"""
A plotting mechanism based on the idea of a "window" into the data.



"""
from __future__ import print_function

#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import numpy as np
import matplotlib
import types
import six
import sys

from collections import defaultdict
from distutils.version import LooseVersion
from numbers import Number

from .base_plot_types import \
    ImagePlotMPL
from .fixed_resolution import \
    FixedResolutionBuffer, \
    OffAxisProjectionFixedResolutionBuffer
from .plot_modifications import callback_registry
from .plot_container import \
    ImagePlotContainer, \
    log_transform, linear_transform, symlog_transform, \
    get_log_minorticks, get_symlog_minorticks, \
    invalidate_data, invalidate_plot, apply_callback
from .base_plot_types import CallbackWrapper

from yt.data_objects.image_array import \
    ImageArray
from yt.extern.six import string_types
from yt.frontends.ytdata.data_structures import \
    YTSpatialPlotDataset
from yt.funcs import \
    mylog, iterable, ensure_list, \
    fix_axis, fix_unitary, obj_length
from yt.units.unit_object import \
    Unit
from yt.units.unit_registry import \
    UnitParseError
from yt.units.yt_array import \
    YTArray, YTQuantity
from yt.utilities.math_utils import \
    ortho_find
from yt.utilities.orientation import \
    Orientation
from yt.utilities.exceptions import \
    YTCannotParseUnitDisplayName, \
    YTPlotCallbackError, \
    YTDataTypeUnsupported, \
    YTInvalidFieldType, \
    YTUnitNotRecognized, \
    YTUnitConversionError

MPL_VERSION = LooseVersion(matplotlib.__version__)

# Some magic for dealing with pyparsing being included or not
# included in matplotlib (not in gentoo, yes in everything else)
try:
    if sys.version_info[0] == 3:
        from matplotlib.pyparsing_py3 import ParseFatalException
    else:
        from matplotlib.pyparsing_py2 import ParseFatalException
except ImportError:
    from pyparsing import ParseFatalException

def get_window_parameters(axis, center, width, ds):
    width = ds.coordinates.sanitize_width(axis, width, None)
    center, display_center = ds.coordinates.sanitize_center(center, axis)
    xax = ds.coordinates.x_axis[axis]
    yax = ds.coordinates.y_axis[axis]
    bounds = (display_center[xax]-width[0] / 2,
              display_center[xax]+width[0] / 2,
              display_center[yax]-width[1] / 2,
              display_center[yax]+width[1] / 2)
    return (bounds, center, display_center)

def get_oblique_window_parameters(normal, center, width, ds, depth=None):
    display_center, center = ds.coordinates.sanitize_center(center, 4)
    width = ds.coordinates.sanitize_width(normal, width, depth)

    if len(width) == 2:
        # Transforming to the cutting plane coordinate system
        center = (center - ds.domain_left_edge)/ds.domain_width - 0.5
        (normal,perp1,perp2) = ortho_find(normal)
        mat = np.transpose(np.column_stack((perp1,perp2,normal)))
        center = np.dot(mat,center)

    w = tuple(el.in_units('code_length') for el in width)
    bounds = tuple(((2*(i % 2))-1)*w[i//2]/2 for i in range(len(w)*2))

    return (bounds, center)

def get_axes_unit(width, ds):
    r"""
    Infers the axes unit names from the input width specification
    """
    if ds.no_cgs_equiv_length:
        return ("code_length",)*2
    if iterable(width):
        if isinstance(width[1], string_types):
            axes_unit = (width[1], width[1])
        elif iterable(width[1]):
            axes_unit = (width[0][1], width[1][1])
        elif isinstance(width[0], YTArray):
            axes_unit = (str(width[0].units), str(width[1].units))
        else:
            axes_unit = None
    else:
        if isinstance(width, YTArray):
            axes_unit = (str(width.units), str(width.units))
        else:
            axes_unit = None
    return axes_unit

def validate_mesh_fields(data_source, fields):
    # this check doesn't make sense for ytdata plot datasets, which
    # load mesh data as a particle field but nonetheless can still
    # make plots with it
    if isinstance(data_source.ds, YTSpatialPlotDataset):
        return
    canonical_fields = data_source._determine_fields(fields)
    invalid_fields = []
    for field in canonical_fields:
        if data_source.ds.field_info[field].particle_type is True:
            invalid_fields.append(field)

    if len(invalid_fields) > 0:
        raise YTInvalidFieldType(invalid_fields)


class PlotWindow(ImagePlotContainer):
    r"""
    A plotting mechanism based around the concept of a window into a
    data source. It can have arbitrary fields, each of which will be
    centered on the same viewpoint, but will have individual zlimits.

    The data and plot are updated separately, and each can be
    invalidated as the object is modified.

    Data is handled by a FixedResolutionBuffer object.

    Parameters
    ----------

    data_source : subclass of :class:`yt.data_objects.selection_data_containers.YTSelectionContainer2D`
        This is the source to be pixelized, which can be a projection,
        slice, or a cutting plane.
    bounds : sequence of floats
        Bounds are the min and max in the image plane that we want our
        image to cover.  It's in the order of (xmin, xmax, ymin, ymax),
        where the coordinates are all in the appropriate code units.
    buff_size : sequence of ints
        The size of the image to generate.
    antialias : boolean
        This can be true or false.  It determines whether or not sub-pixel
        rendering is used during data deposition.
    window_size : float
        The size of the window on the longest axis (in units of inches),
        including the margins but not the colorbar.
    window_size : float
        The size of the window on the longest axis (in units of inches),
        including the margins but not the colorbar.
    right_handed : boolean
        Whether the implicit east vector for the image generated is set to make a right
        handed coordinate system with a north vector and the normal vector, the
        direction of the 'window' into the data.

    """
    def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
                 periodic=True, origin='center-window', oblique=False, right_handed=True,
                 window_size=8.0, fields=None, fontsize=18, aspect=None,
                 setup=False):
        self.center = None
        self._periodic = periodic
        self.oblique = oblique
        self._right_handed = right_handed
        self._equivalencies = defaultdict(lambda: (None, {}))
        self.buff_size = buff_size
        self.antialias = antialias
        self._axes_unit_names = None

        self.aspect = aspect
        skip = list(FixedResolutionBuffer._exclude_fields) + data_source._key_fields
        if fields is None:
            fields = []
        else:
            fields = ensure_list(fields)
        self.override_fields = list(set(fields).intersection(set(skip)))
        self.fields = [f for f in fields if f not in skip]
        super(PlotWindow, self).__init__(data_source, window_size, fontsize)
        self._set_window(bounds) # this automatically updates the data and plot
        self.origin = origin
        if self.data_source.center is not None and oblique is False:
            ax = self.data_source.axis
            xax = self.ds.coordinates.x_axis[ax]
            yax = self.ds.coordinates.y_axis[ax]
            center, display_center = self.ds.coordinates.sanitize_center(
                self.data_source.center, ax)
            center = [display_center[xax], display_center[yax]]
            self.set_center(center)
        for field in self.data_source._determine_fields(self.fields):
            finfo = self.data_source.ds._get_field_info(*field)
            if finfo.take_log:
                self._field_transform[field] = log_transform
            else:
                self._field_transform[field] = linear_transform
        self.setup_callbacks()
        self._setup_plots()

    def __iter__(self):
        for ds in self.ts:
            mylog.warning("Switching to %s", ds)
            self._switch_ds(ds)
            yield self

    def piter(self, *args, **kwargs):
        for ds in self.ts.piter(*args, **kwargs):
            self._switch_ds(ds)
            yield self

    _frb = None
    def frb():
        doc = "The frb property."
        def fget(self):
            if self._frb is None or self._data_valid is False:
                self._recreate_frb()
            return self._frb

        def fset(self, value):
            self._frb = value
            self._data_valid = True

        def fdel(self):
            del self._frb
            self._frb = None
            self._data_valid = False

        return locals()
    frb = property(**frb())

    def _recreate_frb(self):
        old_fields = None
        # If we are regenerating an frb, we want to know what fields we had before
        if self._frb is not None:
            old_fields = list(self._frb.keys())
            old_units = [str(self._frb[of].units) for of in old_fields]

        # Set the bounds
        if hasattr(self,'zlim'):
            bounds = self.xlim+self.ylim+self.zlim
        else:
            bounds = self.xlim+self.ylim

        # Generate the FRB
        self.frb = self._frb_generator(self.data_source, bounds,
                                       self.buff_size, self.antialias,
                                       periodic=self._periodic)

        # At this point the frb has the valid bounds, size, aliasing, etc.
        if old_fields is None:
            self._frb._get_data_source_fields()
        else:
            # Restore the old fields
            for key, unit in zip(old_fields, old_units):
                self._frb[key]
                equiv = self._equivalencies[key]
                if equiv[0] is None:
                    self._frb[key].convert_to_units(unit)
                else:
                    self.frb.set_unit(key, unit, equiv[0], equiv[1])

        # Restore the override fields
        for key in self.override_fields:
            self._frb[key]

    @property
    def width(self):
        Wx = self.xlim[1] - self.xlim[0]
        Wy = self.ylim[1] - self.ylim[0]
        return (Wx, Wy)

    @property
    def bounds(self):
        return self.xlim+self.ylim

    @invalidate_data
    def zoom(self, factor):
        r"""This zooms the window by *factor*.

