docs/notebooks/06_qunits_getting_started.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with qUnits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qrobot\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up a basic qBrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, define a sensorial input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Layer 0 - Unit 0\n",
    "l0_unit0 = qrobot.qunits.SensorialUnit(\"l0_unit_0\", Ts=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SensorialUnit \"l0_unit_0-214657\"\n",
       "     name:\tl0_unit_0\n",
       "     id:\tl0_unit_0-214657\n",
       "     Ts:\t0.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l0_unit0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, define a model and the desired bursts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[model: AngularModel, n: 2, tau: 10]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qrobot.models.AngularModel(n=2, tau=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qrobot.bursts.zeroburst.ZeroBurst at 0x7f4e7d348bb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qrobot.bursts.ZeroBurst()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qrobot.bursts.oneburst.OneBurst at 0x7f4e7d361100>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qrobot.bursts.OneBurst()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use objects like those to create a basic qBrain:\n",
    "\n",
    "<img width=300 src=\"./06_imgs/tutorial_qunits_basicnetwork.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qrobot.models import AngularModel\n",
    "from qrobot.bursts import ZeroBurst, OneBurst\n",
    "\n",
    "# Layer 1 - Unit 0\n",
    "l1_unit0 = qrobot.qunits.QUnit(\n",
    "    name=\"l1_unit0\",\n",
    "    model=AngularModel(n=1, tau=10),\n",
    "    burst=OneBurst(),\n",
    "    Ts=0.3,\n",
    "    in_qunits={0: l0_unit0.id},  # Will receive Input from l0_unit0, dim 0\n",
    ")\n",
    "\n",
    "# Layer 1 - Unit 1\n",
    "l1_unit1 = qrobot.qunits.QUnit(\n",
    "    name=\"l1_unit1\",\n",
    "    model=AngularModel(n=1, tau=25),\n",
    "    burst=ZeroBurst(),\n",
    "    Ts=0.2,\n",
    "    in_qunits={0: l0_unit0.id},  # Will receive input from l0_unit0, dim 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QUnit \"l1_unit0-e96c53\"\n",
       "     name:\tl1_unit0\n",
       "     id:\tl1_unit0-e96c53\n",
       "     model:\t[model: AngularModel, n: 1, tau: 10]\n",
       "     burst:\t<class 'qrobot.bursts.oneburst.OneBurst'>\n",
       "     query:\t[0.0]\n",
       "     Ts:\t0.3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1_unit0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QUnit \"l1_unit1-77319d\"\n",
       "     name:\tl1_unit1\n",
       "     id:\tl1_unit1-77319d\n",
       "     model:\t[model: AngularModel, n: 1, tau: 25]\n",
       "     burst:\t<class 'qrobot.bursts.zeroburst.ZeroBurst'>\n",
       "     query:\t[0.0]\n",
       "     Ts:\t0.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1_unit1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputs and queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the default input for `l0_unit0`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l0_unit0.scalar_reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input units for each qUnit are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'l0_unit_0-214657'}\n",
      "{0: 'l0_unit_0-214657'}\n"
     ]
    }
   ],
   "source": [
    "print(l1_unit0.in_qunits)\n",
    "print(l1_unit1.in_qunits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify `l1_unit1` query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1_unit1.query = 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real-time processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-12 20:16:08,194 — l0_unit_0-214657 — INFO — start:72 — Starting SensorialUnit\n",
      "2023-06-12 20:16:08,206 — l1_unit0-e96c53 — INFO — start:72 — Starting QUnit\n",
      "2023-06-12 20:16:08,218 — l1_unit1-77319d — INFO — start:72 — Starting QUnit\n"
     ]
    }
   ],
   "source": [
    "l0_unit0.start()\n",
    "l1_unit0.start()\n",
    "l1_unit1.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the time evolution of the system from the redis status for 30 seconds changing the input with a random input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      " \"l0_unit_0-214657 class\": \"SensorialUnit\",\n",
      " \"l0_unit_0-214657 output\": \"0.077\",\n",
      " \"l1_unit0-e96c53 class\": \"QUnit\",\n",
      " \"l1_unit0-e96c53 in_qunits\": \"{\\\"0\\\": \\\"l0_unit_0-214657\\\"}\",\n",
      " \"l1_unit0-e96c53 output\": \"1.0\",\n",
      " \"l1_unit0-e96c53 query\": \"[0.0]\",\n",
      " \"l1_unit0-e96c53 state\": \"1\",\n",
      " \"l1_unit1-77319d class\": \"QUnit\",\n",
      " \"l1_unit1-77319d in_qunits\": \"{\\\"0\\\": \\\"l0_unit_0-214657\\\"}\",\n",
      " \"l1_unit1-77319d output\": \"1.0\",\n",
