def debug_callback(context):
from dentate.plot import plot_2D_rate_map
fig_title = '%s %s cell %i' % (
context.population, context.this_selectivity_type_name, context.gid)
fig_options = copy.copy(context.fig_options)
if context.save_fig is not None:
fig_options.saveFig = '%s %s' % (context.save_fig, fig_title)
plot_2D_rate_map(x=context.arena_x_mesh,
y=context.arena_y_mesh,
rate_map=context.rate_map,
peak_rate=context.env.stimulus_config['Peak Rate'][
context.population][context.this_selectivity_type],
title='%s\nNormalized cell position: %.3f' %
(fig_title, context.norm_u_arc_distance),
**fig_options())
def main(config, config_prefix, coords_path, distances_namespace, bin_distance,
selectivity_path, selectivity_namespace, subset_seed, arena_id,
populations, io_size, cache_size, verbose, debug, show_fig, save_fig,
save_fig_dir, font_size, fig_size, colormap, fig_format):
"""
:param config: str (.yaml file name)
:param config_prefix: str (path to dir)
:param coords_path: str (path to file)
:param distances_namespace: str
:param bin_distance: float
:param selectivity_path: str
:param subset_seed: int; for reproducible choice of gids to plot individual rate maps
:param arena_id: str
:param populations: tuple of str
:param io_size: int
:param cache_size: int
:param verbose: bool
:param debug: bool
:param show_fig: bool
:param save_fig: str (base file name)
:param save_fig_dir: str (path to dir)
:param font_size: float
:param fig_format: str
"""
comm = MPI.COMM_WORLD
rank = comm.rank
config_logging(verbose)
env = Env(comm=comm,
config_file=config,
config_prefix=config_prefix,
template_paths=None)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
fig_options = copy.copy(default_fig_options)
fig_options.saveFigDir = save_fig_dir
fig_options.fontSize = font_size
fig_options.figFormat = fig_format
fig_options.showFig = show_fig
fig_options.figSize = fig_size
if save_fig is not None:
save_fig = '%s %s' % (save_fig, arena_id)
fig_options.saveFig = save_fig
population_ranges = read_population_ranges(selectivity_path, comm)[0]
coords_population_ranges = read_population_ranges(coords_path, comm)[0]
if len(populations) == 0:
populations = ('MC', 'ConMC', 'LPP', 'GC', 'MPP', 'CA3c')
valid_selectivity_namespaces = dict()
if rank == 0:
for population in populations:
if population not in population_ranges:
raise RuntimeError(
'plot_input_selectivity_features: specified population: %s not found in '
'provided selectivity_path: %s' %
(population, selectivity_path))
if population not in env.stimulus_config[
'Selectivity Type Probabilities']:
raise RuntimeError(
'plot_input_selectivity_features: selectivity type not specified for '
'population: %s' % population)
valid_selectivity_namespaces[population] = []
with h5py.File(selectivity_path, 'r') as selectivity_f:
for this_namespace in selectivity_f['Populations'][population]:
if f'{selectivity_namespace} {arena_id}' in this_namespace:
valid_selectivity_namespaces[population].append(
this_namespace)
if len(valid_selectivity_namespaces[population]) == 0:
raise RuntimeError(
'plot_input_selectivity_features: no selectivity data in arena: %s found '
'for specified population: %s in provided selectivity_path: %s'
% (arena_id, population, selectivity_path))
valid_selectivity_namespaces = comm.bcast(valid_selectivity_namespaces,
root=0)
selectivity_type_names = dict(
(val, key) for (key, val) in viewitems(env.selectivity_types))
reference_u_arc_distance_bounds = None
reference_v_arc_distance_bounds = None
if rank == 0:
for population in populations:
if population not in coords_population_ranges:
raise RuntimeError(
'plot_input_selectivity_features: specified population: %s not found in '
'provided coords_path: %s' % (population, coords_path))
with h5py.File(coords_path, 'r') as coords_f:
pop_size = population_ranges[population][1]
unique_gid_count = len(
set(coords_f['Populations'][population]
[distances_namespace]['U Distance']['Cell Index'][:]))
if pop_size != unique_gid_count:
raise RuntimeError(
'plot_input_selectivity_features: only %i/%i unique cell indexes found '
'for specified population: %s in provided coords_path: %s'
%
(unique_gid_count, pop_size, population, coords_path))
if reference_u_arc_distance_bounds is None:
try:
reference_u_arc_distance_bounds = \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Max']
except Exception:
raise RuntimeError(
'plot_input_selectivity_features: problem locating attributes '
'containing reference bounds in namespace: %s for population: %s from '
'coords_path: %s' %
(distances_namespace, population, coords_path))
if reference_v_arc_distance_bounds is None:
try:
reference_v_arc_distance_bounds = \
coords_f['Populations'][population][distances_namespace].attrs['Reference V Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference V Max']
except Exception:
raise RuntimeError(
'plot_input_selectivity_features: problem locating attributes '
'containing reference bounds in namespace: %s for population: %s from '
'coords_path: %s' %
(distances_namespace, population, coords_path))
reference_u_arc_distance_bounds = comm.bcast(
reference_u_arc_distance_bounds, root=0)
reference_v_arc_distance_bounds = comm.bcast(
reference_v_arc_distance_bounds, root=0)
u_edges = np.arange(reference_u_arc_distance_bounds[0],
reference_u_arc_distance_bounds[1] + bin_distance / 2.,
bin_distance)
v_edges = np.arange(reference_v_arc_distance_bounds[0],
reference_v_arc_distance_bounds[1] + bin_distance / 2.,
bin_distance)
if arena_id not in env.stimulus_config['Arena']:
raise RuntimeError(
'Arena with ID: %s not specified by configuration at file path: %s'
% (arena_id, config_prefix + '/' + config))
arena = env.stimulus_config['Arena'][arena_id]
arena_x_mesh, arena_y_mesh = None, None
if rank == 0:
arena_x_mesh, arena_y_mesh = \
get_2D_arena_spatial_mesh(arena=arena, spatial_resolution=env.stimulus_config['Spatial Resolution'])
arena_x_mesh = comm.bcast(arena_x_mesh, root=0)
arena_y_mesh = comm.bcast(arena_y_mesh, root=0)
for population in populations:
start_time = time.time()
u_distances_by_gid = dict()
v_distances_by_gid = dict()
distances_attr_gen = \
bcast_cell_attributes(coords_path, population, root=0, namespace=distances_namespace, comm=comm)
for gid, distances_attr_dict in distances_attr_gen:
u_distances_by_gid[gid] = distances_attr_dict['U Distance'][0]
v_distances_by_gid[gid] = distances_attr_dict['V Distance'][0]
if rank == 0:
logger.info(
'Reading %i cell positions for population %s took %.2f s' %