        Parameters
        ----------
        factor : float
            multiplier for the current width

        """
        Wx, Wy = self.width
        centerx = self.xlim[0] + Wx*0.5
        centery = self.ylim[0] + Wy*0.5
        nWx, nWy = Wx/factor, Wy/factor
        self.xlim = (centerx - nWx*0.5, centerx + nWx*0.5)
        self.ylim = (centery - nWy*0.5, centery + nWy*0.5)
        return self

    @invalidate_data
    def pan(self, deltas):
        r"""Pan the image by specifying absolute code unit coordinate deltas.

        Parameters
        ----------
        deltas : Two-element sequence of floats, quantities, or (float, unit)
                 tuples.

            (delta_x, delta_y).  If a unit is not supplied the unit is assumed
            to be code_length.

        """
        if len(deltas) != 2:
            raise RuntimeError(
                "The pan function accepts a two-element sequence.\n"
                "Received %s." % (deltas, ))
        if isinstance(deltas[0], Number) and isinstance(deltas[1], Number):
            deltas = (self.ds.quan(deltas[0], 'code_length'),
                      self.ds.quan(deltas[1], 'code_length'))
        elif isinstance(deltas[0], tuple) and isinstance(deltas[1], tuple):
            deltas = (self.ds.quan(deltas[0][0], deltas[0][1]),
                      self.ds.quan(deltas[1][0], deltas[1][1]))
        elif isinstance(deltas[0], YTQuantity) and isinstance(deltas[1], YTQuantity):
            pass
        else:
            raise RuntimeError(
                "The arguments of the pan function must be a sequence of floats,\n"
                "quantities, or (float, unit) tuples. Received %s." % (deltas, ))
        self.xlim = (self.xlim[0] + deltas[0], self.xlim[1] + deltas[0])
        self.ylim = (self.ylim[0] + deltas[1], self.ylim[1] + deltas[1])
        return self

    @invalidate_data
    def pan_rel(self, deltas):
        r"""Pan the image by specifying relative deltas, to the FOV.

        Parameters
        ----------
        deltas : sequence of floats
            (delta_x, delta_y) in *relative* code unit coordinates

        """
        Wx, Wy = self.width
        self.xlim = (self.xlim[0] + Wx*deltas[0], self.xlim[1] + Wx*deltas[0])
        self.ylim = (self.ylim[0] + Wy*deltas[1], self.ylim[1] + Wy*deltas[1])
        return self

    @invalidate_plot
    def set_unit(self, field, new_unit, equivalency=None,
                 equivalency_kwargs=None):
        """Sets a new unit for the requested field

        parameters
        ----------
        field : string or field tuple
           The name of the field that is to be changed.

        new_unit : string or Unit object
           The name of the new unit.

        equivalency : string, optional
           If set, the equivalency to use to convert the current units to
           the new requested unit. If None, the unit conversion will be done
           without an equivalency

        equivalency_kwargs : string, optional
           Keyword arguments to be passed to the equivalency. Only used if
           ``equivalency`` is set.
        """
        if equivalency_kwargs is None:
            equivalency_kwargs = {}
        field = self.data_source._determine_fields(field)[0]
        field = ensure_list(field)
        new_unit = ensure_list(new_unit)
        if len(field) > 1 and len(new_unit) != len(field):
            raise RuntimeError(
                "Field list {} and unit "
                "list {} are incompatible".format(field, new_unit))
        for f, u in zip(field, new_unit):
            self.frb.set_unit(f, u, equivalency, equivalency_kwargs)
            self._equivalencies[f] = (equivalency, equivalency_kwargs)
        return self

    @invalidate_plot
    def set_origin(self, origin):
        """Set the plot origin.

        Parameters
        ----------
        origin : string or length 1, 2, or 3 sequence.
            The location of the origin of the plot coordinate system. This
            is typically represented by a '-' separated string or a tuple of
            strings. In the first index the y-location is given by 'lower',
            'upper', or 'center'. The second index is the x-location, given as
            'left', 'right', or 'center'. Finally, whether the origin is
            applied in 'domain' space, plot 'window' space or 'native'
            simulation coordinate system is given. For example, both
            'upper-right-domain' and ['upper', 'right', 'domain'] place the
            origin in the upper right hand corner of domain space. If x or y
            are not given, a value is inferred. For instance, 'left-domain'
            corresponds to the lower-left hand corner of the simulation domain,
            'center-domain' corresponds to the center of the simulation domain,
            or 'center-window' for the center of the plot window. In the event
            that none of these options place the origin in a desired location,
            a sequence of tuples and a string specifying the
            coordinate space can be given. If plain numeric types are input,
            units of `code_length` are assumed. Further examples:

         ===============================================    ==================================
         format                                             example
         ===============================================    ==================================
         '{space}'                                          'domain'
         '{xloc}-{space}'                                   'left-window'
         '{yloc}-{space}'                                   'upper-domain'
         '{yloc}-{xloc}-{space}'                            'lower-right-window'
         ('{space}',)                                       ('window',)
         ('{xloc}', '{space}')                              ('right', 'domain')
         ('{yloc}', '{space}')                              ('lower', 'window')
         ('{yloc}', '{xloc}', '{space}')                    ('lower', 'right', 'window')
         ((yloc, '{unit}'), (xloc, '{unit}'), '{space}')    ((0.5, 'm'), (0.4, 'm'), 'window')
         (xloc, yloc, '{space}')                            (0.23, 0.5, 'domain')
         ===============================================    ==================================

        """
        self.origin = origin
        return self

    @invalidate_data
    def _set_window(self, bounds):
        """Set the bounds of the plot window.
        This is normally only called internally, see set_width.


        Parameters
        ----------

        bounds : a four element sequence of floats
            The x and y bounds, in the format (x0, x1, y0, y1)

        """
        if self.center is not None:
            dx = bounds[1] - bounds[0]
            dy = bounds[3] - bounds[2]
            self.xlim = (self.center[0] - dx/2., self.center[0] + dx/2.)
            self.ylim = (self.center[1] - dy/2., self.center[1] + dy/2.)
        else:
            self.xlim = tuple(bounds[0:2])
            self.ylim = tuple(bounds[2:4])
            if len(bounds) == 6:
                self.zlim = tuple(bounds[4:6])
        mylog.info("xlim = %f %f" % self.xlim)
        mylog.info("ylim = %f %f" % self.ylim)
        if hasattr(self,'zlim'):
            mylog.info("zlim = %f %f" % self.zlim)

    @invalidate_data
    def set_width(self, width, unit = None):
        """set the width of the plot window

        parameters
        ----------
        width : float, array of floats, (float, unit) tuple, or tuple of
                (float, unit) tuples.

             Width can have four different formats to support windows with
             variable x and y widths.  They are:

             ==================================     =======================
             format                                 example
             ==================================     =======================
             (float, string)                        (10,'kpc')
             ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
             float                                  0.2
             (float, float)                         (0.2, 0.3)
             ==================================     =======================

             For example, (10, 'kpc') requests a plot window that is 10
             kiloparsecs wide in the x and y directions,
             ((10,'kpc'),(15,'kpc')) requests a window that is 10 kiloparsecs
             wide along the x axis and 15 kiloparsecs wide along the y axis.
             In the other two examples, code units are assumed, for example
             (0.2, 0.3) requests a plot that has an x width of 0.2 and a y
             width of 0.3 in code units.  If units are provided the resulting
             plot axis labels will use the supplied units.
        unit : str
             the unit the width has been specified in. If width is a tuple, this
             argument is ignored. Defaults to code units.
        """
        if isinstance(width, Number):
            if unit is None:
                width = (width, 'code_length')
            else:
                width = (width, fix_unitary(unit))

        axes_unit = get_axes_unit(width, self.ds)

        width = self.ds.coordinates.sanitize_width(
            self.frb.axis, width, None)

        centerx = (self.xlim[1] + self.xlim[0])/2.
        centery = (self.ylim[1] + self.ylim[0])/2.

        self.xlim = (centerx - width[0]/2, centerx + width[0]/2)
        self.ylim = (centery - width[1]/2, centery + width[1]/2)

        if hasattr(self,'zlim'):
            centerz = (self.zlim[1] + self.zlim[0])/2.
            mw = self.ds.arr(width).max()
            self.zlim = (centerz - mw/2.,
                         centerz + mw/2.)

        self.set_axes_unit(axes_unit)

        return self

    @invalidate_data
    def set_center(self, new_center, unit = 'code_length'):
        """Sets a new center for the plot window

        parameters
        ----------
        new_center : two element sequence of floats
            The coordinates of the new center of the image in the
            coordinate system defined by the plot axes. If the unit
            keyword is not specified, the coordinates are assumed to
            be in code units.

        unit : string
            The name of the unit new_center is given in.  If new_center is a
            YTArray or tuple of YTQuantities, this keyword is ignored.

