From e3ae0eb999855a2d62d3e99d87e840fb4a8a2beb Mon Sep 17 00:00:00 2001
From: shimoura <renanshimoura@gmail.com>
Date: Wed, 6 Nov 2024 19:10:41 +0100
Subject: [PATCH] Update humam_tutorial.ipynb to enhance output path handling,
 add network and simulation hash checks, and adjust neuron plotting parameters
 for improved functionality.

---
 humam_tutorial.ipynb | 94 +++++++++++++++++++++++++++-----------------
 1 file changed, 57 insertions(+), 37 deletions(-)

diff --git a/humam_tutorial.ipynb b/humam_tutorial.ipynb
index d5cec11..f5a09de 100644
--- a/humam_tutorial.ipynb
+++ b/humam_tutorial.ipynb
@@ -218,6 +218,7 @@
    "outputs": [],
    "source": [
     "# Get path to the output directory\n",
+    "net_params['outpath'] = os.path.join(os.getcwd(), 'out', 'downscaled')\n",
     "outpath = net_params['outpath']\n",
     "\n",
     "# Get base path\n",
@@ -243,7 +244,7 @@
     "# Set plotting parameters\n",
     "ana_params['plotRasterArea']['low'] = 1600      # Lower bound of the area to plot the raster plot\n",
     "ana_params['plotRasterArea']['high'] = 2000     # Upper bound of the area to plot the raster plot\n",
-    "ana_params['plotRasterArea']['fraction'] = 0.5  # Fraction of neurons to plot in the raster plot"
+    "ana_params['plotRasterArea']['fraction'] = 1.0  # Fraction of neurons to plot in the raster plot"
    ]
   },
   {
@@ -281,27 +282,40 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create NeuroNumbers class\n",
-    "# This class calculates the number of neurons in the network\n",
-    "NN = NeuronNumbers(\n",
-    "    surface_area=net_params['surface_area'],\n",
-    "    **net_params['cytoarchitecture_params']\n",
-    ")\n",
-    "# Create SynapseNumbers class\n",
-    "# This class calculates the number of synapses in the network\n",
-    "SN = SynapseNumbers(\n",
-    "    NN=NN,\n",
-    "    **net_params['predictive_connectomic_params']\n",
-    ")\n",
+    "# Check if the network has already been created\n",
+    "if os.path.exists(os.path.join(outpath, 'network_hash.txt')):\n",
+    "\twith open(os.path.join(outpath, 'network_hash.txt'), 'r') as f:\n",
+    "\t\tnet_hash = f.read()\n",
+    "else:\n",
+    "\t# Create NeuroNumbers class\n",
+    "\t# This class calculates the number of neurons in the network\n",
+    "\tNN = NeuronNumbers(\n",
+    "\t\tsurface_area=net_params['surface_area'],\n",
+    "\t\t**net_params['cytoarchitecture_params']\n",
+    "\t)\n",
+    "\t# Create SynapseNumbers class\n",
+    "\t# This class calculates the number of synapses in the network\n",
+    "\tSN = SynapseNumbers(\n",
+    "\t\tNN=NN,\n",
+    "\t\t**net_params['predictive_connectomic_params']\n",
+    "\t)\n",
     "\n",
-    "# Create Network\n",
-    "# This class prepares the network for simulation, including the initialization of the neurons and synapses\n",
-    "# and the creation of the connectivity matrix. The scaling factors are applied here.\n",
-    "humam = Network(NN, SN, net_params)\n",
-    "# Extract the network dictionary\n",
-    "humam.dump(outpath)\n",
-    "# Get the network hash for further reference\n",
-    "net_hash = humam.getHash()"
+    "\t# Create Network\n",
+    "\t# This class prepares the network for simulation, including the initialization of the neurons and synapses\n",
+    "\t# and the creation of the connectivity matrix. The scaling factors are applied here.\n",
+    "\thumam = Network(NN, SN, net_params)\n",
+    "\t# Extract the network dictionary\n",
+    "\thumam.dump(outpath)\n",
+    "\t# Get the network hash for further reference\n",
+    "\tnet_hash = humam.getHash()\n",
+    "\n",
+    "\t# Save network hash to a file\n",
+    "\twith open(os.path.join(outpath, 'network_hash.txt'), 'w') as f:\n",
+    "\t\tf.write(net_hash)\n",
+    "\n",
+    "# Load the network dictionary\n",
+    "net_folder = os.path.join(outpath, net_hash)\n",
+    "net_dict = networkDictFromDump(net_folder)"
    ]
   },
   {
@@ -328,7 +342,7 @@
    "outputs": [],
    "source": [
     "from figure_theory import meanfield_rate\n",
-    "theo_rates = meanfield_rate(net_hash)"
+    "theo_rates = meanfield_rate(outpath, net_hash)"
    ]
   },
   {
@@ -376,7 +390,8 @@
    "outputs": [],
    "source": [
     "from figure_connectivity_matrices import plot_connectivity_matrices\n",
-    "plot_connectivity_matrices(humam.net, SN, NN)"
+    "# plot_connectivity_matrices(net_dict, SN, NN)\n",
+    "plot_connectivity_matrices(net_dict)"
    ]
   },
   {
@@ -404,22 +419,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Read network dict\n",
-    "net_folder = os.path.join(outpath, net_hash)\n",
-    "net_dict = networkDictFromDump(net_folder)\n",
+    "# Check if the simulation has already run\n",
+    "if os.path.exists(os.path.join(outpath, net_hash, 'sim_hash.txt')):\n",
+    "\twith open(os.path.join(outpath, net_hash, 'sim_hash.txt'), 'r') as f:\n",
+    "\t\tsim_hash = f.read()\n",
+    "else:\n",
+    "\t# Create Simulation class\n",
+    "\tsim = Simulation(sim_params, net_dict)\n",
+    "\t# Extract the simulation dictionary\n",
+    "\tsim.dump(net_folder)\n",
+    "\t# Get the simulation hash\n",
+    "\tsim_hash = sim.getHash()\n",
     "\n",
-    "# Create Simulation class\n",
-    "sim = Simulation(sim_params, net_dict)\n",
-    "# Extract the simulation dictionary\n",
-    "sim.dump(net_folder)\n",
-    "# Get the simulation hash\n",
-    "sim_hash = sim.getHash()\n",
+    "\t# Set output directory according to hashes\n",
+    "\t# and run the simulation\n",
+    "\tdata_path = os.path.join(outpath, net_hash, sim_hash)\n",
+    "\tsim.setup(data_path, num_threads)\n",
+    "\tsim.simulate()\n",
     "\n",
-    "# Set output directory according to hashes\n",
-    "# and run the simulation\n",
-    "data_path = os.path.join(outpath, net_hash, sim_hash)\n",
-    "sim.setup(data_path, num_threads)\n",
-    "sim.simulate()"
+    "\t# Save simulation hash to a file\n",
+    "\twith open(os.path.join(outpath, net_hash, 'sim_hash.txt'), 'w') as f:\n",
+    "\t\tf.write(sim_hash)"
    ]
   },
   {