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October 27, 2022 06:19
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import numpy as np | |
import pandas as pd | |
import json | |
import pickle | |
from pathlib import Path | |
from sonata.circuit import File | |
from sonata.reports.spike_trains import SpikeTrains | |
import pygenn | |
import matplotlib.pyplot as plt | |
from GLIF_models import GLIF1 | |
from tqdm import tqdm | |
import time | |
from utilities import add_model_name_to_df | |
from utilities import spikes_list_to_start_end_times | |
from helper import ( | |
optimize_nodes_df_memory, | |
optimize_edges_df_memory, | |
get_dynamics_params, | |
load_df, | |
save_df, | |
psc_Alpha, | |
) | |
v1_net = File( | |
data_files=[ | |
"./../network/v1_nodes.h5", | |
"./../network/v1_v1_edges.h5", | |
], | |
data_type_files=[ | |
"./../network/v1_node_types.csv", | |
"./../network/v1_v1_edge_types.csv", | |
], | |
) | |
lgn_net = File( | |
data_files=[ | |
"./../network/lgn_nodes.h5", | |
"./../network/lgn_v1_edges.h5", | |
], | |
data_type_files=[ | |
"./../network/lgn_node_types.csv", | |
"./../network/lgn_v1_edge_types.csv", | |
], | |
) | |
bkg_net = File( | |
data_files=[ | |
"./../network/bkg_nodes.h5", | |
"./../network/bkg_v1_edges.h5", | |
], | |
data_type_files=[ | |
"./../network/bkg_node_types.csv", | |
"./../network/bkg_v1_edge_types.csv", | |
], | |
) | |
DYNAMICS_BASE_DIR = Path("./../models/cell_models/nest_2.14_models") | |
SIM_CONFIG_PATH = Path("./../config.json") | |
LGN_V1_EDGE_CSV = Path("./../network/lgn_v1_edge_types.csv") | |
V1_EDGE_CSV = Path("./../network/v1_v1_edge_types.csv") | |
LGN_SPIKES_PATH = Path( | |
"./../inputs/full3_GScorrected_PScorrected_3.0sec_SF0.04_TF2.0_ori270.0_c100.0_gs0.5_spikes.trial_0.h5" | |
) | |
LGN_NODE_DIR = Path("./../network/lgn_node_types.csv") | |
V1_NODE_CSV = Path("./../network/v1_node_types.csv") | |
V1_ID_CONVERSION_FILENAME = Path(".", "pkl_data", "v1_edges_df.pkl") | |
LGN_ID_CONVERSION_FILENAME = Path(".", "pkl_data", "lgn_edges_df.pkl") | |
BKG_V1_EDGE_CSV = Path("./../network/bkg_v1_edge_types.csv") | |
BKG_ID_CONVERSION_FILENAME = Path(".", "pkl_data", "bkg_edges_df.pkl") | |
NUM_RECORDING_TIMESTEPS = 440 | |
#num_steps = 220 | |
ALPHA_TAU = 5.5 # All nodes have alpha-shaped postsynaptic current with tau=5.5 | |
# Parameters/variables used in GLIF3 neuron class | |
param_names = [x for x in GLIF1.get_param_names()] | |
var_names = [x.name for x in GLIF1.get_vars()] | |
### Create base model ### | |
with open(SIM_CONFIG_PATH) as f: | |
sim_config = json.load(f) | |
# model = pygenn.genn_model.GeNNModel(backend="CUDA", preference_kwargs=["generateEmptyStatePushPull=False", "generateExtraGlobalParamPull=False"]) | |
model = pygenn.genn_model.GeNNModel( | |
backend="SingleThreadedCPU", generateEmptyStatePushPull=False, generateExtraGlobalParamPull=False | |
) | |
DT = sim_config["run"]["dt"] | |
model.dT = DT | |
model._model.set_merge_postsynaptic_models(True) | |
model._model.set_default_narrow_sparse_ind_enabled(True) | |
