Adding initial intfire test

.gitignore

@@ -2,6 +2,28 @@
 **/*.egg-info/
 **/*~
 src/pysonata/sonata/tests/tmp
+src/pysonata/build
+src/pysonata/dist
+src/pysonata/.pytest_cache
+**/mechanisms/x86_64
+examples/**/output/log.txt
+examples/**/output/config.json
 .idea
 .cache
 **/mechanisms/x86_64
+/examples/sim_tests/biophysical/one_cell/soma_cm2_hh/.pytest_cache
+/examples/sim_tests/intfire/**/.pytest_cache
+.ipynb_checkpoints/
+/examples/checkouth5.sh
+/examples/300_pointneurons/output/*.dat
+/examples/sim_tests/intfire/*/test/x86_64
+/examples/sim_tests/intfire/*/test/LEMS*.xml
+/examples/sim_tests/intfire/*/test/*.dat
+/examples/sim_tests/intfire/*/test/report*txt
+/examples/sim_tests/intfire/*/test/*spikes
+/examples/sim_tests/intfire/*/test/*.net.nml
+/examples/sim_tests/intfire/*/test/*_nrn.py
+/examples/sim_tests/intfire/*/test/*.mod
+/examples/sim_tests/intfire/*/test/output/*h5
+/examples/sim_tests/intfire/*/test/output/log.txt
+/examples/sim_tests/intfire/*/test/output/*dat

examples/300_cells/analyze_output.py

@@ -0,0 +1,12 @@
+import sys
+from bmtk.analyzer.visualization.spikes import plot_spikes
+
+
+sys.path.append("../sim_tests/shared_components/scripts")
+
+from analyze_output_utils import plot_data
+
+if __name__ == '__main__':
+
+    plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential', show_already=True, max_num_traces=12)
+    plot_spikes('network/internal_nodes.h5', 'network/internal_node_types.csv', 'output/spikes.h5', group_key='model_name')

examples/300_cells/simulation_config.json

@@ -22,7 +22,7 @@
     "v_init": -80
   },
 
-  "node_sets_file": "node_sets.json",
+  "node_sets_file": "$BASE_DIR/node_sets.json",
 
   "inputs": {
     "external_spike_trains": {

examples/300_intfire/build_network.py

@@ -7,12 +7,12 @@
 # Step 1: Create a v1 mock network of 14 cells (nodes) with across 7 different cell "types"
 net = NetworkBuilder("v1")
 
-net.add_nodes(N=240, pop_name='LIF_exc', location='VisL4', ei='e',
+net.add_nodes(N=240, model_name='LIF_exc', location='VisL4', ei='e',
               model_type='point_process',  # use point_process to indicate were are using point model cells
               model_template='nrn:IntFire1',  # Tell the simulator to use the NEURON built-in IntFire1 type cell
               dynamics_params='IntFire1_exc_1.json')
 
-net.add_nodes(N=60, pop_name='LIF_inh', location='VisL4', ei='i',
+net.add_nodes(N=60, model_name='LIF_inh', location='VisL4', ei='i',
               model_type='point_process',
               model_template='nrn:IntFire1',
               dynamics_params='IntFire1_inh_1.json')
@@ -46,7 +46,7 @@ def recurrent_connections(src_cells, trg_cell, n_syns):
               dynamics_params='instanteneousInh.json')
 
 
-net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'},
+net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_inh'},
               iterator='all_to_one',
               connection_rule=recurrent_connections,
               connection_params={'n_syns': 10},
@@ -56,7 +56,7 @@ def recurrent_connections(src_cells, trg_cell, n_syns):
               dynamics_params='instanteneousExc.json')
 
 
-net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},
+net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_exc'},
               iterator='all_to_one',
               connection_rule=recurrent_connections,
               connection_params={'n_syns': 10},
@@ -78,33 +78,33 @@ def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
 
 def select_source_cells(src_cells, trg_cell, n_syns):
     if np.random.random() > 0.1:
-        synapses = [n_syns if src['pop_name'] == 'tON' or src['pop_name'] == 'tOFF' else 0 for src in src_cells]
+        synapses = [n_syns if src['model_name'] == 'tON' or src['model_name'] == 'tOFF' else 0 for src in src_cells]
     else:
-        synapses = [n_syns if src['pop_name'] == 'tONOFF' else 0 for src in src_cells]
+        synapses = [n_syns if src['model_name'] == 'tONOFF' else 0 for src in src_cells]
 
