Changeset 961

Timestamp:

05/04/11 17:44:10 (13 months ago)

Author:

pierre

Message:

Fix for the to_file=False option in NEST. I had a fix also for this, since I saw this was not working. It has been tested in a paralell context, and it works. Spikes saved in memory can also after be saved to file via printSpikes() if needed.

Location:

trunk/src

Files:

• common.py (modified) (3 diffs)
• nest/recording.py (modified) (5 diffs)

trunk/src/common.py

@@ -656 +656 @@
         return (variable in self.celltype.recordable)
 
-    def _add_recorder(self, variable):
+    def _add_recorder(self, variable, to_file):
         """Create a new Recorder for the supplied variable."""
         assert variable not in self.recorders
@@ -665 +665 @@
         logger.debug("Adding recorder for %s to %s" % (variable, self.label))
         population.recorders[variable] = population.recorder_class(variable,
-                                                                   population=population)
+                                                                   population=population, file=to_file)
 
     def _record(self, variable, to_file=True):
@@ -682 +682 @@
             logger.debug("%s.record('%s')", self.label, variable)
             if variable not in self.recorders:
-                self._add_recorder(variable)
+                self._add_recorder(variable, to_file)
             if self.record_filter is not None:
                 self.recorders[variable].record(self.record_filter)
             else:
                 self.recorders[variable].record(self.all_cells)
-            if isinstance(to_file, basestring):
-                self.recorders[variable].file = to_file
+            #if isinstance(to_file, basestring):
+            #    self.recorders[variable].file = to_file
 
     def record(self, to_file=True):

trunk/src/nest/recording.py

@@ -1 @@
-"""
-
-:copyright: Copyright 2006-2011 by the PyNN team, see AUTHORS.
-:license: CeCILL, see LICENSE for details.
-"""
-
 import tempfile
 import os
@@ -32 +26 @@
     def __init__(self, device_type, to_memory=False):
         assert device_type in ("multimeter", "spike_detector")
-        self.type = device_type
-        self.device = nest.Create(device_type)
-        
+        self.type      = device_type
+        self.device    = nest.Create(device_type)
+        self.to_memory = to_memory
         device_parameters = {"withgid": True, "withtime": True}
         if self.type is 'multimeter':
@@ -170 +164 @@
             non_empty_nest_files = [filename for filename in nest_files if os.stat(filename).st_size > 0]
             if len(non_empty_nest_files) > 0:
-                data_list = [numpy.loadtxt(nest_file) for nest_file in non_empty_nest_files]
-                data = numpy.concatenate(data_list)
+                data = numpy.concatenate([numpy.loadtxt(nest_file) for nest_file in non_empty_nest_files])
             if len(non_empty_nest_files) == 0 or data.size == 0:
                 if self.type is "spike_detector":
@@ -199 +192 @@
         # what if the method is called with different values of
         # `compatible_output`? Need to cache these separately.
-        if gather and simulator.state.num_processes > 1:
-            if self._gathered:
-                logger.debug("Loading previously gathered data from cache")
-                self._gathered_file.seek(0)
-                data = numpy.load(self._gathered_file)
+        if not self.to_memory:
+            if gather and simulator.state.num_processes > 1:
+                if self._gathered:
+                    logger.debug("Loading previously gathered data from cache")
+                    self._gathered_file.seek(0)
+                    data = numpy.load(self._gathered_file)
+                else:
+                    local_data = self.read_local_data(compatible_output)
+                    if always_local:
+                        data = local_data # for always_local cells, no need to gather
+                    else:
+                        logger.debug("Gathering data")
+                        data = recording.gather(local_data)
+                    logger.debug("Caching gathered data")
+                    self._gathered_file = tempfile.TemporaryFile()
+                    numpy.save(self._gathered_file, data)
+                    self._gathered = True
             else:
-                local_data = self.read_local_data(compatible_output)
-                if always_local:
-                    data = local_data # for always_local cells, no need to gather
-                else:
-                    logger.debug("Gathering data")
-                    data = recording.gather(local_data)
-                logger.debug("Caching gathered data")
-                self._gathered_file = tempfile.TemporaryFile()
-                numpy.save(self._gathered_file, data)
-                self._gathered = True
-        else:
-            data = self.read_local_data(compatible_output)
-        if len(data.shape) == 1:
-            data = data.reshape((1, data.size))
-        return data
+                data = self.read_local_data(compatible_output)
+            if len(data.shape) == 1:
+                data = data.reshape((1, data.size))
+            return data
+        else:
+            return self.read_data_from_memory(gather, compatible_output)
     
     def read_subset(self, variables, gather, compatible_output, always_local=False):
@@ -296 +292 @@
         if self._device.in_memory():
             events = nest.GetStatus(self._device.device, 'events')[0]
-            for id in filtered_ids:
+            for id in self.filter_recorded(filter):
                 mask = events['senders'] == int(id)
-                N[id] = len(events['times'][mask])
+                N[int(id)] = len(events['times'][mask])
         else:
             spikes = self._get(gather=False, compatible_output=False,
                                filter=filter)
             for id in self.filter_recorded(filter):
-                N[id] = 0
+                N[int(id)] = 0
             ids   = numpy.sort(spikes[:,0].astype(int))
             idx   = numpy.unique(ids)