{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic plotting with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n",
      "[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n"
     ]
    }
   ],
   "source": [
    "# equally spaced numbers:\n",
    "\n",
    "# give range and number of points \n",
    "print(np.linspace(1, 2.0, 11))\n",
    "\n",
    "# give range and step size\n",
    "print(np.arange(1, 2.1, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset is a parabola\n",
    "x = np.arange(1, 21., 1.0)\n",
    "y = x**2 +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-3.0, 300, 'LaTex: $\\\\alpha \\\\beta \\\\gamma$')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# the simplest way to plot\n",
    "plt.plot(x, y, '.b') # format is'[marker][line][color]'\n",
    "\n",
    "# axis ranges\n",
    "plt.axis([-5, 25, 0, 500])\n",
    "\n",
    "# group relevant font stuff in one place\n",
    "font = {'family': 'Courier New', # Arial\n",
    "        'color':  'black',\n",
    "        'size': 16,\n",
    "        }\n",
    "\n",
    "plt.xlabel('x-axis (units)', fontdict=font)\n",
    "plt.ylabel('y-axis (units)', fontdict=font)\n",
    "\n",
    "plt.title('Title of the plot', fontdict=font)\n",
    "\n",
    "plt.text(-3.0, 350, r'some text', fontdict=font, color='g')\n",
    "plt.text(-3.0, 300, r'LaTex: $\\alpha \\beta \\gamma$', fontdict=font, color='b')\n",
    "\n",
    "# make the plot appear\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markers:  \n",
    "character\tdescription  \n",
    "'.'\tpoint marker  \n",
    "','\tpixel marker  \n",
    "'o'\tcircle marker  \n",
    "'v'\ttriangle_down marker  \n",
    "'^'\ttriangle_up marker  \n",
    "'<'\ttriangle_left marker  \n",
    "'>'\ttriangle_right marker  \n",
    "'1'\ttri_down marker  \n",
    "'2'\ttri_up marker  \n",
    "'3'\ttri_left marker  \n",
    "'4'\ttri_right marker  \n",
    "'s'\tsquare marker  \n",
    "'p'\tpentagon marker  \n",
    "'*'\tstar marker  \n",
    "'h'\thexagon1 marker  \n",
    "'H'\thexagon2 marker  \n",
    "'+'\tplus marker  \n",
    "'x'\tx marker  \n",
    "'D'\tdiamond marker  \n",
    "'d'\tthin_diamond marker  \n",
    "'|'\tvline marker  \n",
    "'_'\thline marker  \n",
    "\n",
    "\n",
    "### Line Styles\n",
    "\n",
    "character\tdescription  \n",
    "'-'\tsolid line style  \n",