     return synapses
 
 
 lgn = NetworkBuilder("lgn")
 pos_x, pos_y = generate_positions(30)
-lgn.add_nodes(N=30, pop_name='tON', ei='e', location='LGN',
+lgn.add_nodes(N=30, model_name='tON', ei='e', location='LGN',
               x=pos_x, y=pos_y,
               model_type='virtual')
 
 pos_x, pos_y = generate_positions(30)
-lgn.add_nodes(N=30, pop_name='tOFF', ei='e', location='LGN',
+lgn.add_nodes(N=30, model_name='tOFF', ei='e', location='LGN',
               x=pos_x, y=pos_y,
               model_type='virtual')
 
 pos_x, pos_y = generate_positions(30)
-lgn.add_nodes(N=30, pop_name='tONOFF', ei='e', location='LGN',
+lgn.add_nodes(N=30, model_name='tONOFF', ei='e', location='LGN',
               x=pos_x, y=pos_y,
               model_type='virtual')
 
 
 
 
-lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_exc'),
+lgn.add_edges(source=lgn.nodes(), target=net.nodes(model_name='LIF_exc'),
               iterator='all_to_one',
               connection_rule=select_source_cells,
               connection_params={'n_syns': 10},
@@ -113,7 +113,7 @@ def select_source_cells(src_cells, trg_cell, n_syns):
               delay=2.0,
               dynamics_params='instanteneousExc.json')
 
-lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_inh'),
+lgn.add_edges(source=lgn.nodes(), target=net.nodes(model_name='LIF_inh'),
               iterator='all_to_one',
               connection_rule=select_source_cells,
               connection_params={'n_syns': 10},
@@ -127,16 +127,16 @@ def select_source_cells(src_cells, trg_cell, n_syns):
 
 
 tw = NetworkBuilder("tw")
-tw.add_nodes(N=30, pop_name='TW', ei='e', location='TW', model_type='virtual')
+tw.add_nodes(N=30, model_name='TW', ei='e', location='TW', model_type='virtual')
 
-tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_exc'),
+tw.add_edges(source=tw.nodes(), target=net.nodes(model_name='LIF_exc'),
              connection_rule=5,
              syn_weight=0.01,
              weight_function='wmax',
              delay=2.0,
              dynamics_params='instanteneousExc.json')
 
-tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_inh'),
+tw.add_edges(source=tw.nodes(), target=net.nodes(model_name='LIF_inh'),
              connection_rule=5,
              syn_weight=0.02,
              weight_function='wmax',

examples/300_intfire/network/lgn_node_types.csv

@@ -1,4 +1,4 @@
-node_type_id model_type ei location pop_name
+node_type_id model_type ei location model_name
 100 virtual e LGN tON
 101 virtual e LGN tOFF
 102 virtual e LGN tONOFF

examples/300_intfire/network/lgn_v1_edge_types.csv

@@ -1,3 +1,3 @@
 edge_type_id target_query source_query delay weight_function syn_weight dynamics_params
-100 pop_name=='LIF_exc' * 2.0 wmax 0.0045 instanteneousExc.json
-101 pop_name=='LIF_inh' * 2.0 wmax 0.0015 instanteneousExc.json
+100 model_name=='LIF_exc' * 2.0 wmax 0.0045 instanteneousExc.json
+101 model_name=='LIF_inh' * 2.0 wmax 0.0015 instanteneousExc.json

examples/300_intfire/network/tw_node_types.csv

@@ -1,2 +1,2 @@
-node_type_id model_type ei location pop_name
+node_type_id model_type ei location model_name
 100 virtual e TW TW

examples/300_intfire/network/tw_v1_edge_types.csv

@@ -1,3 +1,3 @@
 edge_type_id target_query source_query delay weight_function syn_weight dynamics_params
-100 pop_name=='LIF_exc' * 2.0 wmax 0.01 instanteneousExc.json
-101 pop_name=='LIF_inh' * 2.0 wmax 0.02 instanteneousExc.json
+100 model_name=='LIF_exc' * 2.0 wmax 0.01 instanteneousExc.json
+101 model_name=='LIF_inh' * 2.0 wmax 0.02 instanteneousExc.json

examples/300_intfire/network/v1_node_types.csv

@@ -1,3 +1,3 @@
-node_type_id ei pop_name location model_template model_type dynamics_params
+node_type_id ei model_name location model_template model_type dynamics_params
 100 e LIF_exc VisL4 nrn:IntFire1 point_process IntFire1_exc_1.json
 101 i LIF_inh VisL4 nrn:IntFire1 point_process IntFire1_inh_1.json