# model.default_var_location = pygenn.genn_model.genn_wrapper.VarLocation_DEVICE | |
# model.default_sparse_connectivity_location = pygenn.genn_model.genn_wrapper.VarLocation_DEVICE | |
### Add Neuron Populations ### | |
pop_dict = {} | |
### V1 | |
# Add data to dataframe | |
v1_nodes_df_path = Path("./pkl_data/v1_nodes_df.pkl") | |
if v1_nodes_df_path.exists(): | |
v1_nodes_df = load_df(v1_nodes_df_path) | |
else: | |
# Construct df from Sonata format | |
v1_nodes = v1_net.nodes["v1"] | |
v1_nodes_df = v1_nodes.to_dataframe() | |
v1_nodes_df = optimize_nodes_df_memory(v1_nodes_df) # reduce memory; makes indexing faster | |
# Add columns of new data | |
v1_nodes_df["GeNN_node_id"] = 0 # Preallocate as int | |
v1_nodes_df["refractory_countdown"] = 0 # Preallocate as int | |
v1_nodes_df["spike_cut_length"] = 0 # Preallocate as int | |
for node_type_id in v1_nodes_df["node_type_id"].unique(): | |
# Dynamics params | |
dynamics_file = v1_nodes_df.loc[v1_nodes_df["node_type_id"] == node_type_id]["dynamics_params"].iloc[0] | |
dynamics_file = dynamics_file.replace("config", "psc") | |
dynamics_path = Path(DYNAMICS_BASE_DIR, dynamics_file) | |
dynamics_params_correct_units = get_dynamics_params(dynamics_path, DT) | |
for pv_name in param_names + var_names: | |
v1_nodes_df.loc[v1_nodes_df["node_type_id"] == node_type_id, pv_name] = dynamics_params_correct_units[ | |
pv_name | |
] | |
# Model name = pop_name + node_type_id | |
pop_name = v1_nodes_df[v1_nodes_df["node_type_id"] == node_type_id]["pop_name"].iloc[0] | |
model_name = "{}_{}".format(pop_name, node_type_id) | |
v1_nodes_df.loc[v1_nodes_df["node_type_id"] == node_type_id, "model_name"] = model_name | |
# GeNN ID; counts from 0 for each model_name | |
num_nodes = v1_nodes_df.loc[v1_nodes_df["node_type_id"] == node_type_id].shape[0] | |
v1_nodes_df.loc[v1_nodes_df["node_type_id"] == node_type_id, "GeNN_node_id"] = np.arange(num_nodes).astype( | |
"int" | |
) | |
# Reduce memory by dropping columns / downcasting variable types | |
v1_nodes_df = optimize_nodes_df_memory(v1_nodes_df) | |
# Save as pkl so can be reloaded faster | |
save_df(v1_nodes_df, v1_nodes_df_path) | |
# Add V1 nodes as neuron populations (111 node types / model_names) | |
for model_name in v1_nodes_df["model_name"].unique(): | |
# Get data from nodes with this model_name | |
subset_df = v1_nodes_df[v1_nodes_df["model_name"] == model_name] | |
#print(model_name) | |
#subset_df = subset_df.sample(frac=0.333) | |
params = {k: subset_df[k].to_list()[0] for k in param_names} | |
init = {k: subset_df[k].to_list()[0] for k in var_names} | |
num_neurons = len(subset_df) | |
pop_dict[model_name] = model.add_neuron_population( | |
pop_name=model_name, | |
num_neurons=num_neurons, | |
neuron=GLIF1, | |
param_space=params, | |
var_space=init, | |
) | |
# Enable spike recording | |
pop_dict[model_name].spike_recording_enabled = True | |
print("Added population: {}.".format(model_name)) | |
### Add synapses ### | |
syn_dict = {} | |
# V1 to V1 synapses | |
v1_edges_df_path = Path("./pkl_data/v1_edges_df.pkl") | |
if v1_edges_df_path.exists(): | |
v1_edges_df = load_df(v1_edges_df_path) | |
else: | |