        """
        error = RuntimeError(
            "\n"
            "new_center must be a two-element list or tuple of floats \n"
            "corresponding to a coordinate in the plot relative to \n"
            "the plot coordinate system.\n"
        )
        if new_center is None:
            self.center = None
        elif iterable(new_center):
            if len(new_center) != 2:
                raise error
            for el in new_center:
                if not isinstance(el, Number) and not isinstance(el, YTQuantity):
                    raise error
            if isinstance(new_center[0], Number):
                new_center = [self.ds.quan(c, unit) for c in new_center]
            self.center = new_center
        else:
            raise error
        self._set_window(self.bounds)
        return self

    @invalidate_data
    def set_antialias(self,aa):
        self.antialias = aa

    @invalidate_data
    def set_buff_size(self, size):
        """Sets a new buffer size for the fixed resolution buffer

        parameters
        ----------
        size : int or two element sequence of ints
            The number of data elements in the buffer on the x and y axes.
            If a scalar is provided,  then the buffer is assumed to be square.
        """
        if iterable(size):
            self.buff_size = size
        else:
            self.buff_size = (size, size)
        return self

    def set_window_size(self, size):
        """This calls set_figure_size to adjust the size of the plot window.

        This is equivalent to set_figure_size but it still available to maintain
        backwards compatibility.
        """
        self.set_figure_size(size)
        return self

    @invalidate_plot
    def set_axes_unit(self, unit_name):
        r"""Set the unit for display on the x and y axes of the image.

        Parameters
        ----------
        unit_name : string or two element tuple of strings
            A unit, available for conversion in the dataset, that the
            image extents will be displayed in.  If set to None, any previous
            units will be reset.  If the unit is None, the default is chosen.
            If unit_name is '1', 'u', or 'unitary', it will not display the
            units, and only show the axes name. If unit_name is a tuple, the
            first element is assumed to be the unit for the x axis and the
            second element the unit for the y axis.

        Raises
        ------
        YTUnitNotRecognized
            If the unit is not known, this will be raised.

        Examples
        --------

        >>> from yt import load
        >>> ds = load("IsolatedGalaxy/galaxy0030/galaxy0030")
        >>> p = ProjectionPlot(ds, "y", "Density")
        >>> p.set_axes_unit("kpc")

        """
        # blind except because it could be in conversion_factors or units
        if unit_name is not None:
            if isinstance(unit_name, string_types):
                unit_name = (unit_name, unit_name)
            for un in unit_name:
                try:
                    self.ds.length_unit.in_units(un)
                except (YTUnitConversionError, UnitParseError):
                    raise YTUnitNotRecognized(un)
        self._axes_unit_names = unit_name
        return self

    @invalidate_plot
    def toggle_right_handed(self):
        self._right_handed = not self._right_handed


class PWViewerMPL(PlotWindow):
    """Viewer using matplotlib as a backend via the WindowPlotMPL.

    """
    _current_field = None
    _frb_generator = None
    _plot_type = None
    _data_valid = False

    def __init__(self, *args, **kwargs):
        if self._frb_generator is None:
            self._frb_generator = kwargs.pop("frb_generator")
        if self._plot_type is None:
            self._plot_type = kwargs.pop("plot_type")
        self._splat_color = kwargs.pop("splat_color", None)
        PlotWindow.__init__(self, *args, **kwargs)

    def _setup_origin(self):
        origin = self.origin
        axis_index = self.data_source.axis
        xc = None
        yc = None

        if isinstance(origin, string_types):
            origin = tuple(origin.split('-'))[:3]
        if 1 == len(origin):
            origin = ('lower', 'left') + origin
        elif 2 == len(origin) and origin[0] in set(['left','right','center']):
            o0map = {'left': 'lower', 'right': 'upper', 'center': 'center'}
            origin = (o0map[origin[0]],) + origin
        elif 2 == len(origin) and origin[0] in set(['lower','upper','center']):
            origin = (origin[0], 'center', origin[-1])
        elif 3 == len(origin) and isinstance(origin[0], (int, float)):
            xc = self.ds.quan(origin[0], 'code_length')
            yc = self.ds.quan(origin[1], 'code_length')
        elif 3 == len(origin) and isinstance(origin[0], tuple):
            xc = YTQuantity(origin[0][0], origin[0][1])
            yc = YTQuantity(origin[1][0], origin[0][1])

        assert origin[-1] in ['window', 'domain', 'native']

        if origin[2] == 'window':
            xllim, xrlim = self.xlim
            yllim, yrlim = self.ylim
        elif origin[2] == 'domain':
            xax = self.ds.coordinates.x_axis[axis_index]
            yax = self.ds.coordinates.y_axis[axis_index]
            xllim = self.ds.domain_left_edge[xax]
            xrlim = self.ds.domain_right_edge[xax]
            yllim = self.ds.domain_left_edge[yax]
            yrlim = self.ds.domain_right_edge[yax]
        elif origin[2] == 'native':
            return (self.ds.quan(0.0, 'code_length'),
                    self.ds.quan(0.0, 'code_length'))
        else:
            mylog.warn("origin = {0}".format(origin))
            msg = \
                  ('origin keyword "{0}" not recognized, must declare "domain" '
                   'or "center" as the last term in origin.').format(self.origin)
            raise RuntimeError(msg)
        if xc is None and yc is None:
            if origin[0] == 'lower':
                yc = yllim
            elif origin[0] == 'upper':
                yc = yrlim
            elif origin[0] == 'center':
                yc = (yllim + yrlim)/2.0
            else:
                mylog.warn("origin = {0}".format(origin))
                msg = ('origin keyword "{0}" not recognized, must declare "lower" '
                       '"upper" or "center" as the first term in origin.')
                msg = msg.format(self.origin)
                raise RuntimeError(msg)

            if origin[1] == 'left':
                xc = xllim
            elif origin[1] == 'right':
                xc = xrlim
            elif origin[1] == 'center':
                xc = (xllim + xrlim)/2.0
            else:
                mylog.warn("origin = {0}".format(origin))
                msg = ('origin keyword "{0}" not recognized, must declare "left" '
                       '"right" or "center" as the second term in origin.')
                msg = msg.format(self.origin)
                raise RuntimeError(msg)

        x_in_bounds = xc >= xllim and xc <= xrlim
        y_in_bounds = yc >= yllim and yc <= yrlim

        if not x_in_bounds and not y_in_bounds:
            msg = ('origin inputs not in bounds of specified coordinate sytem' +
                   'domain.')
            msg = msg.format(self.origin)
            raise RuntimeError(msg)

        return xc, yc

    def _setup_plots(self):
        from matplotlib.mathtext import MathTextParser
        if self._plot_valid:
            return
        if not self._data_valid:
            self._recreate_frb()
            self._data_valid = True
        self._colorbar_valid = True
        for f in list(set(self.data_source._determine_fields(self.fields))):
            axis_index = self.data_source.axis

            xc, yc = self._setup_origin()

            if self._axes_unit_names is None:
                unit = self.ds.get_smallest_appropriate_unit(
                    self.xlim[1] - self.xlim[0])
                (unit_x, unit_y) = (unit, unit)
            else:
                (unit_x, unit_y) = self._axes_unit_names

