diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 43be2691814d4327e2def8b041b6148af1c0f043..2476922b4ce9989334b5aeed89ec8bdf393e2fba 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -534,21 +534,9 @@
"id": "bea30fc8",
"metadata": {},
"outputs": [
- {
- "ename": "TypeError",
- "evalue": "'Simulation' object is not subscriptable",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [14], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_instantaneous_and_mean_firing_rates\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_instan_mean_firing_rate\n\u001b[0;32m----> 2\u001b[0m plot_instan_mean_firing_rate(M)\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_instantaneous_and_mean_firing_rates.py:17\u001b[0m, in \u001b[0;36mplot_instan_mean_firing_rate\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m 15\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime (ms)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 16\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfiring rate (spikes / s)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlim(\u001b[38;5;241m0\u001b[39m, \u001b[43mM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimulation\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_sim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 18\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m 19\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend()\n",
- "\u001b[0;31mTypeError\u001b[0m: 'Simulation' object is not subscriptable"
- ]
- },
{
"data": {
- "image/png": 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fADXyl97q3lxEJH01MmGIiEj6lDBERCQSJQwREYlECUNERCJRwhARkUhqdMJQ7+YiItHVyIRhSX8eIiIiydTIhCEiIulTwhARkUiUMEREJJKUfUmZWT1gOHAc0BbYBnwOvOLuX2Q+PBERyRVJE4aZ3QicDrxL8KS8lUA9oDswNkwmV7v7Z5kPU0REqluqM4xP3P3GJOPuMLPWQMeqDymbdF+tiEhUSROGu79SfpiZ5QGN3H2ju69k19Py9irqrVZEJH0VXvQ2s8fNrImZNSR4uNFsM7sm86GJiEguiXKXVC933wicBbxK0Ax1QSaDEhGR3BMlYdQ2s9oECeNFd9+JGv9FRGqcKAnjH0AB0BCYaGadgI2ZDEpERHJPhQnD3f/m7u3cfai7O7AIODHzoYmISC5J+cO9RMKkUZSBWEREJIfV6K5B1L25iEh0SROGmbXJZiDZpN9hiIikL1WT1INm1pyga5DxwPvurqYoEZEaKtUvvYeE/UUNBM4G/mRmiwiSx3h3X5SdEEVEJBekvOjt7tsJEwSAmXUBhgB3m9l+7t4/8yGKiEguSOsuKXdfANwD3GNmdTITkoiI5KJK3yXl7juqMhAREcltNfu22uoOQERkL