"""
Defines the PolarMapper class, which maps from a 1-D region in data space
into a 1-D output space.
"""

# Major library imports
from numpy import array

# Enthought library imports
from traits.api import Bool, Float

# Local relative imports
from .abstract_mapper import AbstractMapper

###############################################################
# same as linear mapper at the moment... to be modified later #
###############################################################
class PolarMapper(AbstractMapper):
    """
    Maps a 1-D data space to and from screen space by specifying a range in
    data space and a corresponding fixed line in screen space.

    This class concerns itself only with metric and not with orientation. So, to
    "flip" the screen space orientation, swap the values for **low_pos**
    and **high_pos**.
    """
    #------------------------------------------------------------------------
    # Private traits
    #------------------------------------------------------------------------


    _scale = Float(1.0)   # number of screen space units per data space unit
    _null_screen_range = Bool(False)
    _null_data_range = Bool(False)

    #------------------------------------------------------------------------
    # Public methods
    #------------------------------------------------------------------------

    def map_screen(self, data_array):
        """ map_screen(data_array) -> screen_array

        Converts radius and theta values from *data_array*
        to x and y values and then maps
        values from data space into screen space.
        """
        self._compute_scale()
        if self._null_data_range:
            return array([self.low_pos] * len(data_array))
        else:
            return (data_array - self.range.low) * self._scale + self.low_pos

    def map_data(self, screen_val):
        """ map_data(screen_val) -> data_val

        Maps values from screen space into data space.
        """
        self._compute_scale()
        if self._null_screen_range:
            return array([self.range.low])
        else:
            return (screen_val - self.low_pos) / self._scale + self.range.low

    #------------------------------------------------------------------------
    # Private methods
    #------------------------------------------------------------------------

    def _compute_scale(self):
        if self._cache_valid:
            return

        if self.range is None:
            self._cache_valid = False
            return

        d = self.range
        screen_range = self.high_pos - self.low_pos
        data_range = self._pol_to_rect(d.high) - self._pol_to_rect(d.low)
        if screen_range == 0.0:
            self._null_screen_range = True
        else:
            self._null_screen_range = False
        if data_range == 0.0:
            self._null_data_range = True
        else:
            self._scale = screen_range / data_range
            self._null_data_range = False

        self._cache_valid = True
        return


# EOF