# Load as dataframe | |
v1_edges = v1_net.edges["v1_to_v1"] | |
v1_edges_df = v1_edges.groups[0].to_dataframe() | |
edges_df = v1_edges_df | |
edges_df = optimize_edges_df_memory(edges_df) | |
# Add ID's for GeNN (0-num_neurons in each population) | |
edges_df["source_GeNN_id"] = v1_nodes_df["GeNN_node_id"].iloc[edges_df["source_node_id"]].astype("int32").tolist() | |
edges_df["target_GeNN_id"] = ( | |
v1_nodes_df["GeNN_node_id"].iloc[v1_edges_df["target_node_id"]].astype("int32").tolist() | |
) | |
edges_df["source_model_name"] = v1_nodes_df["model_name"].iloc[edges_df["source_node_id"]].tolist() | |
edges_df["target_model_name"] = v1_nodes_df["model_name"].iloc[edges_df["target_node_id"]].tolist() | |
# Add product of nsyns and syn_weight | |
edges_df["nsyns_x_syn_weight"] = edges_df["nsyns"] * edges_df["syn_weight"] | |
# Reduce memory | |
edges_df = optimize_edges_df_memory(edges_df) | |
# Save as pickle for faster loading | |
save_df(edges_df, v1_edges_df_path) | |
# List of all population pairs | |
source_target_pairs = ( | |
v1_edges_df.drop_duplicates(subset=["source_model_name", "target_model_name"]) | |
.loc[:, ("source_model_name", "target_model_name")] | |
.to_numpy() | |
) | |
# Iterate through population pairs | |
num_pairs = len(source_target_pairs) | |
count = 0 | |
for i, (pop1, pop2) in enumerate(source_target_pairs): | |
# Progress bar | |
if i % 25 == 0: | |
print( | |
"Adding synapse groups... {}% ".format(np.round(100 * i / num_pairs, 2)), | |
end="\n", | |
) | |
# Load source_target df if previously saved | |
synapse_group_name = pop1 + "_to_" + pop2 | |
synapse_group_path = Path("./pkl_data", "source_target_df", synapse_group_name, ".pkl") | |
if synapse_group_path.exists(): | |
source_target = load_df(synapse_group_path) | |
else: | |
source_target = v1_edges_df[ | |
(v1_edges_df["source_model_name"] == pop1) & (v1_edges_df["target_model_name"] == pop2) | |
] | |
save_df(source_target, synapse_group_path) | |
# GeNN weight = product of syn_weight and number of synapses | |
weight = (source_target["nsyns_x_syn_weight"] / 1e3).to_list() # pA -> nA | |
# Delay | |
delay_ms = source_target["delay"] | |
delay_steps = round((delay_ms / DT)).astype("int").to_list() | |
assert len(delay_ms.unique()) == 1 | |
delay_steps = delay_steps[0] | |
# Get list of source and target node ids (GeNN numbering) | |
s_list = source_target[source_target["source_model_name"] == pop1]["source_GeNN_id"].tolist() | |
t_list = source_target[source_target["target_model_name"] == pop2]["target_GeNN_id"].tolist() | |
# Weight update model | |
s_ini = {"g": weight, "d": delay_steps} # , "d": delay_steps} | |
# Postsynaptic current model | |
psc_Alpha_params = {"tau": ALPHA_TAU} | |
psc_Alpha_init = {"x": 0.0} | |
# Add synapse population | |
syn_dict[synapse_group_name] = model.add_synapse_population( | |
pop_name=synapse_group_name, | |
matrix_type="SPARSE_INDIVIDUALG", | |
delay_steps=delay_steps, | |
source=pop1, | |
target=pop2, | |
w_update_model="StaticPulseDendriticDelay", | |
wu_param_space={}, | |
wu_var_space=s_ini, | |
wu_pre_var_space={}, | |
