{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API Features\n",
    "\n",
    "This notebook demonstrates the new API features added in epimodels v0.5.3:\n",
    "\n",
    "- `to_dataframe()` - Export results to pandas DataFrame\n",
    "- `to_dict()` - Get a copy of simulation traces\n",
    "- `summary()` - Get epidemic summary statistics\n",
    "- `copy()` - Create a copy of the model\n",
    "- `reset()` - Clear simulation results\n",
    "- `R0` property - Basic reproduction number\n",
    "- Parameter validation"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:45:03.177290912Z",
     "start_time": "2026-03-09T17:45:03.117223546Z"
    }
   },
   "source": [
    "from epimodels.continuous import SIR, SIS, SEIR, SIRS\n",
    "from epimodels.discrete import SIR as DiscreteSIR\n",
    "from epimodels import ValidationError\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Reproduction Number (R0)\n",
    "\n",
    "The R0 property returns the basic reproduction number for the model. It's calculated from the model parameters after running a simulation."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:40:08.685831613Z",
     "start_time": "2026-03-09T17:40:07.508975915Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "\n",
    "# R0 is None before running the model\n",
    "print(f\"R0 before simulation: {model.R0}\")\n",
    "\n",
    "# Run the model\n",
    "model([1000, 1, 0], [0, 100], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "\n",
    "# Now R0 is available (beta/gamma = 3.0)\n",
    "print(f\"R0 after simulation: {model.R0}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R0 before simulation: None\n",
      "R0 after simulation: 2.9999999999999996\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R0 for different models\n",
    "\n",
    "The R0 property is available for SIR, SIS, SEIR, and SIRS models."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:40:30.431659477Z",
     "start_time": "2026-03-09T17:40:30.336165056Z"
    }
   },
   "source": [
    "# SIS model\n",
    "sis = SIS()\n",
    "sis([1000, 1], [0, 50], 1001, {'beta': 0.5, 'gamma': 0.25})\n",
    "print(f\"SIS R0: {sis.R0}\")\n",
    "\n",
    "# SEIR model\n",
    "seir = SEIR()\n",
    "seir([1000, 0, 1, 0], [0, 50], 1001, {'beta': 0.4, 'gamma': 0.2, 'epsilon': 0.1})\n",
    "print(f\"SEIR R0: {seir.R0}\")\n",
    "\n",
    "# SIRS model\n",
    "sirs = SIRS()\n",
    "sirs([1000, 1, 0], [0, 50], 1001, {'beta': 0.6, 'gamma': 0.3, 'xi': 0.05})\n",
    "print(f\"SIRS R0: {sirs.R0}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIS R0: 2.0\n",
      "SEIR R0: 2.0\n",
      "SIRS R0: 2.0\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics\n",
    "\n",
    "The `summary()` method returns key epidemic statistics from the simulation."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:40:46.078554213Z",
     "start_time": "2026-03-09T17:40:45.939802842Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "model([1000, 1, 0], [0, 100], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "\n",
    "stats = model.summary()\n",
    "print(\"Epidemic Summary:\")\n",
    "for key, value in stats.items():\n",
    "    print(f\"  {key}: {value:.2f}\" if isinstance(value, float) else f\"  {key}: {value}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epidemic Summary:\n",
      "  model: SIR\n",
      "  t_start: 0.00\n",
      "  t_end: 100.00\n",
      "  peak_I: 298.70\n",
      "  peak_time: 37.05\n",
      "  final_S: 60.75\n",
      "  final_R: 936.14\n",
      "  attack_rate: 0.94\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the statistics\n",
    "\n",
    "- **peak_I**: Maximum number of infectious individuals\n",
    "- **peak_time**: Time at which peak I occurs\n",
    "- **final_S**: Final susceptible count\n",
    "- **final_R**: Final removed (recovered) count\n",
    "- **attack_rate**: Proportion of population that was infected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to DataFrame\n",
    "\n",
    "The `to_dataframe()` method exports simulation results to a pandas DataFrame for easy analysis."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:19.768770387Z",
     "start_time": "2026-03-02T18:12:19.245498152Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "model([1000, 1, 0], [0, 50], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "\n",
    "df = model.to_dataframe()\n",
    "print(f\"DataFrame shape: {df.shape}\")\n",
    "print(f\"Columns: {list(df.columns)}\")\n",
    "df.head()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (10, 4)\n",
      "Columns: ['S', 'I', 'R', 'time']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "             S         I         R      time\n",
       "0  1000.000000  1.000000  0.000000  0.000000\n",
       "1   999.995758  1.002827  0.001416  0.014135\n",
       "2   999.952671  1.031536  0.015792  0.155485\n",
       "3   999.447946  1.367801  0.184253  1.568989\n",
       "4   996.610788  3.256427  1.132785  5.924102"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S</th>\n",
       "      <th>I</th>\n",
       "      <th>R</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>999.995758</td>\n",
       "      <td>1.002827</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.014135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>999.952671</td>\n",
       "      <td>1.031536</td>\n",
       "      <td>0.015792</td>\n",
       "      <td>0.155485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>999.447946</td>\n",
       "      <td>1.367801</td>\n",
       "      <td>0.184253</td>\n",
       "      <td>1.568989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>996.610788</td>\n",
       "      <td>3.256427</td>\n",
       "      <td>1.132785</td>\n",
       "      <td>5.924102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:28.272231688Z",
     "start_time": "2026-03-02T18:12:28.153432999Z"
    }
   },
   "source": [
