# Source code for pyqtgraph.functions

    # -*- coding: utf-8 -*-
    """
    functions.py -  Miscellaneous functions with no other home
    Copyright 2010  Luke Campagnola
    Distributed under MIT/X11 license. See license.txt for more infomation.
    """

    from __future__ import division
    import warnings
    import numpy as np
    import decimal, re
    import ctypes
    import sys, struct
    from .python2_3 import asUnicode, basestring
    from .Qt import QtGui, QtCore, USE_PYSIDE
    from . import getConfigOption, setConfigOptions
    from . import debug

    Colors = {
        'b': QtGui.QColor(0,0,255,255),
        'g': QtGui.QColor(0,255,0,255),
        'r': QtGui.QColor(255,0,0,255),
        'c': QtGui.QColor(0,255,255,255),
        'm': QtGui.QColor(255,0,255,255),
        'y': QtGui.QColor(255,255,0,255),
        'k': QtGui.QColor(0,0,0,255),
        'w': QtGui.QColor(255,255,255,255),
        'd': QtGui.QColor(150,150,150,255),
        'l': QtGui.QColor(200,200,200,255),
        's': QtGui.QColor(100,100,150,255),
    }

    SI_PREFIXES = asUnicode('yzafpnµm kMGTPEZY')
    SI_PREFIXES_ASCII = 'yzafpnum kMGTPEZY'

    def siScale(x, minVal=1e-25, allowUnicode=True):
        """
        Return the recommended scale factor and SI prefix string for x.

        Example::

            siScale(0.0001)   # returns (1e6, 'μ')
            # This indicates that the number 0.0001 is best represented as 0.0001 * 1e6 = 100 μUnits
        """

        if isinstance(x, decimal.Decimal):
            x = float(x)

        try:
            if np.isnan(x) or np.isinf(x):
                return(1, '')
        except:
            print(x, type(x))
            raise
        if abs(x) < minVal:
            m = 0
            x = 0
        else:
            m = int(np.clip(np.floor(np.log(abs(x))/np.log(1000)), -9.0, 9.0))

        if m == 0:
            pref = ''
        elif m < -8 or m > 8:
            pref = 'e%d' % (m*3)
        else:
            if allowUnicode:
                pref = SI_PREFIXES[m+8]
            else:
                pref = SI_PREFIXES_ASCII[m+8]
        p = .001**m

        return (p, pref)

    def siFormat(x, precision=3, suffix='', space=True, error=None, minVal=1e-25, allowUnicode=True):
        """
        Return the number x formatted in engineering notation with SI prefix.

        Example::
            siFormat(0.0001, suffix='V')  # returns "100 μV"
        """

        if space is True:
            space = ' '
        if space is False:
            space = ''

        (p, pref) = siScale(x, minVal, allowUnicode)
        if not (len(pref) > 0 and pref[0] == 'e'):
            pref = space + pref

        if error is None:
            fmt = "%." + str(precision) + "g%s%s"
            return fmt % (x*p, pref, suffix)
        else:
            if allowUnicode:
                plusminus = space + asUnicode("±") + space
            else:
                plusminus = " +/- "
            fmt = "%." + str(precision) + "g%s%s%s%s"
            return fmt % (x*p, pref, suffix, plusminus, siFormat(error, precision=precision, suffix=suffix, space=space, minVal=minVal))

    def siEval(s):
        """
        Convert a value written in SI notation to its equivalent prefixless value

        Example::

            siEval("100 μV")  # returns 0.0001
        """

        s = asUnicode(s)
        m = re.match(r'(-?((\d+(\.\d*)?)|(\.\d+))([eE]-?\d+)?)\s*([u' + SI_PREFIXES + r']?).*$', s)
        if m is None:
            raise Exception("Can't convert string '%s' to number." % s)
        v = float(m.groups()[0])
        p = m.groups()[6]
        #if p not in SI_PREFIXES:
            #raise Exception("Can't convert string '%s' to number--unknown prefix." % s)
        if p ==  '':
            n = 0
        elif p == 'u':
            n = -2
        else:
            n = SI_PREFIXES.index(p) - 8
        return v * 1000**n

    class Color(QtGui.QColor):
        def __init__(self, *args):
            QtGui.QColor.__init__(self, mkColor(*args))

        def glColor(self):
            """Return (r,g,b,a) normalized for use in opengl"""
            return (self.red()/255., self.green()/255., self.blue()/255., self.alpha()/255.)

        def __getitem__(self, ind):
            return (self.red, self.green, self.blue, self.alpha)[ind]()

    def mkColor(*args):
        """
        Convenience function for constructing QColor from a variety of argument types. Accepted arguments are:

        ================ ================================================
         'c'             one of: r, g, b, c, m, y, k, w
         R, G, B, [A]    integers 0-255
         (R, G, B, [A])  tuple of integers 0-255
         float           greyscale, 0.0-1.0
         int             see :func:`intColor() <pyqtgraph.intColor>`
         (int, hues)     see :func:`intColor() <pyqtgraph.intColor>`
         "RGB"           hexadecimal strings; may begin with '#'
         "RGBA"
         "RRGGBB"
         "RRGGBBAA"
         QColor          QColor instance; makes a copy.
        ================ ================================================
        """
        err = 'Not sure how to make a color from "%s"' % str(args)
        if len(args) == 1:
            if isinstance(args[0], basestring):
                c = args[0]
                if c[0] == '#':
                    c = c[1:]
                if len(c) == 1:
                    try:
                        return Colors[c]
                    except KeyError:
                        raise Exception('No color named "%s"' % c)
                if len(c) == 3:
                    r = int(c[0]*2, 16)
                    g = int(c[1]*2, 16)
                    b = int(c[2]*2, 16)
                    a = 255
                elif len(c) == 4:
                    r = int(c[0]*2, 16)
                    g = int(c[1]*2, 16)
                    b = int(c[2]*2, 16)
                    a = int(c[3]*2, 16)
                elif len(c) == 6:
                    r = int(c[0:2], 16)
                    g = int(c[2:4], 16)
                    b = int(c[4:6], 16)
                    a = 255
                elif len(c) == 8:
                    r = int(c[0:2], 16)
                    g = int(c[2:4], 16)
                    b = int(c[4:6], 16)
                    a = int(c[6:8], 16)
            elif isinstance(args[0], QtGui.QColor):
                return QtGui.QColor(args[0])
            elif isinstance(args[0], float):
                r = g = b = int(args[0] * 255)
                a = 255
            elif hasattr(args[0], '__len__'):
                if len(args[0]) == 3:
                    (r, g, b) = args[0]
                    a = 255
                elif len(args[0]) == 4:
                    (r, g, b, a) = args[0]
                elif len(args[0]) == 2:
                    return intColor(*args[0])
                else:
                    raise Exception(err)
            elif type(args[0]) == int:
                return intColor(args[0])
            else:
                raise Exception(err)
        elif len(args) == 3:
            (r, g, b) = args
            a = 255
        elif len(args) == 4:
            (r, g, b, a) = args
        else:
            raise Exception(err)

        args = [r,g,b,a]
        args = [0 if np.isnan(a) or np.isinf(a) else a for a in args]
        args = list(map(int, args))
        return QtGui.QColor(*args)

    def mkBrush(*args, **kwds):
        """
        | Convenience function for constructing Brush.
        | This function always constructs a solid brush and accepts the same arguments as :func:`mkColor() <pyqtgraph.mkColor>`
        | Calling mkBrush(None) returns an invisible brush.
        """
        if 'color' in kwds:
            color = kwds['color']
        elif len(args) == 1:
            arg = args[0]
            if arg is None:
                return QtGui.QBrush(QtCore.Qt.NoBrush)
            elif isinstance(arg, QtGui.QBrush):
                return QtGui.QBrush(arg)
            else:
                color = arg
        elif len(args) > 1:
            color = args
        return QtGui.QBrush(mkColor(color))

    def mkPen(*args, **kargs):
        """
        Convenience function for constructing QPen.

