{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A sample of plots\n",
    "\n",
    "The idea of this notebook is to show off a number of plot types, and act as a simple check of the plotting output. It requires `matplotlib` and does not attempt to describe the plots (see the help for the plot constructor for this!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa import data\n",
    "from sherpa.astro import data as astrodata\n",
    "\n",
    "from sherpa import plot\n",
    "from sherpa.astro import plot as astroplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One dimensional data plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.asarray([100, 200, 600, 1200])\n",
    "y1 = np.asarray([2000, 2100, 1400, 3050])\n",
    "\n",
    "d1 = data.Data1D('oned', x1, y1)\n",
    "\n",
    "plot1 = plot.DataPlot()\n",
    "plot1.prepare(d1)\n",
    "plot1.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can have some fun with the plot options (these are a mixture of generic options, such as `xlog`, and ones specific to the plotting backend - which here is `matplotlib` - such as `marker`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1.plot(xlog=True, linestyle='dotted', marker='*', markerfacecolor='orange', markersize=20, color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot object contains the preferences - here we look at the default plot settings. Note that some plot types have different - and even multiple - preference settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xerrorbars': False,\n",
       " 'yerrorbars': True,\n",
       " 'ecolor': None,\n",
       " 'capsize': None,\n",
       " 'barsabove': False,\n",
       " 'xlog': False,\n",
       " 'ylog': False,\n",
       " 'linestyle': 'None',\n",
       " 'drawstyle': 'default',\n",
       " 'color': None,\n",
       " 'marker': '.',\n",
       " 'markerfacecolor': None,\n",
       " 'markersize': None,\n",
       " 'alpha': None,\n",
       " 'xaxis': False,\n",
       " 'ratioline': False,\n",
       " 'linecolor': None}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.DataPlot.plot_prefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error bars - here on the dependent axis - can be displayed too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dy1 = np.asarray([100, 50, 200, 300])\n",
    "d2 = data.Data1D('errors', x1, y1, dy1)\n",
    "\n",
    "plot2 = plot.DataPlot()\n",
    "plot2.prepare(d2)\n",
    "\n",
    "plot2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU/ElEQVR4nO3df7DddZ3f8ecLEgjVjIkxgxgoCTbUQWcKcrVBO60j5YcMXdYdu4QyClsd7FZbZJ1a0J3R3Wpbt7tSdsZVEOiyLitQtGsKdhgW7XTayo+bXUR+JwazJAUJGCS7giXk3T/OJ/QYb/jkwj335N48HzPfyfe8P59zzufD99774vvrnFQVkiS9lIPGPQBJ0v7PsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhTQiSRbs8ThJ9vl3brr9pVHyB1GapiRvSPL1JNuSPJLkX7b6Z5LcmOSPkzwDnJ/kvyf5XJL/BfwUOCbJO5LcleQn7d93DL32VP3PT7IpyY72fueOZeI6oBkW0jS0/9P/r8D3gBXAycDHkpzWupwF3AgsAa5ttfcDFwCLgR3AzcDvA8uALwA3J1k29DbD/be1vu+pqsXAO4C7RzM7ae8MC2l63gYsr6rfrqr/W1WbgK8Aa1v7d6vqT6tqV1U922p/WFX3VdVO4FRgQ1V9tap2VtXXgAeBfzT0HsP9dwK7gLckOayqHquq+2ZlptIQw0KanqOBNyR5evcCfBI4vLU/OsVzhmtvADbv0b6ZwV7KL/Svqr8Gzgb+GfBYkpuTvOmVTUGaPsNCmp5HgUeqasnQsriqzmjtU32M83Dt/zAInGF/E9i6l/5U1S1VdQpwBIO9kK+8ohlIL4NhIU3PncCOJP86yWFJDk7yliRv28fnfws4Nsk/SbIgydnAccBNU3VOcniSs5K8CvgZ8FcMDktJs8qwkKahql4AzgSOBx4BngSuBF6zj89/qj3/48BTwCeAM6vqyb085SDgNxjskfwY+AfAr7/8GUgvT/zyI0lSj3sWkqQuw0KS1GVYSJK6DAtJUteCfpe553Wve12tXLly3MOQpDll/fr1T1bV8qna5mVYrFy5ksnJyXEPQ5LmlCR7frrAizwMJUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVLXvLwpT5IOFJfe+jCX3bZhr+0Xnryai0459hW/z7z8PouJiYnyDm5JB5qzL/8uANd/+KSX9fwk66tqYqo2D0NJkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhI0jyx47nn2fr0s6zfvH3GX9uwkKR5YP3m7Tz4+A62bH+Wc6+8fcYDw7CQpHng9k1PsasG68/v3MXtm56a0dcfWVgkWZTkziTfS3Jfkt9q9VVJ7kiyMcn1SQ5p9UPb442tfeXQa13S6g8lOW1UY5akuWrNMcs4KIP1hQsOYs0xy2b09Ue5Z/Ez4N1V9XeA44HTk6wBPg9cWlV/C9gOfLD1/yCwvdUvbf1IchywFngzcDrwB0kOHuG4JWnOOfHopbzp9Ys5culhXPuhNZx49NIZff2RhUUN/FV7uLAtBbwbuLHVrwF+ua2f1R7T2k9Okla/rqp+VlWPABuBt49q3JI0Vy1etJAVSw6b8aCAEZ+zSHJwkruBJ4BbgR8AT1fVztZlC7Cira8AHgVo7T8Blg3Xp3jO8HtdkGQyyeS2bdtGMBtJOnCNNCyq6oWqOh44ksHewJtG+F5XVNVEVU0sX758VG8jSQekWbkaqqqeBr4DnAQsSbKgNR0JbG3rW4GjAFr7a4CnhutTPEeSNAtGeTXU8iRL2vphwCnAAwxC432t23nAN9v6uvaY1v7tqqpWX9uulloFrAbuHNW4JUm/aEG/y8t2BHBNu3LpIOCGqropyf3AdUk+C/wFcFXrfxXw1SQbgR8zuAKKqrovyQ3A/cBO4CNV9cIIxy1J2sPIwqKq7gFOmKK+iSmuZqqq54B/vJfX+hzwuZkeoyRp33gHtySpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKlrlF+rKkkasUtvfZjLbtvwc7WVF9/84vqFJ6/molOOfcXvk6p6xS+yv5mYmKjJyclxD0OS5pQk66tqYqo2D0NJkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVLXyMIiyVFJvpPk/iT3Jbmw1T+TZGuSu9tyxtBzLkmyMclDSU4bqp/eahuTXDyqMUuSprZghK+9E/h4Vf15ksXA+iS3trZLq+p3hzsnOQ5YC7wZeAPwZ0l2f8v4F4FTgC3AXUnWVdX9Ixy7JGnIyMKiqh4DHmvrO5I8AKx4iaecBVxXVT8DHkmyEXh7a9tYVZsAklzX+hoWkjRLZuWcRZKVwAnAHa300ST3JLk6ydJWWwE8OvS0La22t/qe73FBkskkk9u2bZvpKUjSAW3kYZHk1cDXgY9V1TPAl4A3Ascz2PP4vZl4n6q6oqomqmpi+fLlM/GSkqRmlOcsSLKQQVBcW1XfAKiqHw21fwW4qT3cChw19PQjW42XqEuSZsEor4YKcBXwQFV9Yah+xFC39wL3tvV1wNokhyZZBawG7gTuAlYnWZXkEAYnwdeNatySpF80yj2LdwLvB76f5O5W+yRwTpLjgQJ+CHwYoKruS3IDgxPXO4GPVNULAEk+CtwCHAxcXVX3jXDckqQ9pKrGPYYZNzExUZOTk+MehiTNKUnWV9XEVG3ewS1J6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1NUNiyT/Yuh7siVJB6B92bM4HLgryQ1JTm/fgCdJOoB0w6KqfpPBV5xeBZwPbEjyb5O8ccRjkyTtJ/bpnEUNvk7v8bbsBJYCNyb5nRGOTZK0n+h+B3eSC4EPAE8CVwL/qqqeT3IQsAH4xGiHKEkat25YAK8FfqWqNg8Xq2pXkjNHMyxJ0v6kGxZV9emXaHtgZocjSdofeZ+FJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUte+3GehGXTprQ9z2W0b9tp+4cmrueiUY2dxRJLUl8EnecwvExMTNTk5Oe5hdJ19+XcBuP7DJ415JJIESdZX1cRUbR6GGqMdzz3P1qefZf3m7eMeiiS9JMNiTNZv3s6Dj+9gy/ZnOffK2w0MSfs1z1kwnvMIt296il3tCODzO3dx+6anOPFov2NK0v7JsAAuOuXYF8Ngts4jrDlmGQcFdhUsXHAQa45ZNtL3k6RXwrAYkxOPXsqbXr+YZ57byWVrT3CvQtJ+zbAYo8WLFrJ40UKDQtJ+zxPckqQu9yxm2VQn01defPOL696UJ2l/NLKwSHIU8EfA4UABV1TVZUleC1wPrAR+CPxqVW1PEuAy4Azgp8D5VfXn7bXOA36zvfRnq+qaUY171IZPpkvSXDHKw1A7gY9X1XHAGuAjSY4DLgZuq6rVwG3tMcB7gNVtuQD4EkALl08Dfxd4O/DpJCM7yO+NcpL0i0YWFlX12O49g6raATwArADOAnbvGVwD/HJbPwv4oxq4HViS5AjgNODWqvpxVW0HbgVOH8WYvVFOkqY2Kye4k6wETgDuAA6vqsda0+MMDlPBIEgeHXrallbbW33P97ggyWSSyW3btr2scU51o5wkaRbCIsmrga8DH6uqZ4bbavAphjPySYZVdUVVTVTVxPLly1/Wa+y+UQ68UU6Sho00LJIsZBAU11bVN1r5R+3wEu3fJ1p9K3DU0NOPbLW91Wfc7hvljlx6GNd+aI33P0hSM7KwaFc3XQU8UFVfGGpaB5zX1s8DvjlU/0AG1gA/aYerbgFOTbK0ndg+tdVGYvGihaxYcphBIUlDRnmfxTuB9wPfT3J3q30S+PfADUk+CGwGfrW1fYvBZbMbGVw6+2sAVfXjJP8GuKv1++2q+vEIxy1J2sPIwqKq/ieQvTSfPEX/Aj6yl9e6Grh65kYnSZoOP+5DktRlWEiSugwLSVKXYSFJ6vJTZ/GTYCWpJ4OLkOaXiYmJmpycHPcwJGlOSbK+qiamavMwlCSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpa8G4ByDNFZfe+jCX3bZhr+0Xnryai045dhZHJM2eVNW4xzDjJiYmanJyctzD0Dx29uXfBeD6D5805pFIMyfJ+qqamKrNw1CSpC7DQpLUZVhIkroMC0lS18jCIsnVSZ5Icu9Q7TNJtia5uy1nDLVdkmRjkoeSnDZUP73VNia5eFTjlSTt3Sj3LP4QOH2K+qVVdXxbvgWQ5DhgLfDm9pw/SHJwkoOBLwLvAY4Dzml9JUmzaGT3WVTV/0iych+7nwVcV1U/Ax5JshF4e2vbWFWbAJJc1/reP9PjlSTt3TjOWXw0yT3tMNXSVlsBPDrUZ0ur7a3+C5JckGQyyeS2bdtGMW5JOmDNdlh8CXgjcDzwGPB7M/XCVXVFVU1U1cTy5ctn6mUlSczyx31U1Y92ryf5CnBTe7gVOGqo65GtxkvUJUmzZFb3LJIcMfTwvcDuK6XWAWuTHJpkFbAauBO4C1idZFWSQxicBF83m2OWJI1wzyLJ14B3Aa9LsgX4NPCuJMcDBfwQ+DBAVd2X5AYGJ653Ah+pqhfa63wUuAU4GLi6qu4b1ZglSVMb5dVQ50xRvuol+n8O+NwU9W8B35rBoUmSpsk7uCVJXYaFJKnLsJBehh3PPc/Wp59l/ebt4x6KNCsMC2ma1m/ezoOP72DL9mc598rbDQwdEAwLaZpu3/QUu9oXTD6/cxe3b3pqvAOSZoFhIU3TmmOWcVAG6wsXHMSaY5aNd0DSLJjVO7il+eDEo5fyptcv5pnndnLZ2hM48eil/SdJc5xhIb0MixctZPGihQaFDhgehpIkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6vIjyqV9dOmtD3PZbRt+rrby4ptfXL/w5NVcdMqxsz0saVakqsY9hhk3MTFRk5OT4x6GJM0pSdZX1cRUbR6GkiR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKlrXt6Ul2QbsHnc49hHrwOeHPcgRmg+z8+5zV3zeX6vZG5HV9XyqRrmZVjMJUkm93bH5Hwwn+fn3Oau+Ty/Uc3Nw1CSpC7DQpLUZViM3xXjHsCIzef5Obe5az7PbyRz85yFJKnLPQtJUpdhIUnqMixGLMlRSb6T5P4k9yW5sNVfm+TWJBvav0tbPUl+P8nGJPckeet4Z9CX5OAkf5HkpvZ4VZI72hyuT3JIqx/aHm9s7SvHOvCOJEuS3JjkwSQPJDlpnm23i9rP5L1JvpZk0VzddkmuTvJEknuHatPeVknOa/03JDlvHHOZyl7m9x/az+Y9Sf5LkiVDbZe0+T2U5LSh+umttjHJxdMaRFW5jHABjgDe2tYXAw8DxwG/A1zc6hcDn2/rZwD/DQiwBrhj3HPYhzn+BvAnwE3t8Q3A2rb+ZeDX2/o/B77c1tcC14977J15XQN8qK0fAiyZL9sNWAE8Ahw2tM3On6vbDvj7wFuBe4dq09pWwGuBTe3fpW196bjn9hLzOxVY0NY/PzS/44DvAYcCq4AfAAe35QfAMe3n+XvAcfs8hnH/RzjQFuCbwCnAQ8ARrXYE8FBbvxw4Z6j/i/32xwU4ErgNeDdwU/sFfHLoh/gk4Ja2fgtwUltf0Ppl3HPYy7xe0/6YZo/6fNluK4BH2x/GBW3bnTaXtx2wco8/ptPaVsA5wOVD9Z/rN+5lz/nt0fZe4Nq2fglwyVDbLW1bvrg9p+rXWzwMNYvarvsJwB3A4VX1WGt6HDi8re/+Jd5tS6vtr/4j8AlgV3u8DHi6qna2x8Pjf3Furf0nrf/+aBWwDfhP7RDblUlexTzZblW1Ffhd4C+Bxxhsi/XMj22323S31Zzahnv4pwz2lmBE8zMsZkmSVwNfBz5WVc8Mt9Ug5ufcNcxJzgSeqKr14x7LCCxgsNv/pao6AfhrBocyXjRXtxtAO35/FoNQfAPwKuD0sQ5qhObytupJ8ilgJ3DtKN/HsJgFSRYyCIprq+obrfyjJEe09iOAJ1p9K3DU0NOPbLX90TuBX0ryQ+A6BoeiLgOWJFnQ+gyP/8W5tfbXAE/N5oCnYQuwparuaI9vZBAe82G7AfxD4JGq2lZVzwPfYLA958O2222622qubUOSnA+cCZzbAhFGND/DYsSSBLgKeKCqvjDUtA7YfbXFeQzOZeyuf6BdsbEG+MnQrvR+paouqaojq2olg5Oe366qc4HvAO9r3fac2+45v6/13y//b6+qHgceTfK3W+lk4H7mwXZr/hJYk+RvtJ/R3fOb89tuyHS31S3AqUmWtj2vU1ttv5TkdAaHgH+pqn461LQOWNuuYFsFrAbuBO4CVrcr3g5h8Du7bp/fcNwnbeb7Avw9Bru/9wB3t+UMBsd7bwM2AH8GvLb1D/BFBlctfB+YGPcc9nGe7+L/Xw11TPvh3Aj8Z+DQVl/UHm9s7ceMe9ydOR0PTLZt96cMrpCZN9sN+C3gQeBe4KsMrp6Zk9sO+BqDcy/PM9gr/ODL2VYMjv1vbMuvjXtenfltZHAOYvfflS8P9f9Um99DwHuG6mcwuCLzB8CnpjMGP+5DktTlYShJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEizIMnb2vcOLEryqvY9Em8Z97ikfeVNedIsSfJZBndCH8bgc6f+3ZiHJO0zw0KaJe3zeO4CngPeUVUvjHlI0j7zMJQ0e5YBr2bwjYmLxjwWaVrcs5BmSZJ1DD7KfRWDb3D76JiHJO2zBf0ukl6pJB8Anq+qP0lyMPC/k7y7qr497rFJ+8I9C0lSl+csJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lS1/8DHIX/kXEcddcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2.plot(capsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram-style data (with low and high edges) are handled similarly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kQ/gBIBnCDwDJEH4ASIbwA0AyhB8AkiH8AJAM4QfWwPbP2v6a7R2dq0UdsX1l6bmAs8E7d4E1sv3HknZIeouk453rPgCbHuEH1qjzS8WekfR9SVdHBFeKQk/gVA+wdoOSzpN0vuaP/IGewBE/sEa2H5b0gKTdki6OiFsLjwScldRX4ALWyvavSDoVEX/fuWD4v9p+d0T8c+nZgJVwxA8AyXCOHwCSIfwAkAzhB4BkCD8AJEP4ASAZwg8AyRB+AEiG8ANAMv8Pi6zd3+5SlpgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlo2 = np.asarray([0.1, 0.2, 0.4, 0.8, 1.5])\n",