            # For some plots we may set aspect by hand, such as for spectral cube data.
            # This will likely be replaced at some point by the coordinate handler
            # setting plot aspect.
            if self.aspect is None:
                self.aspect = float((self.ds.quan(1.0, unit_y) /
                                     self.ds.quan(1.0, unit_x)).in_cgs())
            extentx = [(self.xlim[i] - xc).in_units(unit_x) for i in (0, 1)]
            extenty = [(self.ylim[i] - yc).in_units(unit_y) for i in (0, 1)]

            extent = extentx + extenty

            if f in self.plots.keys():
                zlim = (self.plots[f].zmin, self.plots[f].zmax)
            else:
                zlim = (None, None)

            image = self.frb[f]
            if self._field_transform[f] == log_transform:
                msg = None
                use_symlog = False
                if zlim != (None, None):
                    pass
                elif np.nanmax(image) == np.nanmin(image):
                    msg = "Plot image for field %s has zero dynamic " \
                          "range. Min = Max = %f." % (f, np.nanmax(image))
                elif np.nanmax(image) <= 0:
                    msg = "Plot image for field %s has no positive " \
                          "values.  Max = %f." % (f, np.nanmax(image))
                elif not np.any(np.isfinite(image)):
                    msg = "Plot image for field %s is filled with NaNs." % (f,)
                elif np.nanmax(image) > 0. and np.nanmin(image) < 0:
                    msg = "Plot image for field %s has both positive "\
                          "and negative values. Min = %f, Max = %f."\
                          % (f, np.nanmin(image), np.nanmax(image))
                    use_symlog = True
                if msg is not None:
                    mylog.warning(msg)
                    if use_symlog:
                        mylog.warning("Switching to symlog colorbar scaling "\
                                      "unless linear scaling is specified later")
                        self._field_transform[f] = symlog_transform
                        self._field_transform[f].func = None
                    else:
                        mylog.warning("Switching to linear colorbar scaling.")
                        self._field_transform[f] = linear_transform

            font_size = self._font_properties.get_size()

            fig = None
            axes = None
            cax = None
            draw_colorbar = True
            draw_axes = True
            if f in self.plots:
                draw_colorbar = self.plots[f]._draw_colorbar
                draw_axes = self.plots[f]._draw_axes
                if self.plots[f].figure is not None:
                    fig = self.plots[f].figure
                    axes = self.plots[f].axes
                    cax = self.plots[f].cax

            # This is for splatting particle positions with a single
            # color instead of a colormap
            if self._splat_color is not None:
                # make image a rgba array, using the splat color
                greyscale_image = self.frb[f]
                ia = np.zeros((greyscale_image.shape[0],
                               greyscale_image.shape[1],
                               4))
                ia[:, :, 3] = 0.0  # set alpha to 0.0
                locs = greyscale_image > 0.0
                to_rgba = matplotlib.colors.colorConverter.to_rgba
                color_tuple = to_rgba(self._splat_color)
                ia[locs] = color_tuple
                ia = ImageArray(ia)
            else:
                ia = image
            self.plots[f] = WindowPlotMPL(
                ia, self._field_transform[f].name,
                self._field_transform[f].func,
                self._colormaps[f], extent, zlim,
                self.figure_size, font_size,
                self.aspect, fig, axes, cax)

            if not self._right_handed:
                ax = self.plots[f].axes
                ax.invert_xaxis()

            axes_unit_labels = self._get_axes_unit_labels(unit_x, unit_y)

            if self.oblique:
                labels = [r'$\rm{Image\ x'+axes_unit_labels[0]+'}$',
                          r'$\rm{Image\ y'+axes_unit_labels[1]+'}$']
            else:
                coordinates = self.ds.coordinates
                axis_names = coordinates.image_axis_name[axis_index]
                xax = coordinates.x_axis[axis_index]
                yax = coordinates.y_axis[axis_index]

                if hasattr(coordinates, "axis_default_unit_name"):
                    axes_unit_labels = \
                    [coordinates.axis_default_unit_name[xax],
                     coordinates.axis_default_unit_name[yax]]
                labels = [r'$\rm{'+axis_names[0]+axes_unit_labels[0] + r'}$',
                          r'$\rm{'+axis_names[1]+axes_unit_labels[1] + r'}$']

                if hasattr(coordinates, "axis_field"):
                    if xax in coordinates.axis_field:
                        xmin, xmax = coordinates.axis_field[xax](
                            0, self.xlim, self.ylim)
                    else:
                        xmin, xmax = [float(x) for x in extentx]
                    if yax in coordinates.axis_field:
                        ymin, ymax = coordinates.axis_field[yax](
                            1, self.xlim, self.ylim)
                    else:
                        ymin, ymax = [float(y) for y in extenty]
                    self.plots[f].image.set_extent((xmin, xmax, ymin, ymax))
                    self.plots[f].axes.set_aspect("auto")

            x_label, y_label, colorbar_label = self._get_axes_labels(f)

            if x_label is not None:
                labels[0] = x_label
            if y_label is not None:
                labels[1] = y_label

            self.plots[f].axes.set_xlabel(labels[0])
            self.plots[f].axes.set_ylabel(labels[1])

            # Determine the units of the data
            units = Unit(self.frb[f].units, registry=self.ds.unit_registry)
            units = units.latex_representation()

            if colorbar_label is None:
                colorbar_label = image.info['label']
                if hasattr(self, 'projected'):
                    colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
                if units is None or units == '':
                    pass
                else:
                    colorbar_label += r'$\ \ \left('+units+r'\right)$'

            parser = MathTextParser('Agg')
            try:
                parser.parse(colorbar_label)
            except ParseFatalException as err:
                raise YTCannotParseUnitDisplayName(f, colorbar_label, str(err))

            self.plots[f].cb.set_label(colorbar_label)

            # x-y axes minorticks
            if f not in self._minorticks:
                self._minorticks[f] = True
            if self._minorticks[f] is True:
                self.plots[f].axes.minorticks_on()
            else:
                self.plots[f].axes.minorticks_off()

            # colorbar minorticks
            if f not in self._cbar_minorticks:
                self._cbar_minorticks[f] = True
            if (self._cbar_minorticks[f] is True and MPL_VERSION < LooseVersion('2.0.0')
                or self._field_transform[f] == symlog_transform):
                if self._field_transform[f] == linear_transform:
                    self.plots[f].cax.minorticks_on()
                else:
                    vmin = np.float64( self.plots[f].cb.norm.vmin )
                    vmax = np.float64( self.plots[f].cb.norm.vmax )
                    if self._field_transform[f] == log_transform:
                        mticks = self.plots[f].image.norm( get_log_minorticks(vmin, vmax) )
                    else: # symlog_transform
                        flinthresh = 10**np.floor( np.log10( self.plots[f].cb.norm.linthresh ) )
                        mticks = self.plots[f].image.norm( get_symlog_minorticks(flinthresh, vmin, vmax) )
                    self.plots[f].cax.yaxis.set_ticks(mticks, minor=True)
            else:
                self.plots[f].cax.minorticks_off()

            if draw_axes is False:
                self.plots[f]._toggle_axes(draw_axes)

            if draw_colorbar is False:
                self.plots[f]._toggle_colorbar(draw_colorbar)

        self._set_font_properties()
        self.run_callbacks()
        self._plot_valid = True

    def setup_callbacks(self):
        for key in callback_registry:
            ignored = ['PlotCallback']
            if self._plot_type.startswith('OffAxis'):
                ignored += ['ParticleCallback','ClumpContourCallback',
                            'GridBoundaryCallback']
            if self._plot_type == 'OffAxisProjection':
                ignored += ['VelocityCallback','MagFieldCallback',
                            'QuiverCallback','CuttingQuiverCallback',
                            'StreamlineCallback']
            if self._plot_type == 'Particle':
                ignored += ['HopCirclesCallback','HopParticleCallback',
                            'ParticleCallback','ClumpContourCallback',
                            'GridBoundaryCallback', 'VelocityCallback',
                            'MagFieldCallback', 'QuiverCallback',
                            'CuttingQuiverCallback', 'StreamlineCallback',
                            'ContourCallback', ]
            if key in ignored:
                continue
            cbname = callback_registry[key]._type_name
            CallbackMaker = callback_registry[key]
            callback = invalidate_plot(apply_callback(CallbackMaker))
            callback.__doc__ = CallbackMaker.__doc__
            self.__dict__['annotate_'+cbname] = types.MethodType(callback,self)

    @invalidate_plot
    def annotate_clear(self, index=None):
        """
        Clear callbacks from the plot.  If index is not set, clear all
        callbacks.  If index is set, clear that index (ie 0 is the first one
        created, 1 is the 2nd one created, -1 is the last one created, etc.)
        """
        if index is None:
            self._callbacks = []
        else:
            del self._callbacks[index]
        self.setup_callbacks()
        return self

    def run_callbacks(self):
        for f in self.fields:
            keys = self.frb.keys()
            for name, (args, kwargs) in self._callbacks:
                cbw = CallbackWrapper(self, self.plots[f], self.frb, f,
                                      self._font_properties, self._font_color)
                CallbackMaker = callback_registry[name]
                callback = CallbackMaker(*args[1:], **kwargs)
                try:
                    callback(cbw)
                except YTDataTypeUnsupported as e:
                    six.reraise(YTDataTypeUnsupported, e)
                except Exception as e:
                    six.reraise(YTPlotCallbackError,
                                YTPlotCallbackError(callback._type_name, e),
                                sys.exc_info()[2])
            for key in self.frb.keys():
                if key not in keys:
                    del self.frb[key]

    def hide_colorbar(self, field=None):
        """
        Hides the colorbar for a plot and updates the size of the
        plot accordingly.  Defaults to operating on all fields for a
        PlotWindow object.