        Examples::

            mkPen(color)
            mkPen(color, width=2)
            mkPen(cosmetic=False, width=4.5, color='r')
            mkPen({'color': "FF0", width: 2})
            mkPen(None)   # (no pen)

        In these examples, *color* may be replaced with any arguments accepted by :func:`mkColor() <pyqtgraph.mkColor>`    """

        color = kargs.get('color', None)
        width = kargs.get('width', 1)
        style = kargs.get('style', None)
        dash = kargs.get('dash', None)
        cosmetic = kargs.get('cosmetic', True)
        hsv = kargs.get('hsv', None)

        if len(args) == 1:
            arg = args[0]
            if isinstance(arg, dict):
                return mkPen(**arg)
            if isinstance(arg, QtGui.QPen):
                return QtGui.QPen(arg)  ## return a copy of this pen
            elif arg is None:
                style = QtCore.Qt.NoPen
            else:
                color = arg
        if len(args) > 1:
            color = args

        if color is None:
            color = mkColor('l')
        if hsv is not None:
            color = hsvColor(*hsv)
        else:
            color = mkColor(color)

        pen = QtGui.QPen(QtGui.QBrush(color), width)
        pen.setCosmetic(cosmetic)
        if style is not None:
            pen.setStyle(style)
        if dash is not None:
            pen.setDashPattern(dash)
        return pen

    def hsvColor(hue, sat=1.0, val=1.0, alpha=1.0):
        """Generate a QColor from HSVa values. (all arguments are float 0.0-1.0)"""
        c = QtGui.QColor()
        c.setHsvF(hue, sat, val, alpha)
        return c

    def colorTuple(c):
        """Return a tuple (R,G,B,A) from a QColor"""
        return (c.red(), c.green(), c.blue(), c.alpha())

    def colorStr(c):
        """Generate a hex string code from a QColor"""
        return ('%02x'*4) % colorTuple(c)

    def intColor(index, hues=9, values=1, maxValue=255, minValue=150, maxHue=360, minHue=0, sat=255, alpha=255, **kargs):
        """
        Creates a QColor from a single index. Useful for stepping through a predefined list of colors.

        The argument *index* determines which color from the set will be returned. All other arguments determine what the set of predefined colors will be

        Colors are chosen by cycling across hues while varying the value (brightness).
        By default, this selects from a list of 9 hues."""
        hues = int(hues)
        values = int(values)
        ind = int(index) % (hues * values)
        indh = ind % hues
        indv = ind / hues
        if values > 1:
            v = minValue + indv * ((maxValue-minValue) / (values-1))
        else:
            v = maxValue
        h = minHue + (indh * (maxHue-minHue)) / hues

        c = QtGui.QColor()
        c.setHsv(h, sat, v)
        c.setAlpha(alpha)
        return c

    def glColor(*args, **kargs):
        """
        Convert a color to OpenGL color format (r,g,b,a) floats 0.0-1.0
        Accepts same arguments as :func:`mkColor <pyqtgraph.mkColor>`.
        """
        c = mkColor(*args, **kargs)
        return (c.red()/255., c.green()/255., c.blue()/255., c.alpha()/255.)

    def makeArrowPath(headLen=20, tipAngle=20, tailLen=20, tailWidth=3, baseAngle=0):
        """
        Construct a path outlining an arrow with the given dimensions.
        The arrow points in the -x direction with tip positioned at 0,0.
        If *tipAngle* is supplied (in degrees), it overrides *headWidth*.
        If *tailLen* is None, no tail will be drawn.
        """
        headWidth = headLen * np.tan(tipAngle * 0.5 * np.pi/180.)
        path = QtGui.QPainterPath()
        path.moveTo(0,0)
        path.lineTo(headLen, -headWidth)
        if tailLen is None:
            innerY = headLen - headWidth * np.tan(baseAngle*np.pi/180.)
            path.lineTo(innerY, 0)
        else:
            tailWidth *= 0.5
            innerY = headLen - (headWidth-tailWidth) * np.tan(baseAngle*np.pi/180.)
            path.lineTo(innerY, -tailWidth)
            path.lineTo(headLen + tailLen, -tailWidth)
            path.lineTo(headLen + tailLen, tailWidth)
            path.lineTo(innerY, tailWidth)
        path.lineTo(headLen, headWidth)
        path.lineTo(0,0)
        return path

    def eq(a, b):
        """The great missing equivalence function: Guaranteed evaluation to a single bool value."""
        if a is b:
            return True

        try:
            with warnings.catch_warnings(module=np):  # ignore numpy futurewarning (numpy v. 1.10)
                e = a==b
        except ValueError:
            return False
        except AttributeError:
            return False
        except:
            print('failed to evaluate equivalence for:')
            print("  a:", str(type(a)), str(a))
            print("  b:", str(type(b)), str(b))
            raise
        t = type(e)
        if t is bool:
            return e
        elif t is np.bool_:
            return bool(e)
        elif isinstance(e, np.ndarray) or (hasattr(e, 'implements') and e.implements('MetaArray')):
            try:   ## disaster: if a is an empty array and b is not, then e.all() is True
                if a.shape != b.shape:
                    return False
            except:
                return False
            if (hasattr(e, 'implements') and e.implements('MetaArray')):
                return e.asarray().all()
            else:
                return e.all()
        else:
            raise Exception("== operator returned type %s" % str(type(e)))

    def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False, **kargs):
        """
        Take a slice of any orientation through an array. This is useful for extracting sections of multi-dimensional arrays such as MRI images for viewing as 1D or 2D data.

        The slicing axes are aribtrary; they do not need to be orthogonal to the original data or even to each other. It is possible to use this function to extract arbitrary linear, rectangular, or parallelepiped shapes from within larger datasets. The original data is interpolated onto a new array of coordinates using scipy.ndimage.map_coordinates if it is available (see the scipy documentation for more information about this). If scipy is not available, then a slower implementation of map_coordinates is used.

        For a graphical interface to this function, see :func:`ROI.getArrayRegion <pyqtgraph.ROI.getArrayRegion>`

        ==============  ====================================================================================================
        **Arguments:**
        *data*          (ndarray) the original dataset
        *shape*         the shape of the slice to take (Note the return value may have more dimensions than len(shape))
        *origin*        the location in the original dataset that will become the origin of the sliced data.
        *vectors*       list of unit vectors which point in the direction of the slice axes. Each vector must have the same
                        length as *axes*. If the vectors are not unit length, the result will be scaled relative to the
                        original data. If the vectors are not orthogonal, the result will be sheared relative to the
                        original data.
        *axes*          The axes in the original dataset which correspond to the slice *vectors*
        *order*         The order of spline interpolation. Default is 1 (linear). See scipy.ndimage.map_coordinates
                        for more information.
        *returnCoords*  If True, return a tuple (result, coords) where coords is the array of coordinates used to select
                        values from the original dataset.
        *All extra keyword arguments are passed to scipy.ndimage.map_coordinates.*
        --------------------------------------------------------------------------------------------------------------------
        ==============  ====================================================================================================

        Note the following must be true:

            | len(shape) == len(vectors)
            | len(origin) == len(axes) == len(vectors[i])

        Example: start with a 4D fMRI data set, take a diagonal-planar slice out of the last 3 axes

            * data = array with dims (time, x, y, z) = (100, 40, 40, 40)
            * The plane to pull out is perpendicular to the vector (x,y,z) = (1,1,1)
            * The origin of the slice will be at (x,y,z) = (40, 0, 0)
            * We will slice a 20x20 plane from each timepoint, giving a final shape (100, 20, 20)

        The call for this example would look like::

            affineSlice(data, shape=(20,20), origin=(40,0,0), vectors=((-1, 1, 0), (-1, 0, 1)), axes=(1,2,3))

        """
        try:
            import scipy.ndimage
            have_scipy = True
        except ImportError:
            have_scipy = False
        have_scipy = False

        # sanity check
        if len(shape) != len(vectors):
            raise Exception("shape and vectors must have same length.")
        if len(origin) != len(axes):
            raise Exception("origin and axes must have same length.")
        for v in vectors:
            if len(v) != len(axes):
                raise Exception("each vector must be same length as axes.")

        shape = list(map(np.ceil, shape))

        ## transpose data so slice axes come first
        trAx = list(range(data.ndim))
        for x in axes:
            trAx.remove(x)
        tr1 = tuple(axes) + tuple(trAx)
        data = data.transpose(tr1)
        #print "tr1:", tr1
        ## dims are now [(slice axes), (other axes)]

        ## make sure vectors are arrays
        if not isinstance(vectors, np.ndarray):
            vectors = np.array(vectors)
        if not isinstance(origin, np.ndarray):
            origin = np.array(origin)
        origin.shape = (len(axes),) + (1,)*len(shape)

        ## Build array of sample locations.
        grid = np.mgrid[tuple([slice(0,x) for x in shape])]  ## mesh grid of indexes
        x = (grid[np.newaxis,...] * vectors.transpose()[(Ellipsis,) + (np.newaxis,)*len(shape)]).sum(axis=1)  ## magic
        x += origin

        ## iterate manually over unused axes since map_coordinates won't do it for us
        if have_scipy:
            extraShape = data.shape[len(axes):]
            output = np.empty(tuple(shape) + extraShape, dtype=data.dtype)
            for inds in np.ndindex(*extraShape):
                ind = (Ellipsis,) + inds
                output[ind] = scipy.ndimage.map_coordinates(data[ind], x, order=order, **kargs)
        else:
            # map_coordinates expects the indexes as the first axis, whereas
            # interpolateArray expects indexes at the last axis.
            tr = tuple(range(1,x.ndim)) + (0,)
            output = interpolateArray(data, x.transpose(tr))

        tr = list(range(output.ndim))
        trb = []
        for i in range(min(axes)):
            ind = tr1.index(i) + (len(shape)-len(axes))
            tr.remove(ind)
            trb.append(ind)
        tr2 = tuple(trb+tr)

        ## Untranspose array before returning
        output = output.transpose(tr2)
        if returnCoords:
            return (output, x)
        else:
            return output

    def interpolateArray(data, x, default=0.0):
        """
        N-dimensional interpolation similar to scipy.ndimage.map_coordinates.

