s3fs-sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a simple example of how one can access data in the SCEDC s3://scedc-pds dataset using\n",
    "# file system commands if they use s3fs or another FUSE interface.\n",
    "# It is based on this web tutorial: https://www.geophysique.be/2013/09/25/seismicity-rate-using-obspy-and-pandas/\n",
    "# This notebook assumes you have mounted s3://scedc-pds to mount point scedc-pds and mount point is current directory\n",
    "# s3fs GitHub repo: https://github.com/s3fs-fuse/s3fs-fuse\n",
    "#\n",
    "#cd command only needs to be run once\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/ellenyu/scedc-pds/earthquake_catalogs/SCEC_DC\n"
     ]
    }
   ],
   "source": [
    "cd scedc-pds/earthquake_catalogs/SCEC_DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "import time\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = np.arange(2000,2021)  # range of years to look for in catalog\n",
    "format = \"%Y/%m/%d %H:%M:%S.%f\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(-1,8)\n",
    "labels = np.array([\"[%i:%i[\"%(i,i+1) for i in bins])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in years:\n",
    "    catalog_file=str(i)+'.catalog'\n",
    "    df = pd.read_csv(catalog_file,sep=\"\\s+\", comment='#',names = ['Ydate', 'time', 'etype','gtype','mag', 'magtype','lat','lon','depth','quality','evid','nph','nwfs'])\n",
    "    df['dtime'] = df['Ydate'] + ' ' + df['time']\n",
    "    df['Magnitude_Range'] = pd.cut(df['mag'], bins=bins, labels=False)\n",
    "    df['dtime2'] = [dt.datetime.strptime(di, format) for di in df.dtime]\n",
    "    df['Year'] = [dt.datetime.strftime(di, '%Y') for di in df.dtime2]\n",
    "    df0=df0.append(df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rate = pd.crosstab(df0.Year, df0.Magnitude_Range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Magnitude_Range</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>329</td>\n",
       "      <td>2587</td>\n",
       "      <td>14825</td>\n",
       "      <td>2431</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>24</td>\n",
       "      <td>2609</td>\n",
       "      <td>12642</td>\n",
       "      <td>2650</td>\n",
       "      <td>173</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>9</td>\n",
       "      <td>2598</td>\n",
       "      <td>7411</td>\n",
       "      <td>1671</td>\n",
       "      <td>151</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>10</td>\n",
       "      <td>2253</td>\n",
       "      <td>7794</td>\n",
       "      <td>1291</td>\n",
       "      <td>173</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>4</td>\n",
       "      <td>4401</td>\n",
       "      <td>6625</td>\n",
       "      <td>1052</td>\n",
       "      <td>156</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3</td>\n",
       "      <td>4437</td>\n",
       "      <td>7313</td>\n",
       "      <td>1259</td>\n",
       "      <td>164</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3</td>\n",
       "      <td>3924</td>\n",
       "      <td>5916</td>\n",
       "      <td>1180</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6</td>\n",
       "      <td>4058</td>\n",
       "      <td>6257</td>\n",
       "      <td>1027</td>\n",
       "      <td>121</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0</td>\n",
       "      <td>4784</td>\n",
       "      <td>7256</td>\n",
       "      <td>1769</td>\n",
       "      <td>222</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2</td>\n",
       "      <td>6549</td>\n",
       "      <td>8353</td>\n",
       "      <td>1575</td>\n",
       "      <td>221</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6</td>\n",
       "      <td>10461</td>\n",
       "      <td>21368</td>\n",
       "      <td>8679</td>\n",
       "      <td>1314</td>\n",
       "      <td>123</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1</td>\n",
       "      <td>6878</td>\n",
       "      <td>6809</td>\n",
       "      <td>1675</td>\n",
       "      <td>186</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>23</td>\n",
       "      <td>7365</td>\n",
       "      <td>7861</td>\n",
       "      <td>1683</td>\n",
       "      <td>198</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>12</td>\n",
       "      <td>10335</td>\n",
       "      <td>6994</td>\n",
       "      <td>1053</td>\n",
       "      <td>136</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>7</td>\n",
       "      <td>6862</td>\n",
       "      <td>6331</td>\n",
       "      <td>1063</td>\n",
       "      <td>151</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>6</td>\n",
       "      <td>8454</td>\n",
       "      <td>6237</td>\n",
       "      <td>795</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7</td>\n",
       "      <td>8339</td>\n",
       "      <td>6523</td>\n",
       "      <td>1025</td>\n",
       "      <td>122</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>5</td>\n",
       "      <td>9386</td>\n",
       "      <td>6034</td>\n",
       "      <td>808</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>14</td>\n",
       "      <td>13312</td>\n",
       "      <td>6410</td>\n",
       "      <td>882</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>20</td>\n",
       "      <td>24831</td>\n",
       "      <td>29852</td>\n",
       "      <td>5921</td>\n",
       "      <td>1119</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>26</td>\n",
       "      <td>19183</td>\n",
       "      <td>13535</td>\n",
       "      <td>1937</td>\n",
       "      <td>246</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Magnitude_Range  0.0    1.0    2.0   3.0   4.0  5.0  6.0  7.0\n",
       "Year                                                         \n",
       "2000             329   2587  14825  2431   187   16    0    0\n",
       "2001              24   2609  12642  2650   173   15    2    0\n",
       "2002               9   2598   7411  1671   151   16    1    0\n",
       "2003              10   2253   7794  1291   173   24    0    1\n",
       "2004               4   4401   6625  1052   156   17    3    0\n",
       "2005               3   4437   7313  1259   164   27    2    0\n",
       "2006               3   3924   5916  1180   158   13    1    0\n",
       "2007               6   4058   6257  1027   121   15    0    0\n",
       "2008               0   4784   7256  1769   222   22    5    0\n",
       "2009               2   6549   8353  1575   221   28    3    0\n",
       "2010               6  10461  21368  8679  1314  123    7    0\n",
       "2011               1   6878   6809  1675   186   15    1    0\n",
       "2012              23   7365   7861  1683   198   28    3    0\n",
       "2013              12  10335   6994  1053   136   14    0    0\n",
       "2014               7   6862   6331  1063   151   14    1    0\n",
       "2015               6   8454   6237   795    85    9    0    0\n",
       "2016               7   8339   6523  1025   122   11    1    0\n",
       "2017               5   9386   6034   808   106    5    0    0\n",
       "2018              14  13312   6410   882   103    8    1    0\n",
       "2019              20  24831  29852  5921  1119  110    3    1\n",
       "2020              26  19183  13535  1937   246   24    2    0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [cm.hsv(float(i+1)/(len(bins)-1)) for i in bins]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "rate.plot(kind='bar',stacked=True,color=colors)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}