Drift_Fourier_Analysis_Bootstrap_CCL4_NYA_Daily_Means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trend analysis with bootstrap resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python / Numpy implementation of the trend analysis presented in [Gardiner et al., 2008][1]\n",
    "\n",
    "The following model is used to fit the annual trend (drift) + intra-annual variability (i.e., fourier series tuncated at $n$ degrees):\n",
    "\n",
    "$$ F(t, p, \\alpha, \\mathrm{{\\mathbf \\beta}}) = p + \\alpha t + \\left[\\beta_1 \\cos(\\frac{\\pi t}{L}) + \\beta_2 \\sin(\\frac{\\pi t}{L}) + \\beta_3 \\cos(\\frac{2 \\pi t}{L}) + \\beta_4 \\sin(\\frac{2 \\pi t}{L}) + \\cdots +  \\beta_{2n-1} \\cos(\\frac{n \\pi t}{L}) + \\beta_{2n} \\sin(\\frac{n \\pi t}{L}) \\right]$$\n",
    "\n",
    "where $p$ is the intercept, $\\alpha$ is the trend (slope), $\\mathrm{{\\mathbf \\beta}}$ are the truncated fourier series parameters, and $L$ is the half-period (= 0.5 if $t$ is in years).\n",
    "\n",
    "Model parameters are estimated using the weighted linear least squares method. Bootstrap resampling can be used to evaluate the confidence intervals on the parameter estimates (more robust in case of non-normally distributed random effects in the data).\n",
    "\n",
    "Author: B. Bovy | GIRPAS, ULg\n",
    "\n",
    "[1]: http://dx.doi.org/10.5194/acp-8-6719-2008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fourier_basis(x, degree, half_period):\n",
    "    \"\"\"Returns a 2-d array of fourier basis.\"\"\"\n",
    "    A = np.ones((x.size, 2 * degree + 1))\n",
    "    \n",
    "    for d in range(1, degree + 1):\n",
    "        A[:, 2*d-1] = np.cos(d * np.pi * x / half_period)\n",
    "        A[:, 2*d] = np.sin(d * np.pi * x / half_period)\n",
    "    \n",
    "    return A\n",
    "\n",
    "\n",
    "def fit_driftfourier(x, data, weights, degree, half_period=0.5):\n",
    "    \"\"\"\n",
    "    Fit y = f(x - x.min()) to data where f is given by\n",
    "    fourier series + drift.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    x : 1-d array\n",
    "        x-coordinates\n",
    "    data : 1-d array\n",
    "        data values\n",
    "    weights : 1-d array\n",
    "        weights (>=0)\n",
    "    degree : int\n",
    "        degree of fourier series\n",
    "    half_period : float\n",
    "        half period\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    intercept : float\n",
    "        intercept at x.min()\n",
    "    slope : float\n",
    "        slope (drift) for the normalized data\n",
    "        (x - x.min())\n",
    "    pfourier : 1-d array\n",
    "        Fourier series parameters for the\n",
    "        normalized data\n",
    "    f_drift : callable\n",
    "        Can be used to calculate the drift\n",
    "        given any (non-normalized) x\n",
    "    f_fourier : callable\n",
    "        Can be used to calculate fourier series\n",
    "    f_driftfourier : callable\n",
    "        Can be used to calculate drift + fourier\n",
    "    residual_std : float\n",
    "        estimated standard deviation of residuals\n",
    "    A : 2-d array\n",
    "        matrix of \"coefficients\"\n",
    "    \n",
    "    \"\"\"\n",
    "    xmin = x.min()\n",
    "    xnorm = x - xmin\n",
    "    \n",
    "    # coefficient matrix\n",
    "    A = np.ones((x.size, 2 * degree + 2))\n",
    "    A[:, 1] = xnorm\n",
    "    A[:, 2:] = fourier_basis(xnorm, degree, half_period)[:, 1:]\n",
    "    \n",
    "    # linear weighted least squares\n",
    "    results = np.linalg.lstsq(A * weights[:, np.newaxis],\n",
    "                              data * weights)\n",
    "    \n",
    "    params = results[0]\n",
    "    intercept = params[0]\n",
    "    slope = params[1]\n",
    "    pfourier = params[2:]\n",
    "    \n",
    "    f_drift = lambda t: slope * (t - xmin) + intercept\n",
    "    f_fourier = lambda t: np.sum(fourier_basis(t - xmin, degree,\n",
    "                                               half_period)[:, 1:]\n",
    "                                 * pfourier[np.newaxis, :],\n",
    "                                 axis=1) + intercept\n",
    "    f_driftfourier = lambda t: f_drift(t) + f_fourier(t) - intercept\n",
    "    \n",
    "    residual_std = np.sqrt(results[1][0] / (x.size - 2 * degree + 2)) \n",