    "xhi2 = np.asarray([0.2, 0.4, 0.6, 1.1, 2.0])\n",
    "y2 = np.asarray([10, 12, 3, 0, 4])\n",
    "\n",
    "data3 = data.Data1DInt('int1', xlo2, xhi2, y2)\n",
    "\n",
    "plot3 = plot.DataHistogramPlot()\n",
    "plot3.prepare(data3)\n",
    "plot3.plot(xlog=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to see the data drawn \"like a histogram\" then we need to set the `linestyle` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3.plot(xlog=True, linestyle='solid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram-style plots are an example of a plot using a different name for the preference settings, in this case `histo_prefs`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xerrorbars': False,\n",
       " 'yerrorbars': True,\n",
       " 'ecolor': None,\n",
       " 'capsize': None,\n",
       " 'barsabove': False,\n",
       " 'xlog': False,\n",
       " 'ylog': False,\n",
       " 'linestyle': 'None',\n",
       " 'drawstyle': 'default',\n",
       " 'color': None,\n",
       " 'alpha': None,\n",
       " 'marker': '.',\n",
       " 'markerfacecolor': None,\n",
       " 'markersize': None,\n",
       " 'linecolor': None}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.DataHistogramPlot.histo_prefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we explicitly set the error values, but we can also use one of the chi-square statistics to come up with error values. In this case it's just the square-root of the data value (so, for $x \\sim 1$ bin, we have an error of 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sherpa.stats import Chi2DataVar\n",
    "\n",
    "plot4 = plot.DataHistogramPlot()\n",
    "plot4.prepare(data3, stat=Chi2DataVar())\n",
    "plot4.plot(linestyle='dashed', marker=None, ecolor='orange', capsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PHA-related plots\n",
    "\n",
    "We start with an ARF from a rather simple instrument. This time we also use the `SplitPlot` class to create multiple plots (although this could be done just as easily with `matplotlib` functions):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = np.arange(0.1, 11, 0.01)\n",
    "elo = energies[:-1]\n",
    "ehi = energies[1:]\n",
    "\n",
    "arf = 100 * np.ones_like(elo)\n",
    "arf[elo < 4] = 200\n",
    "\n",
    "darf = astrodata.DataARF('arf', elo, ehi, arf)\n",
    "aplot = astroplot.ARFPlot()\n",
    "aplot.prepare(darf)\n",
    "\n",
    "splot = plot.SplitPlot()\n",
    "\n",
    "splot.addplot(aplot)\n",
    "splot.addplot(aplot, xlog=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preferences for the split plot has a different \"flavor\" to the other types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'left': None,\n",
       " 'right': None,\n",
       " 'bottom': None,\n",
       " 'top': None,\n",
       " 'wspace': 0.3,\n",
       " 'hspace': 0.4}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splot.plot_prefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does allow us to tweak the plot layout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "splot.reset()\n",
    "splot.plot_prefs['hspace'] = 0.6\n",
    "\n",
    "splot.addplot(aplot)\n",
    "splot.addplot(aplot, xlog=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A PHA, which matches the ARF, can be created (with a sinusoidal pattern just to show something different):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chans = np.arange(1, len(elo) + 1, dtype=np.int16)\n",
    "counts = 5 + 5 * np.sin(elo * 4)\n",
    "counts = counts.astype(np.int)\n",
    "\n",
    "dpha = astrodata.DataPHA('pha', chans, counts)\n",
    "\n",
    "pplot = astroplot.DataPHAPlot()\n",
    "pplot.prepare(dpha)\n",
    "\n",
    "pplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the ARF to the data allows us to change to `energy` units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpha.set_arf(darf)\n",
    "dpha.set_analysis('energy')\n",
    "\n",
    "pplot.prepare(dpha)\n",
    "pplot.plot(linestyle='solid', marker=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping the data - in this case in 20-channel groups - allows us to check the \"x errorbar\" handling (the 'errors' here just indicate the bin width, and so match the overplotted orange line):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpha.group_bins(20)\n",