5JWwjCz5mbWN1PBZIuh+2pFRNIVpXvzd8PuzVsA04F/mdkdmQ9NRERySZQzjKZh9+YjgH+5++HAoMyGJSIiuSZKwqgV/ur7XGBc1AWb2YNmttLMPo8bdqOZLTWzaeHf0Lhx15rZXDObbWanpfUpREQk46IkjJuA/wLz3P0TM+sKzIkw30PA4ATD/+Luh4R/rwKYWS9gJNA7nOceM8uP8gFERCQ7Kvwdhrs/AzwT934+8K0I8000s84R4zgTeNLdC4EFZjYX6A98GHF+ERHJsCgXvbub2VuxpiUz62tm1+/BOn9sZp+FTVbNw2HtgMVx0ywJh2WUeqsVEYkuSpPUP4FrgZ0A7v4ZQfNRZdwLdAMOAZYDfw6HJ7rPNeHu3MwuM7PJZjZ51apVlQpCvdWKiKQvSsJo4O6Tyg2rVK+17r7C3YvdvYQgEcX6oloCdIibtD2wLMky7nf3fu7er1WrVpUJQ0REKiFKwlhtZt0Ij/jN7ByCs4O0lXvGxtlA7A6ql4CRZlY37ODwAKB8khIRkWoUpfPBq4D7gR5mthRYAJxf0Uxm9gRB1+gtzWwJ8FtgoJkdQpB8CoAfAbj7F2b2NPAlwdnLVe5enO6HERGRzImSMJq7+yAzawjkufsmMzsdWJhqJnc/L8HgB1JMfwtwS4R4RESkGkS66G1mfdx9S5gsRgJ7cpeUiIjshaKcYZwD/MfMzgeOBb4PnJrRqEREJOdE+eHe/PCs4gWC30qc6u7bMh1YNrg6OBcRiSxpwjCzGZT9LUQLIB/42Mxw9722m3P9DENEJH2pzjCGZy0KERHJeakSxjp33xg+B0NERGq4VAnjcYKzjCkETVPxLTkOdM1gXCIikmOSJgx3Hx7+75K9cEREJFdFua0WMxtBcEutA++5+wuZDEpERHJPlO7N7wEuB2YQ9P10uZn9PdOBZYO6NxcRiS7KGcYJwEHuHut88GGC5LHXUvfmIiLpi9I1yGygY9z7DsBnmQlHRERyVZQzjH2AmWYW6278COBDM3sJwN3PyFRwIiKSO6IkjDEZj0JERHJelIQxGdjm7iVm1h3oAbzm7jszG5qIiOSSKNcwJgL1zKwd8BZwMfBQJoMSEZHcEyVhmLtvBUYAd7n72UDvzIYlIiK5JlLCMLOjCB7L+ko4LD9zIWWPfoYhIhJdlITxM+Ba4Pnw2dtdgXcyG1am6YcYIiLpivIApYkE1zFi7+cDP81kUCIiknuSnmGY2f1m1ifJuIZmdkn42FYREakBUp1h3APcECaNz4FVQD3gAKAJ8CDwWMYjFBGRnJCqe/NpwLlm1gjoB7QBtgEz3X12dsITEZFcEeUaxmbg3cyHIiIiuSzKXVLfWK7+zUVEIquRCUPdm4uIpC9ywjCzhpkMREREcluUJ+4dbWZfAjPD9weHT+ETEZEaJMoZxl+A04A1AO4+HTg+k0GJiEjuidQk5e6Lyw0qzkAsIiKSw6I8D2OxmR0NuJnVIegWZGZmwxIRkVwT5QzjcuAqoB2wBDgEuDKDMYmISA6KcoZxoLuX6TPKzI4BPshMSJmnu2pFRNIX5QzjrojDyjCzB81spZl9HjeshZm9YWZzwv/N48Zda2ZzzWy2mZ0WLXwREcmWpGcY4UOTjgZamdkv40Y1IdoDlB4C7gb+HTdsNPCWu481s9Hh+1Fm1gsYSfAkv7bAm2bW3d11cV1EJEekOsOoAzQiSCqN4/42AudUtODwORpryw0+E3g4fP0wcFbc8CfdvdDdFwBzgf7RPoKIiGRDqt5qJwATzOwhd19YRevb192Xh8tfbmatw+HtgI/iplsSDhMRkRwR5aL3VjP7I0FzUb3YQHc/qQrjSHQdOmHPgGZ2GXAZQMeOHaswBBERSSXKRe/HgFlAF+B3QAHwSSXXt8LM2gCE/1eGw5cAHeKmaw8sS7QAd7/f3fu5e79WrVpVMgwREUlXlISxj7s/AOx09wnufgkwoJLrewm4MHx9IfBi3PCRZlbXzLoQPNVvUiXXEZl6NxcRiS5Kk9TO8P9yMxtGcOTfvqKZzOwJYCDQ0syWAL8FxgJPm9mlwCLg2wDu/oWZPQ18CRQBV2XyDilT/+YiImmLkjBuNrOmwNUEv79oAvyiopnc/bwko05OMv0twC0R4hERkWqQMmGYWT5wgLuPAzYAJ2YlKhERyTkpr2GEzUJnZCkWERHJYVGapP5nZncDTwFbYgPdfWrGohIRkZwTJWEcHf6/KW6YA1X5OwwREclxFSYMd//GXrfwxL8NFBGRBCI9ce+bRjfVioikr0YmDBERSZ8ShoiIRFLhNQwzG5Fg8AZghruvTDBORES+gaLcJXUpcBTwTvh+IEFX5N3N7CZ3fyRDsYmISA6JkjBKgJ7uvgLAzPYF7gWOBCYCShgiIjVAlGsYnWPJIrQS6O7ua9nVMeFeSb3ViohEF+UM4z0zGwc8E77/FjDRzBoC6zMVWCaps1oRkfRFSRhXESSJYwh+wvBv4Fl3d9QZoYhIjRHll94O/Cf8ExGRGqrCaxhmNsLM5pjZBjPbaGabzGxjNoITEZHcEaVJ6nbgdHefmelgREQkd0W5S2qFkoWIiEQ5w5hsZk8BLwCFsYHu/lymghIRkdwTJWE0AbYCp8YNc2CvTxj6HYaISHRR7pK6OBuBZJOpg3MRkbQlTRhm9mt3v93M7oLdnzTk7j/NaGQiIpJTUp1hxC50T85GICIiktuSJgx3f9nM8oGD3P2aLMYkIiI5KOVtte5eDByepVhERCSHRblL6lMze4mg88EtsYG6rVZEpGaJkjBaAGuAk+KGfTNuq63uAERE9iI187Za3VUrIpI23VYrIiKRpDrD+DL8r9tqRUQkZcL4DjAOaObuf81SPCIikqNS3VZ7uJl1Ai4xs+Zm1iL+L1sBiohIbkh1hnEfMB7oCkyBMh0weThcRERqiFS/9P4b8Dczu9fdr6jKlZpZAbAJKAaK3L1feNbyFNAZKADOdfd1Vbne8lzd1YqIRFbhA5SqOlnEOdHdD3H3fuH70cBb7n4A8Fb4XkREckSUJ+5ly5nAw+Hrh4Gzqi8UEREpr7oShgOvm9kUM7ssHLavuy8HCP+3TjSjmV1mZpPNbPKqVauyFK6IiETpGiQTjnH3ZWbWGnjDzGZFndHd7wfuB+jXr58uQoiIZEm1nGG4+7Lw/0rgeaA/sMLM2gCE/1dWR2wiIpJY1hOGmTU0s8ax1wTPCv8ceAm4MJzsQuDFbMcmIiLJVUeT1L7A8xb0AFgLeNzdx5vZJ8DTZnYpsAj4djXEJiIiSWQ9Ybj7fODgBMPXACdnNZZsrkxEZC+XS7fVZo26NxcRSV+NTBgiIpI+JQwREYlECUNERCJRwhARkUiUMEREJJKanTB0X62ISGQ1MmHYN/y+2kkL1nLuPz5kZ3FJdYciIt8gNTJhfNNd/cw0Ji1Yy7L12yq9jBP++A4X/WtSFUYlInu76uqtVrLAqPyZ1MI1W1m4ZmsVRlOzLd+wjQ3bdtJjvybVHYpIpekMI4d8uWwjnUe/wszlG6s7lFLrt+6o7hAy7tGPFrJ8Q+XPxqI46ra3GXzne2wpLKqS5fW7+U1+++LnVbIsyYwN23ZSXPLNulCqhJHEzOUbWbdlz3aWS9Zt5YpHp7B9Z3Gk6V+dsRyAN75csdu44hLnyUmLIu3Ykj2qfHNhEdMXr48US8whN73B3JWb05qnOtz08pe8N2cVS9Zt5fJHopf5qk2FXP/C51z8r0+STvPponWc9Kd3q+Sa0NjXIj/6JaXVmwt5+MOFVbIsqXqbC4s4+Hevc+urM6tsmc9NXcK9786rsuVVhhJGEkP++h5n/v2DPVrGzeNm8trnX/P2rODRHu6e8gizONzT5+ft3pR0/8T5jH5uBkfd9jZvfLmCzqNfYeP2naXjz7j7fa597jMAtu8MdmxfLNtQZhk/fnwqZ/79AzYniOGZyYtZsXF7wrimhUlm9eZCnpy0iMKiYh54f0GVHj2VlDhbd5SNa0thEU9MWsSazYUsXruVF6ctZeuOIhas3sLL05eVmfbBDxZwwQOT+P24Lxn/xde8O3vX41QKi4rZUZR4Z18SlvmaJAcHF/9rEmff8z/mr97CZ0vWs27LDh79aCEeznftczPoPPqV3co6pvx3/kjcvBV59KOFLI1wHSr2fWwpLKKogqQ2d+Vmxn++vPT9rK+Ds9rPlqyvcD1FxSWlifjLZRt5e9YKthQWlX6eWBwVxVAZ8WX43pxVFR74xLaRDdt2ppwuXrL4i0ucf0yYx8btOxl+13v85vkZkeN9efqyMmUUJYYdRSVs3VFESbnt65dPT+cP46vmgKOyavQ1DK/gvtpFa3dvw5+2eD2btu+kZaO69GyTuj06djNWrK48PXkxo56dwdtXn0CXlg35w/jZDDloPw7u0AyAGUs2lJkPgtPa9+asKlNRYkctr81YzoCu+9Cwbi0+W7KBz5Zs4LYRfVm9uRCAKx6bSsHYYQAsXLOFd2cHj7TduqOIRnVrsX1nMfe+O4+Vm7bzxKTF9GzThNd+dtxuZxS/emY6jevV4oH3FjCpYC2jnws2mAZ18jmvf8cy0365bCNdWjakfp38lGWzdP028gzaNK0PwF/fmsNf35rD9DGn0rRBbeav2sxJf54AwEvTljF7xSbWltupfzh/Daf3bcuni9eVDpuyMHidn7frWKj3mP/SsG4tpv/21N3iGHHP/4DEZ2Wzvt7IO7N3PQb4pnEzadmwDm/NWsn1L3zOpN+czBOTFgHwwqdL6d22KQBfb9hOUUkJ7Zs34OH/FXDjy1+WWe5f3viKwqIShvdtS618Y5+GdXh71kpGxpXlknVbuf6FoMlp1u8HM2/VZqYuXMcZB7ejaYPapdMVFhVz1WOf8ubMFfx+3Jcc3W0fHv/hgNLxC9dsoWHdWrRsVJeSEmfQHUGZvnjVMbRqXJfBd74HwB1vfMXt5/Tl7ZkrqV8nHzNjS2ER23cW065ZfU7q0ZpeY/7LjuISCsYOY+jf3itdx+3f6su5R3RgzAtf8NTkxazcuJ1Lj+3Csg3bOSSs2/GmLlrH+M+/5oyD23JQu6a7F3yckhLnwQ8WcPMrM3ngwn6c3HNfLngguBlj1OAeXDGwGyUlzvQl6zm0Y3MAClZv4ZZXgjKfu3ITjevVZr+m9WhSr3bS9QD8c+J8/vT6V8xcvpFhfdrQoUV99m/dmJemL+W212ZxW3h2+PnSjVxxQjc6tGhQZv7tO4vpccN4Hr6kPz33awzAyk2F9P7tfxkzvBeXHNsFd2fqovUc1rFZwrs1D7x+PHVr5VFYVMKPT9yfX512IMUlzg1xzY/uztjXZjG8b1v6tE9dflXNoma+XNSvXz+fPHly2vMtW7+No8e+zR++1YfvHBFspHNXbmLR2q0c1bUltfKNA657DYCCscNYuXE7Hy9YS5eWDRl+1/uly5l5U7AhfzhvDX9+YzbH7t+SN2fu/qDAu797KMP7tuUHD0/mzZlBc9NtI/pwbbjjvW1EH77TrwNdf/MqAL8Y1J3nPl3CBQM68dQni5lTQZNQnkHsYOTtq08o3dEC/OSk/Znw1Sry84xPF60vHf7clUeX7izjFYwdRufRr1RUhAB0bNGARWu3Mrj3ftx3weHMXbmJQXdMBOC+7x3O4IP2K522uMRxd2rlBzvy+HXM+v1getwwHoCLj+nMmOG96HLtq5FiqMj8W4eWluuC24Zywh/f5btHduTyE7rx4bw1nPfPj0qn/d0Zvfn+UZ1wh50lJRx4/fjI66lfO58PRp/EYb9/o3RYwdhhnHvfh0wqWFtm2oPaNeHzpYmvU9393UNp3qAOlzz0CYXhWdHpB7fd7Ywqld5tm/DKT48DdpXzX0cews+enFZmuv6dW+wWWzJXDuzGPWFzyHVDe3JLuaaWRnVrJTxzHfeTY0uTws7iEt6ZtZLLHplSOv7RS4+k2J0LHwySQIcW9enXqQXn9e9I/y4tytSTS47pwpjTe5UZNn3MqTz68UL++N/ZPP2jo1i6fiu/eGp66fhY2fVs04R7zz+Mf0ycz8XHdOaA1o0oKnHyzErr5QUPfMx7c1aXif/5K4/m7ATbCcC5/drzy1MOZL+m9SgucR7+XwE3jQsS1flHduSxjxeVmX7/1o3KHIy9dfUJnBxuq7N+P5i3Z63kysemlo5v16w+3zmiA3e88VWZ5dz3vcO5/NEp1MnP46tbhiSMrSJmNsXd+6U9nxJGkDCS7SQLxg5j0B0TmLtyMyf1aF3avASpK1O8c/u1Z/3Wnbye4NpEIi0b1S09S6gqB+7bmNkrNlU43YhD2/Hcp0vTXn7B2GH87MlPeXHarh3bRUd35obhvVi3dQen3/U+yzdsZ9JvTqbEYcBtbyVd1s8HHcCdb85JO4ZEvn14e56ZsgQou9O75eyDuO75qr1ofN/3DuPyR6dWPGEW3HXeoXRs0WCPm1X31NgRfXhlxnLuOu9QDrnpjYpniFP+4KVR3Vr8+dyD+VFcwum8TwMKquBuvjHDe5Xu7NPRaZ8GTLjmxMgHWVUt1oKQLiWMNKSTMP787YO5+pnpCcfJLsmOgp+8bAAj7/8owRzfPE3q1WLj9qq5C0qCJs+tO6LdvFBTZTth6KJ3BZQsoknWZFJTkgWgZFHFlCxyjxIGwcVNERFJTQkDSu8UERGR5GrkbbV5m7/myvwX6Dn3I3xbU67Mn13dIYmIpGWetwMqdw2jsmpkwsjfvJxf134aZgOz4depb88WEck5LxcPqHiiKlYjE0bRfgdzwPZ/c8tZB7GzuGS3H1aJiOS6EozTs7zOGpkwsDx2UouSvNpc98IMamoxiIikQxe9RUQkEiWMb6g7v3NIdYcge7k+FfTzJDWPEkYOGTW4R5Ut66xD2/HzQQdUON2wPm149oqjefGqYxKO/+rmITSul5kmu2ljTqnUfFed2K2KI0ntxtN7lXn/0bUnV2o5824dytWndC99P6xPm7Tmf+nHx/DsFUdFnr5t03qlr7+6eQhzbhnCO78amHT61o3rMv/WoQC0alyXhy/pn1Z8UV07pOrqeTIn92id8XV02qdBxROV870BHbltRJ+k4ydfP6jCZcy/dSj/uugIJl5zYtrr31NKGGno2rIh955/GCOP6MA1px3Ivy46gtN671s6/n+jTwIos4Md3nfXTmHkER24/Zy+pe+njTmFgrHDGDO8Fw9dfARXDOzGpcd24ZendGfK9YMoGDus9Kf/HVrUjxxnrEKec3j70mENkvQee+fIQzi8U/PSHnMBpt4Q7Mi/f1Qn6tTK49FLjywd94NjuzB6SI+kXRIcd0BLAG49u+xGMeGagWXe99ivMc0a1KFg7DBm/X4wQJmyAfjpSfvTJtzpXXViNy4Y0ImPf3Myvzr1QPZv3QiAHx3fNekOPJaQvntkR5rWT30rXOvGdctsgF/87jTeH3Uik68fxEXHdOGlHx9D77ZB78R1a+3abO4671BuGN5rt+XFjDyiAzee3otfDOpOfp7xk5MPKP1e/37+YaXTXTCgEwBXDOzG7ef05e7vHlo67sxD2gLQt30zDu/UonRagKb1a/NEXO+0sfIHeOQHu763OrXyqJ2fR61yXee/9+sTS88kbhvRh7w8Y8I1A3njF8fTomEdXv3pcbRsVIeebZow4ZqBZb73ri0bsuC2oVw5sBu3jejDX0cekrQcYgrGDuNHJ+xK+PNvHcpFR3fmuqE9mfSbk7l+WE+g7M44WZIsGDuMi47unHBcfE+wfzynLxcd3ZnLju/KuJ8cy9M/Kru87w3oWH723dZTMHYYH4Tb9xGdm1MwdhgTrjmRD0afxLn92ifdvgAGdG3BYz84kuO7t+LG03tzXv+OpXUegs5GY5o3qEO/TkGvu9cN7cnUG05h5BEd+NHxXXnysgFMG3MKeXnGiT1a07ESCWtP1ci+pJZv2MZRt71dpsfYeLef05ehfdpQO9845Y6JLFq7lRtP78WFR3dO2CXxA+8v4N535zL5+lN4dcZy+rRrynG3vwOU7T02trH1GjOerTuK+eJ3p9GwbsVH7zuLSzDgvTmradusPpsLd/Ktez/kioHd+OUp3TGgVn5eaX9YL151TGkCiA2bd+tQXv/ia34/7ktO7NGa4X3b0qBOfplE4e4UFpVQr3Y+hUXF1M7LIy/P2L6zmL6/e51bz+5TJgntKCohP88oLnHyDIpKnDr5eewoDpbx59dnc9fbc/nXRUdwYo/WbNtRzFOfLGLE4e0TdjW9s7iEXmPGc/NZB7F9Zwnn9e/ID/49mYlfreJfFx/BiQcmP2r85dPTeG7qUq4f1pOl67dxzWkH0qBO0IV73Vp5nHPfh0xZuK60x9CbzuzNiMPas3DNFpav387AA1uVKcN5tw7d7bkkKzdu54N5qzn70PZ0v+41urZqyPifHw/semrf398p+4Cb+O8ikVlfb2TFxkJO6N6qNNZYHZuzYhOL121lYPfW7CwpoW6t/NLvKdabb6xOfTB3Nef/38fc/q2+/PrZzxjUc1/+78J+vPnlCjq3bFiaYGPf5QndW/G7M3rTtll9thQW8dynS/nekR0T1u/yikucRz9ayIjD2tE47nvcsHUnB9/0eun7ds3q8/avTqBurXxKSrzMZxj1n89YtmEbj8QdjMQ+22MfL+LsQ9uV2TYmF6zFDHq3bcrHC9bSuvGuxws8/+kSmtavzSUP7doXPHXZAL4TdkuT6ODG3dlcWBTW9RIO+u1/ARjWtw1/G3koj360kHMOb0+92vll6sF/pizhxANbsU+jumWWF/t87321ms4tG9K+eX1emraM+au3MDrBGVVJidPnxv9y/fBejDyiA12ufZUm9Wrx2Y2nUVziFJc4dWpl7ni+sn1J4