        Parameters
        ----------

        field : string, field tuple, or list of strings or field tuples (optional)
            The name of the field(s) that we want to hide the colorbar. If None
            is provided, will default to using all fields available for this
            object.

        Examples
        --------

        This will save an image with no colorbar.

        >>> import yt
        >>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
        >>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
        >>> s.hide_colorbar()
        >>> s.save()

        This will save an image with no axis or colorbar.

        >>> import yt
        >>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
        >>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
        >>> s.hide_axes()
        >>> s.hide_colorbar()
        >>> s.save()
        """
        if field is None:
            field = self.fields
        field = ensure_list(field)
        for f in field:
            self.plots[f].hide_colorbar()
        return self

    def show_colorbar(self, field=None):
        """
        Shows the colorbar for a plot and updates the size of the
        plot accordingly.  Defaults to operating on all fields for a
        PlotWindow object.  See hide_colorbar().

        Parameters
        ----------

        field : string, field tuple, or list of strings or field tuples (optional)
            The name of the field(s) that we want to show the colorbar.
        """
        if field is None:
            field = self.fields
        field = ensure_list(field)
        for f in field:
            self.plots[f].show_colorbar()
        return self

    def hide_axes(self, field=None):
        """
        Hides the axes for a plot and updates the size of the
        plot accordingly.  Defaults to operating on all fields for a
        PlotWindow object.

        Parameters
        ----------

        field : string, field tuple, or list of strings or field tuples (optional)
            The name of the field(s) that we want to hide the axes.

        Examples
        --------

        This will save an image with no axes.

        >>> import yt
        >>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
        >>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
        >>> s.hide_axes()
        >>> s.save()

        This will save an image with no axis or colorbar.

        >>> import yt
        >>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
        >>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
        >>> s.hide_axes()
        >>> s.hide_colorbar()
        >>> s.save()
        """
        if field is None:
            field = self.fields
        field = ensure_list(field)
        for f in field:
            self.plots[f].hide_axes()
        return self

    def show_axes(self, field=None):
        """
        Shows the axes for a plot and updates the size of the
        plot accordingly.  Defaults to operating on all fields for a
        PlotWindow object.  See hide_axes().

        Parameters
        ----------

        field : string, field tuple, or list of strings or field tuples (optional)
            The name of the field(s) that we want to show the axes.
        """
        if field is None:
            field = self.fields
        field = ensure_list(field)
        for f in field:
            self.plots[f].show_axes()
        return self

class AxisAlignedSlicePlot(PWViewerMPL):
    r"""Creates a slice plot from a dataset

    Given a ds object, an axis to slice along, and a field name
    string, this will return a PWViewerMPL object containing
    the plot.

    The plot can be updated using one of the many helper functions
    defined in PlotWindow.

    Parameters
    ----------
    ds : `Dataset`
         This is the dataset object corresponding to the
         simulation output to be plotted.
    axis : int or one of 'x', 'y', 'z'
         An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
         or the axis name itself
    fields : string
         The name of the field(s) to be plotted.
    center : A sequence of floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    origin : string or length 1, 2, or 3 sequence.
         The location of the origin of the plot coordinate system. This
         is typically represented by a '-' separated string or a tuple of
         strings. In the first index the y-location is given by 'lower',
         'upper', or 'center'. The second index is the x-location, given as
         'left', 'right', or 'center'. Finally, whether the origin is
         applied in 'domain' space, plot 'window' space or 'native'
         simulation coordinate system is given. For example, both
         'upper-right-domain' and ['upper', 'right', 'domain'] place the
         origin in the upper right hand corner of domain space. If x or y
         are not given, a value is inferred. For instance, 'left-domain'
         corresponds to the lower-left hand corner of the simulation domain,
         'center-domain' corresponds to the center of the simulation domain,
         or 'center-window' for the center of the plot window. In the event
         that none of these options place the origin in a desired location,
         a sequence of tuples and a string specifying the
         coordinate space can be given. If plain numeric types are input,
         units of `code_length` are assumed. Further examples:

         ===============================================    ==================================
         format                                             example
         ===============================================    ==================================
         '{space}'                                          'domain'
         '{xloc}-{space}'                                   'left-window'
         '{yloc}-{space}'                                   'upper-domain'
         '{yloc}-{xloc}-{space}'                            'lower-right-window'
         ('{space}',)                                       ('window',)
         ('{xloc}', '{space}')                              ('right', 'domain')
         ('{yloc}', '{space}')                              ('lower', 'window')
         ('{yloc}', '{xloc}', '{space}')                    ('lower', 'right', 'window')
         ((yloc, '{unit}'), (xloc, '{unit}'), '{space}')    ((0.5, 'm'), (0.4, 'm'), 'window')
         (xloc, yloc, '{space}')                            (0.23, 0.5, 'domain')
         ===============================================    ==================================
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    right_handed : boolean
         Whether the implicit east vector for the image generated is set to make a right
         handed coordinate system with a normal vector, the direction of the
         'window' into the data.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    field_parameters : dictionary
         A dictionary of field parameters than can be accessed by derived
         fields.
    data_source: YTSelectionContainer object
         Object to be used for data selection. Defaults to ds.all_data(), a
         region covering the full domain

    Examples
    --------

    This will save an image in the file 'sliceplot_Density.png'

    >>> from yt import load
    >>> ds = load('IsolatedGalaxy/galaxy0030/galaxy0030')
    >>> p = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
    >>> p.save('sliceplot')

    """
    _plot_type = 'Slice'
    _frb_generator = FixedResolutionBuffer

    def __init__(self, ds, axis, fields, center='c', width=None, axes_unit=None,
                 origin='center-window', right_handed=True, fontsize=18, field_parameters=None,
                 window_size=8.0, aspect=None, data_source=None):
        # this will handle time series data and controllers
        axis = fix_axis(axis, ds)
        (bounds, center, display_center) = \
            get_window_parameters(axis, center, width, ds)
        if field_parameters is None:
            field_parameters = {}

        if ds.geometry == "spherical" or ds.geometry == "cylindrical":
            mylog.info("Setting origin='native' for %s geometry." % ds.geometry)
            origin = 'native'

        if isinstance(ds, YTSpatialPlotDataset):
            slc = ds.all_data()
            slc.axis = axis
            if slc.axis != ds.parameters["axis"]:
                raise RuntimeError("Original slice axis is %s." %
                                   ds.parameters["axis"])
        else:
            slc = ds.slice(axis, center[axis], field_parameters=field_parameters,
                           center=center, data_source=data_source)
            slc.get_data(fields)
        validate_mesh_fields(slc, fields)
        PWViewerMPL.__init__(self, slc, bounds, origin=origin,
                             fontsize=fontsize, fields=fields,
                             window_size=window_size, aspect=aspect, right_handed=right_handed)
        if axes_unit is None:
            axes_unit = get_axes_unit(width, ds)
        self.set_axes_unit(axes_unit)

class ProjectionPlot(PWViewerMPL):
    r"""Creates a projection plot from a dataset

    Given a ds object, an axis to project along, and a field name
    string, this will return a PWViewerMPL object containing
    the plot.

    The plot can be updated using one of the many helper functions
    defined in PlotWindow.

