content: """
  The classes here provide support for using custom classes with
  matplotlib, e.g., those that do not expose the array interface but know
  how to convert themselves to arrays.  It also supports classes with
  units and units conversion.  Use cases include converters for custom
  objects, e.g., a list of datetime objects, as well as for objects that
  are unit aware.  We don't assume any particular units implementation;
  rather a units implementation must provide the register with the Registry
  converter dictionary and a ConversionInterface.  For example,
  here is a complete implementation which supports plotting with native
  datetime objects::
  
      import matplotlib.units as units
      import matplotlib.dates as dates
      import matplotlib.ticker as ticker
      import datetime
  
      class DateConverter(units.ConversionInterface):
  
          @staticmethod
          def convert(value, unit, axis):
              'convert value to a scalar or array'
              return dates.date2num(value)
  
          @staticmethod
          def axisinfo(unit, axis):
              'return major and minor tick locators and formatters'
              if unit!='date': return None
              majloc = dates.AutoDateLocator()
              majfmt = dates.AutoDateFormatter(majloc)
              return AxisInfo(majloc=majloc,
                              majfmt=majfmt,
                              label='date')
  
          @staticmethod
          def default_units(x, axis):
              'return the default unit for x or None'
              return 'date'
  
      # finally we register our object type with a converter
      units.registry[datetime.date] = DateConverter()
  
  """
  from __future__ import (absolute_import, division, print_function,
                          unicode_literals)
  
  
  import six
  from matplotlib.cbook import iterable, is_numlike, safe_first_element
  import numpy as np
  
  
  class AxisInfo(object):
      """information to support default axis labeling and tick labeling, and
         default limits"""
      def __init__(self, majloc=None, minloc=None,
                   majfmt=None, minfmt=None, label=None,
                   default_limits=None):
          """
          majloc and minloc: TickLocators for the major and minor ticks
          majfmt and minfmt: TickFormatters for the major and minor ticks
          label: the default axis label
          default_limits: the default min, max of the axis if no data is present
          If any of the above are None, the axis will simply use the default
          """
          self.majloc = majloc
          self.minloc = minloc
          self.majfmt = majfmt
          self.minfmt = minfmt
          self.label = label
          self.default_limits = default_limits
  
  
  class ConversionInterface(object):
      """
      The minimal interface for a converter to take custom instances (or
      sequences) and convert them to values mpl can use
      """
      @staticmethod
      def axisinfo(unit, axis):
          'return an units.AxisInfo instance for axis with the specified units'
          return None
  
      @staticmethod
      def default_units(x, axis):
          'return the default unit for x or None for the given axis'
          return None
  
      @staticmethod
      def convert(obj, unit, axis):
          """
          convert obj using unit for the specified axis.  If obj is a sequence,
          return the converted sequence.  The output must be a sequence of
          scalars that can be used by the numpy array layer
          """
          return obj
  
      @staticmethod
      def is_numlike(x):
          """
          The matplotlib datalim, autoscaling, locators etc work with
          scalars which are the units converted to floats given the
          current unit.  The converter may be passed these floats, or
          arrays of them, even when units are set.  Derived conversion
          interfaces may opt to pass plain-ol unitless numbers through
          the conversion interface and this is a helper function for
          them.
          """
          if iterable(x):
              for thisx in x:
                  return is_numlike(thisx)
          else:
              return is_numlike(x)
  
  
  class Registry(dict):
      """
      register types with conversion interface
      """
      def __init__(self):
          dict.__init__(self)
          self._cached = {}
  
      def get_converter(self, x):
          'get the converter interface instance for x, or None'
  
          if not len(self):
              return None  # nothing registered
          # DISABLED idx = id(x)
          # DISABLED cached = self._cached.get(idx)
          # DISABLED if cached is not None: return cached
  
          converter = None
          classx = getattr(x, '__class__', None)
  
          if classx is not None:
              converter = self.get(classx)
  
          if isinstance(x, np.ndarray) and x.size:
              xravel = x.ravel()
              try:
                  # pass the first value of x that is not masked back to
                  # get_converter
                  if not np.all(xravel.mask):
                      # some elements are not masked
                      converter = self.get_converter(
                          xravel[np.argmin(xravel.mask)])
                      return converter
              except AttributeError:
                  # not a masked_array
                  # Make sure we don't recurse forever -- it's possible for
                  # ndarray subclasses to continue to return subclasses and
                  # not ever return a non-subclass for a single element.
                  next_item = xravel[0]
                  if (not isinstance(next_item, np.ndarray) or
                      next_item.shape != x.shape):
                      converter = self.get_converter(next_item)
                  return converter
  
          if converter is None:
              try:
                  thisx = safe_first_element(x)
              except (TypeError, StopIteration):
                  pass
              else:
                  if classx and classx != getattr(thisx, '__class__', None):
                      converter = self.get_converter(thisx)
                      return converter
  
          # DISABLED self._cached[idx] = converter
          return converter
  
  
  registry = Registry()
  