Channel sinuosities

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Variations in submarine channel sinuosity as a function of latitude and slope"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The purpose of this notebook is to provide the data and the plotting / analysis scripts that were used in the paper: Sylvester, Z., Pirmez, C., Cantelli, A., & Jobe, Z. R. (2013). Global (latitudinal) variation in submarine channel sinuosity: COMMENT. Geology, 41(5), e287\u2013e287. [doi:10.1130/G33548C.1](http://dx.doi.org/10.1130/G33548C.1). If you have comments or questions, please send them to zoltan_dot_sylvester_at_gmail_dot_com.\n",
      "\n",
      "The original article that is discussed in the comment and the reply are as follows:\n",
      "\n",
      "Peakall, J., Kane, I. A., Masson, D. G., Keevil, G., McCaffrey, W., & Corney, R. (2011). Global (latitudinal) variation in submarine channel sinuosity. Geology, 40(1), 11\u201314. [doi:10.1130/G32295.1](http://dx.doi.org/doi:10.1130/G32295.1).\n",
      "\n",
      "Peakall, J., Wells, M. G., Cossu, R., Kane, I. A., Masson, D. G., Keevil, G. M., et al. (2013). Global (latitudinal) variation in submarine channel sinuosity: REPLY. Geology, 41(5), e288\u2013e288. [doi:10.1130/G34319Y.1](http://dx.doi.org/doi:10.1130/G34319Y.1)."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Read and plot data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from scipy import stats\n",
      "pd.set_option('display.mpl_style', 'default') # nicer plots than the default matplotlib style"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fileurl = 'https://dl.dropboxusercontent.com/u/25694950/channel_sinuosities.csv'\n",
      "data = pd.read_csv(fileurl)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 189
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The majority of the data comes from the book by Clark and Pickering (1996), which also forms the basis of the analysis by Peakall et al. (2011); graphs in the book were digitized using Graphclick. Data for the Amazon Channel comes from Pirmez and Flood (1995) (provided by Carlos Pirmez); and Popescu et al. (2001) (centerline digitized from map in paper and half-wavelength sinuosities calculated from centerline). See full references in paper.\n",
      "\n",
      "Let's have a look at the first 20 lines of the data table:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Slope</th>\n",
        "      <th>Sinuosity</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Large_river</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>   DeSoto</td>\n",
        "      <td> 0.00148</td>\n",
        "      <td> 1.24586</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>   DeSoto</td>\n",
        "      <td> 0.00228</td>\n",
        "      <td> 1.31765</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   DeSoto</td>\n",
        "      <td> 0.00181</td>\n",
        "      <td> 2.04757</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>   DeSoto</td>\n",
        "      <td> 0.00187</td>\n",
        "      <td> 2.00569</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   DeSoto</td>\n",
        "      <td> 0.00251</td>\n",
        "      <td> 2.23903</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.00440</td>\n",
        "      <td> 1.45093</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.00464</td>\n",
        "      <td> 1.36212</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.00537</td>\n",
        "      <td> 1.09572</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.01455</td>\n",
        "      <td> 1.56045</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.01575</td>\n",
        "      <td> 1.07500</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> Arguello</td>\n",
        "      <td> 0.06686</td>\n",
        "      <td> 1.10460</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00092</td>\n",
        "      <td> 1.02485</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00136</td>\n",
        "      <td> 1.06459</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00145</td>\n",
        "      <td> 1.05607</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00143</td>\n",
        "      <td> 1.13271</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00223</td>\n",
        "      <td> 1.01634</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00249</td>\n",
        "      <td> 1.08446</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00308</td>\n",
        "      <td> 1.30300</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00320</td>\n",
        "      <td> 1.17812</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> Monterey</td>\n",
        "      <td> 0.00334</td>\n",
        "      <td> 1.04472</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "        Name    Slope  Sinuosity  Latitude  Large_river\n",
        "0     DeSoto  0.00148    1.24586        28            0\n",
        "1     DeSoto  0.00228    1.31765        28            0\n",
        "2     DeSoto  0.00181    2.04757        28            0\n",
        "3     DeSoto  0.00187    2.00569        28            0\n",
        "4     DeSoto  0.00251    2.23903        28            0\n",
        "5   Arguello  0.00440    1.45093        33            0\n",
        "6   Arguello  0.00464    1.36212        33            0\n",
        "7   Arguello  0.00537    1.09572        33            0\n",
        "8   Arguello  0.01455    1.56045        33            0\n",
        "9   Arguello  0.01575    1.07500        33            0\n",
        "10  Arguello  0.06686    1.10460        33            0\n",
        "11  Monterey  0.00092    1.02485        35            0\n",
        "12  Monterey  0.00136    1.06459        35            0\n",
        "13  Monterey  0.00145    1.05607        35            0\n",
        "14  Monterey  0.00143    1.13271        35            0\n",
        "15  Monterey  0.00223    1.01634        35            0\n",
        "16  Monterey  0.00249    1.08446        35            0\n",