    Parameters
    ----------
    ds : `Dataset`
        This is the dataset object corresponding to the
        simulation output to be plotted.
    axis : int or one of 'x', 'y', 'z'
         An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
         or the axis name itself
    fields : string
         The name of the field(s) to be plotted.
    center : A sequence of floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    origin : string or length 1, 2, or 3 sequence.
         The location of the origin of the plot coordinate system. This
         is typically represented by a '-' separated string or a tuple of
         strings. In the first index the y-location is given by 'lower',
         'upper', or 'center'. The second index is the x-location, given as
         'left', 'right', or 'center'. Finally, whether the origin is
         applied in 'domain' space, plot 'window' space or 'native'
         simulation coordinate system is given. For example, both
         'upper-right-domain' and ['upper', 'right', 'domain'] place the
         origin in the upper right hand corner of domain space. If x or y
         are not given, a value is inferred. For instance, 'left-domain'
         corresponds to the lower-left hand corner of the simulation domain,
         'center-domain' corresponds to the center of the simulation domain,
         or 'center-window' for the center of the plot window. In the event
         that none of these options place the origin in a desired location,
         a sequence of tuples and a string specifying the
         coordinate space can be given. If plain numeric types are input,
         units of `code_length` are assumed. Further examples:

         ===============================================    ==================================
         format                                             example
         ===============================================    ==================================
         '{space}'                                          'domain'
         '{xloc}-{space}'                                   'left-window'
         '{yloc}-{space}'                                   'upper-domain'
         '{yloc}-{xloc}-{space}'                            'lower-right-window'
         ('{space}',)                                       ('window',)
         ('{xloc}', '{space}')                              ('right', 'domain')
         ('{yloc}', '{space}')                              ('lower', 'window')
         ('{yloc}', '{xloc}', '{space}')                    ('lower', 'right', 'window')
         ((yloc, '{unit}'), (xloc, '{unit}'), '{space}')    ((0.5, 'm'), (0.4, 'm'), 'window')
         (xloc, yloc, '{space}')                            (0.23, 0.5, 'domain')
         ===============================================    ==================================

    right_handed : boolean
         Whether the implicit east vector for the image generated is set to make a right
         handed coordinate system with the direction of the
         'window' into the data.
    data_source : YTSelectionContainer Object
         Object to be used for data selection.  Defaults to a region covering
         the entire simulation.
    weight_field : string
         The name of the weighting field.  Set to None for no weight.
    max_level: int
         The maximum level to project to.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    method : string
         The method of projection.  Valid methods are:

         "integrate" with no weight_field specified : integrate the requested
         field along the line of sight.

         "integrate" with a weight_field specified : weight the requested
         field by the weighting field and integrate along the line of sight.

         "mip" : pick out the maximum value of the field in the line of sight.

         "sum" : This method is the same as integrate, except that it does not
         multiply by a path length when performing the integration, and is
         just a straight summation of the field along the given axis. WARNING:
         This should only be used for uniform resolution grid datasets, as other
         datasets may result in unphysical images.
    proj_style : string
         The method of projection--same as method keyword.  Deprecated as of
         version 3.0.2.  Please use method instead.
    window_size : float
         The size of the window in inches. Set to 8 by default.
    aspect : float
         The aspect ratio of the plot.  Set to None for 1.
    field_parameters : dictionary
         A dictionary of field parameters than can be accessed by derived
         fields.
    data_source: YTSelectionContainer object
         Object to be used for data selection. Defaults to ds.all_data(), a
         region covering the full domain

    Examples
    --------

    Create a projection plot with a width of 20 kiloparsecs centered on the
    center of the simulation box:

    >>> from yt import load
    >>> ds = load('IsolateGalaxygalaxy0030/galaxy0030')
    >>> p = ProjectionPlot(ds, "z", "density", width=(20, "kpc"))

    """
    _plot_type = 'Projection'
    _frb_generator = FixedResolutionBuffer

    def __init__(self, ds, axis, fields, center='c', width=None, axes_unit=None,
                 weight_field=None, max_level=None, origin='center-window',
                 right_handed=True, fontsize=18, field_parameters=None, data_source=None,
                 method = "integrate", proj_style = None, window_size=8.0,
                 aspect=None):
        axis = fix_axis(axis, ds)
        # proj_style is deprecated, but if someone specifies then it trumps
        # method.
        if proj_style is not None:
            method = proj_style
        # If a non-weighted integral projection, assure field-label reflects that
        if weight_field is None and method == "integrate":
            self.projected = True
        (bounds, center, display_center) = \
                get_window_parameters(axis, center, width, ds)
        if field_parameters is None: field_parameters = {}

        # We don't use the plot's data source for validation like in the other
        # plotting classes to avoid an exception
        test_data_source = ds.all_data()
        validate_mesh_fields(test_data_source, fields)

        if isinstance(ds, YTSpatialPlotDataset):
            proj = ds.all_data()
            proj.axis = axis
            if proj.axis != ds.parameters["axis"]:
                raise RuntimeError("Original projection axis is %s." %
                                   ds.parameters["axis"])
            if weight_field is not None:
                proj.weight_field = proj._determine_fields(weight_field)[0]
            else:
                proj.weight_field = weight_field
        else:
            proj = ds.proj(fields, axis, weight_field=weight_field,
                           center=center, data_source=data_source,
                           field_parameters=field_parameters, method=method,
                           max_level=max_level)
        PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
                             right_handed=right_handed, fontsize=fontsize, window_size=window_size,
                             aspect=aspect)
        if axes_unit is None:
            axes_unit = get_axes_unit(width, ds)
        self.set_axes_unit(axes_unit)

class OffAxisSlicePlot(PWViewerMPL):
    r"""Creates an off axis slice plot from a dataset

    Given a ds object, a normal vector defining a slicing plane, and
    a field name string, this will return a PWViewerMPL object
    containing the plot.

    The plot can be updated using one of the many helper functions
    defined in PlotWindow.

    Parameters
    ----------
    ds : :class:`yt.data_objects.static_output.Dataset`
         This is the dataset object corresponding to the
         simulation output to be plotted.
    normal : a sequence of floats
         The vector normal to the slicing plane.
    fields : string
         The name of the field(s) to be plotted.
    center : A sequence of floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    north_vector : a sequence of floats
         A vector defining the 'up' direction in the plot.  This
         option sets the orientation of the slicing plane.  If not
         set, an arbitrary grid-aligned north-vector is chosen.
    right_handed : boolean
         Whether the implicit east vector for the image generated is set to make a right
         handed coordinate system with the north vector and the normal, the direction of the
         'window' into the data.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    field_parameters : dictionary
         A dictionary of field parameters than can be accessed by derived
         fields.
    data_source : YTSelectionContainer Object
         Object to be used for data selection.  Defaults ds.all_data(), a
         region covering the full domain.
    """

    _plot_type = 'OffAxisSlice'
    _frb_generator = FixedResolutionBuffer

    def __init__(self, ds, normal, fields, center='c', width=None,
                 axes_unit=None, north_vector=None, right_handed=True, fontsize=18,
                 field_parameters=None, data_source=None):
        (bounds, center_rot) = get_oblique_window_parameters(normal,center,width,ds)
        if field_parameters is None:
            field_parameters = {}

        if isinstance(ds, YTSpatialPlotDataset):
            cutting = ds.all_data()
            cutting.axis = 4
            cutting._inv_mat = ds.parameters["_inv_mat"]
        else:
            cutting = ds.cutting(normal, center, north_vector=north_vector,
                                 field_parameters=field_parameters,
                                 data_source=data_source)
            cutting.get_data(fields)
        validate_mesh_fields(cutting, fields)
        # Hard-coding the origin keyword since the other two options
        # aren't well-defined for off-axis data objects
        PWViewerMPL.__init__(self, cutting, bounds, fields=fields,
                             origin='center-window',periodic=False,
                             right_handed=right_handed, oblique=True, fontsize=fontsize)
        if axes_unit is None:
            axes_unit = get_axes_unit(width, ds)
        self.set_axes_unit(axes_unit)

class OffAxisProjectionDummyDataSource(object):
    _type_name = 'proj'
    _key_fields = []
    def __init__(self, center, ds, normal_vector, width, fields,
                 interpolated, resolution = (800,800), weight=None,
                 volume=None, no_ghost=False, le=None, re=None,
                 north_vector=None, method="integrate",
                 data_source=None):
        self.center = center
        self.ds = ds
        self.axis = 4 # always true for oblique data objects
        self.normal_vector = normal_vector
        self.width = width
        if data_source is None:
            self.dd = ds.all_data()
        else:
            self.dd = data_source
        fields = self.dd._determine_fields(fields)
        self.fields = fields
        self.interpolated = interpolated
        self.resolution = resolution
        if weight is not None:
            weight = self.dd._determine_fields(weight)[0]
        self.weight_field = weight
        self.volume = volume
        self.no_ghost = no_ghost
        self.le = le
        self.re = re
        self.north_vector = north_vector
        self.method = method
        self.orienter = Orientation(normal_vector, north_vector=north_vector)

    def _determine_fields(self, *args):
        return self.dd._determine_fields(*args)

class OffAxisProjectionPlot(PWViewerMPL):
    r"""Creates an off axis projection plot from a dataset

    Given a ds object, a normal vector to project along, and
    a field name string, this will return a PWViewerMPL object
    containing the plot.