wu_post_var_space={}, | |
postsyn_model=psc_Alpha, | |
ps_param_space=psc_Alpha_params, | |
ps_var_space=psc_Alpha_init, | |
) | |
# syn_dict[synapse_group_name].pop.set_max_dendritic_delay_timesteps( | |
# max_dendritic_delay_slots | |
# ) | |
syn_dict[synapse_group_name].set_sparse_connections(np.array(s_list), np.array(t_list)) | |
print("Added all {} synapse groups.".format(i)) | |
lgn_node_df_path = Path("./pkl_data/lgn_node_df.pkl") | |
if lgn_node_df_path.exists(): | |
with open(lgn_node_df_path, "rb") as f: | |
lgn_node_df = pickle.load(f) | |
else: | |
lgn_nodes = lgn_net.nodes["lgn"] | |
lgn_node_df = lgn_nodes.to_dataframe() | |
# Add model_name column to account for duplicate pop_names (which have different dynamics_parameters) | |
lgn_node_df = add_model_name_to_df(lgn_node_df) | |
lgn_model_names = lgn_node_df["model_name"].unique() | |
with open(lgn_node_df_path, "wb") as f: | |
pickle.dump(lgn_node_df, f) | |
lgn_model_names = lgn_node_df["model_name"].unique() | |
spikes_path = Path("./pkl_data/spikes.pkl") | |
if spikes_path.exists(): | |
with open(spikes_path, "rb") as f: | |
spikes = pickle.load(f) | |
else: | |
spikes_from_sonata = SpikeTrains.from_sonata(LGN_SPIKES_PATH) | |
spikes_df = spikes_from_sonata.to_dataframe() | |
lgn_spiking_nodes = spikes_df["node_ids"].unique().tolist() | |
spikes_list = [] | |
for n in lgn_spiking_nodes: | |
spikes_list.append( | |
spikes_df[spikes_df["node_ids"] == n]["timestamps"].to_list() | |
) | |
start_spike, end_spike, spike_times = spikes_list_to_start_end_times( | |
spikes_list | |
) # Convert to GeNN format | |
# Group into one list | |
spikes = [start_spike, end_spike, spike_times] | |
# Save as pickle | |
if spikes_path.parent.exists() == False: | |
Path.mkdir(spikes_path.parent, parents=True) | |
with open(spikes_path, "wb") as f: | |
pickle.dump(spikes, f) | |
(start_spike, end_spike, spike_times) = spikes | |
# Add population | |
for i, lgn_model_name in enumerate(lgn_model_names): | |
num_neurons = lgn_node_df[lgn_node_df["model_name"] == lgn_model_name].shape[0] | |
pop_dict[lgn_model_name] = model.add_neuron_population( | |
lgn_model_name, | |
num_neurons, | |
"SpikeSourceArray", | |
{}, | |
{"startSpike": start_spike, "endSpike": end_spike}, | |
) | |
pop_dict[lgn_model_name].set_extra_global_param("spikeTimes", spike_times) | |
print("Added {}".format(lgn_model_name)) | |
#import pdb | |
#pdb.set_trace() | |
### Run simulation | |
start = time.time() | |
model.build(force_rebuild=True) | |
#import pickle | |
#with open("model.p","wb") as f: | |
# pickle.dump(f,model) | |
stop = time.time() | |
print("Duration = {}s".format(stop - start)) | |
start = time.time() | |
NUM_RECORDING_TIMESTEPS = 10000 | |
num_steps = 100000 # 3000000 | |
model.load(num_recording_timesteps=NUM_RECORDING_TIMESTEPS) # TODO: How big to calculate for GPU size? | |
for i in tqdm(range(NUM_RECORDING_TIMESTEPS)): | |
model.step_time() | |
stop = time.time() | |
print("Duration = {}s".format(stop - start)) | |
print([v.spike_event_times for v in pop_dict.values()]) |
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