        This function returns linearly-interpolated values sampled from a regular
        grid of data.

        *data* is an array of any shape containing the values to be interpolated.
        *x* is an array with (shape[-1] <= data.ndim) containing the locations
            within *data* to interpolate.

        Returns array of shape (x.shape[:-1] + data.shape[x.shape[-1]:])

        For example, assume we have the following 2D image data::

            >>> data = np.array([[1,   2,   4  ],
                                 [10,  20,  40 ],
                                 [100, 200, 400]])

        To compute a single interpolated point from this data::

            >>> x = np.array([(0.5, 0.5)])
            >>> interpolateArray(data, x)
            array([ 8.25])

        To compute a 1D list of interpolated locations::

            >>> x = np.array([(0.5, 0.5),
                              (1.0, 1.0),
                              (1.0, 2.0),
                              (1.5, 0.0)])
            >>> interpolateArray(data, x)
            array([  8.25,  20.  ,  40.  ,  55.  ])

        To compute a 2D array of interpolated locations::

            >>> x = np.array([[(0.5, 0.5), (1.0, 2.0)],
                              [(1.0, 1.0), (1.5, 0.0)]])
            >>> interpolateArray(data, x)
            array([[  8.25,  40.  ],
                   [ 20.  ,  55.  ]])

        ..and so on. The *x* argument may have any shape as long as
        ```x.shape[-1] <= data.ndim```. In the case that
        ```x.shape[-1] < data.ndim```, then the remaining axes are simply
        broadcasted as usual. For example, we can interpolate one location
        from an entire row of the data::

            >>> x = np.array([[0.5]])
            >>> interpolateArray(data, x)
            array([[  5.5,  11. ,  22. ]])

        This is useful for interpolating from arrays of colors, vertexes, etc.
        """
        prof = debug.Profiler()

        nd = data.ndim
        md = x.shape[-1]
        if md > nd:
            raise TypeError("x.shape[-1] must be less than or equal to data.ndim")

        # First we generate arrays of indexes that are needed to
        # extract the data surrounding each point
        fields = np.mgrid[(slice(0,2),) * md]
        xmin = np.floor(x).astype(int)
        xmax = xmin + 1
        indexes = np.concatenate([xmin[np.newaxis, ...], xmax[np.newaxis, ...]])
        fieldInds = []
        totalMask = np.ones(x.shape[:-1], dtype=bool) # keep track of out-of-bound indexes
        for ax in range(md):
            mask = (xmin[...,ax] >= 0) & (x[...,ax] <= data.shape[ax]-1)
            # keep track of points that need to be set to default
            totalMask &= mask

            # ..and keep track of indexes that are out of bounds
            # (note that when x[...,ax] == data.shape[ax], then xmax[...,ax] will be out
            #  of bounds, but the interpolation will work anyway)
            mask &= (xmax[...,ax] < data.shape[ax])
            axisIndex = indexes[...,ax][fields[ax]]
            axisIndex[axisIndex < 0] = 0
            axisIndex[axisIndex >= data.shape[ax]] = 0
            fieldInds.append(axisIndex)
        prof()

        # Get data values surrounding each requested point
        fieldData = data[tuple(fieldInds)]
        prof()

        ## Interpolate
        s = np.empty((md,) + fieldData.shape, dtype=float)
        dx = x - xmin
        # reshape fields for arithmetic against dx
        for ax in range(md):
            f1 = fields[ax].reshape(fields[ax].shape + (1,)*(dx.ndim-1))
            sax = f1 * dx[...,ax] + (1-f1) * (1-dx[...,ax])
            sax = sax.reshape(sax.shape + (1,) * (s.ndim-1-sax.ndim))
            s[ax] = sax
        s = np.product(s, axis=0)
        result = fieldData * s
        for i in range(md):
            result = result.sum(axis=0)

        prof()

        if totalMask.ndim > 0:
            result[~totalMask] = default
        else:
            if totalMask is False:
                result[:] = default

        prof()
        return result

    def subArray(data, offset, shape, stride):
        """
        Unpack a sub-array from *data* using the specified offset, shape, and stride.

        Note that *stride* is specified in array elements, not bytes.
        For example, we have a 2x3 array packed in a 1D array as follows::

            data = [_, _, 00, 01, 02, _, 10, 11, 12, _]

        Then we can unpack the sub-array with this call::

            subArray(data, offset=2, shape=(2, 3), stride=(4, 1))

        ..which returns::

            [[00, 01, 02],
             [10, 11, 12]]

        This function operates only on the first axis of *data*. So changing
        the input in the example above to have shape (10, 7) would cause the
        output to have shape (2, 3, 7).
        """
        #data = data.flatten()
        data = data[offset:]
        shape = tuple(shape)
        stride = tuple(stride)
        extraShape = data.shape[1:]
        #print data.shape, offset, shape, stride
        for i in range(len(shape)):
            mask = (slice(None),) * i + (slice(None, shape[i] * stride[i]),)
            newShape = shape[:i+1]
            if i < len(shape)-1:
                newShape += (stride[i],)
            newShape += extraShape
            #print i, mask, newShape
            #print "start:\n", data.shape, data
            data = data[mask]
            #print "mask:\n", data.shape, data
            data = data.reshape(newShape)
            #print "reshape:\n", data.shape, data

        return data

    def transformToArray(tr):
        """
        Given a QTransform, return a 3x3 numpy array.
        Given a QMatrix4x4, return a 4x4 numpy array.

        Example: map an array of x,y coordinates through a transform::

            ## coordinates to map are (1,5), (2,6), (3,7), and (4,8)
            coords = np.array([[1,2,3,4], [5,6,7,8], [1,1,1,1]])  # the extra '1' coordinate is needed for translation to work

            ## Make an example transform
            tr = QtGui.QTransform()
            tr.translate(3,4)
            tr.scale(2, 0.1)

            ## convert to array
            m = pg.transformToArray()[:2]  # ignore the perspective portion of the transformation

            ## map coordinates through transform
            mapped = np.dot(m, coords)
        """
        #return np.array([[tr.m11(), tr.m12(), tr.m13()],[tr.m21(), tr.m22(), tr.m23()],[tr.m31(), tr.m32(), tr.m33()]])
        ## The order of elements given by the method names m11..m33 is misleading--
        ## It is most common for x,y translation to occupy the positions 1,3 and 2,3 in
        ## a transformation matrix. However, with QTransform these values appear at m31 and m32.
        ## So the correct interpretation is transposed:
        if isinstance(tr, QtGui.QTransform):
            return np.array([[tr.m11(), tr.m21(), tr.m31()], [tr.m12(), tr.m22(), tr.m32()], [tr.m13(), tr.m23(), tr.m33()]])
        elif isinstance(tr, QtGui.QMatrix4x4):
            return np.array(tr.copyDataTo()).reshape(4,4)
        else:
            raise Exception("Transform argument must be either QTransform or QMatrix4x4.")

    def transformCoordinates(tr, coords, transpose=False):
        """
        Map a set of 2D or 3D coordinates through a QTransform or QMatrix4x4.
        The shape of coords must be (2,...) or (3,...)
        The mapping will _ignore_ any perspective transformations.

        For coordinate arrays with ndim=2, this is basically equivalent to matrix multiplication.
        Most arrays, however, prefer to put the coordinate axis at the end (eg. shape=(...,3)). To
        allow this, use transpose=True.

