ngs-11-python-and-graphing.ipynb

{
 "metadata": {
  "name": "ngs-11-python-and-graphing"
 },
 "nbformat": 3,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "source": [
      "## Using Python to load some data and do some plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "Let's get some workout data and plot it!"
     ]
    },
    {
     "cell_type": "code",
     "input": [
      "import urllib",
      "fp = urllib.urlopen('http://ged.msu.edu/angus/tutorials-2012/files/workout.csv')",
      "for line in fp:",
      "    print line,"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "workouts = []",
      "",
      "fp = urllib.urlopen('http://ged.msu.edu/angus/tutorials-2012/files/workout.csv')",
      "for line in fp:",
      "    line = line.strip()",
      "    if line:",
      "        workouts.append(line)",
      "workouts"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "# let's use the CSV library: http://docs.python.org/library/csv.html",
      "import csv",
      "",
      "workouts = []",
      "",
      "fp = urllib.urlopen('http://lyorn.idyll.org/~t/transfer/workout.csv')",
      "reader = csv.reader(fp)",
      "for line in reader:",
      "    if line:",
      "        if not line[0].startswith('#'):",
      "            workouts.append(line)",
      "",
      "workouts"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "# http://docs.python.org/library/datetime.html",
      "from datetime import datetime",
      "workouts2 = []",
      "for (date, exercise, miles, duration) in workouts:",
      "    miles = int(miles)",
      "    duration = int(duration)",
      "    exercise = exercise.strip()",
      "    date = datetime.strptime(date, \"%Y, %b-%d\")",
      "    workouts2.append([date, exercise, miles, duration])",
      "    ",
      "workouts = workouts2",
      "workouts"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "durations = []",
      "for row in workouts:",
      "    durations.append(row[3])",
      "durations"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "days = []",
      "for row in workouts:",
      "    date = row[0]",
      "    day = date.day",
      "    days.append(day)",
      "days"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "plot(days, durations, 'b-', linewidth=3)",
      "xlabel('Day of month')",
      "ylabel('Duration of workout')",
      "title('Titus\\'s workout')",
      "grid()",
      "xticks(range(1, max(days) + 1))",
      "axis(ymax=70)",
      "savefig('/root/Dropbox/ngs-day2.pdf')"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "source": [
      "# Loading matplotlib gallery code",
      "",
      "Go to http://matplotlib.sourceforge.net/gallery.html, find the source code link, and",
      "then put it in a ipynb cell after '%load' and it will be loaded into the next cell."
     ]
    },
    {
     "cell_type": "code",
     "input": [
      "%load http://matplotlib.sourceforge.net/mpl_examples/api/radar_chart.py"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      "import numpy as np",
      "",
      "import matplotlib.pyplot as plt",
      "from matplotlib.projections.polar import PolarAxes ",
      "from matplotlib.projections import register_projection ",
      "",
      "def radar_factory(num_vars, frame='circle'): ",
      "    \"\"\"Create a radar chart with `num_vars` axes.\"\"\" ",
      "    # calculate evenly-spaced axis angles ",
      "    theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars) ",
      "    # rotate theta such that the first axis is at the top ",
      "    theta += np.pi/2 ",
      "",
      "    def draw_poly_frame(self, x0, y0, r): ",
      "        # TODO: use transforms to convert (x, y) to (r, theta)",
      "        verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta] ",
      "        return plt.Polygon(verts, closed=True, edgecolor='k') ",
      "        ",
      "    def draw_circle_frame(self, x0, y0, r): ",
      "        return plt.Circle((x0, y0), r) ",
      "",
      "    frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame} ",
      "    if frame not in frame_dict: ",
      "        raise ValueError, 'unknown value for `frame`: %s' % frame ",
      "",
      "    class RadarAxes(PolarAxes): ",
      "        \"\"\"Class for creating a radar chart (a.k.a. a spider or star chart) ",
      "        ",
      "        http://en.wikipedia.org/wiki/Radar_chart ",
      "        \"\"\" ",
      "        name = 'radar' ",
      "        # use 1 line segment to connect specified points ",
      "        RESOLUTION = 1 ",
      "        # define draw_frame method ",
      "        draw_frame = frame_dict[frame] ",
      "        ",
      "        def fill(self, *args, **kwargs): ",
      "            \"\"\"Override fill so that line is closed by default\"\"\" ",
      "            closed = kwargs.pop('closed', True) ",
      "            return super(RadarAxes, self).fill(closed=closed, *args, **kwargs) ",
      "        ",
      "        def plot(self, *args, **kwargs): ",
      "            \"\"\"Override plot so that line is closed by default\"\"\" ",
      "            lines = super(RadarAxes, self).plot(*args, **kwargs) ",
      "            for line in lines: ",
      "                self._close_line(line) ",
      "        ",
      "        def _close_line(self, line): ",
      "            x, y = line.get_data() ",
      "            # FIXME: markers at x[0], y[0] get doubled-up ",
      "            if x[0] != x[-1]: ",