    The plot can be updated using one of the many helper functions
    defined in PlotWindow.

    Parameters
    ----------
    ds : :class:`yt.data_objects.static_output.Dataset`
        This is the dataset object corresponding to the
        simulation output to be plotted.
    normal : a sequence of floats
        The vector normal to the slicing plane.
    fields : string
        The name of the field(s) to be plotted.
    center : A sequence of floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    depth : A tuple or a float
         A tuple containing the depth to project through and the string
         key of the unit: (width, 'unit').  If set to a float, code units
         are assumed
    weight_field : string
         The name of the weighting field.  Set to None for no weight.
    max_level: int
         The maximum level to project to.
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    north_vector : a sequence of floats
         A vector defining the 'up' direction in the plot.  This
         option sets the orientation of the slicing plane.  If not
         set, an arbitrary grid-aligned north-vector is chosen.
    right_handed : boolean
         Whether the implicit east vector for the image generated is set to make a right
         handed coordinate system with the north vector and the normal, the direction of the
         'window' into the data.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    method : string
         The method of projection.  Valid methods are:

         "integrate" with no weight_field specified : integrate the requested
         field along the line of sight.

         "integrate" with a weight_field specified : weight the requested
         field by the weighting field and integrate along the line of sight.

         "sum" : This method is the same as integrate, except that it does not
         multiply by a path length when performing the integration, and is
         just a straight summation of the field along the given axis. WARNING:
         This should only be used for uniform resolution grid datasets, as other
         datasets may result in unphysical images.
    data_source: YTSelectionContainer object
         Object to be used for data selection. Defaults to ds.all_data(), a
         region covering the full domain
    """
    _plot_type = 'OffAxisProjection'
    _frb_generator = OffAxisProjectionFixedResolutionBuffer

    def __init__(self, ds, normal, fields, center='c', width=None,
                 depth=(1, '1'), axes_unit=None, weight_field=None,
                 max_level=None, north_vector=None, right_handed=True,
                 volume=None, no_ghost=False, le=None, re=None,
                 interpolated=False, fontsize=18, method="integrate",
                 data_source=None):
        (bounds, center_rot) = \
          get_oblique_window_parameters(normal,center,width,ds,depth=depth)
        fields = ensure_list(fields)[:]
        oap_width = ds.arr((bounds[1] - bounds[0],
                            bounds[3] - bounds[2],
                            bounds[5] - bounds[4]))
        OffAxisProj = OffAxisProjectionDummyDataSource(
            center_rot, ds, normal, oap_width, fields, interpolated,
            weight=weight_field,  volume=volume, no_ghost=no_ghost,
            le=le, re=re, north_vector=north_vector, method=method,
            data_source=data_source)

        validate_mesh_fields(OffAxisProj, fields)

        if max_level is not None:
            OffAxisProj.dd.max_level = max_level

        # If a non-weighted, integral projection, assure field label
        # reflects that
        if weight_field is None and OffAxisProj.method == "integrate":
            self.projected = True

        # Hard-coding the origin keyword since the other two options
        # aren't well-defined for off-axis data objects
        PWViewerMPL.__init__(
            self, OffAxisProj, bounds, fields=fields, origin='center-window',
            periodic=False, oblique=True, right_handed=right_handed,
            fontsize=fontsize)
        if axes_unit is None:
            axes_unit = get_axes_unit(width, ds)
        self.set_axes_unit(axes_unit)

    def _recreate_frb(self):
        super(OffAxisProjectionPlot, self)._recreate_frb()

class WindowPlotMPL(ImagePlotMPL):
    """A container for a single PlotWindow matplotlib figure and axes"""
    def __init__(self, data, cbname, cblinthresh, cmap, extent, zlim,
                 figure_size, fontsize, aspect, figure, axes, cax):
        from matplotlib.ticker import ScalarFormatter
        self._draw_colorbar = True
        self._draw_axes = True
        self._fontsize = fontsize
        self._figure_size = figure_size

        # Compute layout
        fontscale = float(fontsize) / 18.0
        if fontscale < 1.0:
            fontscale = np.sqrt(fontscale)

        if iterable(figure_size):
            fsize = figure_size[0]
        else:
            fsize = figure_size
        self._cb_size = 0.0375*fsize
        self._ax_text_size = [1.2*fontscale, 0.9*fontscale]
        self._top_buff_size = 0.30*fontscale
        self._aspect = ((extent[1] - extent[0])/(extent[3] - extent[2])).in_cgs()
        self._unit_aspect = aspect

        size, axrect, caxrect = self._get_best_layout()

        super(WindowPlotMPL, self).__init__(
            size, axrect, caxrect, zlim, figure, axes, cax)

        self._init_image(data, cbname, cblinthresh, cmap, extent, aspect)

        # In matplotlib 2.1 and newer we'll be able to do this using
        # self.image.axes.ticklabel_format
        # See https://github.com/matplotlib/matplotlib/pull/6337
        formatter = ScalarFormatter(useMathText=True)
        formatter.set_scientific(True)
        formatter.set_powerlimits((-2, 3))
        self.image.axes.xaxis.set_major_formatter(formatter)
        self.image.axes.yaxis.set_major_formatter(formatter)
        if cbname == 'linear':
            self.cb.formatter.set_scientific(True)
            self.cb.formatter.set_powerlimits((-2, 3))
            self.cb.update_ticks()


def SlicePlot(ds, normal=None, fields=None, axis=None, *args, **kwargs):
    r"""
    A factory function for
    :class:`yt.visualization.plot_window.AxisAlignedSlicePlot`
    and :class:`yt.visualization.plot_window.OffAxisSlicePlot` objects.  This
    essentially allows for a single entry point to both types of slice plots,
    the distinction being determined by the specified normal vector to the
    slice.

    The returned plot object can be updated using one of the many helper
    functions defined in PlotWindow.

    Parameters
    ----------

    ds : :class:`yt.data_objects.static_output.Dataset`
        This is the dataset object corresponding to the
        simulation output to be plotted.
    normal : int or one of 'x', 'y', 'z', or sequence of floats
        This specifies the normal vector to the slice.  If given as an integer
        or a coordinate string (0=x, 1=y, 2=z), this function will return an
        :class:`AxisAlignedSlicePlot` object.  If given as a sequence of floats,
        this is interpreted as an off-axis vector and an
        :class:`OffAxisSlicePlot` object is returned.
    fields : string
         The name of the field(s) to be plotted.
    axis : int or one of 'x', 'y', 'z'
         An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
         or the axis name itself.  If specified, this will replace normal.

    The following are nominally keyword arguments passed onto the respective
    slice plot objects generated by this function.

    center : A sequence floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    origin : string or length 1, 2, or 3 sequence.
         The location of the origin of the plot coordinate system for
         `AxisAlignedSlicePlot` object; for `OffAxisSlicePlot` objects this
         parameter is discarded. This is typically represented by a '-'
         separated string or a tuple of strings. In the first index the
         y-location is given by 'lower', 'upper', or 'center'. The second index
         is the x-location, given as 'left', 'right', or 'center'. Finally, the
         whether the origin is applied in 'domain' space, plot 'window' space or
         'native' simulation coordinate system is given. For example, both
         'upper-right-domain' and ['upper', 'right', 'domain'] place the
         origin in the upper right hand corner of domain space. If x or y
         are not given, a value is inferred. For instance, 'left-domain'
         corresponds to the lower-left hand corner of the simulation domain,
         'center-domain' corresponds to the center of the simulation domain,
         or 'center-window' for the center of the plot window. In the event
         that none of these options place the origin in a desired location,
         a sequence of tuples and a string specifying the
         coordinate space can be given. If plain numeric types are input,
         units of `code_length` are assumed. Further examples:

         ===============================================    ==================================
         format                                             example
         ===============================================    ==================================
         '{space}'                                          'domain'
         '{xloc}-{space}'                                   'left-window'
         '{yloc}-{space}'                                   'upper-domain'
         '{yloc}-{xloc}-{space}'                            'lower-right-window'
         ('{space}',)                                       ('window',)
         ('{xloc}', '{space}')                              ('right', 'domain')
         ('{yloc}', '{space}')                              ('lower', 'window')
         ('{yloc}', '{xloc}', '{space}')                    ('lower', 'right', 'window')
         ((yloc, '{unit}'), (xloc, '{unit}'), '{space}')    ((0.5, 'm'), (0.4, 'm'), 'window')
         (xloc, yloc, '{space}')                            (0.23, 0.5, 'domain')
         ===============================================    ==================================
    north_vector : a sequence of floats
        A vector defining the 'up' direction in the `OffAxisSlicePlot`; not
        used in `AxisAlignedSlicePlot`.  This option sets the orientation of the
        slicing plane.  If not set, an arbitrary grid-aligned north-vector is
        chosen.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    field_parameters : dictionary
         A dictionary of field parameters than can be accessed by derived
         fields.
    data_source : YTSelectionContainer Object
         Object to be used for data selection.  Defaults to a region covering
         the entire simulation.