        """

        if transpose:
            ## move last axis to beginning. This transposition will be reversed before returning the mapped coordinates.
            coords = coords.transpose((coords.ndim-1,) + tuple(range(0,coords.ndim-1)))

        nd = coords.shape[0]
        if isinstance(tr, np.ndarray):
            m = tr
        else:
            m = transformToArray(tr)
            m = m[:m.shape[0]-1]  # remove perspective

        ## If coords are 3D and tr is 2D, assume no change for Z axis
        if m.shape == (2,3) and nd == 3:
            m2 = np.zeros((3,4))
            m2[:2, :2] = m[:2,:2]
            m2[:2, 3] = m[:2,2]
            m2[2,2] = 1
            m = m2

        ## if coords are 2D and tr is 3D, ignore Z axis
        if m.shape == (3,4) and nd == 2:
            m2 = np.empty((2,3))
            m2[:,:2] = m[:2,:2]
            m2[:,2] = m[:2,3]
            m = m2

        ## reshape tr and coords to prepare for multiplication
        m = m.reshape(m.shape + (1,)*(coords.ndim-1))
        coords = coords[np.newaxis, ...]

        # separate scale/rotate and translation
        translate = m[:,-1]
        m = m[:, :-1]

        ## map coordinates and return
        mapped = (m*coords).sum(axis=1)  ## apply scale/rotate
        mapped += translate

        if transpose:
            ## move first axis to end.
            mapped = mapped.transpose(tuple(range(1,mapped.ndim)) + (0,))
        return mapped

    def solve3DTransform(points1, points2):
        """
        Find a 3D transformation matrix that maps points1 onto points2.
        Points must be specified as either lists of 4 Vectors or
        (4, 3) arrays.
        """
        import numpy.linalg
        pts = []
        for inp in (points1, points2):
            if isinstance(inp, np.ndarray):
                A = np.empty((4,4), dtype=float)
                A[:,:3] = inp[:,:3]
                A[:,3] = 1.0
            else:
                A = np.array([[inp[i].x(), inp[i].y(), inp[i].z(), 1] for i in range(4)])
            pts.append(A)

        ## solve 3 sets of linear equations to determine transformation matrix elements
        matrix = np.zeros((4,4))
        for i in range(3):
            ## solve Ax = B; x is one row of the desired transformation matrix
            matrix[i] = numpy.linalg.solve(pts[0], pts[1][:,i])

        return matrix

    def solveBilinearTransform(points1, points2):
        """
        Find a bilinear transformation matrix (2x4) that maps points1 onto points2.
        Points must be specified as a list of 4 Vector, Point, QPointF, etc.

        To use this matrix to map a point [x,y]::

            mapped = np.dot(matrix, [x*y, x, y, 1])
        """
        import numpy.linalg
        ## A is 4 rows (points) x 4 columns (xy, x, y, 1)
        ## B is 4 rows (points) x 2 columns (x, y)
        A = np.array([[points1[i].x()*points1[i].y(), points1[i].x(), points1[i].y(), 1] for i in range(4)])
        B = np.array([[points2[i].x(), points2[i].y()] for i in range(4)])

        ## solve 2 sets of linear equations to determine transformation matrix elements
        matrix = np.zeros((2,4))
        for i in range(2):
            matrix[i] = numpy.linalg.solve(A, B[:,i])  ## solve Ax = B; x is one row of the desired transformation matrix

        return matrix

    def rescaleData(data, scale, offset, dtype=None, clip=None):
        """Return data rescaled and optionally cast to a new dtype::

            data => (data-offset) * scale

        """
        if dtype is None:
            dtype = data.dtype
        else:
            dtype = np.dtype(dtype)

        try:
            if not getConfigOption('useWeave'):
                raise Exception('Weave is disabled; falling back to slower version.')
            try:
                import scipy.weave
            except ImportError:
                raise Exception('scipy.weave is not importable; falling back to slower version.')

            ## require native dtype when using weave
            if not data.dtype.isnative:
                data = data.astype(data.dtype.newbyteorder('='))
            if not dtype.isnative:
                weaveDtype = dtype.newbyteorder('=')
            else:
                weaveDtype = dtype

            newData = np.empty((data.size,), dtype=weaveDtype)
            flat = np.ascontiguousarray(data).reshape(data.size)
            size = data.size

            code = """
            double sc = (double)scale;
            double off = (double)offset;
            for( int i=0; i<size; i++ ) {
                newData[i] = ((double)flat[i] - off) * sc;
            }
            """
            scipy.weave.inline(code, ['flat', 'newData', 'size', 'offset', 'scale'], compiler='gcc')
            if dtype != weaveDtype:
                newData = newData.astype(dtype)
            data = newData.reshape(data.shape)
        except:
            if getConfigOption('useWeave'):
                if getConfigOption('weaveDebug'):
                    debug.printExc("Error; disabling weave.")
                setConfigOptions(useWeave=False)

            #p = np.poly1d([scale, -offset*scale])
            #d2 = p(data)
            d2 = data - float(offset)
            d2 *= scale

            # Clip before converting dtype to avoid overflow
            if dtype.kind in 'ui':
                lim = np.iinfo(dtype)
                if clip is None:
                    # don't let rescale cause integer overflow
                    d2 = np.clip(d2, lim.min, lim.max)
                else:
                    d2 = np.clip(d2, max(clip[0], lim.min), min(clip[1], lim.max))
            else:
                if clip is not None:
                    d2 = np.clip(d2, *clip)
            data = d2.astype(dtype)
        return data

    def applyLookupTable(data, lut):
        """
        Uses values in *data* as indexes to select values from *lut*.
        The returned data has shape data.shape + lut.shape[1:]

        Note: color gradient lookup tables can be generated using GradientWidget.
        """
        if data.dtype.kind not in ('i', 'u'):
            data = data.astype(int)

        return np.take(lut, data, axis=0, mode='clip')

    def makeRGBA(*args, **kwds):
        """Equivalent to makeARGB(..., useRGBA=True)"""
        kwds['useRGBA'] = True
        return makeARGB(*args, **kwds)

    def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
        """
        Convert an array of values into an ARGB array suitable for building QImages,
        OpenGL textures, etc.

        Returns the ARGB array (unsigned byte) and a boolean indicating whether
        there is alpha channel data. This is a two stage process:

            1) Rescale the data based on the values in the *levels* argument (min, max).
            2) Determine the final output by passing the rescaled values through a
               lookup table.

        Both stages are optional.

        ============== ==================================================================================
        **Arguments:**
        data           numpy array of int/float types. If
        levels         List [min, max]; optionally rescale data before converting through the
                       lookup table. The data is rescaled such that min->0 and max->*scale*::

                          rescaled = (clip(data, min, max) - min) * (*scale* / (max - min))

                       It is also possible to use a 2D (N,2) array of values for levels. In this case,
                       it is assumed that each pair of min,max values in the levels array should be
                       applied to a different subset of the input data (for example, the input data may
                       already have RGB values and the levels are used to independently scale each
                       channel). The use of this feature requires that levels.shape[0] == data.shape[-1].
        scale          The maximum value to which data will be rescaled before being passed through the
                       lookup table (or returned if there is no lookup table). By default this will
                       be set to the length of the lookup table, or 255 if no lookup table is provided.
        lut            Optional lookup table (array with dtype=ubyte).
                       Values in data will be converted to color by indexing directly from lut.
                       The output data shape will be input.shape + lut.shape[1:].
                       Lookup tables can be built using ColorMap or GradientWidget.
        useRGBA        If True, the data is returned in RGBA order (useful for building OpenGL textures).
                       The default is False, which returns in ARGB order for use with QImage
                       (Note that 'ARGB' is a term used by the Qt documentation; the *actual* order
                       is BGRA).
        ============== ==================================================================================
        """
        profile = debug.Profiler()

        if data.ndim not in (2, 3):
            raise TypeError("data must be 2D or 3D")
        if data.ndim == 3 and data.shape[2] > 4:
            raise TypeError("data.shape[2] must be <= 4")

        if lut is not None and not isinstance(lut, np.ndarray):
            lut = np.array(lut)

        if levels is None:
            # automatically decide levels based on data dtype
            if data.dtype.kind == 'u':
                levels = np.array([0, 2**(data.itemsize*8)-1])
            elif data.dtype.kind == 'i':
                s = 2**(data.itemsize*8 - 1)
                levels = np.array([-s, s-1])
            elif data.dtype.kind == 'b':
                levels = np.array([0,1])
            else:
                raise Exception('levels argument is required for float input types')
        if not isinstance(levels, np.ndarray):
            levels = np.array(levels)
        if levels.ndim == 1:
            if levels.shape[0] != 2:
                raise Exception('levels argument must have length 2')
        elif levels.ndim == 2:
            if lut is not None and lut.ndim > 1:
                raise Exception('Cannot make ARGB data when both levels and lut have ndim > 2')
            if levels.shape != (data.shape[-1], 2):
                raise Exception('levels must have shape (data.shape[-1], 2)')
        else:
            raise Exception("levels argument must be 1D or 2D (got shape=%s)." % repr(levels.shape))

        profile()

        # Decide on maximum scaled value
        if scale is None:
            if lut is not None:
                scale = lut.shape[0] - 1
            else:
                scale = 255.