      " \"l1_unit1-77319d query\": \"[0.8]\",\n",
      " \"l1_unit1-77319d state\": \"0\"\n",
      "}\n",
      "29 /30 seconds\n"
     ]
    },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import json\n",
    "from random import randint\n",
    "from IPython.display import clear_output\n",
    "\n",
    "statuses = []\n",
    "refresh_time = 0.5  # Read statuses every 0.5 seconds\n",
    "\n",
    "for i in range(int(30 * (1 / refresh_time))):\n",
    "    # Wait and then clean the output\n",
    "    time.sleep(refresh_time)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    # Change input every 2 second\n",
    "    if (i * refresh_time) % 2 == 0:\n",
    "        l0_unit0.scalar_reading = randint(0, 1000) / 1000\n",
    "\n",
    "    # Read statused and store it\n",
    "    status = qrobot.qunits.redis_utils.redis_status()\n",
    "    statuses.append(status)\n",
    "\n",
    "    # Print output\n",
    "    print(json.dumps(status, indent=1, sort_keys=True))\n",
    "    print(int(i * refresh_time), \"/30 seconds\")\n",
    "\n",
    "    # Plot graph\n",
    "    qbrain_graph = qrobot.graph(status)\n",
    "    qrobot.draw(qbrain_graph)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read manually the latest outputs of the qunits found on the redis database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "l1_unit0.get_burst_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "l1_unit1.get_burst_output()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stop the processing loops:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-12 20:16:40,653 — l0_unit_0-214657 — INFO — stop:83 — Stopping SensorialUnit\n",
      "2023-06-12 20:16:40,655 — l0_unit_0-214657 — INFO — stop:85 — Cleaning redis\n",
      "2023-06-12 20:16:40,661 — l1_unit0-e96c53 — INFO — stop:83 — Stopping QUnit\n",
      "2023-06-12 20:16:40,663 — l1_unit0-e96c53 — INFO — stop:85 — Cleaning redis\n",
      "2023-06-12 20:16:40,671 — l1_unit1-77319d — INFO — stop:83 — Stopping QUnit\n",
      "2023-06-12 20:16:40,674 — l1_unit1-77319d — INFO — stop:85 — Cleaning redis\n"
     ]
    }
   ],
   "source": [
    "l0_unit0.stop()\n",
    "l1_unit0.stop()\n",
    "l1_unit1.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flush the redis to clean all traces (should not be necessary if the qUnits processing loops stopped correctly):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qrobot.qunits.redis_utils.redis_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-12 20:16:40,703 — redis — INFO — flush_redis:81 — Flushing redis database\n"
     ]
    }
   ],
   "source": [
    "qrobot.qunits.redis_utils.flush_redis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the time evolution of such time period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "status_df = pd.DataFrame(statuses)\n",
    "units = [l0_unit0.id + \" output\", l1_unit0.id + \" output\", l1_unit1.id + \" output\"]\n",
    "status_df = status_df[units]\n",
    "status_df = status_df.astype(np.float64)\n",
    "status_df.index = status_df.index * refresh_time\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
    "# Plot time evolution\n",
    "styles = [\"g\", \"y\", \"b\"]\n",
    "status_df[units].plot(style=styles, ax=ax)\n",
    "# Plot queries as dashed lines\n",
    "ax.hlines(\n",
    "    y=l1_unit0.query, xmin=0, xmax=30, linewidth=1, color=\"y\", linestyles=\"dashed\"\n",
    ")\n",
    "ax.hlines(\n",
    "    y=l1_unit1.query, xmin=0, xmax=30, linewidth=1, color=\"b\", linestyles=\"dashed\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Focusing on `l1_unit0`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUnit \"l1_unit0-e96c53\"\n",
      "     name:\tl1_unit0\n",
      "     id:\tl1_unit0-e96c53\n",
      "     model:\t[model: AngularModel, n: 1, tau: 10]\n",
      "     burst:\t<class 'qrobot.bursts.oneburst.OneBurst'>\n",
      "     query:\t[0.0]\n",
      "     Ts:\t0.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(l1_unit0)\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
    "# Plot time evolution\n",
    "units = [l0_unit0.id + \" output\", l1_unit0.id + \" output\"]\n",
    "styles = [\"g\", \"b\"]\n",
    "status_df[units].plot(style=styles, ax=ax)\n",
    "# Plot time windows\n",
    "t_start = status_df[l1_unit0.id + \" output\"].dropna().index[0]\n",
    "t_step = l1_unit0.model.tau * l1_unit0.Ts\n",
    "t_windows = np.arange(t_start, 31, t_step)\n",
    "plt.vlines(x=t_windows, ymin=0, ymax=1, colors=\"gray\", ls=\"dotted\", lw=1)\n",
    "# Plot query as dashed line\n",
    "ax.hlines(y=l1_unit0.query, xmin=t_start, xmax=30, linewidth=1, color=\"b\", ls=\"dashed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Focusing on `l1_unit1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUnit \"l1_unit1-77319d\"\n",
      "     name:\tl1_unit1\n",
      "     id:\tl1_unit1-77319d\n",
      "     model:\t[model: AngularModel, n: 1, tau: 25]\n",