    Raises
    ------

    AssertionError
        If a proper normal axis is not specified via the normal or axis
        keywords, and/or if a field to plot is not specified.

    Examples
    --------

    >>> from yt import load
    >>> ds = load("IsolatedGalaxy/galaxy0030/galaxy0030")
    >>> slc = SlicePlot(ds, "x", "density", center=[0.2,0.3,0.4])
    >>>
    >>> slc = SlicePlot(ds, [0.4, 0.2, -0.1], "pressure",
    ...                 north_vector=[0.2,-0.3,0.1])

    """
    # Make sure we are passed a normal
    # we check the axis keyword for backwards compatibility
    if normal is None: normal = axis
    if normal is None:
        raise AssertionError("Must pass a normal vector to the slice!")

    # to keep positional ordering we had to make fields a keyword; make sure
    # it is present
    if fields is None:
        raise AssertionError("Must pass field(s) to plot!")

    # use an AxisAlignedSlicePlot where possible, e.g.:
    # maybe someone passed normal=[0,0,0.2] when they should have just used "z"
    if iterable(normal) and not isinstance(normal, string_types):
        if np.count_nonzero(normal) == 1:
            normal = ("x","y","z")[np.nonzero(normal)[0][0]]
        else:
            normal = np.array(normal, dtype='float64')
            np.divide(normal, np.dot(normal,normal), normal)

    # by now the normal should be properly set to get either a On/Off Axis plot
    if iterable(normal) and not isinstance(normal, string_types):
        # OffAxisSlicePlot has hardcoded origin; remove it if in kwargs
        if 'origin' in kwargs:
            msg = "Ignoring 'origin' keyword as it is ill-defined for " \
                  "an OffAxisSlicePlot object."
            mylog.warn(msg)
            del kwargs['origin']

        return OffAxisSlicePlot(ds, normal, fields, *args, **kwargs)
    else:
        # north_vector not used in AxisAlignedSlicePlots; remove it if in kwargs
        if 'north_vector' in kwargs:
            msg = "Ignoring 'north_vector' keyword as it is ill-defined for " \
                  "an AxisAlignedSlicePlot object."
            mylog.warn(msg)
            del kwargs['north_vector']

        return AxisAlignedSlicePlot(ds, normal, fields, *args, **kwargs)

def plot_2d(ds, fields, center='c', width=None, axes_unit=None,
            origin='center-window', fontsize=18, field_parameters=None, 
            window_size=8.0, aspect=None, data_source=None):
    r"""Creates a plot of a 2D dataset

    Given a ds object and a field name string, this will return a
    PWViewerMPL object containing the plot.

    The plot can be updated using one of the many helper functions
    defined in PlotWindow.

    Parameters
    ----------
    ds : `Dataset`
         This is the dataset object corresponding to the
         simulation output to be plotted.
    fields : string
         The name of the field(s) to be plotted.
    center : A sequence of floats, a string, or a tuple.
         The coordinate of the center of the image. If set to 'c', 'center' or
         left blank, the plot is centered on the middle of the domain. If set to
         'max' or 'm', the center will be located at the maximum of the
         ('gas', 'density') field. Centering on the max or min of a specific
         field is supported by providing a tuple such as ("min","temperature") or
         ("max","dark_matter_density"). Units can be specified by passing in *center*
         as a tuple containing a coordinate and string unit name or by passing
         in a YTArray. If a list or unitless array is supplied, code units are
         assumed. For plot_2d, this keyword accepts a coordinate in two dimensions.
    width : tuple or a float.
         Width can have four different formats to support windows with variable
         x and y widths.  They are:

         ==================================     =======================
         format                                 example
         ==================================     =======================
         (float, string)                        (10,'kpc')
         ((float, string), (float, string))     ((10,'kpc'),(15,'kpc'))
         float                                  0.2
         (float, float)                         (0.2, 0.3)
         ==================================     =======================

         For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
         wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
         window that is 10 kiloparsecs wide along the x axis and 15
         kiloparsecs wide along the y axis.  In the other two examples, code
         units are assumed, for example (0.2, 0.3) requests a plot that has an
         x width of 0.2 and a y width of 0.3 in code units.  If units are
         provided the resulting plot axis labels will use the supplied units.
    origin : string or length 1, 2, or 3 sequence.
         The location of the origin of the plot coordinate system. This
         is typically represented by a '-' separated string or a tuple of
         strings. In the first index the y-location is given by 'lower',
         'upper', or 'center'. The second index is the x-location, given as
         'left', 'right', or 'center'. Finally, whether the origin is
         applied in 'domain' space, plot 'window' space or 'native'
         simulation coordinate system is given. For example, both
         'upper-right-domain' and ['upper', 'right', 'domain'] place the
         origin in the upper right hand corner of domain space. If x or y
         are not given, a value is inferred. For instance, 'left-domain'
         corresponds to the lower-left hand corner of the simulation domain,
         'center-domain' corresponds to the center of the simulation domain,
         or 'center-window' for the center of the plot window. In the event
         that none of these options place the origin in a desired location,
         a sequence of tuples and a string specifying the
         coordinate space can be given. If plain numeric types are input,
         units of `code_length` are assumed. Further examples:

         ===============================================    ==================================
         format                                             example
         ===============================================    ==================================
         '{space}'                                          'domain'
         '{xloc}-{space}'                                   'left-window'
         '{yloc}-{space}'                                   'upper-domain'
         '{yloc}-{xloc}-{space}'                            'lower-right-window'
         ('{space}',)                                       ('window',)
         ('{xloc}', '{space}')                              ('right', 'domain')
         ('{yloc}', '{space}')                              ('lower', 'window')
         ('{yloc}', '{xloc}', '{space}')                    ('lower', 'right', 'window')
         ((yloc, '{unit}'), (xloc, '{unit}'), '{space}')    ((0.5, 'm'), (0.4, 'm'), 'window')
         (xloc, yloc, '{space}')                            (0.23, 0.5, 'domain')
         ===============================================    ==================================
    axes_unit : string
         The name of the unit for the tick labels on the x and y axes.
         Defaults to None, which automatically picks an appropriate unit.
         If axes_unit is '1', 'u', or 'unitary', it will not display the
         units, and only show the axes name.
    fontsize : integer
         The size of the fonts for the axis, colorbar, and tick labels.
    field_parameters : dictionary
         A dictionary of field parameters than can be accessed by derived
         fields.
    data_source: YTSelectionContainer object
         Object to be used for data selection. Defaults to ds.all_data(), a 
         region covering the full domain
    """
    if ds.dimensionality != 2:
        raise RuntimeError("plot_2d only plots 2D datasets!")
    if ds.geometry in ["cartesian", "polar", "spectral_cube"]:
        axis = "z"
    elif ds.geometry == "cylindrical":
        axis = "theta"
    # Part of the convenience of plot_2d is to eliminate the use of the
    # superfluous coordinate, so we do that also with the center argument
    if not isinstance(center, string_types) and obj_length(center) == 2:
        c0_string = isinstance(center[0], string_types)
        c1_string = isinstance(center[1], string_types)
        if not c0_string and not c1_string:
            if obj_length(center[0]) == 2 and c1_string:
                center = ds.arr(center[0], center[1])
            elif not isinstance(center, YTArray):
                center = ds.arr(center, 'code_length')
            center.convert_to_units("code_length")
        center = ds.arr([center[0], center[1], 
                         ds.domain_center[2]])
    return AxisAlignedSlicePlot(ds, axis, fields, center=center, width=width,
                                axes_unit=axes_unit, origin=origin, 
                                fontsize=fontsize,
                                field_parameters=field_parameters, 
                                window_size=window_size, aspect=aspect,
                                data_source=data_source)