    "    \n",
    "    return (intercept, slope, pfourier,\n",
    "            f_drift, f_fourier, f_driftfourier,\n",
    "            residual_std, A)\n",
    "\n",
    "\n",
    "def cf_driftfourier(x, data, weights, degree,\n",
    "                    half_period=0.5, nboot=5000,\n",
    "                    percentiles=(2.5, 50., 97.5)):\n",
    "    \"\"\"\n",
    "    Calculate confidence intervals for the fitted\n",
    "    parameters from fourier series + drift modelling,\n",
    "    using bootstrap resampling.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    nboot : int\n",
    "        number of bootstrap replicates\n",
    "    percentiles : sequence of floats\n",
    "        percentiles of parameter estimate\n",
    "        distributions to return \n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    perc : dict\n",
    "        percentiles for each parameter\n",
    "        distribution.\n",
    "    boot_intercept : 1-d array\n",
    "        intercept estimates from bootstrapped\n",
    "        datasets.\n",
    "    boot_slope : 1-d array\n",
    "        slope estimates.\n",
    "    boot_pfourier : 2-d array\n",
    "        fourier parameters estimates.\n",
    "    \n",
    "    See Also\n",
    "    --------\n",
    "    :func:`fit_driftfourier`\n",
    "    \n",
    "    \"\"\"\n",
    "    # first fit without bootstrapping\n",
    "    results = fit_driftfourier(x, data, weights,\n",
    "                               degree, half_period)\n",
    "    f_driftfourier = results[5]\n",
    "    A = results[7]\n",
    "    model = f_driftfourier(x)\n",
    "    residuals = data - model\n",
    "    \n",
    "    # generate bootstrap resamples of residuals\n",
    "    # and new datasets from these resamples\n",
    "    resample_residuals = np.random.choice(\n",
    "        residuals, size=(x.size, nboot), replace=True\n",
    "    )\n",
    "    boot_dataset = model[:, np.newaxis] + resample_residuals\n",
    "    \n",
    "    # fit all bootstrap datasets at once\n",
    "    results_boot = np.linalg.lstsq(A * weights[:, np.newaxis],\n",
    "                                   boot_dataset * weights[:, np.newaxis])\n",
    "    \n",
    "    params_boot = results_boot[0]\n",
    "    \n",
    "    # compute percentiles\n",
    "    perc_boot = np.column_stack(\n",
    "        np.percentile(params_boot, percentiles, axis=1)\n",
    "    )\n",
    "    \n",
    "    perc = {'intercept' : perc_boot[0],\n",
    "            'slope' : perc_boot[1],\n",
    "            'pfourier' : perc_boot[2:]}\n",
    "    \n",
    "    intercept = params_boot[0]\n",
    "    slope = params_boot[1]\n",
    "    pfourier = params_boot[2:]\n",
    "    \n",
    "    return perc, intercept, slope, pfourier\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_slope_percent(slope, intercept):\n",
    "    \"\"\"\n",
    "    Express `slope` as percentage, taking\n",
    "    the `intercept` value as reference (100%).\n",
    "    \"\"\"\n",
    "    return slope / intercept * 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCL4 NY4 data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ccl4_nya = pd.read_csv('data/CCL4_NYA.csv', header=None,\n",
    "                       names=['time', 'col_density'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values in the `time` axis are given in decimal years. We first need to express them as real date & time values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>col_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-08-05 18:28:48.000001</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.121000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-05 18:28:48.000001</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.165000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-05 18:28:48.000001</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.140000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-05 18:28:48.000001</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.138000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-05 18:28:48.000001</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.119000e+15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time   col_density\n",
       "1996-08-05 18:28:48.000001  1996.595  2.121000e+15\n",
