Merge branch 'develop' of https://github.com/shixnya/bmtk into develop

.gitignore

@@ -29,6 +29,9 @@ docs/tutorial/sim_ch*
 docs/tutorial/sim_dyn_syn
 examples/**/output*/**
 examples/bio_simulated_annealing/updated_weights/*
+examples/filter_movie/boc/*
+examples/filter_movie/bob_images/*
+examples/filter_movie/movies/*
 /.eggs
 benchmarks/.asv
 **/x86_64/**

bmtk/__init__.py

@@ -20,4 +20,4 @@
 # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #
-__version__ = '1.0.8'
+__version__ = '1.1.0'

bmtk/simulator/bionet/modules/iclamp.py

@@ -130,7 +130,7 @@ def __init__(self, input_type, **mod_args):
     def initialize(self, sim):
         # Get select node gids, but only for those nodes that are on the current rank (if running on multiple cores)
         select_gids = list(sim.net.get_node_set(self._node_set).gids())
-        gids_on_rank = list(set(select_gids) & set(select_gids))
+        gids_on_rank = list(set(select_gids) & set(sim.local_gids))
 
         for gid in gids_on_rank:
             cell = sim.net.get_cell_gid(gid)

bmtk/simulator/filternet/default_setters/cell_loaders.py

@@ -203,11 +203,7 @@ def default_cell_loader(node, template_name, dynamics_params):
             cell = sep_ts_onoff_cell
 
         elif model_name == 'LGNOnOFFCell':
-            wts = [node['weight_dom_0'], node['weight_dom_1']]
-            kpeaks = [node['kpeaks_dom_0'], node['kpeaks_dom_1']]
-            delays = [node['delay_dom_0'], node['delay_dom_1']]
-            # transfer_function = ScalarTransferFunction('s')
-            temporal_filter = TemporalFilterCosineBump(wts, kpeaks, delays)
+            temporal_filter = TemporalFilterCosineBump(t_weights, t_kpeaks, t_delays)
 
             spatial_filter_on = GaussianSpatialFilter(sigma=node['sigma_on'], origin=origin, translate=translate)
             on_linear_filter = SpatioTemporalFilter(spatial_filter_on, temporal_filter, amplitude=20)

bmtk/simulator/pointnet/modules/record_spikes.py

@@ -122,16 +122,23 @@ def _get_path(file_name):
         self._tmp_file_base = 'tmp_spike_times'
         self._spike_labels = os.path.join(self._tmp_dir, self._tmp_file_base)
 
-        self._spike_writer = SpikeTrains(cache_dir=tmp_dir, cache_to_disk=cache_to_disk)
-        self._spike_writer.delimiter = '\t'
-        self._spike_writer.gid_col = 0
-        self._spike_writer.time_col = 1
-        self._spike_writer.compression = compression
+        # saving them to initialize SpikeTrains in initialize()
+        self._tmp_dir = tmp_dir
+        self._cache_to_disk = cache_to_disk
+        self._compression = compression
+
         self._sort_order = sort_order.none if not spikes_sort_order else sort_order_lu[spikes_sort_order]
 
         self._spike_detector = None
 
     def initialize(self, sim):
+        # Now this function takes care of initializing the spike_writer.
+        self._spike_writer = SpikeTrains(cache_dir=self._tmp_dir, cache_to_disk=self._cache_to_disk)
+        self._spike_writer.delimiter = '\t'
+        self._spike_writer.gid_col = 0
+        self._spike_writer.time_col = 1
+        self._spike_writer.compression = self._compression
+
         self._spike_detector = create_spike_detector(self._spike_labels)
         nest.Connect(sim.net.gid_map.gids, self._spike_detector)
 

bmtk/tests/simulator/bionet/test_iclamp.py

@@ -38,6 +38,10 @@ def __init__(self):
         self.cell = None
         self.vm = None
 