        "17  Monterey  0.00308    1.30300        35            0\n",
        "18  Monterey  0.00320    1.17812        35            0\n",
        "19  Monterey  0.00334    1.04472        35            0\n",
        "\n",
        "[20 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10,8))\n",
      "data['Name'].value_counts().plot(kind='bar');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAFAALKTyxNN+sMV8Cx2851voEDrfQgfv+RY6eM+30IEZKQAAgBhgRgoAACALZqQAAAAK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\nAJAjFlIAAAA5+v83sR2GptLU9gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc60dd30>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10,8))\n",
      "groups = data.groupby('Large_river')\n",
      "labels = ('no large river','large river')\n",
      "colors = (plt.rcParams['axes.color_cycle'][4],plt.rcParams['axes.color_cycle'][0])\n",
      "for name, group in groups:\n",
      "    plot(group.Slope, group.Sinuosity, marker='o', linestyle='', ms=8, label=labels[name], color=colors[name])\n",
      "legend(loc='best')\n",
      "xlabel('valley slope', fontsize=18)\n",
      "ylabel('sinuosity', fontsize=18)\n",
      "axis([0.0,0.03,1.0,3.0]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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J2Puxjx0RERGRBtToY8elWCIiIiKDYGJnYpIkYWl+ITLnL8E98x5H5vwlWJpfCIfDIfse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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc9cbbf0>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10,8))\n",
      "groups = data.groupby('Large_river')\n",
      "labels = ('no large river','large river')\n",
      "colors = (plt.rcParams['axes.color_cycle'][4],plt.rcParams['axes.color_cycle'][0])\n",
      "for name, group in groups:\n",
      "    plot(group.Latitude, group.Sinuosity, marker='o', linestyle='', ms=8, label=labels[name], color=colors[name])\n",
      "legend(loc='best')\n",
      "xlabel('latitude', fontsize=18)\n",
      "ylabel('sinuosity', fontsize=18)\n",
      "axis([0.0,70,1.0,3.0]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M8xPD/ELH7MQwPzHMTx42bSri3Y1EREQULO49qrLq6moUFK1CmcUKN3TQw4Os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\nXhWvOx46dAgHDhxo9kaEel9++SX27NmDiRMn+j2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VYvpVfTlLgkxP74s3KpS1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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc956b90>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Analysis of correlations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def bootstrap_regression(x,y,nit):\n",
      "    slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)\n",
      "    r = np.zeros(nit)\n",
      "    n = len(x)\n",
      "    for i in range(1,nit):\n",
      "        x1 = x[np.random.randint(n, size=n)]\n",
      "        y1 = y[np.random.randint(n, size=n)]\n",
      "        slope, intercept, r_value2, p_value2, std_err = stats.linregress(x1,y1)\n",
      "        r[i] = r_value2\n",
      "    if r_value>0:\n",
      "        p_value_boot = len(r[r >= r_value])/float(len(r))\n",
      "    else:\n",
      "        p_value_boot = len(r[r <= r_value])/float(len(r))\n",
      "    return r_value, p_value, p_value_boot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r_values = np.zeros([3,1])\n",
      "p_values = np.zeros([3,1])\n",
      "p_values_boot = np.zeros([3,1])\n",
      "r_values[0], p_values[0], p_values_boot[0] = bootstrap_regression(data.Latitude,data.Sinuosity,1000)\n",
      "# for variable pairs that involve slope, we can only use those data points that have slope measurements:\n",
      "r_values[1], p_values[1], p_values_boot[1] = bootstrap_regression(data.Slope[isnan(data.Slope)==0],data.Sinuosity[isnan(data.Slope)==0],1000)\n",
      "r_values[2], p_values[2], p_values_boot[2] = bootstrap_regression(data.Latitude[isnan(data.Slope)==0],data.Slope[isnan(data.Slope)==0],1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a dataframe that contains the result of the regression:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.hstack(([[292],[268],[268]],r_values,r_values**2,\n",
      "                             p_values,p_values_boot)),columns=['n','Pearson R','R squared','p-value',\n",
      "                            'p-value bootstrap'],index=('latitude and sinuosity','slope and sinuosity','latitude and slope'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n</th>\n",
        "      <th>Pearson R</th>\n",
        "      <th>R squared</th>\n",
        "      <th>p-value</th>\n",
        "      <th>p-value bootstrap</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>latitude and sinuosity</th>\n",
        "      <td> 292</td>\n",
        "      <td>-0.347308</td>\n",
        "      <td> 0.120623</td>\n",
        "      <td> 1.058797e-09</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>slope and sinuosity</th>\n",
        "      <td> 268</td>\n",
        "      <td>-0.277373</td>\n",
        "      <td> 0.076936</td>\n",
        "      <td> 4.020125e-06</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>latitude and slope</th>\n",
        "      <td> 268</td>\n",
        "      <td> 0.202860</td>\n",
        "      <td> 0.041152</td>\n",
        "      <td> 8.370798e-04</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 138,
       "text": [
        "                          n  Pearson R  R squared       p-value  \\\n",
        "latitude and sinuosity  292  -0.347308   0.120623  1.058797e-09   \n",
        "slope and sinuosity     268  -0.277373   0.076936  4.020125e-06   \n",
        "latitude and slope      268   0.202860   0.041152  8.370798e-04   \n",
        "\n",
        "                        p-value bootstrap  \n",
        "latitude and sinuosity                  0  \n",
        "slope and sinuosity                     0  \n",
        "latitude and slope                      0  \n",
        "\n",
        "[3 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}