       "1996-08-05 18:28:48.000001  1996.595  2.165000e+15\n",
       "1996-08-05 18:28:48.000001  1996.595  2.140000e+15\n",
       "1996-08-05 18:28:48.000001  1996.595  2.138000e+15\n",
       "1996-08-05 18:28:48.000001  1996.595  2.119000e+15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def decimal_year_to_datetime(val):\n",
    "    \"\"\"\n",
    "    Convert `val` given in decimal years into a\n",
    "    `datetime.datetime` object, via the computation of the\n",
    "    total duration of the year in seconds.\n",
    "    \"\"\"\n",
    "    year = int(val)\n",
    "    frac = val - year\n",
    "    \n",
    "    year_dt = datetime(year, 1, 1)\n",
    "    year_delta = datetime(year + 1, 1, 1) - year_dt\n",
    "    year_delta_seconds = year_delta.total_seconds()\n",
    "    \n",
    "    return year_dt + timedelta(seconds=year_delta_seconds * frac)\n",
    "\n",
    "\n",
    "decimal_years_to_datetimes = np.vectorize(decimal_year_to_datetime)   \n",
    "\n",
    "# assign the datetimes as index of the dataset\n",
    "ccl4_nya.index = pd.DatetimeIndex(decimal_years_to_datetimes(ccl4_nya['time'].values))\n",
    "\n",
    "\n",
    "ccl4_nya.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit below is performed from the daily mean column densities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>col_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1996</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">8</th>\n",
       "      <th>5</th>\n",
       "      <td>1996.595</td>\n",
       "      <td>2.136600e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996.618</td>\n",
       "      <td>2.163800e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1996.649</td>\n",
       "      <td>2.154000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1996.666</td>\n",
       "      <td>2.277143e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>6</th>\n",
       "      <td>1996.681</td>\n",
       "      <td>2.383000e+15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               time   col_density\n",
       "1996 8 5   1996.595  2.136600e+15\n",
       "       14  1996.618  2.163800e+15\n",
       "       25  1996.649  2.154000e+15\n",
       "       31  1996.666  2.277143e+15\n",
       "     9 6   1996.681  2.383000e+15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_day = [ccl4_nya.index.year, ccl4_nya.index.month, ccl4_nya.index.day]\n",
    "\n",
    "ccl4_nya_daily_means = ccl4_nya.groupby(by_day).apply(np.mean)\n",
    "\n",
    "ccl4_nya_daily_means.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit trend only (no fourier parameters added to the model), without bootstrapping**. Also express trend as percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = ccl4_nya_daily_means['time'].values\n",
    "y = ccl4_nya_daily_means['col_density'].values\n",
    "w = np.ones_like(x)\n",
    "fourier_degree = 0   # fit trend only\n",
    "\n",
    "res = fit_driftfourier(x, y, w, fourier_degree)\n",
    "intercept, slope, pfourier, residuals_std = res[0:3] + (res[6],)\n",
    "f_drift, f_fourier, f_driftfourier = res[3:6]\n",
    "slope_percent = get_slope_percent(slope, intercept)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitted trend: -1.208E+13 molec./cm2/yr | -0.666 %/yr\n",
      "fitted intercept at xmin: 1.814E+15 molec./cm2\n",
      "fitted fourier parameters: []\n",
      "std of residuals: 3.186E+14 molec./cm2\n"
     ]
    }
   ],
   "source": [
    "print(\"fitted trend: {:.3E} molec./cm2/yr | {:.3f} %/yr\".format(slope, slope_percent))\n",
    "print(\"fitted intercept at xmin: {:.3E} molec./cm2\".format(intercept))\n",
    "print(\"fitted fourier parameters: {}\".format(pfourier))\n",
    "print(\"std of residuals: {:.3E} molec./cm2\".format(residuals_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot data + fitted line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<matplotlib.legend.Legend at 0x7fb9fe7e8278>"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/python/miniconda3/envs/main_py34/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
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3gRZXSyTSR+o+FhHpWbQt/mOBQyIz+0U8S93HIiLdizbwrye8JK+Ip3khvbKI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba008ae1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccl4_nya_daily_means.plot(x='time', y='col_density', kind='scatter', label='data')\n",
