def __call__(self, **params):
p = ParamOverrides(self, params)
measured_sheets = [
s for s in topo.sim.objects(CFSheet).values()
if hasattr(s, 'measure_maps') and s.measure_maps
]
results = Layout()
# Could easily be extended to measure CoG of all projections
# and e.g. register them using different names (e.g. "Afferent
# XCoG"), but then it's not clear how the PlotGroup would be
# able to find them automatically (as it currently supports
# only a fixed-named plot).
requested_proj = p.proj_name
for sheet in measured_sheets:
for proj in sheet.in_connections:
if (proj.name == requested_proj) or \
(requested_proj == '' and (proj.src != sheet)):
cog_data = self._update_proj_cog(p, proj)
for key, data in cog_data.items():
name = proj.name[0].upper() + proj.name[1:]
results.set_path((key, name), data)
if p.measurement_storage_hook:
p.measurement_storage_hook(results)
return results
def __call__(self, **params):
p = ParamOverrides(self, params)
measured_sheets = [s for s in topo.sim.objects(CFSheet).values()
if hasattr(s,'measure_maps') and s.measure_maps]
results = Layout()
# Could easily be extended to measure CoG of all projections
# and e.g. register them using different names (e.g. "Afferent
# XCoG"), but then it's not clear how the PlotGroup would be
# able to find them automatically (as it currently supports
# only a fixed-named plot).
requested_proj=p.proj_name
for sheet in measured_sheets:
for proj in sheet.in_connections:
if (proj.name == requested_proj) or \
(requested_proj == '' and (proj.src != sheet)):
cog_data = self._update_proj_cog(p, proj)
for key, data in cog_data.items():
name = proj.name[0].upper() + proj.name[1:]
results.set_path((key, name), data)
if p.measurement_storage_hook:
p.measurement_storage_hook(results)
return results
def _populate_grid(self, results):
trees = []
for coord, viewgroup in results.items():
for path, stack in viewgroup.data.items():
grid_container = Layout()
coord_map = stack.add_dimension(f.Y, 0, coord[1])
coord_map = coord_map.add_dimension(f.X, 0, coord[0])
grid_container.set_path(path, coord_map)
trees.append(grid_container)
return Layout.merge(trees)
def _collate_results(self, p):
results = Layout()
timestamp = self.metadata.timestamp
axis_name = p.x_axis.capitalize()
axis_feature = [f for f in self.features if f.name.lower() == p.x_axis][0]
if axis_feature.cyclic:
axis_feature.values.append(axis_feature.range[1])
curve_label = ''.join([p.measurement_prefix, axis_name, 'Tuning'])
dimensions = [features.Time, features.Duration] + [f for f in self.outer] + [axis_feature]
pattern_dimensions = self.outer + self.inner
pattern_dim_label = '_'.join(f.name.capitalize() for f in pattern_dimensions)
for label in self.measurement_product:
# Deconstruct label into source name and feature_values
name = label[0]
f_vals = label[1:]
# Get data and metadata from the DistributionMatrix objects
dist_matrix = self._featureresponses[name][f_vals][p.x_axis]
curve_responses = dist_matrix.distribution_matrix
output_metadata = self.metadata.outputs[name]
rows, cols = output_metadata['shape']
# Create top level NdMapping indexing over time, duration, the outer
# feature dimensions and the x_axis dimension
if (curve_label, name) not in results:
vmap = HoloMap(kdims=dimensions,
group=curve_label, label=name)
vmap.metadata = AttrDict(**output_metadata)
results.set_path((curve_label, name), vmap)
metadata = AttrDict(timestamp=timestamp, **output_metadata)
# Populate the ViewMap with measurements for each x value
for x in curve_responses[0, 0]._data.iterkeys():
y_axis_values = np.zeros(output_metadata['shape'], activity_dtype)
for i in range(rows):
for j in range(cols):
y_axis_values[i, j] = curve_responses[i, j].get_value(x)
key = (timestamp,)+f_vals+(x,)
im = Image(y_axis_values, bounds=output_metadata['bounds'],
label=name, group=' '.join([curve_label, 'Response']),
vdims=['Response'])
im.metadata = metadata.copy()
results[(curve_label, name)][key] = im
if axis_feature.cyclic and x == axis_feature.range[0]:
symmetric_key = (timestamp,)+f_vals+(axis_feature.range[1],)
results[(curve_label, name)][symmetric_key] = im
if p.store_responses:
info = (p.pattern_generator.__class__.__name__, pattern_dim_label, 'Response')
results.set_path(('%s_%s_%s' % info, name), self._responses[name])
return results
def _collate_results(self, responses, label):
time = self.metadata.timestamp
dims = [f.Time, f.Duration]
response_label = label + ' Response'
results = Layout()
for label, response in responses.items():
name, duration = label
path = (response_label.replace(' ', ''), name)
label = ' '.join([name, response_label])
metadata = self.metadata['outputs'][name]
if path not in results:
vmap = HoloMap(key_dimensions=dims)
vmap.metadata = AttrDict(**metadata)
results.set_path(path, vmap)
im = Image(response, metadata['bounds'], label=label, group='Activity')
im.metadata=AttrDict(timestamp=time)
results[path][(time, duration)] = im
return results
def _collate_results(self, p):
"""
Collate responses into the results dictionary containing a
ProjectionGrid for each measurement source.