(len(u_distances_by_gid), population,
time.time() - start_time))
for this_selectivity_namespace in valid_selectivity_namespaces[
population]:
start_time = time.time()
if rank == 0:
logger.info('Reading from %s namespace for population %s...' %
(this_selectivity_namespace, population))
gid_count = 0
gathered_cell_attributes = defaultdict(list)
gathered_component_attributes = defaultdict(list)
u_distances_by_cell = list()
v_distances_by_cell = list()
u_distances_by_component = list()
v_distances_by_component = list()
rate_map_sum_by_module = defaultdict(
lambda: np.zeros_like(arena_x_mesh))
start_time = time.time()
selectivity_attr_gen = NeuroH5CellAttrGen(
selectivity_path,
population,
namespace=this_selectivity_namespace,
comm=comm,
io_size=io_size,
cache_size=cache_size)
for iter_count, (
gid,
selectivity_attr_dict) in enumerate(selectivity_attr_gen):
if gid is not None:
gid_count += 1
this_selectivity_type = selectivity_attr_dict[
'Selectivity Type'][0]
this_selectivity_type_name = selectivity_type_names[
this_selectivity_type]
input_cell_config = \
get_input_cell_config(selectivity_type=this_selectivity_type,
selectivity_type_names=selectivity_type_names,
selectivity_attr_dict=selectivity_attr_dict)
rate_map = input_cell_config.get_rate_map(x=arena_x_mesh,
y=arena_y_mesh)
u_distances_by_cell.append(u_distances_by_gid[gid])
v_distances_by_cell.append(v_distances_by_gid[gid])
this_cell_attrs, component_count, this_component_attrs = input_cell_config.gather_attributes(
)
for attr_name, attr_val in viewitems(this_cell_attrs):
gathered_cell_attributes[attr_name].append(attr_val)
gathered_cell_attributes['Mean Rate'].append(
np.mean(rate_map))
if component_count > 0:
u_distances_by_component.extend(
[u_distances_by_gid[gid]] * component_count)
v_distances_by_component.extend(
[v_distances_by_gid[gid]] * component_count)
for attr_name, attr_val in viewitems(
this_component_attrs):
gathered_component_attributes[attr_name].extend(
attr_val)
this_module_id = this_cell_attrs['Module ID']
if debug and rank == 0:
fig_title = '%s %s cell %i' % (
population, this_selectivity_type_name, gid)
if save_fig is not None:
fig_options.saveFig = '%s %s' % (save_fig,
fig_title)
plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
rate_map=rate_map,
peak_rate=env.stimulus_config['Peak Rate']
[population][this_selectivity_type],
title='%s\nModule: %i' %
(fig_title, this_module_id),
**fig_options())
rate_map_sum_by_module[this_module_id] = np.add(
rate_map, rate_map_sum_by_module[this_module_id])
if debug and iter_count >= 10:
break
cell_count_hist, _, _ = np.histogram2d(u_distances_by_cell,
v_distances_by_cell,
bins=[u_edges, v_edges])
component_count_hist, _, _ = np.histogram2d(
u_distances_by_component,
v_distances_by_component,
bins=[u_edges, v_edges])
if debug:
context.update(locals())
gathered_cell_attr_hist = dict()
gathered_component_attr_hist = dict()
for key in gathered_cell_attributes:
gathered_cell_attr_hist[key], _, _ = \
np.histogram2d(u_distances_by_cell, v_distances_by_cell, bins=[u_edges, v_edges],
weights=gathered_cell_attributes[key])
for key in gathered_component_attributes:
gathered_component_attr_hist[key], _, _ = \
np.histogram2d(u_distances_by_component, v_distances_by_component, bins=[u_edges, v_edges],
weights=gathered_component_attributes[key])
gid_count = comm.gather(gid_count, root=0)
cell_count_hist = comm.gather(cell_count_hist, root=0)
component_count_hist = comm.gather(component_count_hist, root=0)
gathered_cell_attr_hist = comm.gather(gathered_cell_attr_hist,
root=0)
gathered_component_attr_hist = comm.gather(
gathered_component_attr_hist, root=0)
rate_map_sum_by_module = dict(rate_map_sum_by_module)
rate_map_sum_by_module = comm.gather(rate_map_sum_by_module,
root=0)
if rank == 0:
gid_count = sum(gid_count)
cell_count_hist = np.sum(cell_count_hist, axis=0)
component_count_hist = np.sum(component_count_hist, axis=0)
merged_cell_attr_hist = defaultdict(
lambda: np.zeros_like(cell_count_hist))
merged_component_attr_hist = defaultdict(
lambda: np.zeros_like(component_count_hist))
for each_cell_attr_hist in gathered_cell_attr_hist:
for key in each_cell_attr_hist:
merged_cell_attr_hist[key] = np.add(
merged_cell_attr_hist[key],
each_cell_attr_hist[key])
for each_component_attr_hist in gathered_component_attr_hist:
for key in each_component_attr_hist:
merged_component_attr_hist[key] = np.add(
merged_component_attr_hist[key],
each_component_attr_hist[key])
merged_rate_map_sum_by_module = defaultdict(
lambda: np.zeros_like(arena_x_mesh))
for each_rate_map_sum_by_module in rate_map_sum_by_module:
for this_module_id in each_rate_map_sum_by_module:
merged_rate_map_sum_by_module[this_module_id] = \
np.add(merged_rate_map_sum_by_module[this_module_id],
each_rate_map_sum_by_module[this_module_id])
logger.info('Processing %i %s %s cells took %.2f s' %
(gid_count, population, this_selectivity_type_name,
time.time() - start_time))
if debug:
context.update(locals())
for key in merged_cell_attr_hist:
fig_title = '%s %s cells %s distribution' % (
population, this_selectivity_type_name, key)
if save_fig is not None:
fig_options.saveFig = '%s %s' % (save_fig, fig_title)
if colormap is not None:
fig_options.colormap = colormap
title = '%s %s cells\n%s distribution' % (
population, this_selectivity_type_name, key)
fig = plot_2D_histogram(
merged_cell_attr_hist[key],
x_edges=u_edges,
y_edges=v_edges,
norm=cell_count_hist,
ylabel='Transverse position (um)',
xlabel='Septo-temporal position (um)',
title=title,
cbar_label='Mean value per bin',
cbar=True,
**fig_options())
close_figure(fig)
for key in merged_component_attr_hist:
fig_title = '%s %s cells %s distribution' % (
population, this_selectivity_type_name, key)
if save_fig is not None:
fig_options.saveFig = '%s %s' % (save_fig, fig_title)
title = '%s %s cells\n%s distribution' % (
population, this_selectivity_type_name, key)
fig = plot_2D_histogram(
merged_component_attr_hist[key],
x_edges=u_edges,
y_edges=v_edges,
norm=component_count_hist,
ylabel='Transverse position (um)',
xlabel='Septo-temporal position (um)',
title=title,
cbar_label='Mean value per bin',
cbar=True,
**fig_options())
close_figure(fig)
for this_module_id in merged_rate_map_sum_by_module:
fig_title = '%s %s Module %i summed rate maps' % \
(population, this_selectivity_type_name, this_module_id)
if save_fig is not None:
fig_options.saveFig = '%s %s' % (save_fig, fig_title)