    "plt.plot(x, f_drift(x), label='fitted annual trend')\n",
    "plt.plot(x, f_driftfourier(x),\n",
    "         label='fitted annual trend + intra-annual variability')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit trend only, with bootstrap resampling**. Compute 2.5% and 97.5% percentiles (i.e., the 95% interval) for each fitted parameter. Compute also the percentiles for trend in %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ci95_percentiles = (2.5, 97.5)\n",
    "\n",
    "cf, boot_intercept, boot_slope, boot_pfourier = cf_driftfourier(\n",
    "    x, y, w, fourier_degree, nboot=20000,\n",
    "    percentiles=ci95_percentiles\n",
    ")\n",
    "\n",
    "boot_slope_percent = get_slope_percent(boot_slope, boot_intercept)\n",
    "cf['slope_percent'] = np.percentile(boot_slope_percent, ci95_percentiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% CI for trend: (-2.151E+13, -2.483E+12) molec./cm2/yr\n",
      "95% CI for trend: (-1.142, -0.143) %/yr\n",
      "95% CI for intercept: (1.731E+15, 1.900E+15) molec./cm2\n",
      "percentiles for fourier parameters: []\n"
     ]
    }
   ],
   "source": [
    "print(\"95% CI for trend: ({:.3E}, {:.3E}) molec./cm2/yr\".format(*cf['slope']))\n",
    "print(\"95% CI for trend: ({:.3f}, {:.3f}) %/yr\".format(*cf['slope_percent']))\n",
    "print(\"95% CI for intercept: ({:.3E}, {:.3E}) molec./cm2\".format(*cf['intercept']))\n",
    "print(\"percentiles for fourier parameters: {}\".format(cf['pfourier']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histograms of the distributions of the annual trend and intercept estimates obtained from bootstrap resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pEgN+By+/M98RK2Uf1mMngwDcmZfe/E4nSX4HyGTS/OUb8wAUcpmuPOj34/c4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba00834550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(13, 3))\n",
    "\n",
    "def plot_bootdist(v, ax, label):\n",
    "    sns.distributions.distplot(v, ax=ax)\n",
    "    plt.setp(ax, xlabel=label, yticks=[])\n",
    "\n",
    "plot_bootdist(boot_slope, axes[0], 'trend (molec./cm2/yr)')\n",
    "plot_bootdist(boot_slope_percent, axes[1], 'trend (%/yr)')\n",
    "plot_bootdist(boot_intercept, axes[2], 'intercept (molec./cm2)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of daily means vs. raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9fe42f0f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/python/miniconda3/envs/main_py34/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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Dr88i6fwRziRlEB6xrkTl2nv8Mu9MnCeTZkW5ZvYYv6qqemAfsB3QA52AA4qi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\ngEjgF0IIIQLI/wPXaKAU3EdxnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fe7f9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccl4_nya_daily_stds = ccl4_nya.groupby(by_day).apply(np.std)\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "ccl4_nya.plot(x='time', y='col_density', kind='scatter', ax=ax,\n",
    "              label='raw measurements')\n",
    "ccl4_nya_daily_means.plot(x='time', y='col_density',\n",
    "                          kind='scatter', ax=ax, color='r',\n",
    "                          label='daily means')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of daily mean column densities with errorbars (stdev)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9fe45f400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/python/miniconda3/envs/main_py34/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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vu5xX2to40JHdYyAiI1Pgj1jc4/hbT2ofcXx+aOg4/Uqm+o1Trc3E5QT2qJtz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\nERGRDFHgFxERyZD/D5neoPSNJXneAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba0081ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccl4_nya_daily_stds = ccl4_nya.groupby(by_day).apply(np.std)\n",
    "\n",
    "ccl4_nya_daily_means.plot(x='time', y='col_density',\n",
    "                          kind='scatter', color='r',\n",
    "                          yerr=ccl4_nya_daily_stds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Main (py34)",
   "language": "python",
   "name": "main_py34"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}