+    @property
+    def local_gids(self):
+        return [0]
+
     def get_node_set(self, _):
         return self
 

bmtk/tests/utils/reports/spike_trains/test_spikes_core.py

@@ -30,18 +30,29 @@ def test_psg_variable():
     times = np.linspace(0.0, 3.0, 1000)
     fr = np.exp(-np.power(times - 1.0, 2) / (2*np.power(.5, 2)))*5
 
-    psg = PoissonSpikeGenerator(population='test', seed=0.0)
+    psg = PoissonSpikeGenerator(population='test', seed=100)
     psg.add(node_ids=range(10), firing_rate=fr, times=times)
 
     assert(psg.populations == ['test'])
     assert(np.all(psg.node_ids() == list(range(10))))
-    assert(psg.n_spikes() == 59)
-    assert(np.allclose(psg.time_range(), (139.32107933711294, 2901.3003727909172)))
-    assert(psg.to_dataframe().shape == (59, 3))
-    assert(np.allclose(psg.get_times(node_id=0), [442.8378, 520.3624, 640.3880, 1099.0661, 1393.0794, 1725.6109],
-                       atol=1.0e-3))
-    assert(np.allclose(psg.get_times(node_id=9), [729.6267, 885.2469, 1047.7728, 1276.3554, 1543.6557, 1669.9070,
-                                                  1881.3605], atol=1.0e-3))
+    assert(psg.n_spikes() == 54)
+    assert(np.allclose(psg.time_range(), (170.22331575431056, 2004.337420574704)))
+    assert(psg.to_dataframe().shape == (54, 3))
+    assert(np.allclose(psg.get_times(node_id=0), [268.2470, 519.8341, 963.7072, 1004.7012, 1054.3159, 1388.2418, 1727.0501], atol=1.0e-3))
+    assert(np.allclose(psg.get_times(node_id=9), [302.6400,  706.5132, 719.6730, 897.8392, 1192.0589, 1201.5878, 2004.3374], atol=1.0e-3))
+
+
+def test_psg_none_seed():
+    times = np.linspace(0.0, 3.0, 1000)
+    fr = np.exp(-np.power(times - 1.0, 2) / (2*np.power(.5, 2)))*5
+
+    psg0 = PoissonSpikeGenerator(population='test', seed=0)
+    psg0.add(node_ids=range(10), firing_rate=fr, times=times)
+
+    psg_none = PoissonSpikeGenerator(population='test', seed=None)
+    psg_none.add(node_ids=range(10), firing_rate=fr, times=times)
+
+    assert(psg0.n_spikes() != psg_none.n_spikes() and not np.allclose(psg0.n_spikes(), psg_none.n_spikes()))
 
 
 def test_equals():
@@ -78,7 +89,8 @@ def test_subset():
 
 
 if __name__ == '__main__':
-    test_psg_fixed()
+    # test_psg_fixed()
     # test_psg_variable()
+    test_psg_none_seed()
     # test_equals()
     # test_subset()

bmtk/utils/reports/compartment/compartment_writer.py

@@ -411,6 +411,8 @@ def merge(self):
         barrier()
         if self._mpi_size > 1 and self._mpi_rank == 0:
             h5final = h5py.File(self._final_fpath, 'w')
+            add_hdf5_version(h5final)
+            add_hdf5_magic(h5final)
 
             tmp_reports = [CompartmentReader(name) for name in self.temp_files if os.path.exists(name)]
             populations = set()

bmtk/utils/reports/spike_trains/spike_trains.py

@@ -114,7 +114,7 @@ def __init__(self, population=None, seed=None, output_units='ms', **kwargs):
         if population is not None and 'default_population' not in kwargs:
             kwargs['default_population']= population
 
-        if seed:
+        if seed is not None:
             np.random.seed(seed)
 
         super(SpikeGenerator, self).__init__(units=output_units, **kwargs)

docs/autodocs/source/conf.py

@@ -41,6 +41,7 @@
     'numpydoc',
     'sphinx.ext.autosummary',
     'nbsphinx',
+    'IPython.sphinxext.ipython_console_highlighting'
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docs/autodocs/source/tutorials.rst