examples/300_intfire/network/v1_v1_edge_types.csv

@@ -1,5 +1,5 @@
 edge_type_id target_query source_query delay weight_function syn_weight dynamics_params
 100 model_type=='point_process'&ei=='i' ei=='i' 2.0 wmax 0.01 instanteneousInh.json
 101 model_type=='point_process'&ei=='e' ei=='i' 2.0 wmax 0.15 instanteneousInh.json
-102 pop_name=='LIF_inh' ei=='e' 2.0 wmax 0.3 instanteneousExc.json
-103 pop_name=='LIF_exc' ei=='e' 2.0 wmax 0.002 instanteneousExc.json
+102 model_name=='LIF_inh' ei=='e' 2.0 wmax 0.3 instanteneousExc.json
+103 model_name=='LIF_exc' ei=='e' 2.0 wmax 0.002 instanteneousExc.json

examples/300_intfire/plot_spikes.py

@@ -1,3 +1,3 @@
 from bmtk.analyzer.visualization.spikes import plot_spikes
 
-plot_spikes('network/v1_nodes.h5', 'network/v1_node_types.csv', 'output/spikes.h5', group_key='pop_name')
+plot_spikes('network/v1_nodes.h5', 'network/v1_node_types.csv', 'output/spikes.h5', group_key='model_name')

examples/300_intfire/simulation_config.json

@@ -1,7 +1,8 @@
 {
   "manifest": {
-    "$OUTPUT_DIR": "./output",
-    "$INPUT_DIR": "./inputs",
+    "$BASE_DIR": ".",
+    "$OUTPUT_DIR": "$BASE_DIR/output",
+    "$INPUT_DIR": "$BASE_DIR/inputs",
     "$COMPONENT_DIR": "../shared_components"
   },
 
@@ -16,7 +17,7 @@
 
   "target_simulator":"NEURON",
 
-  "node_sets_file": "node_sets.json",
+  "node_sets_file": "$BASE_DIR/node_sets.json",
 
   "conditions": {
     "celsius": 34.0,

examples/300_intfire/test_pynn.py

@@ -0,0 +1,7 @@
+from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan
+import pyNN.neuron as sim
+
+simulation_plan = load_sonata_simulation_plan("simulation_config.json")
+simulation_plan.setup(sim)
+net = import_from_sonata("circuit_config.json", sim)
+simulation_plan.execute(net)