    "\n",
    "pplot.prepare(dpha, stat=Chi2DataVar())\n",
    "pplot.plot(xerrorbars=True, yerrorbars=True)\n",
    "pplot.overplot(linestyle='solid', alpha=0.5, marker=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how a model looks for this dataset - in this case a simple sinusoidal model which is multiplied by the ARF (shown earlier), and so is not going to match the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apply_arf(sin)\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   sin.period   thawed            4        1e-10           10           \n",
      "   sin.offset   thawed            0            0  3.40282e+38           \n",
      "   sin.ampl     thawed            1        1e-05  3.40282e+38           \n"
     ]
    }
   ],
   "source": [
    "from sherpa.models.basic import Sin\n",
    "from sherpa.astro.instrument import Response1D\n",
    "\n",
    "mdl = Sin()\n",
    "mdl.period = 4\n",
    "\n",
    "# Note that the response information - in this case the ARF and channel-to-energy mapping - needs\n",
    "# to be applied to the model, which is done by the Response1D class in this example.\n",
    "#\n",
    "rsp = Response1D(dpha)\n",
    "full_model = rsp(mdl)\n",
    "print(full_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `ModelHistogram` class does not use the grouping of the PHA dataset, so it shows the model evaluated per channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplot = astroplot.ModelHistogram()\n",
    "mplot.prepare(dpha, full_model)\n",
    "\n",
    "mplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discontinuity at 4 keV is because of the step function in the ARF (200 cm$^2$ below this energy and 100 cm$^2$ above it).\n",
    "\n",
    "The `ModelPHAHistogram` class does group the model to match the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplot2 = astroplot.ModelPHAHistogram()\n",
    "mplot2.prepare(dpha, full_model)\n",
    "\n",
    "mplot.plot()\n",
    "mplot2.overplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object-less plots\n",
    "\n",
    "There are a number of plot classes that don't need a data object, such as scatter plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lrDQ+B/wX4JsicgrwrpqMyGg4RrvgjPO9uPsr9Z5hjEXK8Wn0qOoRVf0b4FLg3DQHicgJIvKMiDwrIhtF5O/87WeIyBoR2SIid4lIi799nP96i//+7NC5vu5v3ywiHypj7MYQGO0O7VK+F/PLGEYh5aw0/pf7Q1W/JiJ/mfK448AHVPU1EWkGnhKRR4C/Av4fVb1TRG4BPg983/+/W1XPFJGrgX8CrhKRdwBXA/OA6cAqETlbVXNl3INRAU5w9vXnR6XgLOV7GUl+GcOoB4NmhIvIY8A/q+rPQ9uWq+qSsi8mMh54Cs/M9b+A01S1X0QuAG5Q1Q+JyC/8v58WkSZgLzAZ+BqAqv6jf65gv6TrWUZ49TBnsGGMHYaaEX4G8FUROVdV/87fFnuyEgPIAp3AmcDNwMvAEVXt93fpAk73/z4d2AngK5SjwCR/++rQacPHGDVmJDi0DcOoPWl8GkeADwJTReQhEZlQ7kVUNaeq7wLagfOAt5V7jrSIyBIRWSci6w4cOFCry5TE+iXUnkqesX0uhjF00qw0xF8R/IWIXIdnXqpoyqmqR0TkceACYKKINPnnbgd2+bvtAmYAXb55agJwKLTdET4mfI3lwHLwzFOVjHMojPZIo0agkmec9pg71uzgkQ17uGz+NK45f2atbsEwRixpVhq3uD9U9XbgOuCXaS8gIpNFZKL/94nAJcAm4HHgk/5u1wIP+n+v9F/jv/8r9RwvK4Gr/eiqM4CzgGfSjqNejPZIo0agkmec5pg71uzgGw88z29eOsg3HnieO9bsqNqYbZVjjBYGXWmo6g8irzvxcjbSMg34se/XyAB3q+rDIvICcKeI/APwH8CP/P1/BPybiGwBDuNFTKGqG0XkbuAFoB/4UiNGTo32SKNGoJJnnOaYRzbsKXpdjdWGrT6N0UTNy4io6nPAH8ds34rn34hufxP4VMK5vgN8p9pjrAbh6CIL0aweSVFbVyxoR/z/0zzjNKGzl82fxm9eOljwuhpYgqAxmrDaU1Ugbib5pfefOdzDGvHEPVegYNsVC9pTn2+wCDC3qqi2T8NWn8ZowpRGFbCZZG1I8kPU8llfc/7MqjvALUHQGE2Y0qgCNpOsDUnPdSQ+a8tzGVuM5mRY6xFeJUbzl6QWpH1ecfvZszYamdEQ+GA9wuvAaJtJ1lIwl/Ojinuuo+1ZG6OL0W6uNqVhFFGNmVIppTPaf1TG2Ga0m6tNaTQ4w2GKGapQH0zpVPKjGskmqZE8dqN8RnvggymNBqbWM/6k/YY6UxpM6ZT7oxrJNuKRPHajckazCdWURgNT6xl/qf2GMlNKo3TK+VGNZHPWSB67YcRhSqOBSRK+blXQNr6F7mO9iYI9rcCK2+9L7z+zYuFW7eV5LW3EtTYdRcfeNr6Fmx/fMirNFsbYwJRGAxMnfN2q4HhfHgUyQuIqIq2wrYVQrubyvFY24kqr5ZYzjvDY28a3sOzhjWaqMkY0pjQanKjwdasCl11TahWRVtiOBMddLWzE5ZqOKvVPuLHf/PgWM1UZIx5TGiMMtyro7cuTx1tplFodpBW2g+3XqBFAQxlXuSusofonRnsopjE2MKUxwoiaO0r5NCohKQN7KBFAtVI4Qx1XuSusoQr9kbCiM4zBMKXRIJQjWGsVzpckhIcyw+7c3s1nfrg6ELQrvrCoQBlVWkoEqhOZVM6zrIbQH82hmMbYwJRGA9AIsfyd27u5adWLgYM9LISHMsO+f30Xvf15wKtOe//6rgKHfiXhwG6/4TD3mNA3xjqmNBqAtDPmWph5Ord3c9/6Lu7t7KI/50dkUegnGcoMO1oO070eSjiw228kmHsa1RdkGJViSqMBSDNjrsVqJBq+C55j/d1nnspXLj67KmakKxe0c++6nfTllOascKXfNKla4cD1nPmXqwAaYQVpGNXGlEYDkGbGXIvM4mj4ruDlfEQVxlDMSAtntbFiyQXBvQFBcttICgeuRAFYNrgxGjGl0SAMNmMuN7M4zaw4fM5sRvhUx4yintvVMiNF/RgZEZZdPj9VW9xG8CNUogAsxNYYjZjSaEDiBH45mcVpZ8VpZvHVzCoPC968Kksf3MDc01qHXSGkoRIF0CirJMOoJqY0GoxSAj9tZnE5s+LBZvFO8N23vgspMe60CigjQt7vFpnLKzeterHAHNaoVKoAGmGVZBjVxJRGgxEn8N12J6wGm/XWwiziQmfvW99VcuUymAJadvl8lj64gVxeUeC3Ww6ydtvhEeEkNgVgGKY0Go4430XcyiNsqnKKpVahqKVWLuVGFF1z/kzmntbKTate5LdbDgbnvH9914g34wxHeO1IDOkdiWM2BjCl0WBEBX6SwF44q43Ne3uCWfu45nhTVjUoVaK90gJ+X7n4bNZuO+w54bMZ7lm3k/68NlSZknKoVUh0qfuq9JrD+bwsDHnkU3OlISIzgJ8AU/Fyu5ar6vdE5BTgLmA2sA34tKp2i4gA3wM+AhwDrlPV9f65rgW+6Z/6H1T1x7Uef7VJ84ONCvy4qKm28S0sfXAD/XnPP3C8L1/kH6iWcEhaucQptM17e3hkwx4umz+Na86fWfLe3Tl3HXmDO5/ZUXGZkkYQQtUOr01zX5Vcc7ifl4Uhj3zqsdLoB/5aVdeLSCvQKSKPAtcBj6nqjSLyNeBrwFeBy4Cz/H/nA98HzveVzLeBDjzl0ykiK1W1uw73UBUq+cEmRU1lRMjlB/Kto/4BoKrCIW7lEl2B9LzRx//1i80A/OalgwCB4oi7d8f86RMq9sFUSwgNVcFW24+U5r4queZwC20LQx751FxpqOoeYI//d4+IbAJOBy4HLvJ3+zHwBJ7SuBz4iaoqsFpEJorINH/fR1X1MICveD4MrKj1PVSLSn+wcVFTqJLNDEQiqVLkPA9fK85nkFTRtpzCieEVyE2rXix4/5ENewKlEb33+9d3cZ/vXG9pyrB08Tw27D5aMkIrjnKEUNK9VWP2XW0/UtqWueVec7iFtoUhj3zq6tMQkdnAHwNrgKm+QgHYi2e+Ak+h7Awd1uVvS9oevcYSYAnAzJkzo28PK0MVcNHjly6eR/ex3mAFEj1vkLgX4zOA4pVI3LakH3V4fC5B77L504IVhnuddO9KoVLbsPtoqgituDG451BKCJVSDNWafSf5kSpZxaQVruX6rhpBaFsU2simbkpDRN4C3Ad8RVVf9VwXHqqqIhKtbVcRqrocWA7Q0dFRlXNWi7Q/2FIlOZKOn3taa0GpjrAwjfMZAEHNqd4+b9uzO4/wZp9XkXawwolx43OrijifRnTs4IXxOiUiUJbgLnd1UEox1HL2PZRVTK2EqwltYyjURWmISDOewvipqt7vb94nItNUdY9vftrvb98FzAgd3u5v28WAOcttf6KW464FaX6wYQHXG3FwJx3vtif5DsICetGcSWze2zPQMhZ4+NndbNrbE5xPMpIoPEsJ4GvOn1nkAE+696gSuS8yxrTPKI2SKaUYajn7Hm4fgmFUm3pETwnwI2CTqv7fobdWAtcCN/r/Pxja/mURuRPPEX7UVyy/AL4rIu4Xdynw9VqPvxS1Cl10As61dC0nAS5OSH3p/WcWCcXVWw+REc8PAhQoDIB5005OvFYpAVzpMxlMcEfPW24trsHOX83Zd3isw+1DMIxqI6q1teCIyIXAb4Dn8Sa1AN/A82vcDcwEtuOF3B72lcy/4jm5jwF/pqrr/HN9zj8W4Duq+j9LXbujo0PXrVtX5TvyqFXoohM4beNbeGTDniABLivwV5fOjS3wFxZS4PklnJAarCptuCx6mO9+4pzEFUP0mmEnetqKuOEeHmn8J3Hndec52HOcJzbvL8rzGK5ku7iVnjl+jZGEiHSqakfce/WInnoKEoNiPhizvwJfSjjXbcBt1Rtd5dTC7BAVOEsXzwsS4Mrps1EqW9yxcFYbSxfP4661O9i451U0r4jAvOkTuOrcZBNT+PhK8gbilNVgz6/Uee9f31VwLmfOu2z+NG5YuSHo47FiyQV1EdhxYy1nddEIiYqGUQrLCK+QWpgdCnwZ/Xm6j/VW1GfDrUZKzfo7t3cHOR9NGeFT580sKoteLuVUug338HD7RldMg5l44s7lzHm/23KQnP9Gb065z28zG6YWAjptGZg4hjvxzjDSYEqjQmrhPG0b3xL4GPLqvR7M1t42vgURQVTJZosFal69KKmo0Ay/n8sr0yeeOOR7iCYixq1yknp4wICSa8oIiBSYruKedcG5shneflorz+86Sl6Ll7bR17US0NHvRTkrUnOaGyMBUxpDoNqhi93HehEG+mg/smFPyX4Tndu7ueGhjUFmeD6fD95bNGcSTRmhN+dVk723s4srQyuJWjloF84aqImV1+JaUknKtqDce04Bb9zh1VOciS0ageX8OdmMoEB/TmluyjBv+oTAUQ5w06oXA7NWtQV09HuR9jnX2mlupi+jGpjSaCDaxrcUOKV/81LpqKnVWw/R1z+gKHJ5CgoafqpjBnes2YECuVxxN72hrpSSnOHhmli9MQI5TtlGVyCIkMt5K4hdR96gc3t3yVBjR1SJRMuvZDNCPq/k1VPOGUgVfVUp5TznNCu1SjHTl1EtTGk0ENGVBpSeBS+aM4nmpgy9vuJozhbmVlyxoL0g9yEqGIeyUkoSQqu3HiqoiZURSSWQk5L/7lm3kzuf2cH9oSzxtDNmd3/hVUw+NzA2Ad591qlcNn9ayU6IQ6Wc5+z2iwZEDJbxPhhm+jKqhSmNGIZrGb9oziTGNWeCH7ebBUedxOHVwoovLOL+9V0oFJif3PtpWsSWut9wCLArWeKyzOOEUHAPfXkyGeH6C89ILZCjwnX11kP0+6uCcCZ70oy5c3s3n1n+dFHElFvFRMOLsxnhKxefXVeBmua7FQ2ISDL1pT0fDH/NKWP0YEojwnAu46NC3s0uh9I3w72XZMOPu19X3nzetJO5/eltwXFuFZQRaMpmaMp4lXbDQmgojuCkBL7e/jzir1hKne++9V30+iuJcMSUG5NbufTnlExGWHb5/ODYegjUtN+tsIAXv5pxnO+lnO9qLQI3qon5W0YOpjQi1HrWGZ25R38kUSVQ5CPoS983IbrCCNvwk6Ks/umRTTyzzas2Hy4+CANms7x6PpKrz5vJ9IknBueKmr4caQRykgBcunheoDCXPbyRpYvnJZ6vVMSUG9MVC9pjV2z1EKhpv1txK8S4+y33u1rtwI1qYf6WkYUpjQjlLuPTzpA6t3cHM90+P6IpIwz6I1m99VBQ/hwgE6kJNVhmdka88ul59a737jNPLWjUtGjOJDIZIe+Pae22wduTZMRTPC6vo9SPPs5XEeffSBKA3cd6yas3tuN9XjXcJAF/xYJ27vJXEk1ZCUJ5wyQJznoI1HK+W+HxhItRJoUvD6aQG3kWb/6WkYUpjQjlzDrDwrIpm+GTC9uL/Arh/aI29TQ/krCJJiOej8DZ9iHevn9fOEtaPVOM4JmRLps/rSgqJ1xJJqmojAAX+k7j6AppsB+9E4BRZXb9hWfQemIzi+ZMSsw3SQoddgmMrpSI4GWzZ8QzoqnC5r09ZQmfWgvXSlc0pRTdYOcbCbP4cpRpoyvAsYApjRjSzjqjDssVa3Zw77qdQcJaVKhGBbKbsQ8240xyZl+5oL1IWAPcs25ncK1MBj7wtilMbh3HvOkTipzSq7ceIlx/rCkjLP6jaax8dneQaOiytsMrlDBpf/Th55VX5ZYntwb+kbxqbL7JwlnJocPO8e38GC6cFryExaUPbiiZ5xKmXsK13BXNYEJysPMVfEdjWgI3AmmV6UhQgGMBUxpDIBqVo3gO2DvW7ChoJBTNXP7kwnbmT5+QOowyyZmtFPsL7l/f5SfHeeQVVm3aR0tTBiju5qd4Qrs/583+l10+n+5jvQXXdyuWUuNL86NfNGdSYC4Lj68volDD+SZQHDoc9scU3KtfP8udPp/X1KaORjSRVENIBivVCiom15M0yrQRP6OxiCmNIRCNynG+imikS5JQdbPIzXt7ihRIXOXaqDP7ygWe8nFNj5xpKoxbLTjB3JQR+nKKZCQYc0bg4rdP5Yvve2swg4+GqObKEMBxuPu5/sIzuPWpV4KIoIx4K4Sw8M9mKFitJD2/RXMm0ZyVYKXR3JThc38ym1ufeoV8XmlpTh8J1YghqdUQku7Z3bTqxaBi8kgVuI34GY1FTGkMkXBUjiv3ncvFN/qJRkWF/RxhpzgU+irCZqiwMxsIzE1rtx1m7mmtXLmgnXt9ZZDNChkIwmLnT5/AvdKF4pmC+n05nVP41R/288X3vTUY60+vXzRQvtwP/ex5oy/WiR2+l6wfyhqukhudMbvVTDjn4441O4L9rzp3ZpFAi5uJLpzVxoolFwQ+DWcSvGTeaYPa+eMKI1bib6iljb1aQnLhrDa+cvHZqSomN7K/oNSKttHHPpowpVElnFC7MiakM46onyPqlwjPMKNmKGeT/tsHng/as7pQ3C+9/0xWLLmgQCg6obph91H6c97+UWtTXgdWEu4H6FYyLuT1lie3IlCUK7J666FA+fXnlW89uIENu48GQQHRGXP3sd6CviBhhQGeQ3swwkLiu584J/azSDquVGHEuH4lcdccLGqsGlQzFHiwczWSv6CUAoj7bBtp7GMBUxpVJklgJSau+bbmqFO8KTvgA7lyQXuRMurc3s2daweEbR6vdlV4DNFmR03ZTFEuQ8bf0BLKPA9nVX+qY0YQ8goEoa9hh6oX+TSgiHJ5ZcWagdIfg82Yu4/1Bl0EM0KRTyXuWZZTbjz83AoUWExhxHKcsENJXExLNUOBS52rUfwFlSiARhn7WMGURh1I+iHEZX8vnNXGHWt2kMvlCxzQ0R/8/eu7yOULr9N9rLeom53zswD05/Jc/PapPLZpHzmFlqxww8fmF1z7Gw88X5BVvb/neJF/Qxkoprh08TyWPbyxaOWiDBQrjGs3G6ZtfAsZEVQ1qFUV9wzjssyjCmyw555UGHEw80+cYConTyK8ugmXg28Uk0qj+AsqUQCNMvaxgimNElTLThr3Q3Db42yzSx/cEDQQ6s/FO6CjsUzZjCdswyGoUfIKc049iSeyGfL9eRApCkmNrkSmtI4LHKnRDPHevjzLn3w5MLO5Y8Mmt8EUAHh+GZfx3p9Xvvmz53ngP7o4e2prUa+NlqYM110wO3DwK/BUQjXguOceVWBuv0pqN6U1HxWEvfrRdfd0dpHP5+nPe618//7jpdvr1ppqmsKGQiUKoFHGPlYwpZFANe2k0R9CuJtbeObpzCelMsAdzuHdm1NP6PjO5b4EheFYtWlf4CfJ5bzmTOEf2xUL2rk7FFU1b/qEAkeqE36CZxLbfuhYoDAyAtHLu9WPu1+HqucbuWJBO8f7CpdMefUy09du6+aezi4uOntyQajxxj2vFlQDTjIvJQmg6KotzeeaJJjSmI9iQ7NDzyKn8K0yckpqRTVNYUMZQzUTII3qY0ojgaHYSV3JEGWg8mxSEb9oXkc0AzxcVC98/tVbDxWZlu5YsyNWcIfZevD1gfaoQuDvCEduuWS/vMIND20MhJkLLz7Qc5x9r74ZdMkDv5BhxsvodvLQ+UnC9xumty/PwZ7jiVno4AnXxzbtCyUrCic2Z8lmvHwOF3kWNyutRACV64RNe76li+exYffRILoOvxCho5ycknKum+T0buRZuSmAxsaURgKV2kk7t3fzmR8OzKrvemYHX3jPnKBchvsxRGeeYfPJ0sXzgtyLaOhqUKk1X1gq2/X8VjxzR3vbeLYfPlYwtkzIWS14qwgn+MNms7CvJKowXW5HNjPgrA/ncpxz+gSmnHwCU1rHFWTFR5P6wFMAk1vHBU7wOMJZ3uAJ11Wb9gX7O2f9/OkTisqjuL/LUfbVWl1Ga425UGQX0NA2voWlKzfQH8oxSVM7CgY3p5W6j6HcY6MrG6M+mNJIIG1MOBT+iFdvjXTTUwrKZbj6VOE8iLAj1gl/l3sBBDkNyx7eWOCQ7g0J+q/e+2wQfpsVmDmpWGnMndrKpr09gKeoLpgzic37eoIorV1H3uDkcU0Fq5WwMAuXHu/PwyVvm8zRY72s3d4N6gn+57qOMq65p6ho4bLL5/PNnz1foByuv/AMLpl3mndeP4oMPOW2cFYbZ09tpXVcE7c8uTU4xnXcC177JxxqE6XO7d3ctOrFilaXSaG44c+q3y9rctcXLwjCeuee1lqUYxI9Z7h0TFM2A6pFE4YopVbJlaygo1F41WoMVQmmuIYfUxolGCwmPC7Of9Gcwm56jnB9KheK+t1PnFMUShvuMtfbn+dbP3uenBY7md05e97o46of/I7w5URg3rST+d3LhV30XtzXE/ydAVpPbC7IaF/h13cSPMXzwVCWOMDBnuMF9/Ti3p4ixRT1L4R/5FefNzPIyXDXj0aRbdx9tMCsd/PjWwL/heCvPFzVXjylJjAkU6ITiG7VJAxeE+yONTsKeo7EheJGF0/hXBgoHZ4dLuwY7qcR94yjlFoll7uCjlOAgzWGcsdVW7hbPkZjYEqjTJLi/I/3ebWcvvOJc1jxhUX84Ncv85hvRnGCKGqKckIjyXkLAzP+OOtNBti451Ui+omTT2zhtt++UlSLSfHKiKhqQQSQq+EUdiznFSa3jisY2+TWcQXXiSoMp9iyvvM++iNfungeJzQnO6Y7t3dzw8oN9OWUe9ftZMWSC1g0x+sE6I5xM9yeN/rYuOdVLps/LZixxwnCwWz7cdWHRWDp4nmJAumONTv4xgPPA17osfts40JxJSOoen6ilpRmzkIfkGfaUr/6L35hx1ICv9QquVw/T1QBegEPyY2hoHbC3fIxGgNTGmUSjfNXCATuPet2BmaG5Z/tKDAxbNx9lHvW7SzrB//rzfuDhkhRMkBLs1fq/OmXDxYojsOv9xbslw0pCueQDYfWto1vKVJK0fsBz4Ry59qdBauXgjE5/4N4Z4/LBA+vKqI+iLjOe9/9xDlFQi4slFyobVJtr1LCK2lFoOplzyfxyIY9Ba9FCptbRQWzu1baWXd0NRA2BaU9VylfTjl+nuj3/VMdM4JqyUmrlVoJd8vHaAxqrjRE5DZgMbBfVef7204B7gJmA9uAT6tqt4gI8D3gI8Ax4DpVXe8fcy3wTf+0/6CqP6712OOICoT71ncFZp1oUb/ojzOua1yY8Kx40ZxJfG/Vi7FjiDZTmntaKzc+solNe17lteO5gn3F71vRc7w/EI7OFHNPZxcrvrCoICM7TF+oZaoz4yycOZHO7d2oekrrugtm8/TWQxx8vZdd3W94x/V7q64rFrR75iS/vWr4vqMrkO5jvWwJmc9gYOUSXonc/PgWdsf0J48TIIMJr4IVgX//bmXo+nbEfU6XzZ9WkLPSMauNM6e2Fjnjo8fGOeqjhKOtknwG9ZxdJ61MkhpDQe2Ee6XhuEZ1qcdK43bgX4GfhLZ9DXhMVW8Uka/5r78KXAac5f87H/g+cL6vZL4NdOD9pjtFZKWqxk/Da0xUINyfYBoZ7LikSrYtTRnec9bkIOdCgOkTT2Dvq8dR3wnqFIaL0nl255HYHI1cXrn1qa0o3iogkxkI9ewNCfembLEfxgnP+dMncMPKDQPVZP2IpSv9xLvbfvtKQUKhAnet3UHruKZgTH05DZoihZtE9fblg9pW4dFHO+9FfUmunLsrpBinhNrGtwwqvK5c0B74UO5f31XUtwOKZ/Yuou2utTt4Yc+rrNveTef2bu7xn22lEUtJ+w238zdOAQ62Wgk/12reg4XjDj81Vxqq+qSIzI5svhy4yP/7x8ATeErjcuAn6iUKrBaRiSIyzd/3UVU9DCAijwIfBlbUevyDEXXkRmeTST+WqIAIV7I93pdnVSg3IZuBA6/1ouqVNH/vWZMBz7Ye7h+ehKcLvH2ipqX9Pce5f31XQeOjMLlcnkc27ClQSP055fSJJwaO6nhlBf++cW/Btkc27GHuaa3c29lVkCsSrm0FnpL8wNwp3Le+K1Bqq7cOFEXszSmnTxzH3qNv+kpxoNS6U0LOSRs3Y4+LBrpyQTvzpk8IHO3RJMyosL/m/Jl0H+styFXJ+c/B+bfKjVhKqhwwkpy/cd9rc2CPLobLpzFVVZ1heC8w1f/7dGBnaL8uf1vS9iJEZAmwBGDmzNqXZYgLjXTCyvkx4kIkowJCoaCAoZOi0XwKVHn0hX088eIBcrl8yUS+NDy+eT+5nBbZ9R3ZjHDZ/Gms2XqooG+Fm7UnrawyAu+aMZFthwac5fOmnczqrYeCSruCF6H15EsHCgo3NmUz/OoP+wI/zT2dXXzuT2YXjHHXkTeDv/M64CyWUE/0uIq6N/58E8t/s7XAFOfMafet7yKX987llE0pYR9XIgWK/UFpzTVx+1UaIptmVl+LFUyS4hsNDuzhXvE1CsPuCFdVFZEhir6C8y0HlgN0dHRU7bxxJIVGxplcoj+WqIBwlWxvWvUiT710sCAD+qpzZ7J538bEMhTgzdjPndXG+h3dgbAtlTTnCCsMobgUyMxJJ7Fh91Fu+Nj8wIEedo5v3ttTpHAE+Og7pwcCw/H01kNcde7MwM8hAhfNncIX3/fWQPG6/horQuXSXemQOASKTFJJTto71uwoyPlwxzc3ZYJn6nxTrrR7krB3+TThlrjueUf9W2lt8Un7ucmEJBRzDDNUU9hQKXCc+7k/86dPGPEObFstDTBcSmOfiExT1T2++Wm/v30XMCO0X7u/bRcD5iy3/Yk6jDMgbpaRFBrpZrthYRzXlClOQHzl4rNZ88rhQCmoL5WWLp7HXWt3sGH30aLqtt6O8PudR/jA26YGIb6PbdpX8p6iSuWSd0xlzqknFQjWLftfY8v+1xDgO58oLqp38+MvFZ33PWedys9+v7to+3NdR9m4+/lAqeUVvr1yA3cu8RLe3DOeP30C2QwDyi8DJzZnacpKkEENnt/j0yHfyuqth5h7WmtiJNXyJ18uGI/gmZmc78TV8go7wsPZ+XGRV+BFTr37rFO5bP60RIVVKicjPNbofgtntbF08bzA5Lbs4Y0la1QNxRRWjVWJ+1673J87n9kxrMmA1cLCfQcYLqWxErgWuNH//8HQ9i+LyJ14jvCjvmL5BfBdEXGf0qXA1+s12KRZRlJoZNv4Fr69cgN5v3zEVefOiI3ESXIwfnJhe5AElwe+9bPnyTrHb8IYna3/0Rf2BYUA4xYZEto/6kd4oy9Hz/H+gmKA4fN/84HnCwRW5/ZudofMRADjW7JF1XDD54jmlPT5VXyBAke3hkaRy3uFFpuyGS59xxQATm0dx5UL2tm8t4dlD23khT2vFjihwyYp9/m9GSmM+MX3zino8vepjhkFjvBbfv0yj/9hP7m88vTL3hid0ox+9uFItsEi5FwWuAtdHWz22n2st8DkVkpgDcUUltb/Ntgs2+X+9OeTzYQjDQv3HaAeIbcr8FYJp4pIF14U1I3A3SLyeWA78Gl/95/jhdtuwQu5/TMAVT0sIn8PrPX3W+ac4vUgaZaRtFq4Y82OwDmcyyvz/UqxgxGeaTdlJHBw5xXy/QNtYZsyUhRt5HAJV4K3X7RMevhVuBSU4iWqZcVbFfX3D5T1cOT9Z7FwllcccfmTLxeN4VhvjijnzW7j9zuPeKuEyOomIwQBBOGkyeh58+oJ8nfOmBgIn3CSnSNOqLpoLcfE8c1c3TGDS+adVtCm9voLzwgSCbMZ4Ve+woCBMiBOaSZ99oNFyIVL17u6Wu4zqyTDO0oaU1hcWC/Eh0FX6lcZbULWwn0HqEf01GcS3vpgzL4KfCnhPLcBt1VxaKkp9QOIWy3ctXZH0etrzp9ZNMsML9ejs7nrLzwjiArKyEDiXHOTlxuxatM+thx4PbjGebPb+H3X0aCO1RUL2jnQc5xfvuCZqOJWD3HkFBa0T2Di+BYefaHQvNWU8Z5FnLCOQ/Bm81/7yNuDSC/Pae2tSF57M4eqVzdq6eJ5sUmT4XO5THNHNMkOCOzondu7g+cajtYCePWNPm5/ehs9x/sL2tQu/81WlvjFJXdH+pZD+jIgjuhnesWC9oJ7yuW99VSc+TJMuQKr1LiSVg3feOD5ojDocASa9bgY/LmOpnstxbA7wkcC5f4App58AnA0eD2uKcPfPvA8d63dUVgjioF+29HZXOuJzSy7fH7gUM8CV583k9ZxTdz61CtFYbbH+/Pc8NGB2eHmvT2D+jTAs8erFK46uo/18R87jhTtu+zyc1g4q42bEpIOozRlhVeP99O5vbvAxIJCz5sDK5LevsJs8UVzJvHoxr384MmtBaVNXKa5Y960kwtMYe+Y1srmvT3csWYHd6/dyTK/x0h/yAkkDPRjfzGSTJhXuPWpV7jrixcAFOSTOId7OTPm6GcqeDku0ZwWgOsumD2oySf6fiWCKim66e51O4vCoOMy+cuNyKrGmNNea7gYa05yUxopKSep6KK5U4Ly3dkM/L7rKGu3dcf6CY73eY2QXKROOErGCVrFy43YsOsoG3a/GlvG49muo2zetzHoieEKHYavFeb0thP56DnT6Dnez4v7elgbKlcy59ST2HrgtYL9Mwz07o5mRCfR5/cKuXvtTq6/8IyitrEO19/cPePO7d3c+tQrRfuFE+6coxU8E9fH3jmdh57bM9Dx0DcnLbt8ftCfxCHqCca1MSVa3GrCdfhzrXMnR0q9p2HRnEk0ZSQojX7FgnauWNDOfeu7+NWmfex9daAAZFJ0WBKVCqq4VfN967sKAgwWzmrjuV1Hi7oUpgn1jY4JBpIjN+/tCSZBbrJUqXBtJEE91pzkpjSqjAvFBM+n8IG3TSlI1IviYvpdpI4zC9zw0Ebed/ZkL/PZ9y8813W0pInJKSAoDJuNM03t7n6D2363DVQLTCbNWWHOqScVnbspO1CE0IXepo1nDpt+Xj3eX1BR1o3PJf45J2pR7w3f1+IS7sLKR4BXDr5epEzzqnQf62Xp4nkFSjQjXlHHcI0uwVMkTSHzFnjKKWxeSkM4d8dbHWmwSnLC947pEwpMfJfNn5bq3I5KBVXcqvl+/zvjOGtqK1+97O1DXsW43BcX3JDTgeTS3r6hCddGEtSjzX8zGKY0qkz4yywop7aOK4hbn9l2YoEvArwZ+VfvfdYrsx4q8bHqhX00Z4Vz2icUZB6DJ/guPPNUjr7Rx4ZdR8npgAKaNalQ6J/rO6KjZpFwqW1Hzhfw0cXMRXO9qKVSPchLETb9XLmgnR/8+uXA3+Kc8GteOcwnF7YHcf0u/+X6C88ImljFVV1tbsowrilTdM2m7ECCXPh+8lpY1BGgY3YbAqzb3s0da3Zwb2cXn1zYXrZgiubuuJViLucJUSeIXRRWXLOtNFRS4jzJbDRv+oSCfV2L33KFcHRMConBDUltjCu91nAK6tHovymFKY0qE/0yz/d/kC4pbvPenlgnclSRwECS2LzTJ7Bpz6vBikDxBN+TflnusKm/L6ds2V9oWpo+8UTOmtrK/p7jBT0wsllB81poxtL4FcRjm/ax8/CxshVGeEXiWpp+6f1n8s4ZEwOl4XD9RsY1l47rD8x4GWHBzImc7d9bEf5qZdGcSUW+BMcpJ7V4jaQipqrefq8VbSnBFNeMa1eomCLq1foSvLLm0eoA15w/s6gz42CCJ7xPOX6GUqac7mO9wecUNkOWS1R4wkBdtmyo94wAH3jblIqukXSt4RbUlSjZkYopjSoT/jJHS4tcsaC94Ac6GK7c9vzpE7hXuogLsvVlU0niEu3AE+JzTj2pQGElnSqnBF3/wpx28jjOntrKkxEfxyknNdMx6xTGt2RZ+exur2lSxisu+I0HnmfLvp7EfJBScf0LZ7Vx3QWzWf6breTyytpt3azd1k1zVmjKFOaB5EJKasWSC7jxkU1FyuHE5gyHE2761NZxJbs3OkHsQmfdPWZkYAV03QWz2bjnVU5ozgb9VSrtQRG3T5rch6gp577Qimfz3h5+uXEvfquOIc/aw8Kzc3t3QeFCIKj5tWrTPp586cCQfBH1ENT1crY3ilM/DaY0qkA4lNY5SxfOKuzCFy7hPa7ZmylDcZKdQ/CyjL9y8dkF9ZqqSV5hzuS3xK5y0rLv1eMciJnlHznWx6MvFPpy+vNaVMYjDpHkmlbOSR5Xxv3Sd3glzB7dtC9YMfW80cfNj29h0ZxJnNCcLTrfnqNvFm0DL7z4ytBn6a7tPsOwIM5HwmizGeGq87y+5eFWra5He7hJlZtcPLJhT+CnSTKFVWrHLyjtkZGgUGNGCpXsJe+Yyp+HOjXGUU5dq2jhwiDpL1fo9xiqsKyVwK2Xs72RnPppMKVRgrTmgrCd3/WoANh15A2aspmCHuALZw2UhUhK0ANPsLgsYyC2dHk1mHPqSYk1qgSvhIdqcg0rhdiiiYPVvCpFTgnKqEc/gzgneXgsT2zeH6y88jrQn72lKcOH550WO87wiicjXhHFqPAMd/lziYBJ0WCqXhXgcMFDV8bdOcU37+0JFEp4DOFmTlEqteOHV7+7j7zBimd2BL1DwrzZlxuSmStMkoKL1qZKKuhZilItBaopcOvlbK/FdWq5cjGlkYBTBn05pTkrrFhyQezDX731UEH0UVzUyNXnDdQ3uvnxLew68kZROfCWrJDHKyCYEVh2+fyC6yWVLh8KghfqGSeDBxSJMKPtxKLWrgX7QlH2+FBQhW/+7Hl2HHq9qP922/gWT/iqBmagvHpRX1Nax8WWaXc/xkOvx9vqL3nHVE5tHcfBnuNBeZK4WX44EfDWp17h+gvPYPlvthY8v2g+R1PWNXkayP525eYH6pYRZPu75lpAsEIKJxMO1Y4/L1Q8UCIrjXAE12C11iotZxK+h11H3uBOX4FVEmgQbSlQbcFeL2d7ta9T65WLKY0E4lqPJpV4CDtZo1EjubwyfeKJQKi2km+qcAIjmxE+9+4zuO1328j5TlOAJT9Zx9aDr/NGX66oZlM1yLiy56ECiUBBX/FcXksqDBi6whC86KXO7d3BM8krgUB2z9PlZriwzUxGuP7dZxT0Cr973c4ixSF4meLREu/gmaEumjuFDbuP8sSLB+j3o5zCDZBcrka4gVVeNVbhXnjWQEfFO9bsIJdzyYFeNr9bdV42fxprtx0OvicZCJprQXVnz3G92sNJoNEIrrS11iotZxLOx0nbwMwRVVzqP7daCPZKlXS5s/xqO/VrvUIypZGARF4fjIvOwfvAVyy5oMCnAcXd/MIfZC6XZ/7pEwbyLnwB1O8LmP5cnm/+7PlBTTxNvpnkVb/I4MZdR/l919HU95jLK09s3s/n/mQ2P/RLlmQzwhf8EiaDNXeqFtkMnD21lYUz2wqywKNhsi/t6ykQ+Lmc+v4NZc3WQ1w0dwoXzZ3C0WO9HH69l1NOaglKxed8n5D7rMKf59IHny9Qyu6HtnlvT0F+RzYzsAITvIz0aH92Vw23c3u3Z4J0SjAPnz63ndMnnhgIBlfc0CVyuu1xvjB3znKUiRNe4fa4vf3eKscptoWz2orCfcuttZZEWDlEV03u/XKFZVRxuZYCtTLFlOtsr3SWX02nfq1XSKY0ErhiQXvBrPWJzfuDekZR4j7wpL4IzpY75eQTaG7qIZfzXp/QnA1amEI6n0A+7ykbJwBu/PmmspQGwC9f2Ec2tOpx57z+wjMCRVJr8gorntlBU0YKZvNROiOlTdy+ircadCG8Lb45cfXWQ0G0VE5h6YMbuOuLF/DdT5zjmR9/uDrWT+SSCMNCH7xquxl/NpFTr83tgpltPBOKyLr58ZfYuPuoHxY9cHAmI0Vmr7BQdZnuXuXgN4KilOEffTkzyKT2uHmFp146yNpthxMFWrm11uKundbnUK6wTFI0jeI4boSkw1qHI5vSSGDhrDY+1TGDFa5Udl7L+gJEfwzOAX7bb19h6/7XeGzTPpoywgffPpUnNu/nsU37ArNQWjGdB367xRMAH553WmJo7WBEy424RDtXoDzjJ4O4XiFRoX7K+GYOH+sr2JYhOTIsirvnOH9EwThD1z1zyls4ZXwzndu7ixzxvTkN2sRGTUruM3SZ3lHe2T6BpR+dF+twz0YUWl9O2RWJvtp15E1+umaHHwLsJWtmRIp8VI6ocHe5DE3ZDFedV1hSv5wZZMHKNq9cdd4Mdh4+FjT4igq0qEkljdCJM8PUwucwWM+RRqJRkg5r+YxMaZTgygXtZdtc43C22zvX7hhooKSe0Nn36ptB34EkCTvxxCZefbM/dvWRV698SKUKI4moQL3q3BkIxRV8Ad579uSi64ufu5CGjK9hsqFSE1k/PjVJj7xy8DW2lHCm3LV2B/OmT0AonO0HpVB2Fa/IWpoyLP3ovIGItVBp+YzAFy70/E7u2TRnhRNjwnjB+2z/9PyZTA+Zo6J0bu/mplUvDoTa5rwvgXOWuz7sjnJmkHFmHIC12w4XfZ+jWezLLp/PNefPLIoeC183yQxTbZ9Do4SjltuEaqTkXFSCKY0ShL8ArueD2+4Y7MuU1AAIPB2xYffRIMImScQeeaO/5DgrMSBl8DLCB5vdgyfMnd04Tg88GKOwyjFr5fNw8Tum8tZQ18BcXvn4u6bz8HN7yOe91rBRU1Ep+vOe4gjv159T/u3pbfz7xr0FSrEpA1edO7OgIKFbabqmTACr/rCfya3jEFXeMX0CX3zfWxMz/F2BwqSZfFgYulDbpqy30giHaEdJO4NMEl5x3+eCnBMt7Bvixh4V3GlCaqvhc2gEc08lTahGo7JwmNIYBPfhx31p0nyZ3Jc+iVwePvi2yQBFZTUqISMwd2prbPZ2NpRz0dSU4XN/Mjs22c4FAaj/96c6ZrBwVhubY87p9hsKCvzqD/vZP/3kgu2HXu/lri96volndx4peD5p+p/3xWiW6Ipo9qTxfHjeabSe2Fy0r6tI64RWuDzL3p7jfPF9bw2cyLf99hVe3v8aiqcw/j5kjoqLXrpr7Y5gIhENta3WLDVOeMV9n5cunhfUyYKBci/uO37Tqhdjk1QHC6mths+hEcw9jaC4GglTGoOQ9KOJW4rHfZnclz4ckx9OJgOvXEU0WqscBM95+8mF3qzuxkc2Fb3fMbuN3v48z/mO8lwun1iO2wk+VIOGTq5672AlSyoll9eiPiThvtxeoqSQ8/NmbvjYfDbuPsqBnuM89od9RSuPjMALe+KVXJhth44VJABGa1799PpF3LTqxaJS8P25AcHqakglrTrD35Pe/jzf8hM7HU3ZTEEiZ5qVbJI/IY3CiX5vu4/1FvRuyWS80vzhhEaXRxIW3FcsaC+oguCo5ky7Ecw9jaC4GglTGiUY7EeT5ssUNXF1H+ul542+ghn+yeOauO23r8SOwZWecN37inIQBC4OZTDfsWZHUX0lxesd4ZSV59cW5k07mbXbDsdmNZ895S0sfuf0ojDQcnRGi2/+Ch/jxitQtHL44vveykVzpwR5A3NPa+VvH3g+yBpuyghXnV9oRvrGA88XKQwBprSOK+hXMRjONxTuWOdWjl+5+Gye3nqooOeEKxUfJklYhr8nxAQSfHJhfJ+OpJVsUt+KUr0swueP+9669939L3t4I1f4TmxnPguvhsI+kHkp2xlXynCbexpBcTUSpjRK4GZk0R9NeEaY5ssU/dLf/PiWwLySES8rOy4nosWfUYf7OLsENyeMVeHxzfuZ0joOiG+B6hhQGN7M/vantwUz67bxLXzrwQEBvGlvD+87uy8QUuEw0DjBFyaD56O4aO4U7lq7g2f91Y0A15w3k+/4Ia+Pb94fKEEXyhqetUf7ZrhEyfCzjK7QBK8b4sffdXpinatoFFT4+bgQ3mh+wl1LLuAHv36ZrQdf55TxzZw1tTXx/sOE+3Fv3H20KJCgJSuBkzpK0ko2bjtQsC1clSBqOk363oa7K7pOg2Hl4r774TySOB/IaGS4FVcjYUqjBNEZWVhhOCr5MrnzHvdt2l5Yq7ekaMoKF82dkljOYuGsNq5Y0M6yhzYGwrg/p/x0zQ7u6ezine0TSl7bKRooriYbFvDgZWTPnHQSN6zc4HWfywpXnTeT+dMncMNDGwNfjRPcTdmBaq9PbN7PEy8eCHp2OPOPM3Wt3nqI98+dEhQ1VKXAvBdW2O4acau5Kxa0c4/f1KkpK3yqwysUuGH3Uc6b3cbx/jwXzJnE7U9vCz7H95w1mVUvFDfGEjyFoqqx+QnLP9sRKLN1fqZ4KadoXI/wsInyj/zw3qTjk1aySdsTe1nEmE7jvrfR87pOg1HlsmjOpEQfiDH6MaVRglotSxfO8sp73/KkVybDlRVvyngrizQNeV4/XhxR1dufZ922bpoyxZ3pHKUE41XnzuTZroFIIFVPkbiwU2eeueb8mcw9rbVg1ZPNCO+fO9ClMBw+mmTaaMoIzU2ZoJhf2/iW4NrRwnbOXxOnRFd8obCHQ7iAZFNWmH/6hAJfBcBvXjpQ0OfBRSyV6uMBEf9EX56bVr0YO5mI7hs3cy+lMNy9JTmVS0VGhVel5djh0zqxF85qC3wg+bzS0lw7O/9IKhk+VjClMQi1WpbGOaHVb09ailIhvDCwkvjw/NOCtqpNWUEgyDBOEozXnD+THYdeD2o+jWvOFDmn3arCmUn6885M5nUpdOHD2ayQCQnjONNGLq984O1TePwP+8nlPTu6M3PE+YKSCH9GNz++pcDv0+/3KY/2pI4K2LSCKQhs6MsXJFfGrTjCQRDi2/7jZu6lSPr+JUVGhbdVMuFJ+313E4daCXSX21RJFVyjtpjSGCYumz+tICInyfwSpVQIb7j5TzQ+3h072A/8ax95O5fMO63gOGdmciYLR9ScMX/6BO5dt9MbC3DDR4uVU/SYKa3jgoq/UTNKqXDnJKIFJIHEc0fNNWkIR1T9dsvBkpFzC2cVlsFf9vDG1I2TqkGt7fC1On+cP8tCXRsHUxrDRLhH9LxpJwc9sNPOdPv680HWdV6LneZxpoVKZ5th80/0fOHZbHjlkctrbPe96DHgVRROMqOUGyO/cFZbQVHCJ148UDJZrhJcRFVcdnUU51yOU1xGPGn9WcbwMKKUhoh8GPgekAVuVdUbh3lIQyLaIzoNcUK31jbfUjPK6Htp4tnLMaNUEiMfPn+tbOJp/V0W418+af1ZxvAgWqtsrSojIlngReASoAtYC3xGVV9IOqajo0PXrVtXpxEaUBshPdKdoSN9/MOBPbPhRUQ6VbUj9r0RpDQuAG5Q1Q/5r78OoKr/mHSMKQ3DMIzyKaU0MvUezBA4HdgZet3lbzMMwzDqxEhSGqkQkSUisk5E1h04cGC4h2MYhjGqGElKYxcwI/S63d9WgKouV9UOVe2YPHly3QZnGIYxFhhJSmMtcJaInCEiLcDVwMphHpNhGMaYYsSE3Kpqv4h8GfgFXsjtbaq6cZiHZRiGMaYYMUoDQFV/Dvx8uMdhGIYxVhkxIbeVICIHgO1lHjaBcLGloe2b9H7c9ui2Uq/Df58KFHYIqgy776Hva/c9+Pa09xl+Xa17ThpTpfuO5vueqKrxTmFVtX+hf8Dyau2b9H7c9ui2Uq8jf6+z+7b7Hg33nfRete7Z7rs6+44kR3i9eKiK+ya9H7c9uq3U63LGmBa776Hva/c9+PZy7tPuu3pU7b5HtXlqLCAi6zQhc3M0Y/c9dhiL9wyNe9+20hj5LB/uAQwTdt9jh7F4z9Cg920rDcMwDCM1ttIwDMMwUmNKwzAMw0iNKQ3DMAwjNaY0RhEiMkdEfiQi9w73WOqJiHxcRH4oIneJyKXDPZ56ISJvF5FbROReEfkvwz2eeiIiJ/nVrBcP91jqhYhcJCK/8T/zi4ZrHKY0GhwRuU1E9ovIhsj2D4vIZhHZIiJfA1DVrar6+eEZaXUp875/pqpfAP4cuGo4xlstyrzvTar658CngXcPx3irRTn37fNV4O76jrL6lHnfCrwGnIDXT2h4qFbGof2rzT/gvcACYENoWxZ4GZgDtADPAu8IvX/vcI97mO77n4EFwz32et438DHgEeCa4R57ve4br+Xz1cB1wOLhHnsd7zvjvz8V+OlwjdlWGg2Oqj4JHI5sPg/Yot7Kohe4E7i87oOrIeXct3j8E/CIqq6v91irSbmft6quVNXLgD+t70irS5n3fRGwCLgG+IKIjFg5Vs59q2ref78bGFfHYRYwoqrcGgFxrW/PF5FJwHeAPxaRr2uJ/ukjlNj7Bv4SuBiYICJnquotwzG4GpL0eV8EXIEnQEZj9efY+1bVLwOIyHXAwZAwHS0kfd5XAB8CJgL/OgzjAkxpjCpU9RCeXX9Moar/AvzLcI+j3qjqE8ATwzyMYUNVbx/uMdQTVb0fuH+4xzFil3VjnFStb0chdt8edt+jm4a+b1MaI5Ox2vrW7tvu2+57mDGl0eCIyArgaWCuiHSJyOdVtR9wrW83AXfrKGt9a/dt92333Zj3bQULDcMwjNTYSsMwDMNIjSkNwzAMIzWmNAzDMIzUmNIwDMMwUmNKwzAMw0iNKQ3DMAwjNaY0DMMwjNSY0jAMwzBSY0rDMOqAiPy5iPze//eKiDw+3GMyjEqwjHDDqCMi0gz8CvhvqvrQcI/HMMrFVhqGUV++B/xKVR8aqz3djZGNKQ3DqBN+06BZwN/B6OrpbowdrAmTYdQBEVkI/A3wnlHYac4YQ9hKwzDqw5eBU4DHfWf4rcM9IMOoBHOEG8YwEerpfglw6yjs6W6MQkxpGIZhGKkx85RhGIaRGlMahmEYRmpMaRiGYRipMaVhGIZhpMaUhmEYhpEaUxqGYRhGakxpGIZhGKkxpWEYhmGkxpSGYRiGkZr/H4kXfvlVo+p0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1273)\n",
    "\n",
    "# I've never used the Wald distribution before, so let's see how it looks...\n",
    "#\n",
    "z1 = np.random.wald(1000, 20, size=1000)\n",
    "z2 = np.random.wald(1000, 2000, size=1000)\n",
    "\n",
    "splot = plot.ScatterPlot()\n",
    "splot.prepare(z1, z2, xlabel='z$_1$', ylabel='z$_2$', name='(z$_1$, z$_2$)')\n",
    "splot.plot(xlog=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and cumulative plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cplot = plot.CDFPlot()\n",
    "\n",
    "cplot.prepare(z1, xlabel='z', name='z')\n",
    "cplot.plot(xlog=True)\n",
    "\n",
    "cplot.prepare(z2)\n",
    "cplot.overplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is a small sampling of the available plot types (although most are variants of plotting either the data or a model)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}