      "     burst:\t<class 'qrobot.bursts.zeroburst.ZeroBurst'>\n",
      "     query:\t[0.8]\n",
      "     Ts:\t0.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(l1_unit1)\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
    "# Plot time evolution\n",
    "units = [l0_unit0.id + \" output\", l1_unit1.id + \" output\"]\n",
    "styles = [\"g\", \"b\"]\n",
    "status_df[units].plot(style=styles, ax=ax)\n",
    "# Plot time windows\n",
    "t_start = status_df[l1_unit1.id + \" output\"].dropna().index[0]\n",
    "t_step = l1_unit1.model.tau * l1_unit1.Ts\n",
    "t_windows = np.arange(t_start, 31, t_step)\n",
    "plt.vlines(x=t_windows, ymin=0, ymax=1, colors=\"gray\", ls=\"dotted\", lw=1)\n",
    "# Plot query as dashed line\n",
    "ax.hlines(y=l1_unit1.query, xmin=t_start, xmax=30, linewidth=1, color=\"b\", ls=\"dashed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detailed logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the last 30 lines of the log:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-12 20:16:40,114 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,208 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 4/25\n",
      "2023-06-12 20:16:40,212 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,220 — l0_unit_0-214657 — DEBUG — _unit_task:79 — scalar_reading=0.077\n",
      "2023-06-12 20:16:40,220 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,325 — l0_unit_0-214657 — DEBUG — _unit_task:79 — scalar_reading=0.077\n",
      "2023-06-12 20:16:40,325 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,329 — l1_unit0-e96c53 — DEBUG — _unit_task:198 — Temporal window event 4/10\n",
      "2023-06-12 20:16:40,331 — l1_unit0-e96c53 — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,416 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 5/25\n",
      "2023-06-12 20:16:40,421 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,429 — l0_unit_0-214657 — DEBUG — _unit_task:79 — scalar_reading=0.077\n",
      "2023-06-12 20:16:40,429 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,535 — l0_unit_0-214657 — DEBUG — _unit_task:79 — scalar_reading=0.077\n",
      "2023-06-12 20:16:40,535 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,624 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 6/25\n",
      "2023-06-12 20:16:40,626 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,633 — l1_unit0-e96c53 — DEBUG — _unit_task:198 — Temporal window event 5/10\n",
      "2023-06-12 20:16:40,635 — l1_unit0-e96c53 — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,640 — l0_unit_0-214657 — DEBUG — _unit_task:79 — scalar_reading=0.077\n",
      "2023-06-12 20:16:40,640 — l0_unit_0-214657 — DEBUG — _unit_task:80 — Writing input on redis\n",
      "2023-06-12 20:16:40,653 — l0_unit_0-214657 — INFO — stop:83 — Stopping SensorialUnit\n",
      "2023-06-12 20:16:40,655 — l0_unit_0-214657 — INFO — stop:85 — Cleaning redis\n",
      "2023-06-12 20:16:40,661 — l1_unit0-e96c53 — INFO — stop:83 — Stopping QUnit\n",
      "2023-06-12 20:16:40,663 — l1_unit0-e96c53 — INFO — stop:85 — Cleaning redis\n",
      "2023-06-12 20:16:40,671 — l1_unit1-77319d — INFO — stop:83 — Stopping QUnit\n",
      "2023-06-12 20:16:40,674 — l1_unit1-77319d — INFO — stop:85 — Cleaning redis\n",
      "2023-06-12 20:16:40,703 — redis — INFO — flush_redis:81 — Flushing redis database\n",
      "2023-06-12 20:16:40,705 — redis — DEBUG — flush_redis:82 — Previous redis state: {}\n",
      "2023-06-12 20:16:40,708 — redis — DEBUG — flush_redis:85 — Current redis state: {}\n"
     ]
    }
   ],
   "source": [
    "def print_log(filter_by: \"list[str]\" = None, n_lines: int = 30):\n",
    "    log_file = qrobot._logger.log_file()\n",
    "    i = 0\n",
    "    with open(log_file) as log:\n",
    "        for line in log.readlines()[-n_lines:]:\n",
    "            if filter_by is None or all(x in line for x in filter_by):\n",
    "                print(line, end=\"\")\n",
    "                i += 1\n",
    "            if i >= n_lines:\n",
    "                break\n",
    "\n",
    "\n",
    "print_log()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the last lines of the log only for `l1_unit1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-12 20:16:35,810 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 8/25\n",
      "2023-06-12 20:16:35,814 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.82]\n",
      "2023-06-12 20:16:36,016 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 9/25\n",
      "2023-06-12 20:16:36,017 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.82]\n",
      "2023-06-12 20:16:36,218 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 10/25\n",
      "2023-06-12 20:16:36,219 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.82]\n",
      "2023-06-12 20:16:36,421 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 11/25\n",
