hist2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "import pandas as pd\n",
    "data=pd.read_csv('./data for figs/MEQs_3b.csv')\n",
    "data2=pd.read_csv('./data for figs/MEQs_3c.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Histograms\n",
    "\n",
    "\n",
    "Demonstrates how to plot histograms with matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "        vertical-align: top;\n",
       "    }\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>x(m)</th>\n",
       "      <th>y(m)</th>\n",
       "      <th>z(m)</th>\n",
       "      <th>Mw</th>\n",
       "      <th>Time(s)</th>\n",
       "      <th>fracture index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1178.000000</td>\n",
       "      <td>1.178000e+03</td>\n",
       "      <td>1178.000000</td>\n",
       "      <td>1178.000000</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>335580.470381</td>\n",
       "      <td>4.263117e+06</td>\n",
       "      <td>-2274.859818</td>\n",
       "      <td>-1.611279</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>1450.815789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>177.603031</td>\n",
       "      <td>2.247767e+02</td>\n",
       "      <td>212.260321</td>\n",
       "      <td>1.209229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>938.130293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>335093.648400</td>\n",
       "      <td>4.262577e+06</td>\n",
       "      <td>-2708.110133</td>\n",
       "      <td>-3.185777</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>335480.194500</td>\n",
       "      <td>4.262929e+06</td>\n",
       "      <td>-2451.108409</td>\n",
       "      <td>-2.620719</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>657.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>335592.924250</td>\n",
       "      <td>4.263160e+06</td>\n",
       "      <td>-2305.741621</td>\n",
       "      <td>-1.900529</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>1375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>335678.488300</td>\n",
       "      <td>4.263271e+06</td>\n",
       "      <td>-2090.320360</td>\n",
       "      <td>-0.796131</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>2285.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>336008.286800</td>\n",
       "      <td>4.263601e+06</td>\n",
       "      <td>-1850.906004</td>\n",
       "      <td>1.289696</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3110.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x(m)          y(m)         z(m)           Mw  Time(s)  \\\n",
       "count    1178.000000  1.178000e+03  1178.000000  1178.000000   1178.0   \n",
       "mean   335580.470381  4.263117e+06 -2274.859818    -1.611279   4500.0   \n",
       "std       177.603031  2.247767e+02   212.260321     1.209229      0.0   \n",
       "min    335093.648400  4.262577e+06 -2708.110133    -3.185777   4500.0   \n",
       "25%    335480.194500  4.262929e+06 -2451.108409    -2.620719   4500.0   \n",
       "50%    335592.924250  4.263160e+06 -2305.741621    -1.900529   4500.0   \n",
       "75%    335678.488300  4.263271e+06 -2090.320360    -0.796131   4500.0   \n",
       "max    336008.286800  4.263601e+06 -1850.906004     1.289696   4500.0   \n",
       "\n",
       "       fracture index  \n",
       "count     1178.000000  \n",
       "mean      1450.815789  \n",
       "std        938.130293  \n",
       "min          7.000000  \n",
       "25%        657.000000  \n",
       "50%       1375.000000  \n",
       "75%       2285.000000  \n",
       "max       3110.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate data and plot a simple histogram\n",
    "-----------------------------------------\n",
    "\n",
    "To generate a 1D histogram we only need a single vector of numbers. For a 2D\n",
    "histogram we'll need a second vector. We'll generate both below, and show\n",
    "the histogram for each vector.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '3-1b Injection')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_bins = 20\n",
    "\n",
    "x = np.asarray(data['Mw'].tolist())\n",
    "y = np.asarray(data2['Mw'].tolist())\n",
    "plt.ylabel('Number of Events')\n",
    "plt.xlabel('Predicted Magnitude')\n",
    "plt.hist(x, bins = n_bins, label = '3-1b')\n",
    "plt.hist(y, bins = n_bins, label = '3-1c')\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='upper right')\n",
    "#plt.suptitle('3-1b Injection', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "bins = 20\n",
    "\n",
    "pyplot.hist(x, bins, alpha=0.5, label='3-1b')\n",
    "pyplot.hist(y, bins, alpha=0.5, label='3-1c')\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/podgrk/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/Users/podgrk/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    }
   ],
   "source": [
    "#N_points = 100000\n",
    "n_bins = 20\n",
    "\n",
    "error = np.asarray(data['Mw'].tolist())\n",
    "#mag = np.asarray(data['MAGNITUDE'].tolist())\n",
    "\n",
    "# Generate a normal distribution, center at x=0 and y=5\n",
    "x = error\n",
    "#y = mag\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "#plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "#plt.show()\n",
    "\n",
    "# We can set the number of bins with the `bins` kwarg\n",
    "axs[0].hist(x, bins=n_bins)\n",
