content: """This is the Bokeh charts interface. It gives you a high level API to build
  complex plot is a simple way.
  
  This is the Line class which lets you build your Line charts just
  passing the arguments to the Chart class and calling the proper functions.
  """
  #-----------------------------------------------------------------------------
  # Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
  #
  # Powered by the Bokeh Development Team.
  #
  # The full license is in the file LICENSE.txt, distributed with this software.
  #-----------------------------------------------------------------------------
  
  #-----------------------------------------------------------------------------
  # Imports
  #-----------------------------------------------------------------------------
  from __future__ import absolute_import
  
  from six import string_types
  import numpy as np
  
  from ..utils import cycle_colors
  from .._builder import Builder, create_and_build
  from ...models import ColumnDataSource, DataRange1d, GlyphRenderer, Range1d
  from ...models.glyphs import Line as LineGlyph
  from ...properties import Any
  
  #-----------------------------------------------------------------------------
  # Classes and functions
  #-----------------------------------------------------------------------------
  
  
  def Line(values, index=None, **kws):
      """ Create a line chart using :class:`LineBuilder <bokeh.charts.builder.line_builder.LineBuilder>` to
      render the geometry from values and index.
  
      Args:
          values (iterable): iterable 2d representing the data series
              values matrix.
          index (str|1d iterable, optional): can be used to specify a common custom
              index for all data series as an **1d iterable** of any sort that will be used as
              series common index or a **string** that corresponds to the key of the
              mapping to be used as index (and not as data series) if
              area.values is a mapping (like a dict, an OrderedDict
              or a pandas DataFrame)
  
      In addition the the parameters specific to this chart,
      :ref:`userguide_charts_generic_arguments` are also accepted as keyword parameters.
  
      Returns:
          a new :class:`Chart <bokeh.charts.Chart>`
  
      Examples:
  
      .. bokeh-plot::
          :source-position: above
  
          import numpy as np
          from bokeh.charts import Line, output_file, show
  
          # (dict, OrderedDict, lists, arrays and DataFrames are valid inputs)
          xyvalues = np.array([[2, 3, 7, 5, 26], [12, 33, 47, 15, 126], [22, 43, 10, 25, 26]])
  
          line = Line(xyvalues, title="line", legend="top_left", ylabel='Languages')
  
          output_file('line.html')
          show(line)
  
      """
      return create_and_build(LineBuilder, values, index=index, **kws)
  
  
  class LineBuilder(Builder):
      """This is the Line class and it is in charge of plotting
      Line charts in an easy and intuitive way.
      Essentially, we provide a way to ingest the data, make the proper
      calculations and push the references into a source object.
      We additionally make calculations for the ranges.
      And finally add the needed lines taking the references from the source.
      """
  
      index = Any(help="""
      An index to be used for all data series as follows:
  
      - A 1d iterable of any sort that will be used as
          series common index
  
      - As a string that corresponds to the key of the
          mapping to be used as index (and not as data
          series) if area.values is a mapping (like a dict,
          an OrderedDict or a pandas DataFrame)
  
      """)
  
      def _process_data(self):
          """Calculate the chart properties accordingly from line.values.
          Then build a dict containing references to all the points to be
          used by the line glyph inside the ``_yield_renderers`` method.
          """
          self._data = dict()
          # list to save all the attributes we are going to create
          self._attr = []
          xs = self._values_index
          self.set_and_get("x", "", np.array(xs))
          for col, values in self._values.items():
              if isinstance(self.index, string_types) and col == self.index:
                  continue
  
              # save every new group we find
              self._groups.append(col)
              self.set_and_get("y_", col, values)
  
      def _set_sources(self):
          """
          Push the Line data into the ColumnDataSource and calculate the
          proper ranges.
          """
          self._source = ColumnDataSource(self._data)
          self.x_range = DataRange1d()
  
          y_names = self._attr[1:]
          endy = max(max(self._data[i]) for i in y_names)
          starty = min(min(self._data[i]) for i in y_names)
          self.y_range = Range1d(
              start=starty - 0.1 * (endy - starty),
              end=endy + 0.1 * (endy - starty)
          )
  
      def _yield_renderers(self):
          """Use the line glyphs to connect the xy points in the Line.
          Takes reference points from the data loaded at the ColumnDataSource.
          """
          colors = cycle_colors(self._attr, self.palette)
          for i, duplet in enumerate(self._attr[1:], start=1):
              glyph = LineGlyph(x='x', y=duplet, line_color=colors[i - 1])
              renderer = GlyphRenderer(data_source=self._source, glyph=glyph)
              self._legends.append((self._groups[i-1], [renderer]))
              yield renderer
  