PyLunch_12_Oct_2018_histograms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mbovill/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype([('name', '<i8'), ('g', '<f8'), ('bprp', '<f8'), ('mem', 'S2')])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets get some more data\n",
    "# notice I've added the delimiter keyword argument, \n",
    "# if you don't include this numpy thinks everything is text\n",
    "cluster = np.genfromtxt(\"m67_cleaned.txt\", names=True, dtype=None, delimiter=',')\n",
    "\n",
    "cluster.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13467 1096 12371\n"
     ]
    }
   ],
   "source": [
    "# I want to know what the cluster data looks like, lets split it up to useful bits\n",
    "# we're going to create a boolean array based on a condition, in this case a flag\n",
    "# we can select a subset of a np.array in this way\n",
    "\n",
    "# setting up the mask\n",
    "member_cut = cluster['mem'] == b' Y'\n",
    "# selecting out the members (member_cut = True)\n",
    "members = cluster[member_cut]\n",
    "# selecting out the non members (member_cut = False)\n",
    "non_members = cluster[~member_cut]\n",
    "\n",
    "# lets see how many data points we have in each catagory\n",
    "print(len(cluster), len(members), len(non_members))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqh+qqh8+djnJ30tyZ5Ibklw2bXZZko/P8jwAAMto1lOHT07ysao69li/1d3/uar+MMl1VfW6JPcmefWMzwMAsHRmCq3u/mqSv7HJ+jeSvGSWxwYAWHbeGR4AYBChBQAwiNACABhEaAEADCK0AAAGEVoAAIMILQCAQYQWAMAgQgsAYBChBQAwiNACABhEaAEADCK0AAAGEVoAAIMILQCAQYQWAMAgQgsAYBChBQAwiNACABhEaAEADCK0AAAGEVoAAIMILQCAQYQWAMAgQgsAYBChBQAwiNACABhEaAEADCK0AAAGEVoAAIMILQCAQYQWAMAgQgsAYBChBQAwiNACABhEaAEADLLt0Kqq86rqk1V1d1XdVVVvntZ/taq+XlW3T18vm9+4AADLY88M930oydu6+7NV9cNJbquqm6fbPtDd7519PACA5bXt0Oruo0mOTpe/XVV3Jzl3XoMBACy7ubxGq6r2Jnluks9MS2+qqjuq6qqqOnMezwEAsGxmDq2qekKS65O8pbu/leSKJE9Psi/rR7zed4L77a+qQ1V1aG1tbdYxAAB2nJlCq6oek/XI+nB3fzRJuvv+7n64u7+X5INJLtzsvt19sLtXu3t1ZWVlljEAAHakWf7qsJJcmeTu7n7/hvVzNmz2qiR3bn88AIDlNctfHb4gyWuSfKGqbp/W3pHk0qral6STHE7y+pkmBABYUrP81eHvJ6lNbrpp++MAAOwe3hkeAGAQoQUAMIjQAgAYRGgBAAwitAAABhFaAACDCC0AgEGEFgDAIEILAGAQoQUAMIjQAgAYRGgBAAwitAAABhFaAACDCC0AgEGEFgDAIEILAGCQPYsegNPb3gM3LnoEABjGES0AgEGEFgDAIEILAGAQr9FaQl7XBADLwREtAIBBhBYAwCBCCwBgEKEFADCI0AIAGERoAQAMIrQAAAYRWgAAgwgtAIBBhBYAwCBCCwBgkNPqsw59RiAAcCo5ogUAMIjQAgAYRGgBAAwyLLSq6qKq+lJV3VNVB0Y9DwDATjUktKrqjCT/PslLkzwzyaVV9cwRzwUAsFONOqJ1YZJ7uvur3f3nSX47ycWDngsAYEcaFVrnJvnahutHpjUAgNPGqPfRqk3W+vs2qNqfZP909X9X1ZdmeL6zkvzpDPfnB7N/x7Fvx7Fvx7J/x7FvZ1D/5gfePK99+9e3uuGo0DqS5LwN15+S5L6NG3T3wSQH5/FkVXWou1fn8Vg8kv07jn07jn07lv07jn07ziL27ahTh3+Y5IKqempVPTbJJUluGPRcAAA70pAjWt39UFW9Kcl/SXJGkqu6+64RzwUAsFMN+6zD7r4pyU2jHv84czkFyQnZv+PYt+PYt2PZv+PYt+Oc8n1b3X3yrQAAeNR8BA8AwCBLH1pV9daququq7qyqa6vqLy56pmVVVVdV1QNVdeeGtSdV1c1V9eXp+5mLnHGZnWD/vqeqvlhVd1TVx6rqiYuccVlttm833PZLVdVVddYiZlt2J9q3VfXPpo9Zu6uq3r2o+ZbdCf67sK+qbq2q26vqUFVduMgZl1VVnVdVn6yqu6ef0zdP66f099pSh1ZVnZvknydZ7e5nZ/2F95csdqqldnWSi45bO5Dklu6+IMkt03W25+o8cv/enOTZ3f2cJH+U5O2neqhd4uo8ct+mqs5L8lNJ7j3VA+0iV+e4fVtVP5H1T/t4Tnc/K8l7FzDXbnF1Hvmz++4kv9bd+5L8ynSdR++hJG/r7h9L8vwkb5w+DvCU/l5b6tCa7Enyl6pqT5LH57j362LruvvTSR48bvniJNdMl69J8spTOtQustn+7e5PdPdD09Vbs/6eczxKJ/jZTZIPJPlXOe4Nk9m6E+zbX0xyeXd/d9rmgVM+2C5xgv3bSX5kuvyX4/fatnT30e7+7HT520nuzvqn1JzS32tLHVrd/fWs/0vq3iRHk3yzuz+x2Kl2nSd399Fk/Yc2ydkLnmc3+4Ukv7foIXaLqnpFkq939+cXPcsu9IwkL6yqz1TVp6rqby16oF3mLUneU1Vfy/rvOEe6Z1RVe5M8N8lncop/ry11aE3nVS9O8tQkfy3JD1XVP1zsVPDoVdU7s36Y+8OLnmU3qKrHJ3ln1k+7MH97kpyZ9dMx/zLJdVW12UevsT2/mOSt3X1ekrcmuXLB8yy1qnpCkuuTvKW7v3Wqn3+pQyvJTyb54+5e6+7/m+SjSf72gmfabe6vqnOSZPruFMGcVdVlSX46yc+191uZl6dn/R9gn6+qw1k/JfvZqvqrC51q9ziS5KO97g+SfC/rnyHHfFyW9d9nSfI7SbwYfpuq6jFZj6wPd/exfXpKf68te2jdm+T5VfX46V9TL8n6OVjm54as/58+0/ePL3CWXaeqLkryy0le0d3fWfQ8u0V3f6G7z+7uvd29N+th8Lzu/p8LHm23+N0kL06SqnpGksfGhyDP031J/u50+cVJvrzAWZbW1AVXJrm7u9+/4aZT+ntt6d+wtKp+LcnPZv20y+eS/ONjL9Dk0amqa5O8KOv/Mr0/ybuy/h/U65Kcn/WwfXV3b/aiY07iBPv37Ukel+Qb02a3dvcbFjLgEtts33b3lRtuP5z1v04WA4/SCX5u/2OSq5LsS/LnSX6pu//romZcZifYv19K8m+zfor2/yT5p91926JmXFZV9XeS/LckX8j6UdckeUfWX6d1yn6vLX1oAQDsVMt+6hAAYMcSWgAAgwgtAIBBhBYAwCBCCwBgEKEFADCI0AIAGERoAQAM8v8ALL/AgFIi+EYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now we can see what the data look like\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.hist(members['g'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# that's not great...\n",
    "# lets work on that.\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "### adding and edge color to the bins\n",
    "ax.hist(members['g'], edgecolor='black')\n",
    "\n",
    "ax.set_xlabel(\"g magnitude\",size=20)\n",
    "ax.set_ylabel(\"N\",size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cool. How does that compare to the non-members?\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.hist(non_members['g'], edgecolor='black')\n",
    "# plotting a second histogram\n",
    "ax.hist(members['g'], edgecolor='black')\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"g magnitude\",size=20)\n",
    "ax.set_ylabel(\"N\",size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hmm way more non-members, that's not ideal\n",
    "# and the bins don't align at all! \n",
    "# How do we fix this?\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# explicitly set bins\n",
    "bins = np.arange(6, 20, 0.5)\n",
    "\n",
    "# since we have a large range of values set the histogram values to log\n",
    "ax.hist(non_members['g'], edgecolor='black', bins=bins, log=True)\n",
    "ax.hist(members['g'], edgecolor='black', bins=bins, log=True)\n",
    "\n",
    "ax.set_xlabel(\"g magnitude\",size=20)\n",
    "ax.set_ylabel(\"N\",size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# much better, but we can still make it clearer\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bins = np.arange(6, 20, 0.5)\n",
    "\n",
    "# playing with the transparency of one of the histograms\n",
    "ax.hist(non_members['g'], edgecolor='black', bins=bins, log=True)\n",
    "ax.hist(members['g'], edgecolor='black', bins=bins, log=True, alpha=0.7)\n",
    "\n",
    "ax.set_xlabel(\"g magnitude\",size=20)\n",
    "ax.set_ylabel(\"N\",size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Challenge 1: Redo this plot using only a line for the histogram rather than the bar plot. Select your own colors for the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Challenge 2: Let's say we want to compare the distributions of two populations but don't care as much about the normalization.\n",
    "# Normalize both histograms and replot."
   ]
  }
 ],
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