{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segreg Example One Breakpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "from segreg import analysis\n",
    "from segreg import data\n",
    "from segreg.model import OneBkptSegRegEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['legend.numpoints'] = 1\n",
    "matplotlib.rcParams['figure.figsize'] = (10,5)\n",
    "matplotlib.rcParams['axes.grid'] = True\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indep, dep = data.test2()\n",
    "plt.scatter(indep, dep, s=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate a One-Bkpt Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.384, 19.936, -0.007, -0.102,  0.479])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = OneBkptSegRegEstimator()\n",
    "estimator.fit(indep, dep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.384, 19.936, -0.007, -0.102,  0.479])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['u', 'v', 'm1', 'm2', 'sigma']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.param_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the One-Bkpt Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "func = estimator.model_function\n",
    "func_arr = [func]\n",
    "u = estimator.params[0]\n",
    "extra_pts_arr = [[u]]\n",
    "\n",
    "analysis.plot_models(func_arr=func_arr,\n",
    "                     indep=indep,\n",
    "                     dep=dep,\n",
    "                     extra_pts_arr=extra_pts_arr,\n",
    "                     mark_extra_pts=True,\n",
    "                     scatter_size=15,\n",
    "                     scatter_color=\"gray\",\n",
    "                     marker=\"o\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}