        # Decide on the dtype we want after scaling
        if lut is None:
            dtype = np.ubyte
        else:
            dtype = np.min_scalar_type(lut.shape[0]-1)

        # Apply levels if given
        if levels is not None:
            if isinstance(levels, np.ndarray) and levels.ndim == 2:
                # we are going to rescale each channel independently
                if levels.shape[0] != data.shape[-1]:
                    raise Exception("When rescaling multi-channel data, there must be the same number of levels as channels (data.shape[-1] == levels.shape[0])")
                newData = np.empty(data.shape, dtype=int)
                for i in range(data.shape[-1]):
                    minVal, maxVal = levels[i]
                    if minVal == maxVal:
                        maxVal += 1e-16
                    newData[...,i] = rescaleData(data[...,i], scale/(maxVal-minVal), minVal, dtype=dtype)
                data = newData
            else:
                # Apply level scaling unless it would have no effect on the data
                minVal, maxVal = levels
                if minVal != 0 or maxVal != scale:
                    if minVal == maxVal:
                        maxVal += 1e-16
                    data = rescaleData(data, scale/(maxVal-minVal), minVal, dtype=dtype)

        profile()

        # apply LUT if given
        if lut is not None:
            data = applyLookupTable(data, lut)
        else:
            if data.dtype is not np.ubyte:
                data = np.clip(data, 0, 255).astype(np.ubyte)

        profile()

        # this will be the final image array
        imgData = np.empty(data.shape[:2]+(4,), dtype=np.ubyte)

        profile()

        # decide channel order
        if useRGBA:
            order = [0,1,2,3] # array comes out RGBA
        else:
            order = [2,1,0,3] # for some reason, the colors line up as BGR in the final image.

        # copy data into image array
        if data.ndim == 2:
            # This is tempting:
            #   imgData[..., :3] = data[..., np.newaxis]
            # ..but it turns out this is faster:
            for i in range(3):
                imgData[..., i] = data
        elif data.shape[2] == 1:
            for i in range(3):
                imgData[..., i] = data[..., 0]
        else:
            for i in range(0, data.shape[2]):
                imgData[..., i] = data[..., order[i]]

        profile()

        # add opaque alpha channel if needed
        if data.ndim == 2 or data.shape[2] == 3:
            alpha = False
            imgData[..., 3] = 255
        else:
            alpha = True

        profile()
        return imgData, alpha

    def makeQImage(imgData, alpha=None, copy=True, transpose=True):
        """
        Turn an ARGB array into QImage.
        By default, the data is copied; changes to the array will not
        be reflected in the image. The image will be given a 'data' attribute
        pointing to the array which shares its data to prevent python
        freeing that memory while the image is in use.

        ============== ===================================================================
        **Arguments:**
        imgData        Array of data to convert. Must have shape (width, height, 3 or 4)
                       and dtype=ubyte. The order of values in the 3rd axis must be
                       (b, g, r, a).
        alpha          If True, the QImage returned will have format ARGB32. If False,
                       the format will be RGB32. By default, _alpha_ is True if
                       array.shape[2] == 4.
        copy           If True, the data is copied before converting to QImage.
                       If False, the new QImage points directly to the data in the array.
                       Note that the array must be contiguous for this to work
                       (see numpy.ascontiguousarray).
        transpose      If True (the default), the array x/y axes are transposed before
                       creating the image. Note that Qt expects the axes to be in
                       (height, width) order whereas pyqtgraph usually prefers the
                       opposite.
        ============== ===================================================================
        """
        ## create QImage from buffer
        profile = debug.Profiler()

        ## If we didn't explicitly specify alpha, check the array shape.
        if alpha is None:
            alpha = (imgData.shape[2] == 4)

        copied = False
        if imgData.shape[2] == 3:  ## need to make alpha channel (even if alpha==False; QImage requires 32 bpp)
            if copy is True:
                d2 = np.empty(imgData.shape[:2] + (4,), dtype=imgData.dtype)
                d2[:,:,:3] = imgData
                d2[:,:,3] = 255
                imgData = d2
                copied = True
            else:
                raise Exception('Array has only 3 channels; cannot make QImage without copying.')

        if alpha:
            imgFormat = QtGui.QImage.Format_ARGB32
        else:
            imgFormat = QtGui.QImage.Format_RGB32

        if transpose:
            imgData = imgData.transpose((1, 0, 2))  ## QImage expects the row/column order to be opposite

        profile()

        if not imgData.flags['C_CONTIGUOUS']:
            if copy is False:
                extra = ' (try setting transpose=False)' if transpose else ''
                raise Exception('Array is not contiguous; cannot make QImage without copying.'+extra)
            imgData = np.ascontiguousarray(imgData)
            copied = True

        if copy is True and copied is False:
            imgData = imgData.copy()

        if USE_PYSIDE:
            ch = ctypes.c_char.from_buffer(imgData, 0)
            img = QtGui.QImage(ch, imgData.shape[1], imgData.shape[0], imgFormat)
        else:
            #addr = ctypes.addressof(ctypes.c_char.from_buffer(imgData, 0))
            ## PyQt API for QImage changed between 4.9.3 and 4.9.6 (I don't know exactly which version it was)
            ## So we first attempt the 4.9.6 API, then fall back to 4.9.3
            #addr = ctypes.c_char.from_buffer(imgData, 0)
            #try:
                #img = QtGui.QImage(addr, imgData.shape[1], imgData.shape[0], imgFormat)
            #except TypeError:
                #addr = ctypes.addressof(addr)
                #img = QtGui.QImage(addr, imgData.shape[1], imgData.shape[0], imgFormat)
            try:
                img = QtGui.QImage(imgData.ctypes.data, imgData.shape[1], imgData.shape[0], imgFormat)
            except:
                if copy:
                    # does not leak memory, is not mutable
                    img = QtGui.QImage(buffer(imgData), imgData.shape[1], imgData.shape[0], imgFormat)
                else:
                    # mutable, but leaks memory
                    img = QtGui.QImage(memoryview(imgData), imgData.shape[1], imgData.shape[0], imgFormat)

        img.data = imgData
        return img
        #try:
            #buf = imgData.data
        #except AttributeError:  ## happens when image data is non-contiguous
            #buf = imgData.data

        #profiler()
        #qimage = QtGui.QImage(buf, imgData.shape[1], imgData.shape[0], imgFormat)
        #profiler()
        #qimage.data = imgData
        #return qimage

    def imageToArray(img, copy=False, transpose=True):
        """
        Convert a QImage into numpy array. The image must have format RGB32, ARGB32, or ARGB32_Premultiplied.
        By default, the image is not copied; changes made to the array will appear in the QImage as well (beware: if
        the QImage is collected before the array, there may be trouble).
        The array will have shape (width, height, (b,g,r,a)).
        """
        fmt = img.format()
        ptr = img.bits()
        if USE_PYSIDE:
            arr = np.frombuffer(ptr, dtype=np.ubyte)
        else:
            ptr.setsize(img.byteCount())
            arr = np.asarray(ptr)
            if img.byteCount() != arr.size * arr.itemsize:
                # Required for Python 2.6, PyQt 4.10
                # If this works on all platforms, then there is no need to use np.asarray..
                arr = np.frombuffer(ptr, np.ubyte, img.byteCount())

        arr = arr.reshape(img.height(), img.width(), 4)
        if fmt == img.Format_RGB32:
            arr[...,3] = 255

        if copy:
            arr = arr.copy()

        if transpose:
            return arr.transpose((1,0,2))
        else:
            return arr

    def colorToAlpha(data, color):
        """
        Given an RGBA image in *data*, convert *color* to be transparent.
        *data* must be an array (w, h, 3 or 4) of ubyte values and *color* must be
        an array (3) of ubyte values.
        This is particularly useful for use with images that have a black or white background.