    "'--'\tdashed line style  \n",
    "'-.'\tdash-dot line style  \n",
    "':'\tdotted line style  \n",
    "\n",
    "\n",
    "### Colors\n",
    "\n",
    "The supported color abbreviations are the single letter codes\n",
    "\n",
    "character\tcolor  \n",
    "'b'\tblue  \n",
    "'g'\tgreen  \n",
    "'r'\tred  \n",
    "'c'\tcyan  \n",
    "'m'\tmagenta  \n",
    "'y'\tyellow  \n",
    "'k'\tblack  \n",
    "'w'\twhite  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11bbad410>,\n",
       " <matplotlib.lines.Line2D at 0x11bbad610>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# you can overlap several plot in one call\n",
    "plt.plot(x, x, '-b', x, x**2, ':r')"
   ]
  },
  {
   "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": [
    "# scatter plots\n",
    "a = np.random.randn(50)\n",
    "b = a + np.random.randn(50)\n",
    "plt.scatter(a, b)\n",
    "plt.xlabel('entry a')\n",
    "plt.ylabel('entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# plotting categorical variables\n",
    "names = ['group_a', 'group_b', 'group_c']\n",
    "values = [1, 10, 100]\n",
    "plt.bar(names, values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "# A figure is composed of subplots\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "\n",
    "# create a figure (by default the figure 1)\n",
    "plt.figure() \n",
    "\n",
    "# The subplot() command specifies numrows, numcols, plot_number \n",
    "# where plot_number ranges from 1 to numrows*numcols. \n",
    "plt.subplot(211) \n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mauro/anaconda3/envs/fits/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    }
   ],
   "source": [
    "# Move around figures and subplots\n",
    "\n",
    "plt.figure(1)                # the first figure\n",
    "plt.subplot(221)             # the first subplot in the first figure\n",
    "plt.plot([1, 2, 3])\n",
    "plt.subplot(222)             # the second subplot in the first figure\n",
    "plt.plot([4, 5, 6])\n",
    "plt.subplot(223)             # the first subplot in the first figure\n",
    "plt.plot([1, 2, 8])\n",
    "plt.subplot(224)             # the second subplot in the first figure\n",
    "plt.plot([4, 3, 6])\n",
    "\n",
    "\n",
    "plt.figure(2)                # a second figure\n",
    "plt.plot([4, 5, 6])          # creates a subplot(111) by default\n",
    "\n",