    "# DataFrame allows easy analysis\n",
    "print(f\"Max I: {df['I'].max():.1f}\")\n",
    "print(f\"Time of peak: {df.loc[df['I'].idxmax(), 'time']:.1f}\")\n",
    "print(f\"Final R: {df['R'].iloc[-1]:.1f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max I: 298.7\n",
      "Time of peak: 37.0\n",
      "Final R: 667.8\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy and Reset\n",
    "\n",
    "### Copy a model"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:31.545885881Z",
     "start_time": "2026-03-02T18:12:31.420006360Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "model([1000, 1, 0], [0, 50], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "\n",
    "# Copy without results\n",
    "new_model = model.copy(include_traces=False)\n",
    "print(f\"Original traces: {len(model.traces)}\")\n",
    "print(f\"Copy traces: {len(new_model.traces)}\")\n",
    "\n",
    "# Copy with results\n",
    "full_copy = model.copy(include_traces=True)\n",
    "print(f\"Full copy traces: {len(full_copy.traces)}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original traces: 4\n",
      "Copy traces: 0\n",
      "Full copy traces: 4\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reset a model"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:34.570567087Z",
     "start_time": "2026-03-02T18:12:34.454407126Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "model([1000, 1, 0], [0, 50], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "print(f\"Before reset: {len(model.traces)} traces\")\n",
    "\n",
    "model.reset()\n",
    "print(f\"After reset: {len(model.traces)} traces\")\n",
    "\n",
    "# Can run again with different parameters\n",
    "model([500, 5, 0], [0, 30], 505, {'beta': 0.4, 'gamma': 0.2})\n",
    "print(f\"After re-run: {len(model.traces)} traces\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before reset: 4 traces\n",
      "After reset: 0 traces\n",
      "After re-run: 4 traces\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Validation\n",
    "\n",
    "Models now validate parameters and initial conditions by default."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:42:29.299093959Z",
     "start_time": "2026-03-09T17:42:29.191849176Z"
    }
   },
   "source": [
    "model = SIR()\n",
    "\n",
    "# Missing parameter\n",
    "try:\n",
    "    model([1000, 1, 0], [0, 50], 1001, {'beta': 0.3})  # Missing gamma\n",
    "except ValidationError as e:\n",
    "    print(f\"Validation error: {e}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation error: Missing required parameters: {'gamma'}\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:42:35.603371810Z",
     "start_time": "2026-03-09T17:42:35.501648018Z"
    }
   },
   "source": [
    "# Negative parameter\n",
    "try:\n",
    "    model([1000, 1, 0], [0, 50], 1001, {'beta': -0.3, 'gamma': 0.1})\n",
    "except ValidationError as e:\n",
    "    print(f\"Validation error: {e}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation error: Parameter 'beta' must be non-negative, got -0.3\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:42:57.436860378Z",
     "start_time": "2026-03-09T17:42:57.306668616Z"
    }
   },
   "source": [
    "# Initial conditions exceed population\n",
    "try:\n",
    "    model([1000, 100, 0], [0, 50], 1001, {'beta': 0.3, 'gamma': 0.1})\n",
    "except ValidationError as e:\n",
    "    print(f\"Validation error: {e}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation error: Sum of initial conditions (1100) exceeds total population (1001)\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disable validation\n",
    "\n",
    "Validation can be disabled for performance or special cases."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:48.964909903Z",
     "start_time": "2026-03-02T18:12:48.854333048Z"
    }
   },
   "source": [
    "# Validation disabled - no error raised\n",
    "model([1000, 1, 0], [0, 50], 1001, {'beta': 0.3, 'gamma': 0.1}, validate=False)\n",
    "print(\"No validation error with validate=False\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No validation error with validate=False\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete Example\n",
    "\n",
    "Let's put it all together with a complete example."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T17:45:20.717062254Z",
     "start_time": "2026-03-09T17:45:17.291335835Z"
    }
   },
   "source": [
    "# Create and run model\n",
    "model = SIR()\n",
    "model([990, 10, 0], [0, 500], 1000, {'beta': 0.3, 'gamma': 0.1})\n",
    "\n",
    "# Get R0\n",
    "print(f\"Basic reproduction number (R0): {model.R0:.2f}\")\n",
    "\n",
    "# Get summary\n",
    "stats = model.summary()\n",
    "print(f\"\\nEpidemic Summary:\")\n",
    "print(f\"  Peak infected: {stats['peak_I']:.0f} at day {stats['peak_time']:.0f}\")\n",
    "print(f\"  Attack rate: {stats['attack_rate']*100:.1f}%\")\n",
    "\n",
    "# Export to DataFrame\n",
    "df = model.to_dataframe()\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Time series\n",
    "df.plot(x='time', y=['S', 'I', 'R'], ax=axes[0])\n",
    "axes[0].set_title('SIR Model Dynamics')\n",
    "axes[0].set_xlabel('Time')\n",
    "axes[0].set_ylabel('Population')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Phase portrait\n",
    "axes[1].plot(df['S'], df['I'])\n",
    "axes[1].set_title('Phase Portrait (S vs I)')\n",
    "axes[1].set_xlabel('Susceptible')\n",
    "axes[1].set_ylabel('Infectious')\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic reproduction number (R0): 3.00\n",
      "\n",
      "Epidemic Summary:\n",
      "  Peak infected: 286 at day 31\n",
      "  Attack rate: 94.1%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-02T18:12:54.906857043Z",
     "start_time": "2026-03-02T18:12:54.853978353Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "epimodels (3.12.9)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}