"""
results = Layout()
timestamp = self.metadata.timestamp
dimensions = [features.Time, features.Duration]
pattern_dimensions = self.outer + self.inner
pattern_dim_label = '_'.join(f.name.capitalize() for f in pattern_dimensions)
for labels in self.measurement_product:
in_label, out_label, duration = labels
input_metadata = self.metadata.inputs[in_label]
output_metadata = self.metadata.outputs[out_label]
rows, cols, scs = self._compute_roi(p, output_metadata)
time_key = (timestamp, duration)
view = GridSpace(group='RFs', label=out_label)
rc_response = self._featureresponses[in_label][out_label][duration]
for i, ii in enumerate(rows):
for j, jj in enumerate(cols):
coord = scs.matrixidx2sheet(ii, jj)
im = Image(rc_response[i, j], input_metadata['bounds'],
label=out_label, group='Receptive Field',
value_dimensions=['Weight'])
im.metadata = AttrDict(timestamp=timestamp)
view[coord] = HoloMap((time_key, im), key_dimensions=dimensions,
label=out_label, group='Receptive Field')
view[coord].metadata = AttrDict(**input_metadata)
results.set_path(('%s_Reverse_Correlation' % in_label, out_label), view)
if p.store_responses:
info = (p.pattern_generator.__class__.__name__, pattern_dim_label, 'Response')
results.set_path(('%s_%s_%s' % info, out_label), self._responses[out_label])
return results
def _collate_results(self, p):
results = Layout()
timestamp = self.metadata.timestamp
# Generate dimension info dictionary from features
dimensions = [features.Time, features.Duration] + self.outer
pattern_dimensions = self.outer + self.inner
pattern_dim_label = '_'.join(f.name.capitalize() for f in pattern_dimensions)
for label in self.measurement_product:
name = label[0] # Measurement source
f_vals = label[1:] # Duration and outer feature values
#Metadata
inner_features = dict([(f.name, f) for f in self.inner])
output_metadata = dict(self.metadata.outputs[name], inner_features=inner_features)
# Iterate over inner features
fr = self._featureresponses[name][f_vals]
for fname, fdist in fr.items():
feature = fname.capitalize()
base_name = self.measurement_prefix + feature
# Get information from the feature
fp = [f for f in self.features if f.name.lower() == fname][0]
pref_fn = fp.preference_fn if has_preference_fn(fp)\
else self.preference_fn
if p.selectivity_multiplier is not None:
pref_fn.selectivity_scale = (pref_fn.selectivity_scale[0],
p.selectivity_multiplier)
# Get maps and iterate over them
response = fdist.apply_DSF(pref_fn)
for k, maps in response.items():
for map_name, map_view in maps.items():
# Set labels and metadata
map_index = base_name + k + map_name.capitalize()
map_label = ' '.join([base_name, map_name.capitalize()])
cyclic = (map_name != 'selectivity' and fp.cyclic)
fprange = fp.range if cyclic else (None, None)
value_dimension = Dimension(map_label, cyclic=cyclic, range=fprange)
self._set_style(fp, map_name)
# Create views and stacks
im = Image(map_view, bounds=output_metadata['bounds'],
label=name, group=map_label,
vdims=[value_dimension])
im.metadata=AttrDict(timestamp=timestamp)
key = (timestamp,)+f_vals
if (map_label.replace(' ', ''), name) not in results:
vmap = HoloMap((key, im), kdims=dimensions,
label=name, group=map_label)
vmap.metadata = AttrDict(**output_metadata)
results.set_path((map_index, name), vmap)
else:
results.path_items[(map_index, name)][key] = im
if p.store_responses:
info = (p.pattern_generator.__class__.__name__, pattern_dim_label, 'Response')
results.set_path(('%s_%s_%s' % info, name), self._responses[name])
return results
def test_viewgroup_set_path(self):
tr = Layout()
tr.set_path(('Test', 'Path'), -42)
self.assertEqual(tr.Test.Path, -42)
def test_layout_set_path(self):
tr = Layout()
tr.set_path(('Test', 'Path'), -42)
self.assertEqual(tr.Test.Path, -42)