fig = plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
rate_map=merged_rate_map_sum_by_module[this_module_id],
title='%s %s summed rate maps\nModule %i' %
(population, this_selectivity_type_name,
this_module_id),
**fig_options())
close_figure(fig)
if is_interactive and rank == 0:
context.update(locals())
def main(config, config_prefix, coords_path, distances_namespace, bin_distance,
selectivity_path, selectivity_namespace, spatial_resolution, arena_id,
populations, io_size, cache_size, verbose, debug, show_fig, save_fig,
save_fig_dir, font_size, fig_size, colormap, fig_format):
"""
:param config: str (.yaml file name)
:param config_prefix: str (path to dir)
:param coords_path: str (path to file)
:param distances_namespace: str
:param bin_distance: float
:param selectivity_path: str
:param arena_id: str
:param populations: tuple of str
:param io_size: int
:param cache_size: int
:param verbose: bool
:param debug: bool
:param show_fig: bool
:param save_fig: str (base file name)
:param save_fig_dir: str (path to dir)
:param font_size: float
:param fig_format: str
"""
comm = MPI.COMM_WORLD
rank = comm.rank
config_logging(verbose)
env = Env(comm=comm,
config_file=config,
config_prefix=config_prefix,
template_paths=None)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info(f'{comm.size} ranks have been allocated')
fig_options = copy.copy(default_fig_options)
fig_options.saveFigDir = save_fig_dir
fig_options.fontSize = font_size
fig_options.figFormat = fig_format
fig_options.showFig = show_fig
fig_options.figSize = fig_size
if save_fig is not None:
save_fig = f'{save_fig} {arena_id}'
fig_options.saveFig = save_fig
population_ranges = read_population_ranges(selectivity_path, comm)[0]
coords_population_ranges = read_population_ranges(coords_path, comm)[0]
if len(populations) == 0:
populations = ('MC', 'ConMC', 'LPP', 'GC', 'MPP', 'CA3c')
valid_selectivity_namespaces = dict()
if rank == 0:
for population in populations:
if population not in population_ranges:
raise RuntimeError(
f'plot_input_selectivity_features: specified population: {population} not found in '
f'provided selectivity_path: {selectivity_path}')
if population not in env.stimulus_config[
'Selectivity Type Probabilities']:
raise RuntimeError(
'plot_input_selectivity_features: selectivity type not specified for '
f'population: {population}')
valid_selectivity_namespaces[population] = []
with h5py.File(selectivity_path, 'r') as selectivity_f:
for this_namespace in selectivity_f['Populations'][population]:
if f'{selectivity_namespace} {arena_id}' in this_namespace:
valid_selectivity_namespaces[population].append(
this_namespace)
if len(valid_selectivity_namespaces[population]) == 0:
raise RuntimeError(
f'plot_input_selectivity_features: no selectivity data in arena: {arena_id} found '
f'for specified population: {population} in provided selectivity_path: {selectivity_path}'
)
valid_selectivity_namespaces = comm.bcast(valid_selectivity_namespaces,
root=0)
selectivity_type_names = dict(
(val, key) for (key, val) in viewitems(env.selectivity_types))
reference_u_arc_distance_bounds = None
reference_v_arc_distance_bounds = None
if rank == 0:
for population in populations:
if population not in coords_population_ranges:
raise RuntimeError(
f'plot_input_selectivity_features: specified population: {population} not found in '
f'provided coords_path: {coords_path}')
with h5py.File(coords_path, 'r') as coords_f:
pop_size = population_ranges[population][1]
unique_gid_count = len(
set(coords_f['Populations'][population]
[distances_namespace]['U Distance']['Cell Index'][:]))
if pop_size != unique_gid_count:
raise RuntimeError(
f'plot_input_selectivity_features: only {unique_gid_count}/{pop_size} unique cell indexes found '
f'for specified population: {population} in provided coords_path: {coords_path}'
)
if reference_u_arc_distance_bounds is None:
try:
reference_u_arc_distance_bounds = \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Max']
except Exception:
raise RuntimeError(
'plot_input_selectivity_features: problem locating attributes '
f'containing reference bounds in namespace: {distances_namespace} '
f'for population: {population} from coords_path: {coords_path}'
)
if reference_v_arc_distance_bounds is None:
try:
reference_v_arc_distance_bounds = \
coords_f['Populations'][population][distances_namespace].attrs['Reference V Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference V Max']
except Exception:
raise RuntimeError(
'plot_input_selectivity_features: problem locating attributes '
f'containing reference bounds in namespace: {distances_namespace} '
f'for population: {population} from coords_path: {coords_path}'
)
reference_u_arc_distance_bounds = comm.bcast(
reference_u_arc_distance_bounds, root=0)
reference_v_arc_distance_bounds = comm.bcast(
reference_v_arc_distance_bounds, root=0)
u_edges = np.arange(reference_u_arc_distance_bounds[0],
reference_u_arc_distance_bounds[1] + bin_distance / 2.,
bin_distance)
v_edges = np.arange(reference_v_arc_distance_bounds[0],
reference_v_arc_distance_bounds[1] + bin_distance / 2.,
bin_distance)
if arena_id not in env.stimulus_config['Arena']:
raise RuntimeError(
f'Arena with ID: {arena_id} not specified by configuration at file path: {config_prefix}/{config}'
)
if spatial_resolution is None:
spatial_resolution = env.stimulus_config['Spatial Resolution']
arena = env.stimulus_config['Arena'][arena_id]
arena_x_mesh, arena_y_mesh = None, None
if rank == 0:
arena_x_mesh, arena_y_mesh = \
get_2D_arena_spatial_mesh(arena=arena, spatial_resolution=spatial_resolution)
arena_x_mesh = comm.bcast(arena_x_mesh, root=0)
arena_y_mesh = comm.bcast(arena_y_mesh, root=0)
x0_dict = {}
y0_dict = {}
for population in populations:
start_time = time.time()
u_distances_by_gid = dict()
v_distances_by_gid = dict()
distances_attr_gen = \
bcast_cell_attributes(coords_path, population, root=0, namespace=distances_namespace, comm=comm)
for gid, distances_attr_dict in distances_attr_gen:
u_distances_by_gid[gid] = distances_attr_dict['U Distance'][0]
v_distances_by_gid[gid] = distances_attr_dict['V Distance'][0]
if rank == 0:
logger.info(
f'Reading {len(u_distances_by_gid)} cell positions for population {population} took '
f'{time.time() - start_time:.2f} s')
for this_selectivity_namespace in valid_selectivity_namespaces[
population]:
start_time = time.time()
if rank == 0:
logger.info(
f'Reading from {this_selectivity_namespace} namespace for population {population}...'
