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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotly maps"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "with Plotly's Python API library and Basemap"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook comes in response to <a href=\"https://twitter.com/rjallain/status/496767038782570496\" target=\"_blank\">this</a> Rhett Allain tweet.\n",
      "\n",
      "> Although Plotly does not feature built-in maps functionality (yet), this notebook demonstrates how to *plotly-fy* maps generated by Basemap."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, check the version which version of the Python API library installed on your machine:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import plotly\n",
      "plotly.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'1.2.6'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, if you have a plotly account as well as a credentials file set up on your machine, singing in to Plotly's servers is done automatically while importing `plotly.plotly`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import plotly.plotly as py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import the plotly graph objects (in particular `Contour`) to help build our figure:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from plotly.graph_objs import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data with this notebook will be taken from a NetCDF file, so import netcdf class from the <a href=\"http://docs.scipy.org/doc/scipy/reference/generated/scipy.io.netcdf.netcdf_file.html\" target=\"_blank\">scipy.io</a> module, along with numpy:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np           \n",
      "from scipy.io import netcdf  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, import the Matplotlib <a href=\"http://matplotlib.org/basemap/\" target=\"_blank\">Basemap</a> Toolkit, its installation instructions can found <a href=\"http://matplotlib.org/basemap/users/installing.html\" target=\"_blank\">here</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.basemap import Basemap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "1. Get the data!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data is taken from <a href=\"http://www.esrl.noaa.gov/psd/data/composites/day/\" target=\"_blank\">NOAA Earth System Research Laboratory</a>.\n",
      "\n",
      "Unfortunately, this website does not allow to *code* your output demand and/or use `wget` to download the data. <br>\n",
      "\n",
      "That said, the data used for this notebook can be downloaded in a only a few clicks:\n",
      "\n",
      "- Select *Air Temperature* in **Varaibles**\n",
      "- Select *Surface* in **Analysis level?**\n",
      "- Select *Jul | 1* and *Jul | 31*\n",
      "- Enter *2014* in the **Enter Year of last day of range** field\n",
      "- Select *Anomaly* in **Plot type?**\n",
      "- Select *All* in **Region of globe**\n",
      "- Click on **Create Plot** \n",
      "\n",
      "Then on the following page, click on **Get a copy of the netcdf data file used for the plot** to download the NetCDF on your machine.\n",
      "\n",
      "Note that the data represents the average daily surface air temperature anomaly (in deg. C) for July 2014 with respect to 1981-2010 climatology.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, import the NetCDF file into this IPython session. The following was inspired by this  earthpy blog <a href=\"http://earthpy.org/interpolation_between_grids_with_basemap.html\" target=\"_blank\">post</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Path the downloaded NetCDF file (different for each download)\n",
      "f_path = '/home/etienne/Downloads/compday.Bo3cypJYyE.nc'\n",
      "\n",
      "# Retrieve data from NetCDF file\n",
      "with netcdf.netcdf_file(f_path, 'r') as f:\n",
      "    lon = f.variables['lon'][::]    # copy as list\n",
      "    lat = f.variables['lat'][::-1]  # invert the latitude vector -> South to North\n",
      "    air = f.variables['air'][0,::-1,:]  # squeeze out the time dimension, \n",
      "                                        # invert latitude index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The values `lon` start a 0 degrees and increase eastward to 360 degrees. So, the `air` array is centered about the Pacific Ocean. For a better-looking plot, shift the data so that it is centered about the 0 meridian:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Shift 'lon' from [0,360] to [-180,180], make numpy array\n",
      "tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n] \n",
      "                   for n,l in enumerate(lon)])  # => [0,180]U[-180,2.5]\n",
      "\n",
      "i_east, = np.where(tmp_lon>=0)  # indices of east lon\n",
      "i_west, = np.where(tmp_lon<0)   # indices of west lon\n",
      "lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))  # stack the 2 halves\n",
      "\n",
      "# Correspondingly, shift the 'air' array\n",
      "tmp_air = np.array(air)\n",
