diff --git a/Notebooks/Raster Factory.ipynb b/Notebooks/Raster Factory.ipynb
index 4596539..25ddbc4 100644
--- a/Notebooks/Raster Factory.ipynb	
+++ b/Notebooks/Raster Factory.ipynb	
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=0, dtype=np.float32)\n",
+    "C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
     "#C2 = ra.bulk_load('../Data/lidar/C2', 'C2', filter_treshold=.5, dtype=np.float32)\n",
     "#C3 = ra.bulk_load('../Data/lidar/C3', 'C3', filter_treshold=.5, dtype=np.float32)\n",
     "#C123 = ra.bulk_load('../Data/lidar', 'C123', filter_treshold=.5, dtype=np.float32)"
@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raster = ra.rasterize_cache(C1, 'intensity', 1., 'nearest', True, cache_dir='../Res/')"
+    "raster = ra.rasterize_cache('intensity', C1, 1., 'nearest', False, cache_dir='../Res/enrichment_rasters')"
    ]
   },
   {
diff --git a/raster_assistant.py b/raster_assistant.py
index c5d5a3b..25594ec 100644
--- a/raster_assistant.py
+++ b/raster_assistant.py
@@ -17,12 +17,12 @@ sys.path.append('../triskele/python/')
 import triskele
 import rasterizer
 
-def rasterize_cache(data, field, resolution=1., method='linear', reverse_alt=False, cache_dir='/tmp'):
+def rasterize_cache(field, data, resolution=.5, interpolation='nearest', reverse_direction=False, cache_dir='/tmp'):
     """Cache layer for rasterize"""
     
     cache_dir = Path(cache_dir)
-    name = '{}_{}_{}_{}{}'.format(data.name, field, str(resolution).replace('.', '_'), method,
-                                  '_reversed' if reverse_alt else '')
+    name = '{}_{}_f{}_c{}_r{}_{}{}'.format(field, data.name, data.filter_treshold, data.clip_treshold, resolution,
+                                       interpolation, '_reversed' if reverse_direction else '')
     png_file = cache_dir.joinpath(Path(name + '.png'))
     tif_file = cache_dir.joinpath(Path(name + '.tif'))
     
@@ -30,7 +30,7 @@ def rasterize_cache(data, field, resolution=1., method='linear', reverse_alt=Fal
         print ('WARNING: Loading cached result {}'.format(tif_file))
         raster = triskele.read(tif_file)
     else:
-        raster = rasterizer.rasterize(data.spatial, getattr(data, field), resolution, method, reverse_alt, np.float32)
+        raster = rasterizer.rasterize(data.spatial, getattr(data, field), resolution, interpolation, reverse_direction, np.float32)
         triskele.write(tif_file, raster)
         plt.imsave(png_file, raster)
         
@@ -50,17 +50,20 @@ def find_las(path):
     return las
 
 def extremum_filter(data, treshold=.5):
+    """Return boolean filter of extremums in data, treshold in percentil [0;100]"""
     tresholds = np.percentile(data, [treshold, 100 - treshold])
     return np.logical_or(data < tresholds[0], data > tresholds[1])
 
-def auto_filter(data, treshold=.5):
+def extremum_clip(data, treshold=.5):
+    """Clip extremums in data, treshold in percentil [0;100]"""
     tresholds = np.percentile(data, [treshold, 100 - treshold])
-
-    data[data < tresholds[0]] = tresholds[0]
-    data[data > tresholds[1]] = tresholds[1]
+    np.clip(data, *tresholds, out=data)
     
-def bulk_load(path, name=None, filter_treshold=.1, dtype=None):
-    data = {'file': path, 'name': name}
+def bulk_load(path, name=None, filter_treshold=.1, clip_treshold=.5, dtype=None):
+    data = {'file': path, 
+            'name': name,
+            'filter_treshold': filter_treshold,
+            'clip_treshold': clip_treshold}
 
     attributes = ['x', 'y', 'z', 'intensity', 'num_returns']#, 'scan_angle_rank', 'pt_src_id', 'gps_time']
     for a in attributes:
@@ -84,19 +87,19 @@ def bulk_load(path, name=None, filter_treshold=.1, dtype=None):
     print('\rCreate matrices: [Done]')
         
     print('Filter data...', end='')
-    Z = data['z'].copy()
-    auto_filter(Z)
-    data['spatial'] = np.array((data['x'], data['y'], Z)).T
-    del Z
-    
-    t = .1
-    efilter = np.logical_or(extremum_filter(data['intensity'], t), extremum_filter(data['z']))
-    
-    attributes.append('spatial')
+    z_or_i = np.logical_or(extremum_filter(data['z'], filter_treshold),
+                           extremum_filter(data['intensity'], filter_treshold))
     for i, a in enumerate(attributes):
         print('\rFilter data: [{:3d}%]'.format(int(i/len(attributes) * 100)), end='')
-        data[a] = data[a][np.logical_not(efilter)]
+        data[a] = data[a][~z_or_i]
     print('\rFilter data: [Done]')
+        
+    print('Clip data...', end='')
+    extremum_clip(data['z'], clip_treshold)
+    extremum_clip(data['intensity'], clip_treshold)
+    data['spatial'] = np.array((data['x'], data['y'], data['z'])).T
+    attributes.append('spatial')
+    print('\rClip data: [Done]')
     
     class TMPLAS(object):
         def __init__(self, d):