)
gid_count = 0
gathered_cell_attributes = defaultdict(list)
gathered_component_attributes = defaultdict(list)
u_distances_by_cell = list()
v_distances_by_cell = list()
u_distances_by_component = list()
v_distances_by_component = list()
rate_map_sum_by_module = defaultdict(
lambda: np.zeros_like(arena_x_mesh))
count_by_module = defaultdict(int)
start_time = time.time()
x0_list_by_module = defaultdict(list)
y0_list_by_module = defaultdict(list)
selectivity_attr_gen = NeuroH5CellAttrGen(
selectivity_path,
population,
namespace=this_selectivity_namespace,
comm=comm,
io_size=io_size,
cache_size=cache_size)
for iter_count, (
gid,
selectivity_attr_dict) in enumerate(selectivity_attr_gen):
if gid is not None:
gid_count += 1
this_selectivity_type = selectivity_attr_dict[
'Selectivity Type'][0]
this_selectivity_type_name = selectivity_type_names[
this_selectivity_type]
input_cell_config = \
get_input_cell_config(selectivity_type=this_selectivity_type,
selectivity_type_names=selectivity_type_names,
selectivity_attr_dict=selectivity_attr_dict)
rate_map = input_cell_config.get_rate_map(x=arena_x_mesh,
y=arena_y_mesh)
u_distances_by_cell.append(u_distances_by_gid[gid])
v_distances_by_cell.append(v_distances_by_gid[gid])
this_cell_attrs, component_count, this_component_attrs = input_cell_config.gather_attributes(
)
for attr_name, attr_val in viewitems(this_cell_attrs):
gathered_cell_attributes[attr_name].append(attr_val)
gathered_cell_attributes['Mean Rate'].append(
np.mean(rate_map))
if component_count > 0:
u_distances_by_component.extend(
[u_distances_by_gid[gid]] * component_count)
v_distances_by_component.extend(
[v_distances_by_gid[gid]] * component_count)
for attr_name, attr_val in viewitems(
this_component_attrs):
gathered_component_attributes[attr_name].extend(
attr_val)
this_module_id = this_cell_attrs['Module ID']
if debug and rank == 0:
fig_title = f'{population} {this_selectivity_type_name} cell {gid}'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
rate_map=rate_map,
peak_rate=env.stimulus_config['Peak Rate']
[population][this_selectivity_type],
title=f'{fig_title}\nModule: {this_module_id}',
**fig_options())
x0_list_by_module[this_module_id].append(
selectivity_attr_dict['X Offset'])
y0_list_by_module[this_module_id].append(
selectivity_attr_dict['Y Offset'])
rate_map_sum_by_module[this_module_id] = np.add(
rate_map, rate_map_sum_by_module[this_module_id])
count_by_module[this_module_id] += 1
if debug and iter_count >= 10:
break
if rank == 0:
logger.info(
f'Done reading from {this_selectivity_namespace} namespace for population {population}...'
)
cell_count_hist, _, _ = np.histogram2d(u_distances_by_cell,
v_distances_by_cell,
bins=[u_edges, v_edges])
component_count_hist, _, _ = np.histogram2d(
u_distances_by_component,
v_distances_by_component,
bins=[u_edges, v_edges])
if debug:
context.update(locals())
gathered_cell_attr_hist = dict()
gathered_component_attr_hist = dict()
for key in gathered_cell_attributes:
gathered_cell_attr_hist[key], _, _ = \
np.histogram2d(u_distances_by_cell, v_distances_by_cell, bins=[u_edges, v_edges],
weights=gathered_cell_attributes[key])
for key in gathered_component_attributes:
gathered_component_attr_hist[key], _, _ = \
np.histogram2d(u_distances_by_component, v_distances_by_component, bins=[u_edges, v_edges],
weights=gathered_component_attributes[key])
gid_count = comm.gather(gid_count, root=0)
cell_count_hist = comm.gather(cell_count_hist, root=0)
component_count_hist = comm.gather(component_count_hist, root=0)
gathered_cell_attr_hist = comm.gather(gathered_cell_attr_hist,
root=0)
gathered_component_attr_hist = comm.gather(
gathered_component_attr_hist, root=0)
x0_list_by_module = dict(x0_list_by_module)
y0_list_by_module = dict(y0_list_by_module)
x0_list_by_module = comm.reduce(x0_list_by_module,
op=mpi_op_merge_list_dict,
root=0)
y0_list_by_module = comm.reduce(y0_list_by_module,
op=mpi_op_merge_list_dict,
root=0)
rate_map_sum_by_module = dict(rate_map_sum_by_module)
rate_map_sum_by_module = comm.gather(rate_map_sum_by_module,
root=0)
count_by_module = dict(count_by_module)
count_by_module = comm.reduce(count_by_module,
op=mpi_op_merge_count_dict,
root=0)
if rank == 0:
gid_count = sum(gid_count)
cell_count_hist = np.sum(cell_count_hist, axis=0)
component_count_hist = np.sum(component_count_hist, axis=0)
merged_cell_attr_hist = defaultdict(
lambda: np.zeros_like(cell_count_hist))
merged_component_attr_hist = defaultdict(
lambda: np.zeros_like(component_count_hist))
for each_cell_attr_hist in gathered_cell_attr_hist:
for key in each_cell_attr_hist:
merged_cell_attr_hist[key] = np.add(
merged_cell_attr_hist[key],
each_cell_attr_hist[key])
for each_component_attr_hist in gathered_component_attr_hist:
for key in each_component_attr_hist:
merged_component_attr_hist[key] = np.add(
merged_component_attr_hist[key],
each_component_attr_hist[key])
merged_rate_map_sum_by_module = defaultdict(
lambda: np.zeros_like(arena_x_mesh))
for each_rate_map_sum_by_module in rate_map_sum_by_module:
for this_module_id in each_rate_map_sum_by_module:
merged_rate_map_sum_by_module[this_module_id] = \
np.add(merged_rate_map_sum_by_module[this_module_id],
each_rate_map_sum_by_module[this_module_id])
logger.info(
f'Processing {gid_count} {population} {this_selectivity_type_name} cells '
f'took {time.time() - start_time:.2f} s')
if debug:
context.update(locals())
fig_title = f'{population} {this_selectivity_type_name} field offsets'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
for key in merged_cell_attr_hist:
fig_title = f'{population} {this_selectivity_type_name} cells {key} distribution'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
if colormap is not None:
fig_options.colormap = colormap
title = f'{population} {this_selectivity_type_name} cells\n{key} distribution'
fig = plot_2D_histogram(
merged_cell_attr_hist[key],
x_edges=u_edges,
y_edges=v_edges,
norm=cell_count_hist,
ylabel='Transverse position (um)',
xlabel='Septo-temporal position (um)',
title=title,
cbar_label='Mean value per bin',
cbar=True,
**fig_options())
close_figure(fig)
for key in merged_component_attr_hist:
fig_title = f'{population} {this_selectivity_type_name} cells {key} distribution'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