        Algorithm is taken from Gimp's color-to-alpha function in plug-ins/common/colortoalpha.c
        Credit:
            /*
            * Color To Alpha plug-in v1.0 by Seth Burgess, sjburges@gimp.org 1999/05/14
            *  with algorithm by clahey
            */

        """
        data = data.astype(float)
        if data.shape[-1] == 3:  ## add alpha channel if needed
            d2 = np.empty(data.shape[:2]+(4,), dtype=data.dtype)
            d2[...,:3] = data
            d2[...,3] = 255
            data = d2

        color = color.astype(float)
        alpha = np.zeros(data.shape[:2]+(3,), dtype=float)
        output = data.copy()

        for i in [0,1,2]:
            d = data[...,i]
            c = color[i]
            mask = d > c
            alpha[...,i][mask] = (d[mask] - c) / (255. - c)
            imask = d < c
            alpha[...,i][imask] = (c - d[imask]) / c

        output[...,3] = alpha.max(axis=2) * 255.

        mask = output[...,3] >= 1.0  ## avoid zero division while processing alpha channel
        correction = 255. / output[...,3][mask]  ## increase value to compensate for decreased alpha
        for i in [0,1,2]:
            output[...,i][mask] = ((output[...,i][mask]-color[i]) * correction) + color[i]
            output[...,3][mask] *= data[...,3][mask] / 255.  ## combine computed and previous alpha values

        #raise Exception()
        return np.clip(output, 0, 255).astype(np.ubyte)

    def gaussianFilter(data, sigma):
        """
        Drop-in replacement for scipy.ndimage.gaussian_filter.

        (note: results are only approximately equal to the output of
         gaussian_filter)
        """
        if np.isscalar(sigma):
            sigma = (sigma,) * data.ndim

        baseline = data.mean()
        filtered = data - baseline
        for ax in range(data.ndim):
            s = sigma[ax]
            if s == 0:
                continue

            # generate 1D gaussian kernel
            ksize = int(s * 6)
            x = np.arange(-ksize, ksize)
            kernel = np.exp(-x**2 / (2*s**2))
            kshape = [1,] * data.ndim
            kshape[ax] = len(kernel)
            kernel = kernel.reshape(kshape)

            # convolve as product of FFTs
            shape = data.shape[ax] + ksize
            scale = 1.0 / (abs(s) * (2*np.pi)**0.5)
            filtered = scale * np.fft.irfft(np.fft.rfft(filtered, shape, axis=ax) *
                                            np.fft.rfft(kernel, shape, axis=ax),
                                            axis=ax)

            # clip off extra data
            sl = [slice(None)] * data.ndim
            sl[ax] = slice(filtered.shape[ax]-data.shape[ax],None,None)
            filtered = filtered[sl]
        return filtered + baseline

    def downsample(data, n, axis=0, xvals='subsample'):
        """Downsample by averaging points together across axis.
        If multiple axes are specified, runs once per axis.
        If a metaArray is given, then the axis values can be either subsampled
        or downsampled to match.
        """
        ma = None
        if (hasattr(data, 'implements') and data.implements('MetaArray')):
            ma = data
            data = data.view(np.ndarray)

        if hasattr(axis, '__len__'):
            if not hasattr(n, '__len__'):
                n = [n]*len(axis)
            for i in range(len(axis)):
                data = downsample(data, n[i], axis[i])
            return data

        if n <= 1:
            return data
        nPts = int(data.shape[axis] / n)
        s = list(data.shape)
        s[axis] = nPts
        s.insert(axis+1, n)
        sl = [slice(None)] * data.ndim
        sl[axis] = slice(0, nPts*n)
        d1 = data[tuple(sl)]
        #print d1.shape, s
        d1.shape = tuple(s)
        d2 = d1.mean(axis+1)

        if ma is None:
            return d2
        else:
            info = ma.infoCopy()
            if 'values' in info[axis]:
                if xvals == 'subsample':
                    info[axis]['values'] = info[axis]['values'][::n][:nPts]
                elif xvals == 'downsample':
                    info[axis]['values'] = downsample(info[axis]['values'], n)
            return MetaArray(d2, info=info)

    def arrayToQPath(x, y, connect='all'):
        """Convert an array of x,y coordinats to QPainterPath as efficiently as possible.
        The *connect* argument may be 'all', indicating that each point should be
        connected to the next; 'pairs', indicating that each pair of points
        should be connected, or an array of int32 values (0 or 1) indicating
        connections.
        """

        ## Create all vertices in path. The method used below creates a binary format so that all
        ## vertices can be read in at once. This binary format may change in future versions of Qt,
        ## so the original (slower) method is left here for emergencies:
            #path.moveTo(x[0], y[0])
            #if connect == 'all':
                #for i in range(1, y.shape[0]):
                    #path.lineTo(x[i], y[i])
            #elif connect == 'pairs':
                #for i in range(1, y.shape[0]):
                    #if i%2 == 0:
                        #path.lineTo(x[i], y[i])
                    #else:
                        #path.moveTo(x[i], y[i])
            #elif isinstance(connect, np.ndarray):
                #for i in range(1, y.shape[0]):
                    #if connect[i] == 1:
                        #path.lineTo(x[i], y[i])
                    #else:
                        #path.moveTo(x[i], y[i])
            #else:
                #raise Exception('connect argument must be "all", "pairs", or array')

        ## Speed this up using >> operator
        ## Format is:
        ##    numVerts(i4)   0(i4)
        ##    x(f8)   y(f8)   0(i4)    <-- 0 means this vertex does not connect
        ##    x(f8)   y(f8)   1(i4)    <-- 1 means this vertex connects to the previous vertex
        ##    ...
        ##    0(i4)
        ##
        ## All values are big endian--pack using struct.pack('>d') or struct.pack('>i')

        path = QtGui.QPainterPath()

        #profiler = debug.Profiler()
        n = x.shape[0]
        # create empty array, pad with extra space on either end
        arr = np.empty(n+2, dtype=[('x', '>f8'), ('y', '>f8'), ('c', '>i4')])
        # write first two integers
        #profiler('allocate empty')
        byteview = arr.view(dtype=np.ubyte)
        byteview[:12] = 0
        byteview.data[12:20] = struct.pack('>ii', n, 0)
        #profiler('pack header')
        # Fill array with vertex values
        arr[1:-1]['x'] = x
        arr[1:-1]['y'] = y

        # decide which points are connected by lines
        if eq(connect, 'all'):
            arr[1:-1]['c'] = 1
        elif eq(connect, 'pairs'):
            arr[1:-1]['c'][::2] = 1
            arr[1:-1]['c'][1::2] = 0
        elif eq(connect, 'finite'):
            arr[1:-1]['c'] = np.isfinite(x) & np.isfinite(y)
        elif isinstance(connect, np.ndarray):
            arr[1:-1]['c'] = connect
        else:
            raise Exception('connect argument must be "all", "pairs", "finite", or array')

        #profiler('fill array')
        # write last 0
        lastInd = 20*(n+1)
        byteview.data[lastInd:lastInd+4] = struct.pack('>i', 0)
        #profiler('footer')
        # create datastream object and stream into path

        ## Avoiding this method because QByteArray(str) leaks memory in PySide
        #buf = QtCore.QByteArray(arr.data[12:lastInd+4])  # I think one unnecessary copy happens here

        path.strn = byteview.data[12:lastInd+4] # make sure data doesn't run away
        try:
            buf = QtCore.QByteArray.fromRawData(path.strn)
        except TypeError:
            buf = QtCore.QByteArray(bytes(path.strn))
        #profiler('create buffer')
        ds = QtCore.QDataStream(buf)

        ds >> path
        #profiler('load')

        return path

    #def isosurface(data, level):
        #"""
        #Generate isosurface from volumetric data using marching tetrahedra algorithm.
        #See Paul Bourke, "Polygonising a Scalar Field Using Tetrahedrons"  (http://local.wasp.uwa.edu.au/~pbourke/geometry/polygonise/)

        #*data*   3D numpy array of scalar values
        #*level*  The level at which to generate an isosurface
        #"""

        #facets = []

        ### mark everything below the isosurface level
        #mask = data < level

        #### make eight sub-fields
        #fields = np.empty((2,2,2), dtype=object)
        #slices = [slice(0,-1), slice(1,None)]
        #for i in [0,1]:
            #for j in [0,1]:
                #for k in [0,1]:
                    #fields[i,j,k] = mask[slices[i], slices[j], slices[k]]

