[dataview] fix chunking and loading for n-d data

nixview/constants.py

@@ -10,7 +10,7 @@ settings_recent_files_key = "/".join([organization, application, "recent_files"]
 settings_recent_file_max_count_key =  "/".join([organization, application, "recent_files_max_count"])
 settings_recent_file_max_count = 10
 
-io_chunksize = 10000000
+max_chunksize = 1000000000
 
 PACKAGE_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
 ICONS_FOLDER = os.path.join(PACKAGE_ROOT, "icons")

nixview/ui/plotscreen.py

@@ -347,7 +347,7 @@ class PlotScreen(QWidget):
         try:
             self._data_view = DataView(item, self._file_handler)
         except ValueError as e:
-            print(e)
+            print("error in plotscreen.plot", e)
             return
         self._data_view.request_more() # TODO this is just a test, needs to be removed
         print(self._data_view)

nixview/util/dataview.py

@@ -1,5 +1,5 @@
 import numpy as np
-from nixview.constants import io_chunksize as chunksize
+from nixview.constants import max_chunksize as chunksize
 
 class DataView():
 
@@ -10,8 +10,9 @@ class DataView():
         self._full_shape = item_descriptor.shape
         self._buffer = None
         self._offset = np.zeros(len(self._full_shape), dtype=int)
+        self._fetched_data = np.zeros(len(self._full_shape), dtype=int)
         self._count = None
-        self._max_dim = None
+        self._cut_dim = None
         self.init_buffer()
         self.request_more()
 
@@ -24,36 +25,46 @@ class DataView():
         sl = tuple([slice(o, o + c) for o, c in zip(self._offset, valid_count)])
         self._buffer[sl] = self._file_handler.request_data(self._item_descriptor, self._offset,
                                                            valid_count)
-        self._offset = tuple([sum(x) for x in zip(self._offset, self._count)])
+        new_ofst = np.zeros_like(self._offset)
+        for i, x in enumerate(zip(self._offset, valid_count)):
+            if i == self._cut_dim:
+                new_ofst[i] = sum(x)
         
+        self._offset = tuple(new_ofst)
+        self._fetched_data = tuple([sum(x) for x in zip(self._fetched_data, self._count)])
         
-        #if data is not None and self._buffer is None:
+    def init_chunking(self):
-        #    self._buffer = data
+        """decides on the chunks size for reading. Heuristic is based on the dimensionality of the data and the "best xdim" if available.
-        #    self._offset = data.shape
+        If data is 2D the best xdim is loaded in chunks (if necessary) while the other is fully loaded. For 3D and more it is the last dimension that is cut. If the number of data points in the first n-1 dimensions exceeds the maximum chunksize (settings) an error will be thrown.
-        #else:
+        """
-        #    from IPython import embed
+        max_element_count = chunksize
-        #    embed()
+        if self._item_descriptor.best_xdim is not None:
+            cut_dim = self._item_descriptor.best_xdim 
+        else:
+            cut_dim = len(self._full_shape) - 1
+            if np.prod(self._full_shape[:-1]) > chunksize:
+                raise ValueError("Cannot load data in chunks! maxchunksize too small: product of elements in first %i dimensions exceeds max chunksize! (%i > %i)" % (len(self._full_shape) -1, np.prod(self._full_shape[:-1]), chunksize))
+        chunk_shape = np.zeros(len(self._full_shape), dtype=int)
+        for i, d in enumerate(self._full_shape):
+            if i != cut_dim:
+                chunk_shape[i] = d
+                max_element_count /= d
+
+        chunk_shape[cut_dim] = max_element_count
+        self._cut_dim = cut_dim
+        self._count = chunk_shape
 
     def init_buffer(self):
-        buffer_shape = np.zeros(len(self._full_shape), dtype=int)
+        self.init_chunking()
-        max_dim_count = chunksize
-        max_dim = np.argmax(self._full_shape)
-        for i, d in enumerate(self._full_shape):
-            if i != max_dim:
-                buffer_shape[i] = self._full_shape[i]
-                max_dim_count /= self._full_shape[i]
-        buffer_shape[max_dim] = max_dim_count
-        self._count = buffer_shape
-        self._max_dim = max_dim
         try:
             self._buffer = np.empty(self._full_shape)
         except:
-            raise ValueError("Cannot handle so many data points!") #FIXME
+            raise ValueError("Error reserving buffer! Cannot handle so many data points!") #FIXME
-    
+        print("init buffer")
 
     @property
     def fully_loaded(self):        
-        return self._buffer is not None and self._full_shape == self._offset
+        return np.all(self._buffer is not None and self._fetched_data == self._full_shape)
     
     def __str__(self) -> str:
         r = self._item_descriptor.name + " " + str(self._item_descriptor.entity_type)

nixview/util/file_handler.py

@@ -1,4 +1,3 @@
-from nixview.file_utils import suggested_plotter
 import os
 import nixio as nix
 import numpy as np
@@ -103,6 +102,8 @@ class FileHandler(metaclass=Singleton):
         for i, (o, c) in enumerate(zip(offset, count)):
             if o + c > shape[i]:
                 valid_count[i] = shape[i] - o
+            else:
+                valid_count[i] = c
         return valid_count
     
     def count_is_valid(self, shape, offset, count):