title = f'{population} {this_selectivity_type_name} cells\n{key} distribution'
fig = plot_2D_histogram(
merged_component_attr_hist[key],
x_edges=u_edges,
y_edges=v_edges,
norm=component_count_hist,
ylabel='Transverse position (um)',
xlabel='Septo-temporal position (um)',
title=title,
cbar_label='Mean value per bin',
cbar=True,
**fig_options())
close_figure(fig)
for this_module_id in merged_rate_map_sum_by_module:
num_cells = count_by_module[this_module_id]
x0 = np.concatenate(x0_list_by_module[this_module_id])
y0 = np.concatenate(y0_list_by_module[this_module_id])
fig_title = f'{population} {this_selectivity_type_name} Module {this_module_id} rate map'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
fig = plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
x0=x0,
y0=y0,
rate_map=merged_rate_map_sum_by_module[this_module_id],
title=
(f'{population} {this_selectivity_type_name} rate map\n'
f'Module {this_module_id} ({num_cells} cells)'),
**fig_options())
close_figure(fig)
if is_interactive and rank == 0:
context.update(locals())
def main(config, config_prefix, coords_path, distances_namespace, output_path,
arena_id, populations, use_noise_gen, io_size, chunk_size,
value_chunk_size, cache_size, write_size, verbose, gather,
interactive, debug, debug_count, plot, show_fig, save_fig,
save_fig_dir, font_size, fig_format, dry_run):
"""
:param config: str (.yaml file name)
:param config_prefix: str (path to dir)
:param coords_path: str (path to file)
:param distances_namespace: str
:param output_path: str
:param arena_id: str
:param populations: tuple of str
:param io_size: int
:param chunk_size: int
:param value_chunk_size: int
:param cache_size: int
:param write_size: int
:param verbose: bool
:param gather: bool; whether to gather population attributes to rank 0 for interactive analysis or plotting
:param interactive: bool
:param debug: bool
:param plot: bool
:param show_fig: bool
:param save_fig: str (base file name)
:param save_fig_dir: str (path to dir)
:param font_size: float
:param fig_format: str
:param dry_run: bool
"""
comm = MPI.COMM_WORLD
rank = comm.rank
config_logging(verbose)
env = Env(comm=comm,
config_file=config,
config_prefix=config_prefix,
template_paths=None)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info(f'{comm.size} ranks have been allocated')
if save_fig is not None:
plot = True
if plot:
import matplotlib.pyplot as plt
from dentate.plot import plot_2D_rate_map, default_fig_options, save_figure, clean_axes, close_figure
fig_options = copy.copy(default_fig_options)
fig_options.saveFigDir = save_fig_dir
fig_options.fontSize = font_size
fig_options.figFormat = fig_format
fig_options.showFig = show_fig
if save_fig is not None:
save_fig = '%s %s' % (save_fig, arena_id)
fig_options.saveFig = save_fig
if not dry_run and rank == 0:
if output_path is None:
raise RuntimeError(
'generate_input_selectivity_features: missing output_path')
if not os.path.isfile(output_path):
input_file = h5py.File(coords_path, 'r')
output_file = h5py.File(output_path, 'w')
input_file.copy('/H5Types', output_file)
input_file.close()
output_file.close()
comm.barrier()
population_ranges = read_population_ranges(coords_path, comm)[0]
if len(populations) == 0:
populations = sorted(population_ranges.keys())
reference_u_arc_distance_bounds_dict = {}
if rank == 0:
for population in sorted(populations):
if population not in population_ranges:
raise RuntimeError(
'generate_input_selectivity_features: specified population: %s not found in '
'provided coords_path: %s' % (population, coords_path))
if population not in env.stimulus_config[
'Selectivity Type Probabilities']:
raise RuntimeError(
'generate_input_selectivity_features: selectivity type not specified for '
'population: %s' % population)
with h5py.File(coords_path, 'r') as coords_f:
pop_size = population_ranges[population][1]
unique_gid_count = len(
set(coords_f['Populations'][population]
[distances_namespace]['U Distance']['Cell Index'][:]))
if pop_size != unique_gid_count:
raise RuntimeError(
'generate_input_selectivity_features: only %i/%i unique cell indexes found '
'for specified population: %s in provided coords_path: %s'
%
(unique_gid_count, pop_size, population, coords_path))
try:
reference_u_arc_distance_bounds_dict[population] = \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Max']
except Exception:
raise RuntimeError(
'generate_input_selectivity_features: problem locating attributes '
'containing reference bounds in namespace: %s for population: %s from '
'coords_path: %s' %
(distances_namespace, population, coords_path))
comm.barrier()
reference_u_arc_distance_bounds_dict = comm.bcast(
reference_u_arc_distance_bounds_dict, root=0)
selectivity_type_names = dict([
(val, key) for (key, val) in viewitems(env.selectivity_types)
])
selectivity_type_namespaces = dict()
for this_selectivity_type in selectivity_type_names:
this_selectivity_type_name = selectivity_type_names[
this_selectivity_type]
chars = list(this_selectivity_type_name)
chars[0] = chars[0].upper()
selectivity_type_namespaces[this_selectivity_type_name] = ''.join(
chars) + ' Selectivity %s' % arena_id
if arena_id not in env.stimulus_config['Arena']:
raise RuntimeError(
f'Arena with ID: {arena_id} not specified by configuration at file path: {config_prefix}/{config}'
)
arena = env.stimulus_config['Arena'][arena_id]
arena_x_mesh, arena_y_mesh = None, None
if rank == 0:
arena_x_mesh, arena_y_mesh = \
get_2D_arena_spatial_mesh(arena=arena, spatial_resolution=env.stimulus_config['Spatial Resolution'])
arena_x_mesh = comm.bcast(arena_x_mesh, root=0)
arena_y_mesh = comm.bcast(arena_y_mesh, root=0)
local_random = np.random.RandomState()
selectivity_seed_offset = int(
env.model_config['Random Seeds']['Input Selectivity'])
local_random.seed(selectivity_seed_offset - 1)
selectivity_config = InputSelectivityConfig(env.stimulus_config,
local_random)
if plot and rank == 0:
selectivity_config.plot_module_probabilities(**fig_options())
if (debug or interactive) and rank == 0:
context.update(dict(locals()))
pop_norm_distances = {}
rate_map_sum = {}
x0_dict = {}
y0_dict = {}
write_every = max(1, int(math.floor(write_size / comm.size)))
for population in sorted(populations):
if rank == 0:
logger.info(
f'Generating input selectivity features for population {population}...'