      "air = np.hstack((tmp_air[:,i_west], tmp_air[:,i_east]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "2. Make Contour graph object"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Very simply,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trace1 = Contour(\n",
      "    z=air,\n",
      "    x=lon,\n",
      "    y=lat,\n",
      "    colorscale=\"RdBu\",\n",
      "    zauto=False,  # custom contour levels\n",
      "    zmin=-5,      # first contour level\n",
      "    zmax=5        # last contour level  => colorscale is centered about 0\n",
      ")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "3. Get the coastlines and country boundaries with Basemap"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Basemap module includes data for drawing coastlines and country boundaries onto world maps. Adding coastlines and/or country boundaries on a matplotlib figure is done with the `.drawcoaslines()` or `.drawcountries()` Basemap methods. \n",
      "\n",
      "Next, we will retrieve the Basemap plotting data (or polygons) and convert them to longitude/latitude arrays (inspired by this stackoverflow <a href=\"http://stackoverflow.com/questions/14280312/world-map-without-rivers-with-matplotlib-basemap\" target=\"_blank\">post</a>) and then package them into Plotly `Scatter` graph objects .\n",
      "\n",
      "In other words, the goal is to plot each *continuous* coastline and country boundary lines as 1 Plolty scatter line trace."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make shortcut to Basemap object, \n",
      "# not specifying projection type for this example\n",
      "m = Basemap()     "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make trace-generating function (return a Scatter object)\n",
      "def make_scatter(x,y):\n",
      "    return Scatter(\n",
      "        x=x,\n",
      "        y=y,\n",
      "        mode='lines',\n",
      "        line=Line(color=\"black\"),\n",
      "        name=' '  # no name on hover\n",
      "    )\n",
      "\n",
      "# Functions converting coastline/country polygons to lon/lat traces\n",
      "def polygons_to_traces(poly_paths, N_poly):\n",
      "    ''' \n",
      "    pos arg 1. (poly_paths): paths to polygons\n",
      "    pos arg 2. (N_poly): number of polygon to convert\n",
      "    '''\n",
      "    traces = []  # init. plotting list \n",
      "\n",
      "    for i_poly in range(N_poly):\n",
      "        poly_path = poly_paths[i_poly]\n",
      "        \n",
      "        # get the Basemap coordinates of each segment\n",
      "        coords_cc = np.array(\n",
      "            [(vertex[0],vertex[1]) \n",
      "             for (vertex,code) in poly_path.iter_segments(simplify=False)]\n",
      "        )\n",
      "        \n",
      "        # convert coordinates to lon/lat by 'inverting' the Basemap projection\n",
      "        lon_cc, lat_cc = m(coords_cc[:,0],coords_cc[:,1], inverse=True)\n",
      "        \n",
      "        # add plot.ly plotting options\n",
      "        traces.append(make_scatter(lon_cc,lat_cc))\n",
      "     \n",
      "    return traces\n",
      "\n",
      "# Function generating coastline lon/lat traces\n",
      "def get_coastline_traces():\n",
      "    poly_paths = m.drawcoastlines().get_paths() # coastline polygon paths\n",
      "    N_poly = 91  # use only the 91st biggest coastlines (i.e. no rivers)\n",
      "    return polygons_to_traces(poly_paths, N_poly)\n",
      "\n",
      "# Function generating country lon/lat traces\n",
      "def get_country_traces():\n",
      "    poly_paths = m.drawcountries().get_paths() # country polygon paths\n",
      "    N_poly = len(poly_paths)  # use all countries\n",
      "    return polygons_to_traces(poly_paths, N_poly)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get list of of coastline and country lon/lat traces\n",
      "traces_cc = get_coastline_traces()+get_country_traces()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4. Make a figue object and plot!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Package the `Contour` trace with the coastline and country traces. Note that the `Contour` trace must be placed before the coastline and country traces in order to make all traces visible."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = Data([trace1]+traces_cc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Layout options are set in a `Layout` object:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "title = u\"Average daily surface air temperature anomalies [\\u2103]<br> \\\n",
      "in July 2014 with respect to 1981-2010 climatology\"\n",
      "\n",
      "anno_text = \"Data courtesy of \\\n",
      "<a href='http://www.esrl.noaa.gov/psd/data/composites/day/'>\\\n",
      "NOAA Earth System Research Laboratory</a>\"\n",
      "\n",
      "axis_style = dict(\n",
      "    zeroline=False,\n",
      "    showline=False,\n",
      "    showgrid=False,\n",
      "    ticks='',\n",
      "    showticklabels=False,\n",
      ")\n",
      "\n",
      "layout = Layout(\n",
      "    title=title,\n",
      "    showlegend=False,\n",
      "    hovermode=\"closest\",        # highlight closest point on hover\n",