    "plt.figure(1)                # figure 1 current; subplot(212) still current\n",
    "plt.subplot(221)             # make subplot(211) in figure1 current\n",
    "plt.title('Easy as 1, 2, 3') # subplot 211 title\n",
    "\n",
    "#Save a figure\n",
    "# plt.savefig(\"test.pdf\") \n",
    "\n",
    "# Close and release the memory for figure(1)\n",
    "plt.close(1)\n",
    "plt.close(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# A figure is composed of subplots\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "\n",
    "# Create two subplots sharing y axis\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharey=True)\n",
    "ax1.plot(t1, f(t1), 'bo')\n",
    "ax1.set(title='A tale of 2 subplots', ylabel='Damped oscillation')\n",
    "\n",
    "ax2.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "ax2.set(xlabel='time (s)', ylabel='Undamped')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(432x288) AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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M8uzzY/R0V++yVxVdWl2Soe9hsRAncSgO4lHNI0dJ1siprOrP7OmaWL+qpPSmD10S5bDT19vDMUfPCF2Ubjqv0SP5aiQOXtTbwwtj41OW4akWC1DsnD/srjhogJJHSpIullbPGjmVVfqo57zqpu2hj39qZJTtH35jvX+iSCxJYiGNOKj2nFGxcNidh9a/pd4/UVDySEU9AZDGGjlR7cON7Lss0oiksZDWWlGKheZTn0cK4m5iUy7LNXI0C1bykjQWsl4rSrGQHSWPFNQTAFmukaNZsJKXpLGQ9VpRioXsqNkqBfVUjVcvWzipeg/pfiPSLFjJQ9JYyDoOQLGQFdU8UlBP1VjfiKQTJY0FxUH7Us2jTpWjOy4+uz/xcD99I5J2FzbKad2KRYlGHioO2pOSRx3CRpTcsrWgb0wyrUSNrFq3YpFmZ08DSh51aGR4YdL5ICKtSnEwvSl51KHe4YX1zAcRaVWKg+lNHeZ1qHd4YT3zQURaleJgelPyqEO9E4+ynhAl0kyKg+lNyaMO9Q4vzHpClEgzKQ6mN/V51Kme4YXNmBAl0kyKg+nLPOFm9HkaHBz0oaGh3J5/7ty5LFiwoKFrDB8c5bdPP8/o+GF6urt46XEzU9s6tp3t2bOHxx9/PO9iSEyNxoLiIFxWcWBmW919MM1rquaRgD7cRIoUC6I+DxERSSzz5GFmnzezx8zs3rJjx5vZHWb2QPDvnKzLISIi6WlGzeMLwAUVx9YAP3D3M4AfBL+LiEibyDx5uPuPgCcrDl8E3BjcvhFYnnU5REQkPXn1eZzg7o8ABP++JOpEM1tpZkNmNrR///6mFVBERKK1fIe5u1/v7oPuPjhv3ry8iyMiIuSXPB41sxMBgn8fy6kcIiJSh7ySx23AFcHtK4Bv5VQOERGpQzOG6n4V+Cmw0MweNrN3AuuBN5jZA8Abgt9FRKRNZD7D3N3fHnHX67N+bhERyUbLd5iLiEjrUfIQEZHElDxERCQxJQ8REUlMyUNERBJT8hARkcSUPEREJDElDxERSUzJQ0REElPyEBGRxJQ8REQkMSUPERFJTMlDREQSU/IQEZHElDxERCSxmsnDzFaZ2ZxmFEZERNpDnJrHS4Gfm9nXzewCM7OsCyUiIq2tZvJw92uAM4DPAX8BPGBm/2hmL8+4bCIi0qJi9Xm4uwO/DX7GgDnAzWb28QzLJiIiLarmHuZm9l7gCuBx4LPAancfNbMu4AHg77MtooiItJqayQOYC6xw91+XH3T3w2b21myKJSIiraxm8nD3D1W57750iyMiIu1A8zxERCQxJQ8REUlMyUNERBJT8hARkcSUPEREJDElDxERSUzJQ0REElPyEBGRxJQ8REQkMSUPERFJTMlDREQSU/IQEZHElDxERCSxOEuyZ8bM9gDPAOPAmLsP5lkeERGJJ9fkEVjq7o/nXQgREYlPzVYiIpJY3snDge+Z2VYzWxl2gpmtNLMhMxvav39/k4snIiJh8k4eS9z9d4E3AX9rZn9QeYK7X+/ug+4+OG/evOaXUEREpsg1ebj7vuDfx4BvAq/JszwiIhJPbsnDzI4xs2NLt4E3AvfmVR4REYkvz9FWJwDfNLNSOb7i7v+RY3lERCSm3JKHuz8IvDqv5xcRkfrl3WEuIiJtSMlDREQSU/IQEZHElDxERCQxJQ8REUlMyUNERBJT8hARkcSUPEREJLFW2M9DRIBN2wps2LyLfcMjnNTXy+plC1k+0J93sURCKXmItIBN2wqsvXUHI6PjABSGR1h76w6AlkogSnBSomYrkRawYfOuicRRMjI6zobNu3Iq0VSlBFcYHsE5kuA2bSvkXTTJgZKHSAvYNzyS6Hge2iHBSfMoeYi0gJP6ehMdz0M7JDhpHiUPkRawetlCenu6Jx3r7elm9bKFOZVoqnZIcNI8Sh4iLWD5QD/rViyiv68XA/r7elm3YlFLdUa3Q4KT5tFoK5EcpT16KcvRUKXraLSVgJKHSKqSfHinPTy3GcN9lw/0K1kIoGYrkdQkHcqa9ugljYaSZlLyEElJ0g/vtEYvbdpWYMn6LRQ0GkqaSMlDJCVJk0Eao5fKaztRNBpKsqA+D5GYavVnnNTXG/ohHvbhvWlbgecOjU05nnT0Ulhtp5HricSlmodIDHH6M+IOZS1da3hkdNLxObN6Eg/PrdYk1YrDfaVzKHmIxBCnPyPuXI2o2sKso2Yk/qCPapLq7+vlrjXnKXFIZtRsJRJD3P6MOENZ01zmY/WyhZOG50J0bUfzMyRNqnmIxJDm0hxpXitObUer4UoWVPMQiSHuN/xmXwtq13aqNbmp9iH1UvIQiSHNpTmavcyHVsOVLCh5iOSgmct8VBtCrL4QqZf6PGTaKM3EPnXN7SxZvyVRm3879xtEDSFeeua8tv2bJH9KHjItNPrh387rRkV1qt95//62/Zskf2q2kmmh0U7jqP6BwvAIm7YVMmvqSatZKayZ7Kqbtoeeq74QiUPJQ6aFRjuNo/oNgCnLnqf1gZ/1EutJllMRqaRmK5kWGp1bEdZvUFLe1FOteSxpn0vWTWXaGVAaoZqHTAv1zq0or0W8qLcnchHCUg0m6gP/2tt2cmjscKJaRNZDbLUzoDQi1+RhZhcAnwK6gc+6+/o8yyOdq/KDsm9WD+7Fdv9rb9uJGQwfHJ30AXrNph18+e69eHCN4ZFRDCZ+L1eqwUR9sFcuggi1+1ya0ayknQGlXrklDzPrBv4VeAPwMPBzM7vN3f8rrzJJ5wnrfwAm1ULKP9hLNYKhXz85KXGUOIQmkIMvjLFpW6Fq30iYarWItGeii6TJ3MO+RzXhic1+H7jW3ZcFv68FcPd1UY8ZHBz0oaGhJpVQ2l1lhzMQtPE7I6OHqz6224zxKrHR19szpTbR29PNxWf3c8vWwpTnnNnTxYGDU2sf/UFCi2o60iQ+SYOZbXX3wTSvmWezVT/wm7LfHwZeW3mSma0EVgLMnz+/OSWTXKX1gRnV/xBHtcTRHzQbVSaPkdFx7rx/P+tWLKpZ24HJE/Wi+kLUrCStKs/kYSHHpkSsu18PXA/FmkfWhZJ8pTk8tZGO5aiah1FsTqo2R6LaB35lUtGihdKu8kweDwMnl/3+MmBfTmWRlNWqPUTdn+aHadL+h5Ko5icD3nHO/Ilyhl27y4xT19we+jeXJ5XS3x9VPk3Uk1aX5zyPnwNnmNmpZnYUcBlwW47lkZTUWgqk2v1pb5RUOY8hrLoL0GVMWrrjY8sXsW7FIvp6eybO6ZvVw+Apx09cu6dr6tXG3Wsuf1L+90fRRD1pdbnVPNx9zMxWAZspDtX9vLvvzKs8kp5atYdq99c7PLVaTaf8+NIz54V2aEft9X1o7EjH+oGDoxNNaEB0Jgr5m8tFbUNbXp4k80/UkS55yHWeh7t/B/hOnmWQ9NWqPVS7f+OlixMPTw3rJ1n9jXv4yLd3Tszd2Hjp4okP18FTjo/1wVtrhvfoeO0uuLC/tVotqj9GIsh62RKRODTDXFJXq/ZQ7f56Zj2HfciPHvaJobH1jmBKowktrMYU9ff39/Vy15rzal5TnezSCrS2laSu2v4RS9ZvoTA8MqXFp7x2sXygn9XLFnJSXy/7hkfYsHlX1XWg4nyY17MmVLX1sOL0SUTVmKL6YpaeOS9WubQzoLQCJQ9JXdj+EaXRS6Vv3KWZ2nCkk7p8JFKSvTfidi4nHXkV1ine02WsXraw6kKJUJxEGNWPsnygn4vP7p+UQB24