)
reference_u_arc_distance_bounds = reference_u_arc_distance_bounds_dict[
population]
modular = True
if population in env.stimulus_config[
'Non-modular Place Selectivity Populations']:
modular = False
noise_gen_dict = None
if use_noise_gen:
noise_gen_dict = {}
if modular:
for module_id in range(env.stimulus_config['Number Modules']):
extent_x, extent_y = get_2D_arena_extents(arena)
margin = round(
selectivity_config.place_module_field_widths[module_id]
/ 2.)
arena_x_bounds, arena_y_bounds = get_2D_arena_bounds(
arena, margin=margin)
noise_gen = MPINoiseGenerator(
comm=comm,
bounds=(arena_x_bounds, arena_y_bounds),
tile_rank=comm.rank,
bin_size=0.5,
mask_fraction=0.99,
seed=int(selectivity_seed_offset + module_id * 1e6))
noise_gen_dict[module_id] = noise_gen
else:
margin = round(
np.mean(selectivity_config.place_module_field_widths) / 2.)
arena_x_bounds, arena_y_bounds = get_2D_arena_bounds(
arena, margin=margin)
noise_gen_dict[-1] = MPINoiseGenerator(
comm=comm,
bounds=(arena_x_bounds, arena_y_bounds),
tile_rank=comm.rank,
bin_size=0.5,
mask_fraction=0.99,
seed=selectivity_seed_offset)
this_pop_norm_distances = {}
this_rate_map_sum = defaultdict(lambda: np.zeros_like(arena_x_mesh))
this_x0_list = []
this_y0_list = []
start_time = time.time()
gid_count = defaultdict(lambda: 0)
distances_attr_gen = NeuroH5CellAttrGen(coords_path,
population,
namespace=distances_namespace,
comm=comm,
io_size=io_size,
cache_size=cache_size)
selectivity_attr_dict = dict(
(key, dict()) for key in env.selectivity_types)
for iter_count, (gid,
distances_attr_dict) in enumerate(distances_attr_gen):
req = comm.Ibarrier()
if gid is None:
if noise_gen_dict is not None:
all_module_ids = [-1]
if modular:
all_module_ids = comm.allreduce(set([]),
op=mpi_op_set_union)
for module_id in all_module_ids:
this_noise_gen = noise_gen_dict[module_id]
global_num_fields = this_noise_gen.sync(0)
for i in range(global_num_fields):
this_noise_gen.add(
np.empty(shape=(0, 0), dtype=np.float32), None)
else:
if rank == 0:
logger.info(
f'Rank {rank} generating selectivity features for gid {gid}...'
)
u_arc_distance = distances_attr_dict['U Distance'][0]
v_arc_distance = distances_attr_dict['V Distance'][0]
norm_u_arc_distance = (
(u_arc_distance - reference_u_arc_distance_bounds[0]) /
(reference_u_arc_distance_bounds[1] -
reference_u_arc_distance_bounds[0]))
this_pop_norm_distances[gid] = norm_u_arc_distance
this_selectivity_type_name, this_selectivity_attr_dict = \
generate_input_selectivity_features(env, population, arena,
arena_x_mesh, arena_y_mesh,
gid, (norm_u_arc_distance, v_arc_distance),
selectivity_config, selectivity_type_names,
selectivity_type_namespaces,
noise_gen_dict=noise_gen_dict,
rate_map_sum=this_rate_map_sum,
debug= (debug_callback, context) if debug else False)
if 'X Offset' in this_selectivity_attr_dict:
this_x0_list.append(this_selectivity_attr_dict['X Offset'])
this_y0_list.append(this_selectivity_attr_dict['Y Offset'])
selectivity_attr_dict[this_selectivity_type_name][
gid] = this_selectivity_attr_dict
gid_count[this_selectivity_type_name] += 1
if noise_gen_dict is not None:
for m in noise_gen_dict:
noise_gen_dict[m].tile_rank = (
noise_gen_dict[m].tile_rank + 1) % comm.size
req.wait()
if (iter_count > 0 and iter_count % write_every
== 0) or (debug and iter_count == debug_count):
total_gid_count = 0
gid_count_dict = dict(gid_count.items())
req = comm.Ibarrier()
selectivity_gid_count = comm.reduce(gid_count_dict,
root=0,
op=mpi_op_merge_count_dict)
req.wait()
if rank == 0:
for selectivity_type_name in selectivity_gid_count:
total_gid_count += selectivity_gid_count[
selectivity_type_name]
for selectivity_type_name in selectivity_gid_count:
logger.info(
'generated selectivity features for %i/%i %s %s cells in %.2f s'
% (selectivity_gid_count[selectivity_type_name],
total_gid_count, population,
selectivity_type_name,
(time.time() - start_time)))
if not dry_run:
for selectivity_type_name in sorted(
selectivity_attr_dict.keys()):
req = comm.Ibarrier()
if rank == 0:
logger.info(
f'writing selectivity features for {population} [{selectivity_type_name}]...'
)
selectivity_type_namespace = selectivity_type_namespaces[
selectivity_type_name]
append_cell_attributes(
output_path,
population,
selectivity_attr_dict[selectivity_type_name],
namespace=selectivity_type_namespace,
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size)
req.wait()
del selectivity_attr_dict
selectivity_attr_dict = dict(
(key, dict()) for key in env.selectivity_types)
gc.collect()
if debug and iter_count >= debug_count:
break
pop_norm_distances[population] = this_pop_norm_distances
rate_map_sum[population] = dict(this_rate_map_sum)
if len(this_x0_list) > 0:
x0_dict[population] = np.concatenate(this_x0_list, axis=None)
y0_dict[population] = np.concatenate(this_y0_list, axis=None)
total_gid_count = 0
gid_count_dict = dict(gid_count.items())
req = comm.Ibarrier()
selectivity_gid_count = comm.reduce(gid_count_dict,
root=0,
op=mpi_op_merge_count_dict)
req.wait()
if rank == 0:
for selectivity_type_name in selectivity_gid_count:
total_gid_count += selectivity_gid_count[selectivity_type_name]
for selectivity_type_name in selectivity_gid_count:
logger.info(
'generated selectivity features for %i/%i %s %s cells in %.2f s'
% (selectivity_gid_count[selectivity_type_name],
total_gid_count, population, selectivity_type_name,
(time.time() - start_time)))
if not dry_run:
for selectivity_type_name in sorted(selectivity_attr_dict.keys()):
req = comm.Ibarrier()
if rank == 0:
logger.info(
f'writing selectivity features for {population} [{selectivity_type_name}]...'