e577d/hhx/ulbF8/TbvNGqcP/7xQu80alzp3x2vz67U8hJZsm6rL167xd3d56zY5Ks3bS8dd9wf3vZOo8b51sKiSi9/csFa31lUXGbY8vXb/Nkpi8sM6zRqnF/04MeVXk91++kTU73TqHH+wdxVKafbtqPIpy9el3T8/RPmeadR4/wnjwfLK19OMbG6UFJSUql4ZyxZ79MWrfO1mwsrNX9UX2/Y5gWrN+82fO7KTd5p1Dh/7KOFGV1/Mh/MXeWdRo3zd2evzOp6S0pKvNOocd7v5jdKhy1cvcWXr98Waf7JBWv8hw9/Enn6qjZnxUZfk+E6Ew+Y7JXY59bIM4yvN2xnwG1v8bszevPbl74oHT7/1qHkJXjaXVVbun4bH8xdzbn9OmR8XVt3FFEnP6/0ORR7m43bd/KfyUu4+JjEZ3dRuTubCoOyePSjhVx8TJeETzaMnWFUthfQXLBx+04a1621R+W1p+uv6GFFmfDslCX079Jitwcbye7UvXkaYgkjvi/9/1x+FP06t6jqEGUv8+G8NTz/6RJuP+fg6g5FJGMqmzBq9C/W4h+8omQhAEd124ejuu1T3WGI5KSca6cws8FmNtvM5prZ6OqOR0REAjmVMMwsH/g7MAToBZxnZsnvWRQRkazJqYQB9Afmuvt8d98BPAmcmemVXjEwuz8EExHZG+VawmgHLI57vyQcVsrMLjOzyWY2edWqVZVaSevGZe+h/mXcr29FRCSxXLvoneg+wDK3cbn7/cD9ENwlVZmV5OXZXn3bpIhIdci1M4wlQPyPE9oDy6opFhERiZNrCeMT4AAz62JmdYCRwEvVHJOIiJBjTVLuXmRmPwb+C+QDD7r7FxXMJiIiWZBTCQPA3V8FXq3uOEREpKxca5ISEZEcpYQhIiKRKGGIiEgkShgiIhLJXt29uZmtAhbuwSJaAqurKJyqpLjSo7jSo7jS802Mq5O7t0p3pr06YewpM5tcmT7hM01xpUdxpUdxpUdx7aImKRERiUQJQ0REIqnpCeP+6g4gCcWVHsWVHsWVHsUVqtHXMEREJLqafoYhIiIRKWGIiEgkNTJhmNlgM5ttZnPNbHSW193BzN4xs5lm9oWZ/SwcfqOZLTWzaeHf0Lh5rg1jnW1mp2UwtgIzmxGuf3I4rIWZvWFmc8L/zbMZl5kdGFcm08xso5n9vDrKy8weNLOVZvZ53LC0y8fMDg/Lea6Z/c3MEj04bE/j+qOZzTKzz8zseTNrFg7vbGbb4srtvizHlfb3lqW4noqLqcDMpoXDs1leyfYN1V7HSrl7jfoj6DZ9HtAVqANMB3plcf1tgMPC142Br4BewI3ArxJM3yuMsS7QJYw9P0OxFQAtyw27HRgdvh4N/CHbcZX77r4GOlVHeQHHA4cBn+9J+QCTgKMInjD5GjAkA3GdCtQKX/8hLq7O8dOVW0424kr7e8tGXOXG/xkYUw3llWzfUO11LPZXE88w+gNz3X2+u+8AngTOzNbK3X25u08NX28CZlLuueXlnAk86e6F7r4AmEvwGbLlTODh8PXDwFnVGNfJwDx3T/Xr/ozF5e4TgbUJ1he5fMysDdDE3T/0YMv+d9w8VRaXu7/u7kXh248Inl6ZVLbiSqFayysmPBI/F3gi1TIyFFeyfUO117GYmpgw2gGL494vIfUOO2PMrDNwKPBxOOjHYRPCg3GnndmM14HXzWyKmV0WDtvX3ZdDUKGB1tUQV8xIym7I1V1ekH75tAtfZys+gEsIjjJjupjZp2Y2wcyOC4dlM650vrdsl9dxwAp3nxM3LOvlVW7fkDN1rCYmjERteVm/t9jMGgHPAj93943AvUA34BBgOcFpMWQ33mPc/TBgCHCVmR2fYtqslqMFj+w9A3gmHJQL5ZVKsjiyXW7XAUXAY+Gg5UBHdz8U+CXwuJk1yWJc6X5v2f4+z6PsQUnWyyvBviHppEliyFhsNTFhLAE6xL1vDyzLZgBmVpugQjzm7s8BuPsKdy929xLgn+xqRslavO6+LPy/Eng+jGFFeIobOw1fme24QkOAqe6+Ioyx2ssrlG75LKFs81DG4jOzC4HhwPlh0wRh88Wa8PUUgnbv7tmKqxLfWzbLqxYwAngqLt6slleifQM5VMdqYsL4BDjAzLqER60jgZeytfKwjfQBYKa73xE3vE3cZGcDsTs4XgJGmlldM+sCHEBwQauq42poZo1jrwkumn4erv/