ZWsh1v4ijS7yKJIGJQ/JxPKBfu5acx4PrX8Ld605L3TjIedIU035h2zS1WRrfZCXdFuNHu4wlQ8Jfi8lyKhrHnP0jKpNSHfev3/KpKa4tSOthiutQMlDmiJJU0vSZpnKmk6UarPGw2zYvGtKp/jouE98wC8f6OdwxDXLazlhS7E30vQUtTOg+jukmdRhLk2RZAhuPcN1S53gm7YVuOqm7aEr386Z1cOS9Vtid8TH+YCPKqvBRBNU2MioF4WsjVXrbyynZUskb6p5SFMkaWpppFlmw+ZdoYkD4Nnnxyb1o7zvpu0MfPR7iftSyo+vXrYwcp2dDZt3RTbBmaGmJ2lrSh7SFEmaWhpplqnW7DN6eGpaKU38C0sgcZLY8oH+yGS1b3gken+Pg6NqepK2pmYraZokTS31NsvUs55V1ByJuHNO+ms0s1Wb06JkIe1KNQ/pKFG1hTmzeiIeUVStM/6uNeex8dLFQHHnwcr9x6vVUDQySjqVah6Sq7TXaIqqLcDU/TTKVeuorrUcSJwaitahkk6T206C9dBOgvnJYiG+qJ3+smr737StwLW37QzdAbDac5ZmxVeKu5yISN6y2ElQzVZSU9IZ33ElnQzYqOUD/Wz/8Bv55KWLE3VUazkQkanUbCU1ZbUQX14fykk7qutdJl6kk6nmITVl9SHfLms0qdNbZColD6kpqw/5dvlQ1nIgIlOp2UpqWr1sYeINmuKoZ++OvGhOhshkSh5SU5Yf8vpQFmlPSh4C1B6Kqw95ESmn5CHaE1tEElPykKbtiZ3FREMRyYeShzRlvoVqNyKdRUN1pSnzLZo9m1xEsqXkIU2Zb6ElPkQ6i5KHNGUSXLvMJheReNTnIUD2Q3GzmmgoIvlQ8pCmaKfZ5CJSm5KHNI0mGop0DvV5iIhIYkoeIiKSmJKHiIgkpuQhIiKJKXmIiEhiSh4iIpKYkoeIiCSWS/Iws2vNrGBm24OfN+dRDhERqU+ekwQ3uvsncnx+ERGpk5qtREQksTxrHqvM7M+BIeB/ufuBsJPMbCWwEuDFL34xg4ODTSyiiEhH+N20L2junvY1MbOTgXuAWcGhA8ATwe2rgbuBs4BvAiNAD/Apd/9otesODg760NBQ6uUVEelkZrbV3VP95p1Vs9UYcL67zwTmAc8Af+Lur3L3b7n7o8Bh4MfAOcAjtRKHiIi0jkySh7s/4u6/CG4/A9wHTCynamYnlp3+R8C9WZRDRESykXmfh5ktAAaAn5Ud/jiwBJgPvA7YamZnufvOrMsj0q42bStoPxRpGZkmDzObDdwCvM/dny4dd/fLzew44LC7PxvM89gEnBFyjYkO8/nz52dZXJGWtWlbYdJOjIXhEdbeugNACURykdlQXTProZg4vuzut1be7+5Pu/uzwe3vAD1mNjfkvOvdfdDdB+fNm5dVcUVa2obNuyZt4QswMjrOhs27ciqRTHeZJA8zM+BzwH3u/s8R57w0OA8ze01QlifCzhWZ7vYNjyQ6LpK1rJqtlgCXAzvMbHtw7IMU+zhw988AlwDvMbMxisN1L/Msxg2LdICT+nophCSKk/p6cyiNSEbJw91/AliNcz4NfDqL5xfpNKuXLZzU5wHQ29PN6mULcyyVTGd5zjAXkZhKneIabSWtQslDpE0sH+hXspCWoeQhMs1ovoikQclDZBrRfBFJS5bzPC4ws11mttvM1oTcf7SZ3RTc/7NgJrqIZEjzRSQtWc3z6Ab+FXgT8Erg7Wb2yorT3gkccPfTgY3AdVmURUSO0HwRSUtWNY/XALvd/UF3fwH4GnBRxTkXATcGt28GXl+aNCgi2YiaF6L5IpJUVsmjH/hN2e8PU7aqbuU57j4GPAW8uPJCZrbSzIbMbGj//v0ZFVfayaZtBZas38Kpa25nyfotbNpWyLtIbWP1soX09nRPOqb5IlKPrJJHWA2icvZ4nHO0tpVMUurwLQyP4Bzp8FUCiWf5QD/rViyiv68XA/r7elm3YpE6yyWxrEZbPQycXPb7y4B9Eec8bGYzgBcBT2ZUHukQ1Tp89QEYj+aLSBqySh4/B84ws1OBAnAZ8KcV59wGXAH8lOI6V1s6cW0rjalPlzp8RVpDVmtbjZnZKmAz0A183t13mtlHgSF3v43iqrtfMrPdFGscl2VRljxpTH36tECgSGvIbJJgsEfHdyqOfajs9vPAH2f1/K2g0SYW1Vqm0gKBIq1BM8wz1EgTi2ot4bRAoEhrUPLIUCNNLOoYjqYOX5H8ZbY8iTQ2pl4dwyLSylKveZjZBuBC4AXgV8BfuvtwyHl7gGeAcWDM3QfTLkveGmliUcewiLSyLJqt7gDWBiOurgPWAh+IOHepuz+eQRlaRr1NLOoYFpFWlnqzlbt/L1huBHVPsokAAAcISURBVOBuihMEJSHNBBaRVpZ1h/lfATdF3OfA98zMgf/r7tdnXJa2o45hEWlVdSUPM/s+8NKQu652928F51wNjAFfjrjMEnffZ2YvAe4ws/vd/Uchz7USWAkwf/78eoorIiIpqyt5uPv51e43syuAtwKvj1pyxN33Bf8+ZmbfpLiM+5TkEdRIrgcYHBzsuOVLRETaUep9HmZ2AcUO8re5+8GIc44xs2NLt4E3AvemXRYREclGFvM8Pg0cS7EparuZfQbAzE4ys9JyJScAPzGze4D/BG539//IoCwiIpKB1DvMg21lw47vA94c3H4QeHXazy0iIs2hGeYiIpKYtdMWGmb2DLAr73LEMBdoh8mPKmd62qGMoHKmrV3KudDdj03zgu22MOKudljGxMyGVM70tEM526GMoHKmrZ3KmfY11WwlIiKJKXmIiEhi7ZY82mUJE5UzXe1QznYoI6icaZu25WyrDnMREWkN7VbzEBGRFqDkISIiieWWPMzsAjPbZWa7zWxNyP1Hm9lNwf0/M7MFZfetDY7vMrNlca/ZzHKa2RvMbKuZ7Qj+Pa/sMT8Mrrk9+HlJjuVcYGYjZWX5TNljzg7Kv9vM/sXMLMdyvqOsjNvN7LCZLQ7uy+P1/AMz+4WZjZnZJRX3XWFmDwQ/V5Qdz+P1DC2nmS02s5+a2U4z+6WZXVp23xfM7KGy13NxHmUM7hsvK8dtZcdPDd4fDwTvl6MaKWMj5TSzpRXvzefNbHlwX6qvZcxy/p2Z/Vfw//oDMzul7L703pvu3vQfoJviFrWnAUcB9wCvrDjnb4DPBLcvA24Kbr8yOP9o4NTgOt1xrtnkcg4AJwW3XwUUyh7zQ2CwRV7PBcC9Edf9T+D3AQO+C7wpr3JWnLMIeDDn13MB8DvAF4FLyo4fDzwY/DsnuD0nx9czqpyvAM4Ibp8EPAL0Bb9/ofzcvMoY3PdsxHW/DlwW3P4M8J48y1nx//8kMCvt1zJBOZeWPf97OBLrqb4386p5vAbY7e4PuvsLwNeAiyrOuQi4Mbh9M/D6IBteBHzN3Q+5+0PA7uB6ca7ZtHK6+zYPlp0HdgIzzezoBsuTejmjLmhmJwLHuftPvfju+iKwvEXK+Xbgqw2WpaFyuvsed/8lcLjiscuAO9z9SXc/QHFb5gvyej2jyunu/+3uDwS39wGPAfMaLE+qZYwSvB/Oo/j+gOL7JbfXssIlwHc9YkXxFMQp551lz1++m2uq7828kkc/8Juy3x8OjoWe48VtbZ8CXlzlsXGu2cxylrsY2Obuh8qO/VtQjf3fKTRfNFrOU81sm5n9PzN7Xdn5D9e4ZrPLWXIpU5NHs1/PpI/N6/WsycxeQ/Fb7K/KDv9D0OyxscEvPY2WcaaZDZnZ3aWmIIrvh2E/st11y7yWFGvLle/NtF5LSF7Od1KsSVR7bF3vzbySR1hwV44Zjjon6fFGNFLO4p1mZwHXAX9ddv873H0R8Lrg5/Icy/kIMN/dB4C/A75iZsfFvGZSabyerwUOunv5/i95vJ5JH5vX61n9AsVvnV8C/tLdS9+o1wJnAr9HsYnjAzmWcb4Xl//4U+CTZvbyFK4ZJq3XchGwuexwmq8lJCinmf0ZMAhsqPHYuv72vJLHw8DJZb+/DNgXdY6ZzQBeRLEtMeqxca7ZzHJiZi8Dvgn8ubtPfKtz90Lw7zPAVyhWRXMpZ9D890RQnq0Uv32+Ijj/ZWWPz/31DEz5ZpfT65n0sXm9npGCLwm3A9e4+92l4+7+iBcdAv6Nxl7PhsroR3YcfZBi39YAxYUI+4L3R+JrZlHOwJ8A33T30dKBlF/L2OU0s/OBqyluyneoxmPre2+m1ZGT5IfigowPUuzwLnX6nFVxzt8yueP068Hts5jcYf4gxU6kmtdscjn7gvMvDrnm3OB2D8V223fnWM55QHdw+zSgABwf/P5z4ByOdKK9Oa9yBr93UXyjn5b361l27heY2mH+EMUOyTnB7dxezyrlPAr4AfC+kHNPDP414JPA+pzKOAc4Org9F3iAoHMY+AaTO8z/Jq/Xsuz43cDSrF7LBDE0QPFL4BkVx1N9b9b9RzT6Q3FjqP8O/sirg2MfpZgpAWYGb5DdFEcClH9gXB08bhdlowLCrplXOYFrgOeA7WU/LwGOAbYCv6TYkf4pgg/vnMp5cVCOe4BfABeWXXOQ4vbAv6K4Q6Tl/P9+LnB3xfXyej1/j2Iiew54AthZ9ti/Csq/m2JzUJ6vZ2g5gT8DRiven4uD+7YAO4Ky/jswO6cy/o+gHPcE/76z7JqnBe+P3cH75eic/88XUPzi1VVxzVRfy5jl/D7waNn/621ZvDe1PImIiCSmGeYiIpKYkoeIiCSm5CEiIokpeYiISGJKHiIikpiSh4iIJKbkISIiif1/cKkVGIPk2ywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by default all subplots in a figure are ordered on a rectangular grid\n",