        ### split each cell into 6 tetrahedra
        ### these all have the same 'orienation'; points 1,2,3 circle
        ### clockwise around point 0
        #tetrahedra = [
            #[(0,1,0), (1,1,1), (0,1,1), (1,0,1)],
            #[(0,1,0), (0,1,1), (0,0,1), (1,0,1)],
            #[(0,1,0), (0,0,1), (0,0,0), (1,0,1)],
            #[(0,1,0), (0,0,0), (1,0,0), (1,0,1)],
            #[(0,1,0), (1,0,0), (1,1,0), (1,0,1)],
            #[(0,1,0), (1,1,0), (1,1,1), (1,0,1)]
        #]

        ### each tetrahedron will be assigned an index
        ### which determines how to generate its facets.
        ### this structure is:
        ###    facets[index][facet1, facet2, ...]
        ### where each facet is triangular and its points are each
        ### interpolated between two points on the tetrahedron
        ###    facet = [(p1a, p1b), (p2a, p2b), (p3a, p3b)]
        ### facet points always circle clockwise if you are looking
        ### at them from below the isosurface.
        #indexFacets = [
            #[],  ## all above
            #[[(0,1), (0,2), (0,3)]],  # 0 below
            #[[(1,0), (1,3), (1,2)]],   # 1 below
            #[[(0,2), (1,3), (1,2)], [(0,2), (0,3), (1,3)]],   # 0,1 below
            #[[(2,0), (2,1), (2,3)]],   # 2 below
            #[[(0,3), (1,2), (2,3)], [(0,3), (0,1), (1,2)]],   # 0,2 below
            #[[(1,0), (2,3), (2,0)], [(1,0), (1,3), (2,3)]],   # 1,2 below
            #[[(3,0), (3,1), (3,2)]],   # 3 above
            #[[(3,0), (3,2), (3,1)]],   # 3 below
            #[[(1,0), (2,0), (2,3)], [(1,0), (2,3), (1,3)]],   # 0,3 below
            #[[(0,3), (2,3), (1,2)], [(0,3), (1,2), (0,1)]],   # 1,3 below
            #[[(2,0), (2,3), (2,1)]], # 0,1,3 below
            #[[(0,2), (1,2), (1,3)], [(0,2), (1,3), (0,3)]],   # 2,3 below
            #[[(1,0), (1,2), (1,3)]], # 0,2,3 below
            #[[(0,1), (0,3), (0,2)]], # 1,2,3 below
            #[]  ## all below
        #]

        #for tet in tetrahedra:

            ### get the 4 fields for this tetrahedron
            #tetFields = [fields[c] for c in tet]

            ### generate an index for each grid cell
            #index = tetFields[0] + tetFields[1]*2 + tetFields[2]*4 + tetFields[3]*8

            ### add facets
            #for i in xrange(index.shape[0]):                 # data x-axis
                #for j in xrange(index.shape[1]):             # data y-axis
                    #for k in xrange(index.shape[2]):         # data z-axis
                        #for f in indexFacets[index[i,j,k]]:  # faces to generate for this tet
                            #pts = []
                            #for l in [0,1,2]:      # points in this face
                                #p1 = tet[f[l][0]]  # tet corner 1
                                #p2 = tet[f[l][1]]  # tet corner 2
                                #pts.append([(p1[x]+p2[x])*0.5+[i,j,k][x]+0.5 for x in [0,1,2]]) ## interpolate between tet corners
                            #facets.append(pts)

        #return facets

    def isocurve(data, level, connected=False, extendToEdge=False, path=False):
        """
        Generate isocurve from 2D data using marching squares algorithm.

        ============== =========================================================
        **Arguments:**
        data           2D numpy array of scalar values
        level          The level at which to generate an isosurface
        connected      If False, return a single long list of point pairs
                       If True, return multiple long lists of connected point
                       locations. (This is slower but better for drawing
                       continuous lines)
        extendToEdge   If True, extend the curves to reach the exact edges of
                       the data.
        path           if True, return a QPainterPath rather than a list of
                       vertex coordinates. This forces connected=True.
        ============== =========================================================

        This function is SLOW; plenty of room for optimization here.
        """

        if path is True:
            connected = True

        if extendToEdge:
            d2 = np.empty((data.shape[0]+2, data.shape[1]+2), dtype=data.dtype)
            d2[1:-1, 1:-1] = data
            d2[0, 1:-1] = data[0]
            d2[-1, 1:-1] = data[-1]
            d2[1:-1, 0] = data[:, 0]
            d2[1:-1, -1] = data[:, -1]
            d2[0,0] = d2[0,1]
            d2[0,-1] = d2[1,-1]
            d2[-1,0] = d2[-1,1]
            d2[-1,-1] = d2[-1,-2]
            data = d2

        sideTable = [
            [],
            [0,1],
            [1,2],
            [0,2],
            [0,3],
            [1,3],
            [0,1,2,3],
            [2,3],
            [2,3],
            [0,1,2,3],
            [1,3],
            [0,3],
            [0,2],
            [1,2],
            [0,1],
            []
            ]

        edgeKey=[
            [(0,1), (0,0)],
            [(0,0), (1,0)],
            [(1,0), (1,1)],
            [(1,1), (0,1)]
            ]

        lines = []

        ## mark everything below the isosurface level
        mask = data < level

        ### make four sub-fields and compute indexes for grid cells
        index = np.zeros([x-1 for x in data.shape], dtype=np.ubyte)
        fields = np.empty((2,2), dtype=object)
        slices = [slice(0,-1), slice(1,None)]
        for i in [0,1]:
            for j in [0,1]:
                fields[i,j] = mask[slices[i], slices[j]]
                #vertIndex = i - 2*j*i + 3*j + 4*k  ## this is just to match Bourk's vertex numbering scheme
                vertIndex = i+2*j
                #print i,j,k," : ", fields[i,j,k], 2**vertIndex
                np.add(index, fields[i,j] * 2**vertIndex, out=index, casting='unsafe')
                #print index
        #print index

        ## add lines
        for i in range(index.shape[0]):                 # data x-axis
            for j in range(index.shape[1]):             # data y-axis
                sides = sideTable[index[i,j]]
                for l in range(0, len(sides), 2):     ## faces for this grid cell
                    edges = sides[l:l+2]
                    pts = []
                    for m in [0,1]:      # points in this face
                        p1 = edgeKey[edges[m]][0] # p1, p2 are points at either side of an edge
                        p2 = edgeKey[edges[m]][1]
                        v1 = data[i+p1[0], j+p1[1]] # v1 and v2 are the values at p1 and p2
                        v2 = data[i+p2[0], j+p2[1]]
                        f = (level-v1) / (v2-v1)
                        fi = 1.0 - f
                        p = (    ## interpolate between corners
                            p1[0]*fi + p2[0]*f + i + 0.5,
                            p1[1]*fi + p2[1]*f + j + 0.5
                            )
                        if extendToEdge:
                            ## check bounds
                            p = (
                                min(data.shape[0]-2, max(0, p[0]-1)),
                                min(data.shape[1]-2, max(0, p[1]-1)),
                            )
                        if connected:
                            gridKey = i + (1 if edges[m]==2 else 0), j + (1 if edges[m]==3 else 0), edges[m]%2
                            pts.append((p, gridKey))  ## give the actual position and a key identifying the grid location (for connecting segments)
                        else:
                            pts.append(p)

                    lines.append(pts)

        if not connected:
            return lines

        ## turn disjoint list of segments into continuous lines

        #lines = [[2,5], [5,4], [3,4], [1,3], [6,7], [7,8], [8,6], [11,12], [12,15], [11,13], [13,14]]
        #lines = [[(float(a), a), (float(b), b)] for a,b in lines]
        points = {}  ## maps each point to its connections
        for a,b in lines:
            if a[1] not in points:
                points[a[1]] = []
            points[a[1]].append([a,b])
            if b[1] not in points:
                points[b[1]] = []
            points[b[1]].append([b,a])

        ## rearrange into chains
        for k in list(points.keys()):
            try:
                chains = points[k]
            except KeyError:   ## already used this point elsewhere
                continue
            #print "===========", k
            for chain in chains:
                #print "  chain:", chain
                x = None
                while True:
                    if x == chain[-1][1]:
                        break ## nothing left to do on this chain

                    x = chain[-1][1]
                    if x == k:
                        break ## chain has looped; we're done and can ignore the opposite chain
                    y = chain[-2][1]
                    connects = points[x]
                    for conn in connects[:]:
                        if conn[1][1] != y:
                            #print "    ext:", conn
                            chain.extend(conn[1:])
                    #print "    del:", x
                    del points[x]
                if chain[0][1] == chain[-1][1]:  # looped chain; no need to continue the other direction
                    chains.pop()
                    break

        ## extract point locations
        lines = []
        for chain in points.values():
            if len(chain) == 2:
                chain = chain[1][1:][::-1] + chain[0]  # join together ends of chain
            else:
                chain = chain[0]
            lines.append([p[0] for p in chain])

        if not path:
            return lines ## a list of pairs of points

        path = QtGui.QPainterPath()
        for line in lines:
            path.moveTo(*line[0])
            for p in line[1:]:
                path.lineTo(*p)

        return path

    def traceImage(image, values, smooth=0.5):
        """
        Convert an image to a set of QPainterPath curves.
        One curve will be generated for each item in *values*; each curve outlines the area
        of the image that is closer to its value than to any others.