@@ -11,8 +11,10 @@ Main Tutorials
    BioNet: Multiple Nodes with single cell-type <tutorial_single_pop>
    BioNet: Heterogeneous network <tutorial_multi_pop>
    PointNet: Point-neuron modeling <tutorial_pointnet_modeling>
-   PopNet: Population-based firing rate models <tutorial_population_modeling>
    FilterNet: Full-field flashing movie <tutorial_filter_models>
+   Auditory FilterNet: Generating stimuli from auditory input <tutorial_auditory_filternet>
+   PopNet: Population-based firing rate models <tutorial_population_modeling>
+
 
 
 Also check out the tutorial series from our `annual workshop <workshop>`_.

docs/tutorial/sim_aud/circuit_config.json

@@ -0,0 +1,20 @@
+{
+  "manifest": {
+    "$BASE_DIR": "${configdir}",
+    "$NETWORK_DIR": "$BASE_DIR/network",
+    "$COMPONENTS_DIR": "$BASE_DIR/components"
+  },
+  "components": {
+    "filter_models_dir": "$COMPONENTS_DIR/filter_models"
+  },
+  "networks": {
+    "nodes": [
+      {
+        "nodes_file": "$NETWORK_DIR/aud_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/aud_node_types.csv"
+      }
+    ],
+    "edges": [],
+    "gap_juncs": []
+  }
+}

docs/tutorial/sim_aud/components/filter_models/AUD_filt_partial.json

@@ -0,0 +1,8 @@
+{
+  "b_t": 7,
+  "order_t": 3,
+  "psi": "-pi/2", 
+  "amplitude": 150,
+  "direction": 0,
+  "sigma_f": 1.0
+}

docs/tutorial/sim_aud/config.json

@@ -0,0 +1,4 @@
+{
+  "network": "./circuit_config.json",
+  "simulation": "./simulation_config.json"
+}

docs/tutorial/sim_aud/node_sets.json

@@ -0,0 +1,8 @@
+{
+  "biophysical_nodes": {
+    "model_type": "biophysical"
+  },
+  "point_nodes": {
+    "model_type": "point_process"
+  }
+}

docs/tutorial/sim_aud/run_filternet.py

@@ -0,0 +1,22 @@
+# -*- coding: utf-8 -*-
+
+"""Simulates an example network of 14 cell receiving two kinds of exernal input as defined in configuration file"""
+
+import os, sys
+from bmtk.simulator import filternet
+
+
+def run(config_file):
+    config = filternet.Config.from_json(config_file)
+    config.build_env()
+
+    net = filternet.FilterNetwork.from_config(config)
+    sim = filternet.FilterSimulator.from_config(config, net)
+    sim.run()
+
+
+if __name__ == '__main__':
+    if __file__ != sys.argv[-1]:
+        run(sys.argv[-1])
+    else:
+        run('${CONFIG}')

docs/tutorial/sim_aud/simulation_config.json

@@ -0,0 +1,32 @@
+{
+  "manifest": {
+    "$BASE_DIR": "${configdir}",
+    "$OUTPUT_DIR": "$BASE_DIR/output"
+  },
+  "target_simulator": "LGNModel",
+  "run": {
+    "tstart": 0.0,
+    "tstop": 3000.0,
+    "dt": 0.001
+  },
+  "inputs": {
+  "movie": {
+      "input_type": "audio",
+      "module": "wav_file",
+      "data_file": "$BASE_DIR/audio/sa1.wav",
+      "normalize": "full",
+      "interp_to_freq": true,
+      "padding": "edge"
+   }
+  },
+  "output": {
+    "log_file": "log.txt",
+    "output_dir": "$OUTPUT_DIR",
+    "spikes_file": "spikes.h5",
+    "rates_file_csv": "rates.csv",
+    "spikes_file_csv": "spikes.csv",
+    "spikes_file_h5": "spikes.h5"
+  },
+  "reports": {},
+  "node_sets_file": "$BASE_DIR/node_sets.json"
+}