      "2023-06-12 20:16:36,426 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.82]\n",
      "2023-06-12 20:16:36,630 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 12/25\n",
      "2023-06-12 20:16:36,633 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.82]\n",
      "2023-06-12 20:16:36,836 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 13/25\n",
      "2023-06-12 20:16:36,841 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:37,044 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 14/25\n",
      "2023-06-12 20:16:37,048 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:37,252 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 15/25\n",
      "2023-06-12 20:16:37,258 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:37,462 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 16/25\n",
      "2023-06-12 20:16:37,468 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:37,672 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 17/25\n",
      "2023-06-12 20:16:37,677 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:37,881 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 18/25\n",
      "2023-06-12 20:16:37,885 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:38,089 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 19/25\n",
      "2023-06-12 20:16:38,092 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:38,295 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 20/25\n",
      "2023-06-12 20:16:38,299 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:38,503 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 21/25\n",
      "2023-06-12 20:16:38,510 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:38,713 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 22/25\n",
      "2023-06-12 20:16:38,718 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:38,922 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 23/25\n",
      "2023-06-12 20:16:38,928 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.608]\n",
      "2023-06-12 20:16:39,132 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 24/25\n",
      "2023-06-12 20:16:39,137 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:39,341 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 25/25\n",
      "2023-06-12 20:16:39,346 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:39,349 — l1_unit1-77319d — DEBUG — _unit_task:212 — Querying for state [0.8]\n",
      "2023-06-12 20:16:39,376 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 0\n",
      "2023-06-12 20:16:39,376 — l1_unit1-77319d — DEBUG — _unit_task:218 — Opening a connection to redis...\n",
      "2023-06-12 20:16:39,377 — l1_unit1-77319d — DEBUG — _unit_task:220 — Redis connected: Redis<ConnectionPool<Connection<host=localhost,port=6379,db=0>>>\n",
      "2023-06-12 20:16:39,380 — l1_unit1-77319d — DEBUG — _unit_task:231 — Initializing a new temporal window\n",
      "2023-06-12 20:16:39,582 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 1/25\n",
      "2023-06-12 20:16:39,589 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:39,793 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 2/25\n",
      "2023-06-12 20:16:39,797 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,002 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 3/25\n",
      "2023-06-12 20:16:40,005 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,208 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 4/25\n",
      "2023-06-12 20:16:40,212 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,416 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 5/25\n",
      "2023-06-12 20:16:40,421 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,624 — l1_unit1-77319d — DEBUG — _unit_task:198 — Temporal window event 6/25\n",
      "2023-06-12 20:16:40,626 — l1_unit1-77319d — DEBUG — _unit_task:203 — input_vector=[0.077]\n",
      "2023-06-12 20:16:40,671 — l1_unit1-77319d — INFO — stop:83 — Stopping QUnit\n",
      "2023-06-12 20:16:40,674 — l1_unit1-77319d — INFO — stop:85 — Cleaning redis\n"
     ]
    }
   ],
   "source": [
    "print_log([\"l1_unit1\"], 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time window time: 5.0 seconds\n",
      "2023-06-12 20:16:18,500 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 0\n",
      "2023-06-12 20:16:23,725 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 1\n",
      "2023-06-12 20:16:28,941 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 1\n",
      "2023-06-12 20:16:34,151 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 0\n",
      "2023-06-12 20:16:39,376 — l1_unit1-77319d — DEBUG — _unit_task:216 — Output state = 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Time window time:\", l1_unit1.Ts * l1_unit1.model.tau, \"seconds\")\n",
    "print_log([\"l1_unit1\", \"Output state = \"], 1000)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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