Adds clip_negative argument to normalize_spatial

Used for fixing ellipse fits to 2D spatial RFs with strong surrounds.
Closes #66

pyret/filtertools.py

@@ -438,7 +438,7 @@ def rolling_window(array, window, time_axis=0):
         return arr
 
 
-def normalize_spatial(spatial_filter, scale_factor=1.0):
+def normalize_spatial(spatial_filter, scale_factor=1.0, clip_negative=False):
     """
     Normalizes a spatial frame by doing the following:
     1. mean subtraction using a robust estimate of the mean (ignoring outliers)
@@ -451,7 +451,10 @@ def normalize_spatial(spatial_filter, scale_factor=1.0):
 
     scale_factor : float, optional
         The given filter is resampled at a sampling rate of this ratio times
-        the original sampling rate (default: 1.0)
+        the original sampling rate (Default: 1.0)
+
+    clip_negative : boolean, optional
+        Whether or not to clip negative values to 0. (Default: True)
 
     """
 
@@ -470,8 +473,14 @@ def normalize_spatial(spatial_filter, scale_factor=1.0):
     # normalize by the standard deviation of the pixel values
     rf_centered /= rf_centered.std()
 
-    # return this normalized filter, resampled by the given amount
-    return resample(rf_centered, scale_factor)
+    # resample by the given amount
+    rf_resampled = resample(rf_centered, scale_factor)
+
+    # clip negative values
+    if clip_negative:
+        rf_resampled = np.maximum(rf_resampled, 0)
+
+    return rf_resampled
 
 
 def get_contours(spatial_filter, threshold=10.0):
@@ -560,8 +569,8 @@ def get_ellipse(tx, ty, spatial_filter, pvalue=0.6827):
     """
 
     # preprocess
-    zdata = normalize_spatial(spatial_filter).ravel()
-    zdata /= np.max(zdata)
+    zdata = normalize_spatial(spatial_filter, clip_negative=True).ravel()
+    zdata /= zdata.max()
 
     # get initial parameters
     xm, ym = np.meshgrid(tx, ty)