)
selectivity_type_namespace = selectivity_type_namespaces[
selectivity_type_name]
append_cell_attributes(
output_path,
population,
selectivity_attr_dict[selectivity_type_name],
namespace=selectivity_type_namespace,
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size)
req.wait()
del selectivity_attr_dict
gc.collect()
req = comm.Ibarrier()
req.wait()
if gather:
merged_pop_norm_distances = {}
for population in sorted(populations):
merged_pop_norm_distances[population] = \
comm.reduce(pop_norm_distances[population], root=0,
op=mpi_op_merge_dict)
merged_rate_map_sum = comm.reduce(rate_map_sum,
root=0,
op=mpi_op_merge_rate_map_dict)
merged_x0 = comm.reduce(x0_dict,
root=0,
op=mpi_op_concatenate_ndarray_dict)
merged_y0 = comm.reduce(y0_dict,
root=0,
op=mpi_op_concatenate_ndarray_dict)
if rank == 0:
if plot:
for population in merged_pop_norm_distances:
norm_distance_values = np.asarray(
list(merged_pop_norm_distances[population].values()))
hist, edges = np.histogram(norm_distance_values, bins=100)
fig, axes = plt.subplots(1)
axes.plot(edges[1:], hist)
axes.set_title(f'Population: {population}')
axes.set_xlabel('Normalized cell position')
axes.set_ylabel('Cell count')
clean_axes(axes)
if save_fig is not None:
save_figure(
f'{save_fig} {population} normalized distances histogram',
fig=fig,
**fig_options())
if fig_options.showFig:
fig.show()
close_figure(fig)
for population in merged_rate_map_sum:
for selectivity_type_name in merged_rate_map_sum[
population]:
fig_title = f'{population} {this_selectivity_type_name} summed rate maps'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
rate_map=merged_rate_map_sum[population]
[selectivity_type_name],
title=
f'Summed rate maps\n{population} {selectivity_type_name} cells',
**fig_options())
for population in merged_x0:
fig_title = f'{population} field offsets'
if save_fig is not None:
fig_options.saveFig = f'{save_fig} {fig_title}'
x0 = merged_x0[population]
y0 = merged_y0[population]
fig, axes = plt.subplots(1)
axes.scatter(x0, y0)
if save_fig is not None:
save_figure(f'{save_fig} {fig_title}',
fig=fig,
**fig_options())
if fig_options.showFig:
fig.show()
close_figure(fig)
if interactive and rank == 0:
context.update(locals())
def main(config, config_prefix, coords_path, distances_namespace, output_path,
arena_id, populations, io_size, chunk_size, value_chunk_size,
cache_size, write_size, verbose, gather, interactive, debug, plot,
show_fig, save_fig, save_fig_dir, font_size, fig_format, dry_run):
"""
:param config: str (.yaml file name)
:param config_prefix: str (path to dir)
:param coords_path: str (path to file)
:param distances_namespace: str
:param output_path: str
:param arena_id: str
:param populations: tuple of str
:param io_size: int
:param chunk_size: int
:param value_chunk_size: int
:param cache_size: int
:param write_size: int
:param verbose: bool
:param gather: bool; whether to gather population attributes to rank 0 for interactive analysis or plotting
:param interactive: bool
:param debug: bool
:param plot: bool
:param show_fig: bool
:param save_fig: str (base file name)
:param save_fig_dir: str (path to dir)
:param font_size: float
:param fig_format: str
:param dry_run: bool
"""
comm = MPI.COMM_WORLD
rank = comm.rank
config_logging(verbose)
env = Env(comm=comm,
config_file=config,
config_prefix=config_prefix,
template_paths=None)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
if save_fig is not None:
plot = True
if plot:
import matplotlib.pyplot as plt
from dentate.plot import plot_2D_rate_map, default_fig_options, save_figure, clean_axes
fig_options = copy.copy(default_fig_options)
fig_options.saveFigDir = save_fig_dir
fig_options.fontSize = font_size
fig_options.figFormat = fig_format
fig_options.showFig = show_fig
if save_fig is not None:
save_fig = '%s %s' % (save_fig, arena_id)
fig_options.saveFig = save_fig
if not dry_run and rank == 0:
if output_path is None:
raise RuntimeError(
'generate_input_selectivity_features: missing output_path')
if not os.path.isfile(output_path):
input_file = h5py.File(coords_path, 'r')
output_file = h5py.File(output_path, 'w')
input_file.copy('/H5Types', output_file)
input_file.close()
output_file.close()
comm.barrier()
population_ranges = read_population_ranges(coords_path, comm)[0]
if len(populations) == 0:
populations = sorted(population_ranges.keys())
reference_u_arc_distance_bounds_dict = {}
if rank == 0:
for population in sorted(populations):
if population not in population_ranges:
raise RuntimeError(
'generate_input_selectivity_features: specified population: %s not found in '
'provided coords_path: %s' % (population, coords_path))
if population not in env.stimulus_config[
'Selectivity Type Probabilities']:
raise RuntimeError(
'generate_input_selectivity_features: selectivity type not specified for '
'population: %s' % population)
with h5py.File(coords_path, 'r') as coords_f:
pop_size = population_ranges[population][1]
unique_gid_count = len(
set(coords_f['Populations'][population]
[distances_namespace]['U Distance']['Cell Index'][:]))
if pop_size != unique_gid_count:
raise RuntimeError(
'generate_input_selectivity_features: only %i/%i unique cell indexes found '
'for specified population: %s in provided coords_path: %s'
%
(unique_gid_count, pop_size, population, coords_path))
try:
reference_u_arc_distance_bounds_dict[population] = \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Min'], \
coords_f['Populations'][population][distances_namespace].attrs['Reference U Max']
except Exception:
raise RuntimeError(
'generate_input_selectivity_features: problem locating attributes '
'containing reference bounds in namespace: %s for population: %s from '
'coords_path: %s' %
(distances_namespace, population, coords_path))
comm.barrier()
reference_u_arc_distance_bounds_dict = comm.bcast(
reference_u_arc_distance_bounds_dict, root=0)
selectivity_type_names = dict([
(val, key) for (key, val) in viewitems(env.selectivity_types)
])
selectivity_type_namespaces = dict()
for this_selectivity_type in selectivity_type_names:
this_selectivity_type_name = selectivity_type_names[
this_selectivity_type]
chars = list(this_selectivity_type_name)
chars[0] = chars[0].upper()
selectivity_type_namespaces[this_selectivity_type_name] = ''.join(
chars) + ' Selectivity %s' % arena_id
if arena_id not in env.stimulus_config['Arena']:
raise RuntimeError(
'Arena with ID: %s not specified by configuration at file path: %s'
% (arena_id, config_prefix + '/' + config))
arena = env.stimulus_config['Arena'][arena_id]