    "# to position plots in any arbitrary place use axes\n",
    "\n",
    "# create some data to use for the plot\n",
    "a = np.random.randn(50)\n",
    "b = a + np.random.randn(50)\n",
    "\n",
    "fig, main_ax = plt.subplots()\n",
    "print (fig, main_ax)\n",
    "main_ax.scatter(a, b)\n",
    "main_ax.set_xlim(-5, 5)\n",
    "main_ax.set_ylim(-5, 20)\n",
    "main_ax.set_xlabel('x')\n",
    "main_ax.set_ylabel('y')\n",
    "main_ax.set_title('title')\n",
    "\n",
    "# this is an inset axes over the main axes\n",
    "right_inset_ax = fig.add_axes([.65, .6, .2, .2])\n",
    "right_inset_ax.scatter(a,b)\n",
    "right_inset_ax.set_title('title')\n",
    "#remove ticks\n",
    "right_inset_ax.set_xticks([])\n",
    "right_inset_ax.set_yticks([])\n",
    "\n",
    "# this is another inset axes over the main axes \n",
    "left_inset_ax = fig.add_axes([.2, .6, .2, .2])\n",
    "left_inset_ax.scatter(a,b)\n",
    "left_inset_ax.set_title('title')\n",
    "#remove ticks\n",
    "left_inset_ax.set_xticks([])\n",
    "left_inset_ax.set_yticks([])\n",
    "\n",
    "# this is another inset axes below the main axes\n",
    "left_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
    "left_inset_ax.scatter(a,b)\n",
    "left_inset_ax.set_xlim(0, 0.2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n"
     ]
    }
   ],
   "source": [
    "# plots styles\n",
    "print (plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# Histograms and text\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Useful links\n",
    "\n",
    "Gallery of examples:\n",
    "https://matplotlib.org/gallery/index.html\n",
    "\n",
    "pyplot.plot ('Markers', 'Line Styles', 'Colors'\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
    "\n",
    "pyplot.figure:\n",
    "https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure\n",
    "  \n",
    "line properties:\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D\n",
    "\n",
    "matplotlib fonts:\n",
    "http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samples/\n",
    "\n",
    "stlye sheet reference:\n",
    "https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html\n",
    "\n",
    " "
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}