      "                x = np.concatenate((x, [x[0]])) ",
      "                y = np.concatenate((y, [y[0]])) ",
      "                line.set_data(x, y) ",
      "        ",
      "        def set_varlabels(self, labels): ",
      "            self.set_thetagrids(theta * 180/np.pi, labels) ",
      "        ",
      "        def _gen_axes_patch(self): ",
      "            x0, y0 = (0.5, 0.5) ",
      "            r = 0.5 ",
      "            return self.draw_frame(x0, y0, r)",
      "        ",
      "    register_projection(RadarAxes) ",
      "    return theta ",
      "",
      "",
      "if __name__ == '__main__': ",
      "    #The following data is from the Denver Aerosol Sources and Health study. ",
      "    #See  doi:10.1016/j.atmosenv.2008.12.017    ",
      "    #",
      "    #The data are pollution source profile estimates for five modeled pollution",
      "    #sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical species.",
      "    #The radar charts are experimented with here to see if we can nicely ",
      "    #visualize how the modeled source profiles change across four scenarios:",
      "    #  1) No gas-phase species present, just seven particulate counts on",
      "    #     Sulfate",
      "    #     Nitrate",
      "    #     Elemental Carbon (EC)",
      "    #     Organic Carbon fraction 1 (OC)",
      "    #     Organic Carbon fraction 2 (OC2)",
      "    #     Organic Carbon fraction 3 (OC3)",
      "    #     Pyrolized Organic Carbon (OP)",
      "    #  2)Inclusion of gas-phase specie carbon monoxide (CO) ",
      "    #  3)Inclusion of gas-phase specie ozone (O3). ",
      "    #  4)Inclusion of both gas-phase speciesis present...",
      "    N = 9",
      "    theta = radar_factory(N)",
      "    spoke_labels = ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', ",
      "                    'O3']",
      "    f1_base = [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00]",
      "    f1_CO =   [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00] ",
      "    f1_O3 =   [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03] ",
      "    f1_both = [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01] ",
      "",
      "    f2_base = [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00]",
      "    f2_CO =   [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00] ",
      "    f2_O3 =   [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00] ",
      "    f2_both = [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00] ",
      "",
      "    f3_base = [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00]",
      "    f3_CO =   [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00] ",
      "    f3_O3 =   [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00] ",
      "    f3_both = [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00] ",
      "    ",
      "    f4_base = [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00]",
      "    f4_CO =   [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00] ",
      "    f4_O3 =   [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95] ",
      "    f4_both = [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88] ",
      "",
      "    f5_base = [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]",
      "    f5_CO =   [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00] ",
      "    f5_O3 =   [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00] ",
      "    f5_both = [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16] ",
      "",
      "    fig = plt.figure(figsize=(9,9))",
      "    # adjust spacing around the subplots",
      "    fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)",
      "    title_list = ['Basecase', 'With CO', 'With O3', 'CO & O3']",
      "    data = {'Basecase': [f1_base, f2_base, f3_base, f4_base, f5_base],",
      "            'With CO': [f1_CO, f2_CO, f3_CO, f4_CO, f5_CO],",
      "            'With O3': [f1_O3, f2_O3, f3_O3, f4_O3, f5_O3], ",
      "            'CO & O3': [f1_both, f2_both, f3_both, f4_both, f5_both]}",
      "    colors = ['b', 'r', 'g', 'm', 'y']",
      "    # chemicals range from 0 to 1",
      "    radial_grid = [0.2, 0.4, 0.6, 0.8]",
      "    # If you don't care about the order, you can loop over data_dict.items()",
      "    for n, title in enumerate(title_list):",
      "        ax = fig.add_subplot(2, 2, n+1, projection='radar')",
      "        plt.rgrids(radial_grid)",
      "        ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),",
      "                     horizontalalignment='center', verticalalignment='center')",
      "        for d, color in zip(data[title], colors):",
      "            ax.plot(theta, d, color=color) ",
      "            ax.fill(theta, d, facecolor=color, alpha=0.25)  ",
      "        ax.set_varlabels(spoke_labels)",
      "    # add legend relative to top-left plot",
      "    plt.subplot(2,2,1)",
      "    labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')",
      "    legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)",
      "    plt.setp(legend.get_texts(), fontsize='small')",
      "    plt.figtext(0.5, 0.965,  '5-Factor Solution Profiles Across Four Scenarios', ",
      "               ha='center', color='black', weight='bold', size='large')        ",
      "    plt.show()",
      ""
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "input": [
      ""
     ],
     "language": "python",
     "outputs": []
    }
   ]
  }
 ]
}