arena_x_mesh, arena_y_mesh = None, None
if rank == 0:
arena_x_mesh, arena_y_mesh = \
get_2D_arena_spatial_mesh(arena=arena, spatial_resolution=env.stimulus_config['Spatial Resolution'])
arena_x_mesh = comm.bcast(arena_x_mesh, root=0)
arena_y_mesh = comm.bcast(arena_y_mesh, root=0)
local_random = np.random.RandomState()
selectivity_seed_offset = int(
env.model_config['Random Seeds']['Input Selectivity'])
local_random.seed(selectivity_seed_offset - 1)
selectivity_config = InputSelectivityConfig(env.stimulus_config,
local_random)
if plot and rank == 0:
selectivity_config.plot_module_probabilities(**fig_options())
if (debug or interactive) and rank == 0:
context.update(dict(locals()))
pop_norm_distances = {}
rate_map_sum = {}
write_every = max(1, int(math.floor(write_size / comm.size)))
for population in sorted(populations):
if rank == 0:
logger.info(
'Generating input selectivity features for population %s...' %
population)
reference_u_arc_distance_bounds = reference_u_arc_distance_bounds_dict[
population]
this_pop_norm_distances = {}
this_rate_map_sum = defaultdict(lambda: np.zeros_like(arena_x_mesh))
start_time = time.time()
gid_count = defaultdict(lambda: 0)
distances_attr_gen = NeuroH5CellAttrGen(coords_path,
population,
namespace=distances_namespace,
comm=comm,
io_size=io_size,
cache_size=cache_size)
selectivity_attr_dict = dict(
(key, dict()) for key in env.selectivity_types)
for iter_count, (gid,
distances_attr_dict) in enumerate(distances_attr_gen):
if gid is not None:
u_arc_distance = distances_attr_dict['U Distance'][0]
v_arc_distance = distances_attr_dict['V Distance'][0]
norm_u_arc_distance = (
(u_arc_distance - reference_u_arc_distance_bounds[0]) /
(reference_u_arc_distance_bounds[1] -
reference_u_arc_distance_bounds[0]))
this_pop_norm_distances[gid] = norm_u_arc_distance
this_selectivity_type_name, this_selectivity_attr_dict = \
generate_input_selectivity_features(env, population, arena,
arena_x_mesh, arena_y_mesh,
gid, (norm_u_arc_distance, v_arc_distance),
selectivity_config, selectivity_type_names,
selectivity_type_namespaces,
rate_map_sum=this_rate_map_sum,
debug= (debug_callback, context) if debug else False)
selectivity_attr_dict[this_selectivity_type_name][
gid] = this_selectivity_attr_dict
gid_count[this_selectivity_type_name] += 1
if (iter_count > 0 and iter_count % write_every
== 0) or (debug and iter_count == 10):
total_gid_count = 0
gid_count_dict = dict(gid_count.items())
selectivity_gid_count = comm.reduce(gid_count_dict,
root=0,
op=mpi_op_merge_count_dict)
if rank == 0:
for selectivity_type_name in selectivity_gid_count:
total_gid_count += selectivity_gid_count[
selectivity_type_name]
for selectivity_type_name in selectivity_gid_count:
logger.info(
'generated selectivity features for %i/%i %s %s cells in %.2f s'
% (selectivity_gid_count[selectivity_type_name],
total_gid_count, population,
selectivity_type_name,
(time.time() - start_time)))
if not dry_run:
for selectivity_type_name in sorted(
selectivity_attr_dict.keys()):
if rank == 0:
logger.info(
'writing selectivity features for %s [%s]...' %
(population, selectivity_type_name))
selectivity_type_namespace = selectivity_type_namespaces[
selectivity_type_name]
append_cell_attributes(
output_path,
population,
selectivity_attr_dict[selectivity_type_name],
namespace=selectivity_type_namespace,
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size)
del selectivity_attr_dict
selectivity_attr_dict = dict(
(key, dict()) for key in env.selectivity_types)
if debug and iter_count >= 10:
break
pop_norm_distances[population] = this_pop_norm_distances
rate_map_sum[population] = this_rate_map_sum
total_gid_count = 0
gid_count_dict = dict(gid_count.items())
selectivity_gid_count = comm.reduce(gid_count_dict,
root=0,
op=mpi_op_merge_count_dict)
if rank == 0:
for selectivity_type_name in selectivity_gid_count:
total_gid_count += selectivity_gid_count[selectivity_type_name]
for selectivity_type_name in selectivity_gid_count:
logger.info(
'generated selectivity features for %i/%i %s %s cells in %.2f s'
% (selectivity_gid_count[selectivity_type_name],
total_gid_count, population, selectivity_type_name,
(time.time() - start_time)))
if not dry_run:
for selectivity_type_name in sorted(selectivity_attr_dict.keys()):
if rank == 0:
logger.info('writing selectivity features for %s [%s]...' %
(population, selectivity_type_name))
selectivity_type_namespace = selectivity_type_namespaces[
selectivity_type_name]
append_cell_attributes(
output_path,
population,
selectivity_attr_dict[selectivity_type_name],
namespace=selectivity_type_namespace,
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size)
del selectivity_attr_dict
comm.barrier()
if gather:
merged_pop_norm_distances = {}
for population in sorted(populations):
merged_pop_norm_distances[population] = \
comm.reduce(pop_norm_distances[population], root=0,
op=mpi_op_merge_dict)
rate_map_sum = dict([(key, dict(val.items()))
for key, val in viewitems(rate_map_sum)])
rate_map_sum = comm.gather(rate_map_sum, root=0)
if rank == 0:
merged_rate_map_sum = defaultdict(
lambda: defaultdict(lambda: np.zeros_like(arena_x_mesh)))
for each_rate_map_sum in rate_map_sum:
for population in each_rate_map_sum:
for selectivity_type_name in each_rate_map_sum[population]:
merged_rate_map_sum[population][selectivity_type_name] = \
np.add(merged_rate_map_sum[population][selectivity_type_name],
each_rate_map_sum[population][selectivity_type_name])
if plot:
for population in merged_pop_norm_distances:
norm_distance_values = np.asarray(
list(merged_pop_norm_distances[population].values()))
hist, edges = np.histogram(norm_distance_values, bins=100)
fig, axes = plt.subplots(1)
axes.plot(edges[1:], hist)
axes.set_title('Population: %s' % population)
axes.set_xlabel('Normalized cell position')
axes.set_ylabel('Cell count')
clean_axes(axes)
if save_fig is not None:
save_figure('%s %s normalized distances histogram' %
(save_fig, population),
fig=fig,
**fig_options())
if fig_options.showFig:
fig.show()
for population in merged_rate_map_sum:
for selectivity_type_name in merged_rate_map_sum[
population]:
fig_title = '%s %s summed rate maps' % (
population, this_selectivity_type_name)
if save_fig is not None:
fig_options.saveFig = '%s %s' % (save_fig,
fig_title)
plot_2D_rate_map(
x=arena_x_mesh,
y=arena_y_mesh,
rate_map=merged_rate_map_sum[population]
[selectivity_type_name],
title='Summed rate maps\n%s %s cells' %
(population, selectivity_type_name),
**fig_options())
if interactive and rank == 0:
context.update(locals())