      "    xaxis=XAxis(\n",
      "        axis_style,\n",
      "        range=[lon[0],lon[-1]]  # restrict y-axis to range of lon\n",
      "    ),\n",
      "    yaxis=YAxis(\n",
      "        axis_style,\n",
      "    ),\n",
      "    annotations=Annotations([\n",
      "        Annotation(\n",
      "            text=anno_text,\n",
      "            xref='paper',\n",
      "            yref='paper',\n",
      "            x=0,\n",
      "            y=1,\n",
      "            yanchor='bottom',\n",
      "            showarrow=False\n",
      "        )\n",
      "    ]),\n",
      "    autosize=False,\n",
      "    width=1000,\n",
      "    height=500,\n",
      ")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Package data and layout in a `Figure` object and send it to plotly:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = Figure(data=data, layout=layout)\n",
      "\n",
      "py.iplot(fig, filename=\"maps\", width=1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~etpinard/453/975/500\" height=\"525\" width=\"1000\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7fd0517299d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "See this graph in full screen <a href=\"https://plot.ly/~etpinard/453\" target=\"_blank\">here</a>."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To learn more about Plotly's Python API"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Refer to\n",
      "\n",
      "* our online documentation <a href=\"https://plot.ly/python/\" target=\"_blank\">page</a> or\n",
      "* our <a href=\"https://plot.ly/python/user-guide/\" target=\"_blank\">User Guide</a>."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<hr>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float:right; \\\">\n",
      "    <img src=\"http://i.imgur.com/4vwuxdJ.png\" \n",
      " align=right style=\"float:right; margin-left: 5px; margin-top: -10px\" />\n",
      "</div>\n",
      "\n",
      "<h4 style=\"margin-top:60px;\"> Got Questions or Feedback? </h4>\n",
      "\n",
      "About <a href=\"https://plot.ly\" target=\"_blank\">Plotly</a>\n",
      "\n",
      "* email: feedback@plot.ly \n",
      "* tweet: \n",
      "<a href=\"https://twitter.com/plotlygraphs\" target=\"_blank\">@plotlygraphs</a>\n",
      "\n",
      "<h4 style=\"margin-top:30px;\">Notebook styling ideas</h4>\n",
      "\n",
      "Big thanks to\n",
      "\n",
      "* <a href=\"http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb\" target=\"_blank\">Cam Davidson-Pilon</a>\n",
      "* <a href=\"http://lorenabarba.com/blog/announcing-aeropython/#.U1ULXdX1LJ4.google_plusone_share\" target=\"_blank\">Lorena A. Barba</a>\n",
      "\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, HTML\n",
      "import urllib2\n",
      "url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'\n",
      "display(HTML(urllib2.urlopen(url).read()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "        padding-left: 0.5em; \n",
        "        margin-bottom: 0.5em; \n",
        "        margin-top: 0.2em; \n",
        "    }\n",
        "    a:link{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    a:visited{\n",
        "       font-weight: bold;\n",
        "       color: #1d3b84;\n",
        "    }\n",
        "    a:hover{\n",
        "       font-weight: bold;\n",
        "       color: #1d3b84;\n",
        "    }\n",
        "    a:focus{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    a:active{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    .rendered_html :link {\n",
        "       text-decoration: none; \n",
        "    }\n",
        "    .rendered_html :hover {\n",
        "       text-decoration: none; \n",
        "    }\n",
        "    .rendered_html :visited {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :focus {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :active {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "    } \n",
        "    hr {\n",
        "      color: #f3f3f3;\n",
        "      background-color: #f3f3f3;\n",
        "      height: 1px;\n",
        "    }\n",
        "    blockquote{\n",
        "      display:block;\n",
        "      background: #f3f3f3;\n",
        "      font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "      width:610px;\n",
        "      padding: 15px 15px 15px 15px;\n",
        "      text-align:justify;\n",
        "      text-justify:inter-word;\n",
        "      }\n",
        "      blockquote p {\n",
        "        margin-bottom: 0;\n",
        "        line-height: 125%;\n",
        "        font-size: 100%;\n",
        "        text-align: center;\n",
        "      }\n",
        "   /* element.style {\n",
        "    } */  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ [\"$\",\"$\"], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ [\"$$\",\"$$\"], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: \"center\", // Change this to \"center\" to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {\".MathJax_Display\": {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7fd07d2694d0>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}