CcLILgRezGVecMkd+1V1ecdIqn7BJYZOZDQjrwvfj5qkyZjYYGAWc4e5b44a3MrP88HXXMK75WYwrre8tW3GFBgGz3L20OSeb5ZVs30Au1bGquHK+t/0BQwnuQJgHXJfldR9LcHr4GTAt/BsKPALMCIe/BLSJm+e6MNbZVNHdDgni6kpwx8V04ItYuQD7AG8Bc8L/LbIZV7ieBsAaoGncsKyXF0HCWg7sJDiKu7Qy5QP0I9hRzgPuJuxxoYrjmkvQvh2rY/eF034r/H6nA1OB07McV9rfWzbiCoc/BFxebtpslleyfUO117HYn7oGERGRSGpik5SIiFSCEoaIiESihCEiIpEoYYiISCRKGCIiEokShtRYZtbMzK6Me9/WzP6ToXWdZWZjqmA5fzKzk6oiJpF06bZaqbHC/nrGuftBWVjX/wh+RLd6D5fTCfinu59aNZGJRKczDKnJxgLdLHjOwR8tePbB5wBmdpGZvWBmL5vZAjP7sZn9MuyE7iMzaxFO183MxocdNr5nZj3Kr8TMugOFsWRhZg+Z2b0WPPtgvpmdEHbEN9PMHgqnyQ+n+9yC5xr8AsCDnnr3MbP9slNEIrvUqu4ARKrRaOAgdz8ESs844h1E0GNoPYJfTo9y90PN7C8E3S3cCdxP8OvgOWZ2JHAPUL7J6BiCXwnHax5OdwbwcjjND4BPzOwQgmd/tIud/Vj4AKTQ1HD6ZyvxmUUqTQlDJLl3PHguwSYz20CwY4ega4u+Ya+iRwPP2K4HmtVNsJw2wKpyw152dzezGQTdac8AMLMvCB7aMwHoamZ3Aa8Ar8fNuxJou6cfTiRdShgiyRXGvS6Je19CsO3kAetjZygpbAOaJll2/HJLl+3u68zsYOA04CqCh/pcEk5TL1ymSFbpGobUZJsIHoVZKR48q2CBmX0bgt5Gw518eTOB/dNZtpm1BPLc/VngBoJHisZ0Z1cvryJZo4QhNZYHzzn4ILyw/MdKLuZ84FIzi/Xym+hxvxOBQy2u3SqCdsC7ZjaNoBfVa6H0eQn7A5MrGa9Ipem2WpEsMLO/Ely3eHMPl3M2cJi731A1kYlEpzMMkey4leC5HnuqFrseayqSVTrDEBGRSHSGISIikShhiIhIJEoYIiISiRKGiIhEooQhIiKR/D+VThsc7yQmqwAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -582,15 +570,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "ae19bcc3",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'A' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m plot_resting_state(M, A, label_spikes, data_path)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined"
+ ]
+ }
+ ],
"source": [
"from M2E_visualize_resting_state import plot_resting_state\n",
- "plot_resting_state(M, A, label_spikes, data_path)"
+ "plot_resting_state(M, data_path)"
]
},
{
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
index 8828364dd0f6574e1efe4a80e7625ffae9ac3c8a..f68979ea69033d816fe83a57820b66cb022cd0b3 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -17,6 +17,8 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+from M2E_LOAD_DATA import load_and_create_data
+
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
@@ -32,8 +34,11 @@ def set_boxplot_props(d):
pl.setp(d['means'], marker='x', color='k',
markerfacecolor='k', markeredgecolor='k', markersize=3.)