examples/300_pointneurons/build_network.py

@@ -0,0 +1,157 @@
+import os
+import numpy as np
+
+from bmtk.builder import NetworkBuilder
+from bmtk.builder.bionet import SWCReader
+from bmtk.utils.io.spike_trains import PoissonSpikesGenerator
+from bmtk.builder.aux.node_params import positions_columinar, xiter_random
+
+build_recurrent_edges = True
+
+print('Building internal network')
+# List of non-virtual cell models
+cell_models = [
+    {
+        'model_name': 'Scnn1a', 'ei': 'e',
+        'model_template': 'nest:iaf_psc_alpha',
+        'dynamics_params': '472363762_point.json'
+    },
+    {
+        'model_name': 'Rorb', 'ei': 'e',
+        'model_template': 'nest:iaf_psc_alpha',
+        'dynamics_params': '473863510_point.json'
+    },
+    {
+        'model_name': 'Nr5a1', 'ei': 'e',
+        'model_template': 'nest:iaf_psc_alpha',
+        'dynamics_params': '473863035_point.json'
+    },
+    {
+        'model_name': 'PV1', 'ei': 'i',
+        'model_template': 'nest:iaf_psc_alpha',
+        'dynamics_params': '472912177_point.json'
+    },
+    {
+        'model_name': 'PV2', 'ei': 'i',
+        'model_template': 'nest:iaf_psc_alpha',
+        'dynamics_params': '473862421_point.json'
+    }
+]
+
+'''
+morphologies = {p['model_name']: SWCReader(os.path.join('../shared_components/morphologies',
+                                                        '{}.swc'.format(p['morphology'])))
+                for p in cell_models}
+def build_edges(src, trg, sections=['basal', 'apical'], dist_range=[50.0, 150.0]):
+    """Function used to randomly assign a synaptic location based on the section (soma, basal, apical) and an
+    arc-length dist_range from the soma. This function should be passed into the network and called during the build
+    process.
+
+    :param src: source cell (dict)
+    :param trg: target cell (dict)
+    :param sections: list of target cell sections to synapse onto
+    :param dist_range: range (distance from soma center) to place
+    :return:
+    """
+    # Get morphology and soma center for the target cell
+    swc_reader = morphologies[trg['model_name']]
+    target_coords = [trg['x'], trg['y'], trg['z']]
+
+    sec_ids, sec_xs = swc_reader.choose_sections(sections, dist_range)  # randomly choose sec_ids
+    coords = swc_reader.get_coord(sec_ids, sec_xs, soma_center=target_coords)  # get coords of sec_ids
+    dist = swc_reader.get_dist(sec_ids)
+    swctype = swc_reader.get_type(sec_ids)
+    return sec_ids, sec_xs, coords[0][0], coords[0][1], coords[0][2], dist[0], swctype[0]
+'''
+
+# Build a network of 300 biophysical cells to simulate
+internal = NetworkBuilder("internal")
+for i, model_props in enumerate(cell_models):
+    n_cells = 80 if model_props['ei'] == 'e' else 30  # 80% excitatory, 20% inhib
+
+    # Randomly get positions uniformly distributed in a column
+    positions = positions_columinar(N=n_cells, center=[0, 10.0, 0], max_radius=50.0, height=200.0)
+
+    internal.add_nodes(N=n_cells,
+                       x=positions[:, 0], y=positions[:, 1], z=positions[:, 2],
+                       rotation_angle_yaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi),  # randomly rotate y axis
+                       model_type='point_process',
+                       **model_props)
+
+
+
+if build_recurrent_edges:
+    def n_connections(src, trg, prob=0.5, min_syns=2, max_syns=7):
+        return 0 if np.random.uniform() > prob else np.random.randint(min_syns, max_syns)
+
+    # exc --> exc connections
+    cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'e'},
+                            connection_rule=lambda s, t: np.random.binomial(1, 0.2),
+                            dynamics_params='ExcToExc.json',
+                            model_template='static_synapse',
+                            # syn_weight=2.5,
+                            delay=2.0)
+    cm.add_properties('syn_weight', rule=2.5, dtypes=np.float)
+
+    # exc --> inh connections
+    cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'i'},
+                            connection_rule=lambda s, t: np.random.binomial(1, 0.5),
+                            dynamics_params='ExcToInh.json',
+                            model_template='static_synapse',
+                            # syn_weight=7.0,
+                            delay=2.0)
+    cm.add_properties('syn_weight', rule=7.0, dtypes=np.float)
+
+    # inh --> exc connections
+    cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'e'},
+                            connection_rule=lambda s, t: np.random.binomial(1, 0.5),
+                            #connection_rule=lambda *_: np.random.randint(0, 4),
+                            dynamics_params='InhToExc.json',
+                            model_template='static_synapse',
+                            # syn_weight=-7.5,
+                            delay=2.0)
+    cm.add_properties('syn_weight', rule=-7.5, dtypes=np.float)
+
+    # inh --> inh connections
+    cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'i'},
+                            connection_rule=lambda s, t: np.random.binomial(1, 0.5),
+                            dynamics_params='InhToInh.json',
+                            model_template='static_synapse',
+                            # syn_weight=-3.0,
+                            delay=2.0)
+    cm.add_properties('syn_weight', rule=-3.0, dtypes=np.float)
+
+
+internal.build()
+
+print('Saving internal')
+internal.save(output_dir='network')
+
+
+print('Building external connections')
+external = NetworkBuilder("external")
+external.add_nodes(N=100, model_type='virtual', ei='e')
+cm = external.add_edges(target=internal.nodes(ei='e'), source=external.nodes(),
+                        connection_rule=lambda *_: np.random.binomial(1, .7),
+                        dynamics_params='ExcToExc.json',
+                        model_template='static_synapse',
+                        # syn_weight=50.0,
+                        delay=2.0)
+cm.add_properties('syn_weight', rule=50.0, dtypes=np.float)
+
+
+cm = external.add_edges(target=internal.nodes(ei='i'), source=external.nodes(),
+                        connection_rule=lambda *_: np.random.binomial(1, .7),
+                        dynamics_params='ExcToInh.json',
+                        model_template='static_synapse',
+                        # syn_weight=75.0,
+                        delay=2.0)
+cm.add_properties('syn_weight', rule=65.0, dtypes=np.float)
+
+
+external.build()
+
+print('Saving external')
+external.save(output_dir='network')
+
+