    "#axs[1].hist(y, bins=n_bins)\n",
    "\n",
    "axs[0].set_ylabel('Number of Events')\n",
    "axs[0].set_xlabel('Error Eigenvalue Max')\n",
    "#axs[1].set_xlabel('Magnitude')\n",
    "\n",
    "fig.savefig('Phase2_cycle4-9_MEQ.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating histogram colors\n",
    "-------------------------\n",
    "\n",
    "The histogram method returns (among other things) a `patches` object. This\n",
    "gives us access to the properties of the objects drawn. Using this, we can\n",
    "edit the histogram to our liking. Let's change the color of each bar\n",
    "based on its y value.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1, 2, tight_layout=True)\n",
    "\n",
    "# N is the count in each bin, bins is the lower-limit of the bin\n",
    "N, bins, patches = axs[0].hist(x, bins=n_bins)\n",
    "\n",
    "# We'll color code by height, but you could use any scalar\n",
    "fracs = N / N.max()\n",
    "\n",
    "# we need to normalize the data to 0..1 for the full range of the colormap\n",
    "norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "\n",
    "# Now, we'll loop through our objects and set the color of each accordingly\n",
    "for thisfrac, thispatch in zip(fracs, patches):\n",
    "    color = plt.cm.viridis(norm(thisfrac))\n",
    "    thispatch.set_facecolor(color)\n",
    "\n",
    "# We can also normalize our inputs by the total number of counts\n",
    "axs[1].hist(x, bins=n_bins, density=True)\n",
    "\n",
    "# Now we format the y-axis to display percentage\n",
    "axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a 2D histogram\n",
    "-------------------\n",
    "\n",
    "To plot a 2D histogram, one only needs two vectors of the same length,\n",
    "corresponding to each axis of the histogram.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "autodetected range of [nan, nan] is not finite",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-22454286a80f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1587\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1589\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1591\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    306\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist2d\u001b[0;34m(self, x, y, bins, range, density, weights, cmin, cmax, **kwargs)\u001b[0m\n\u001b[1;32m   7051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7052\u001b[0m         h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range,\n\u001b[0;32m-> 7053\u001b[0;31m                                            normed=density, weights=weights)\n\u001b[0m\u001b[1;32m   7054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/twodim_base.py\u001b[0m in \u001b[0;36mhistogram2d\u001b[0;34m(x, y, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m    698\u001b[0m         \u001b[0mxedges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myedges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m         \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxedges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myedges\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 700\u001b[0;31m     \u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistogramdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    701\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogramdd\u001b[0;34m(sample, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m   1029\u001b[0m                 raise ValueError(\n\u001b[1;32m   1030\u001b[0m                     '`bins[{}]` must be positive, when an integer'.format(i))\n\u001b[0;32m-> 1031\u001b[0;31m             \u001b[0msmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1032\u001b[0m             \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1033\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/py3/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_outer_edges\u001b[0;34m(a, range)\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m             raise ValueError(\n\u001b[0;32m--> 315\u001b[0;31m                 \"autodetected range of [{}, {}] is not finite\".format(first_edge, last_edge))\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;31m# expand empty range to avoid divide by zero\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: autodetected range of [nan, nan] is not finite"
     ]
    },
    {
     "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": [
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "hist = ax.hist2d(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customizing your histogram\n",
    "--------------------------\n",
    "\n",
    "Customizing a 2D histogram is similar to the 1D case, you can control\n",
    "visual components such as the bin size or color normalization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(5, 15), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "# We can increase the number of bins on each axis\n",
    "axs[0].hist2d(x, y, bins=40)\n",
    "\n",
    "# As well as define normalization of the colors\n",
    "axs[1].hist2d(x, y, bins=40, norm=colors.LogNorm())\n",
    "\n",
    "# We can also define custom numbers of bins for each axis\n",
    "axs[2].hist2d(x, y, bins=(80, 10), norm=colors.LogNorm())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "comets line\n"
   ]
  }
 ],
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