-def plot_resting_state(M, A, data_path):
- # label_spikes = M.simulation.label
+def plot_resting_state(M, data_path):
+ # load data
+ # A = load_and_create_data(M)
+
+ label_spikes = M.simulation.label
label = M.simulation.label
t_sim = M.simulation.params["t_sim"]
@@ -148,17 +153,17 @@ def plot_resting_state(M, A, data_path):
# """
# M = MultiAreaModel({})
- # spike data
- # spike_data = {}
- # for area in areas:
- # spike_data[area] = {}
- # for pop in M.structure[area]:
- # spike_data[area][pop] = np.load(os.path.join(data_path,
- # label_spikes,
- # 'recordings',
- # '{}-spikes-{}-{}.npy'.format(label_spikes,
- # area, pop)))
- spike_data = A.spike_data
+ spike data
+ spike_data = {}
+ for area in areas:
+ spike_data[area] = {}
+ for pop in M.structure[area]:
+ spike_data[area][pop] = np.load(os.path.join(data_path,
+ label_spikes,
+ 'recordings',
+ '{}-spikes-{}-{}.npy'.format(label_spikes,
+ area, pop)))
+ # spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index 8828364dd0f6574e1efe4a80e7625ffae9ac3c8a..f68979ea69033d816fe83a57820b66cb022cd0b3 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -17,6 +17,8 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+from M2E_LOAD_DATA import load_and_create_data
+
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
@@ -32,8 +34,11 @@ def set_boxplot_props(d):
pl.setp(d['means'], marker='x', color='k',
markerfacecolor='k', markeredgecolor='k', markersize=3.)
-def plot_resting_state(M, A, data_path):
- # label_spikes = M.simulation.label
+def plot_resting_state(M, data_path):
+ # load data
+ # A = load_and_create_data(M)
+
+ label_spikes = M.simulation.label
label = M.simulation.label
t_sim = M.simulation.params["t_sim"]
@@ -148,17 +153,17 @@ def plot_resting_state(M, A, data_path):
# """
# M = MultiAreaModel({})
- # spike data
- # spike_data = {}
- # for area in areas:
- # spike_data[area] = {}
- # for pop in M.structure[area]:
- # spike_data[area][pop] = np.load(os.path.join(data_path,
- # label_spikes,
- # 'recordings',
- # '{}-spikes-{}-{}.npy'.format(label_spikes,
- # area, pop)))
- spike_data = A.spike_data
+ spike data
+ spike_data = {}
+ for area in areas:
+ spike_data[area] = {}
+ for pop in M.structure[area]:
+ spike_data[area][pop] = np.load(os.path.join(data_path,
+ label_spikes,
+ 'recordings',
+ '{}-spikes-{}-{}.npy'.format(label_spikes,
+ area, pop)))
+ # spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index f38b210c5c65eccb4c2e57d00de0060dc57cf693..2476922b4ce9989334b5aeed89ec8bdf393e2fba 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -570,7 +570,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"id": "ae19bcc3",
"metadata": {
"tags": []
@@ -578,20 +578,19 @@
"outputs": [
{
"ename": "NameError",
- "evalue": "name 'M' is not defined",
+ "evalue": "name 'A' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[1;32m 2\u001b[0m plot_resting_state(M, A, label_spikes, data_path)\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:21\u001b[0m\n\u001b[1;32m 18\u001b[0m ecolor \u001b[38;5;241m=\u001b[39m myblue\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# label_spikes = M.simulation.label\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[43mM\u001b[49m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mlabel\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_boxplot_props\u001b[39m(d):\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(d[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboxes\u001b[39m\u001b[38;5;124m'\u001b[39m])):\n",
- "\u001b[0;31mNameError\u001b[0m: name 'M' is not defined"
+ "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m plot_resting_state(M, A, label_spikes, data_path)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined"
]
}
],
"source": [
"from M2E_visualize_resting_state import plot_resting_state\n",
- "plot_resting_state(M, A, label_spikes, data_path)"
+ "plot_resting_state(M, data_path)"
]
},
{