x_shift)
+        i, j = i_.astype(int), j_.astype(int)
+        return self.image[i, j]

lenstronomy/LightModel/light_model.py

@@ -9,7 +9,8 @@ class LightModel(object):
     """
     class to handle source and lens light models
     """
-    def __init__(self, light_model_list, smoothing=0.0000001):
+    def __init__(self, light_model_list, smoothing=0.0000001, 
+                 file_path=None, data_class=None):
         self.profile_type_list = light_model_list
         self.func_list = []
         for profile_type in light_model_list:
@@ -65,6 +66,11 @@ def __init__(self, light_model_list, smoothing=0.0000001):
             elif profile_type == 'DOUBLE_CHAMELEON':
                 from lenstronomy.LightModel.Profiles.chameleon import DoubleChameleon
                 self.func_list.append(DoubleChameleon())
+            elif profile_type == 'FROM_FILE':
+                if file_path is None or data_class is None:
+                    raise ValueError('Warning! You must provide the path to the FITS file and pixel size for profile of type', profile_type)
+                from lenstronomy.LightModel.Profiles.fromfile import FromFile
+                self.func_list.append(FromFile(file_path, data_class))
             else:
                 raise ValueError('Warning! No light model of type', profile_type, ' found!')
 

lenstronomy/Sampling/likelihood.py

@@ -142,6 +142,9 @@ def logL(self, args):
             if np.min(flux) < 0:
                 logL -= 10**10
         self.imSim.reset_point_source_cache(bool=False)
+        if np.isnan(logL):
+            print("WARNING : logL returns NaN, changed to penalty")
+            logL = -10**15
         return logL, None
 
     def solver_penalty(self, kwargs_lens, kwargs_ps, kwargs_cosmo, tolerance):