added binning for 2D images

kabuki/actor.py

@@ -32,10 +32,12 @@ class Actor:
 
         if isinstance(cfg, ConfigDialog2D): #TODO
             cfg.sel_cmap.value   = plt.palette
+            cfg.sp_binning.value = plt.binning
 
             cfg.sel_cmap.on_change("value",   cb(plt.change_cmap))
             cfg.sp_vmin.on_change("value",    cb(plt.change_vmin))
             cfg.sp_vmax.on_change("value",    cb(plt.change_vmax))
+            cfg.sp_binning.on_change("value", cb(plt.change_binning))
 
 
     def delete(self):

kabuki/cfgdlgs/cfgdlg2d.py

@@ -18,16 +18,17 @@ class ConfigDialog2D(Column):
         self.sel_cmap = sel_cmap = Select(title="Colormap", options=CMAPS)
         self.sp_vmin = sp_vmin = Spinner(title="Minimum Value")
         self.sp_vmax = sp_vmax = Spinner(title="Maximum Value")
+        self.sp_binning = sp_binning = Spinner(title="Binning", low=1)
         super().__init__(
             sp_cache_size,
             sel_cmap,
-            Row(sp_vmin, sp_vmax)
+            Row(sp_vmin, sp_vmax),
+            sp_binning
         )
 
 
 
 #TODO:
-#- binning
 #- log z
 
 

kabuki/plots/plot2d.py

@@ -16,6 +16,8 @@ class Plot2D(Object):
 
         self.source = source = ColumnDataSource(data=data)
 
+        self.binning = 1
+
         self.cmap = cmap = LinearColorMapper()
         self.change_cmap("Viridis")
         cbar = ColorBar(color_mapper=cmap)
@@ -32,6 +34,7 @@ class Plot2D(Object):
 
     def set(self, _times, values):
         image = values[-1]
+        image = rebin(image, self.binning)
         self.source.data.update(image=[image])
 
 
@@ -45,5 +48,31 @@ class Plot2D(Object):
     def change_vmax(self, value):
         self.cmap.high = value
 
+    def change_binning(self, value):
+        self.binning = value
+
+
+
+def rebin(img, binning):
+    if binning == 1:
+        return img
+
+    old_shape = np.array(img.shape)
+    new_ny, new_nx = new_shape = old_shape // binning
+    cut_shape = new_shape * binning
+
+    left_ny, left_nx = offsets = (old_shape - cut_shape) // 2
+    right_ny, right_nx = cut_shape + offsets
+
+    meaning_shape = (
+        new_ny, binning,
+        new_nx, binning
+    )
+
+    img = img[left_ny:right_ny, left_nx:right_nx]
+    img.shape = meaning_shape
+    img = img.mean(axis=(1, 3))
+    return img
+