import json
import numpy as np
import matplotlib.pyplot as plt

import mpld3
from mpld3 import plugins, utils

class VoronoiHighlightLines(plugins.PluginBase):
    """A plugin to highlight lines on hover
    
    
    see also http://bl.ocks.org/mbostock/8033015
    
    """

    JAVASCRIPT = """
    mpld3.register_plugin("voronoi_highlightlines", VoronoiHighlightLines);
    VoronoiHighlightLines.prototype = Object.create(mpld3.Plugin.prototype);
    VoronoiHighlightLines.prototype.constructor = VoronoiHighlightLines;
    VoronoiHighlightLines.prototype.requiredProps = ["line_ids"];
    VoronoiHighlightLines.prototype.defaultProps = {alpha_bg:0.3, alpha_fg:1.0}
    function VoronoiHighlightLines(fig, props){
        mpld3.Plugin.call(this, fig, props);
    };

    VoronoiHighlightLines.prototype.draw = function(){
      var alpha_fg = this.props.alpha_fg;
      var alpha_bg = this.props.alpha_bg;    
      
      // get the data for the voronoi mesh
      data = new Array();
      for(var i=0; i<this.props.line_ids.length; i++){
         var line = mpld3.get_element(this.props.line_ids[i], this.fig);
         
         console.log()
         
         for (j=0; j<line.data.length; j++){
             var obj = {}
             obj.x = line.data[j][line.props.xindex]
             obj.y = line.data[j][line.props.yindex]
             obj.line = line
             obj.line_id = this.props.line_ids[i]
             obj.fig = this.fig
             data.push(obj)
         }
      }
        
      var ax = mpld3.get_element(this.props.line_ids[0], this.fig).ax

      // we hide the transform from data coordinates to svg
      // coordinates in the voronoi
      var transform_x = function(d){return ax.x(d)+ax.position[0]};
      var transform_y = function(d){return ax.y(d)+ax.position[1]};
      
      var voronoi = d3.geom.voronoi()
                    .x(function(d) { return transform_x(d.x); })
                    .y(function(d) { return transform_y(d.y); })      
                    .clipExtent([ax.position, [ax.position[0]+ax.width, ax.position[1]+ax.height]]);

      

      var voronoiGroup =  this.fig.canvas.append("svg:g")
                  .attr("class", "voronoi");
      
      voronoiGroup.selectAll("path")
          .data(voronoi(d3.nest()
                      .key(function(d) { return d.x + "," + d.y; })
                      .rollup(function(v) { return v[0]; })
                      .entries(data)
                      .map(function(d) { return d.values; })))
        .enter().append("path")
          .attr("d", function(d) { 
                    var ret = "M" + d.join(" L") + "Z";
                    return ret; })
          .datum(function(d) {return d.point; })
          .on("mouseover", mouseover)
          .on("mouseout", mouseout);
    
    
      function mouseover(d) {
        d3.select(d.line.path[0][0])
            .style("stroke-opacity", alpha_fg);        
      }

      function mouseout(d) {
         d3.select(d.line.path[0][0])
                .style("stroke-opacity", alpha_bg);  
      }    
    };
    """

    def __init__(self, lines, css):
        
        self.css_ = css or ""

        self.lines = lines
        self.dict_ = {"type": "voronoi_highlightlines",
                      "line_ids": [utils.get_id(line) for line in lines],
                      "alpha_bg": lines[0].get_alpha(),
                      "alpha_fg": 1.0}


# controls the coloring etc. of the voronoi mesh
css = """
.voronoi path 
{
  fill: none;
  pointer-events: all;
  stroke: red;
  stroke-opacity: .1;  
}
"""
        
np.random.seed(100)
N_paths = 50
N_steps = 100

x = np.linspace(0, 10, N_steps)
y = 0.1 * (np.random.random((N_paths, N_steps)) - 0.5)
y = y.cumsum(1)

fig, ax = plt.subplots()
lines = ax.plot(x, y.T, color='blue', lw=4, alpha=0.2)

plugins.clear(fig)
plugins.connect(fig, VoronoiHighlightLines(lines, css))

mpld3.display()



# controls the coloring etc. of the voronoi mesh
css = """
.voronoi path 
{
  fill: none;
  pointer-events: all;
  stroke: red;
  stroke-opacity: .1;  
}
"""
        
np.random.seed(100)

x = np.random.laplace(size=(2, 10))
y = np.random.laplace(size=(2, 10))

fig, ax = plt.subplots()
lines = ax.plot(x, y, 'k', linewidth=4, alpha=.5)

plugins.clear(fig)
plugins.connect(fig, VoronoiHighlightLines(lines, css))

mpld3.display()

fig, ax = plt.subplots()
rainbow = ax._get_lines.color_cycle
data = [ ]
for j in range(x.shape[1]):
    ax.scatter(x[:, j],y[:, j], c=next(rainbow))

for j in range(x.shape[1]):
    print "\n"
    for entry in zip(x[:, j],y[:, j]):
        print entry