        If image is RGB or RGBA, then the shape of values should be (nvals, 3/4)
        The parameter *smooth* is expressed in pixels.
        """
        try:
            import scipy.ndimage as ndi
        except ImportError:
            raise Exception("traceImage() requires the package scipy.ndimage, but it is not importable.")

        if values.ndim == 2:
            values = values.T
        values = values[np.newaxis, np.newaxis, ...].astype(float)
        image = image[..., np.newaxis].astype(float)
        diff = np.abs(image-values)
        if values.ndim == 4:
            diff = diff.sum(axis=2)

        labels = np.argmin(diff, axis=2)

        paths = []
        for i in range(diff.shape[-1]):
            d = (labels==i).astype(float)
            d = gaussianFilter(d, (smooth, smooth))
            lines = isocurve(d, 0.5, connected=True, extendToEdge=True)
            path = QtGui.QPainterPath()
            for line in lines:
                path.moveTo(*line[0])
                for p in line[1:]:
                    path.lineTo(*p)

            paths.append(path)
        return paths

    IsosurfaceDataCache = None
    def isosurface(data, level):
        """
        Generate isosurface from volumetric data using marching cubes algorithm.
        See Paul Bourke, "Polygonising a Scalar Field"
        (http://paulbourke.net/geometry/polygonise/)

        *data*   3D numpy array of scalar values. Must be contiguous.
        *level*  The level at which to generate an isosurface

        Returns an array of vertex coordinates (Nv, 3) and an array of
        per-face vertex indexes (Nf, 3)
        """
        ## For improvement, see:
        ##
        ## Efficient implementation of Marching Cubes' cases with topological guarantees.
        ## Thomas Lewiner, Helio Lopes, Antonio Wilson Vieira and Geovan Tavares.
        ## Journal of Graphics Tools 8(2): pp. 1-15 (december 2003)

        ## Precompute lookup tables on the first run
        global IsosurfaceDataCache
        if IsosurfaceDataCache is None:
            ## map from grid cell index to edge index.
            ## grid cell index tells us which corners are below the isosurface,
            ## edge index tells us which edges are cut by the isosurface.
            ## (Data stolen from Bourk; see above.)
            edgeTable = np.array([
                0x0  , 0x109, 0x203, 0x30a, 0x406, 0x50f, 0x605, 0x70c,
                0x80c, 0x905, 0xa0f, 0xb06, 0xc0a, 0xd03, 0xe09, 0xf00,
                0x190, 0x99 , 0x393, 0x29a, 0x596, 0x49f, 0x795, 0x69c,
                0x99c, 0x895, 0xb9f, 0xa96, 0xd9a, 0xc93, 0xf99, 0xe90,
                0x230, 0x339, 0x33 , 0x13a, 0x636, 0x73f, 0x435, 0x53c,
                0xa3c, 0xb35, 0x83f, 0x936, 0xe3a, 0xf33, 0xc39, 0xd30,
                0x3a0, 0x2a9, 0x1a3, 0xaa , 0x7a6, 0x6af, 0x5a5, 0x4ac,
                0xbac, 0xaa5, 0x9af, 0x8a6, 0xfaa, 0xea3, 0xda9, 0xca0,
                0x460, 0x569, 0x663, 0x76a, 0x66 , 0x16f, 0x265, 0x36c,
                0xc6c, 0xd65, 0xe6f, 0xf66, 0x86a, 0x963, 0xa69, 0xb60,
                0x5f0, 0x4f9, 0x7f3, 0x6fa, 0x1f6, 0xff , 0x3f5, 0x2fc,
                0xdfc, 0xcf5, 0xfff, 0xef6, 0x9fa, 0x8f3, 0xbf9, 0xaf0,
                0x650, 0x759, 0x453, 0x55a, 0x256, 0x35f, 0x55 , 0x15c,
                0xe5c, 0xf55, 0xc5f, 0xd56, 0xa5a, 0xb53, 0x859, 0x950,
                0x7c0, 0x6c9, 0x5c3, 0x4ca, 0x3c6, 0x2cf, 0x1c5, 0xcc ,
                0xfcc, 0xec5, 0xdcf, 0xcc6, 0xbca, 0xac3, 0x9c9, 0x8c0,
                0x8c0, 0x9c9, 0xac3, 0xbca, 0xcc6, 0xdcf, 0xec5, 0xfcc,
                0xcc , 0x1c5, 0x2cf, 0x3c6, 0x4ca, 0x5c3, 0x6c9, 0x7c0,
                0x950, 0x859, 0xb53, 0xa5a, 0xd56, 0xc5f, 0xf55, 0xe5c,
                0x15c, 0x55 , 0x35f, 0x256, 0x55a, 0x453, 0x759, 0x650,
                0xaf0, 0xbf9, 0x8f3, 0x9fa, 0xef6, 0xfff, 0xcf5, 0xdfc,
                0x2fc, 0x3f5, 0xff , 0x1f6, 0x6fa, 0x7f3, 0x4f9, 0x5f0,
                0xb60, 0xa69, 0x963, 0x86a, 0xf66, 0xe6f, 0xd65, 0xc6c,
                0x36c, 0x265, 0x16f, 0x66 , 0x76a, 0x663, 0x569, 0x460,
                0xca0, 0xda9, 0xea3, 0xfaa, 0x8a6, 0x9af, 0xaa5, 0xbac,
                0x4ac, 0x5a5, 0x6af, 0x7a6, 0xaa , 0x1a3, 0x2a9, 0x3a0,
                0xd30, 0xc39, 0xf33, 0xe3a, 0x936, 0x83f, 0xb35, 0xa3c,
                0x53c, 0x435, 0x73f, 0x636, 0x13a, 0x33 , 0x339, 0x230,
                0xe90, 0xf99, 0xc93, 0xd9a, 0xa96, 0xb9f, 0x895, 0x99c,
                0x69c, 0x795, 0x49f, 0x596, 0x29a, 0x393, 0x99 , 0x190,
                0xf00, 0xe09, 0xd03, 0xc0a, 0xb06, 0xa0f, 0x905, 0x80c,
                0x70c, 0x605, 0x50f, 0x406, 0x30a, 0x203, 0x109, 0x0
                ], dtype=np.uint16)

            ## Table of triangles to use for filling each grid cell.
            ## Each set of three integers tells us which three edges to
            ## draw a triangle between.
            ## (Data stolen from Bourk; see above.)
            triTable = [
                [],
                [0, 8, 3],
                [0, 1, 9],
                [1, 8, 3, 9, 8, 1],
                [1, 2, 10],
                [0, 8, 3, 1, 2, 10],
                [9, 2, 10, 0, 2, 9],
                [2, 8, 3, 2, 10, 8, 10, 9, 8],
                [3, 11, 2],
                [0, 11, 2, 8, 11, 0],
                [1, 9, 0, 2, 3, 11],
                [1, 11, 2, 1, 9, 11, 9, 8, 11],
                [3, 10, 1, 11, 10, 3],
                [0, 10, 1, 0, 8, 10, 8, 11, 10],
                [3, 9, 0, 3, 11, 9, 11, 10, 9],
                [9, 8, 10, 10, 8, 11],
                [4, 7, 8],
                [4, 3, 0, 7, 3, 4],
                [0, 1, 9, 8, 4, 7],
                [4, 1, 9, 4, 7, 1, 7, 3, 1],
                [1, 2, 10, 8, 4, 7],
                [3, 4, 7, 3, 0, 4, 1, 2, 10],
                [9, 2, 10, 9, 0, 2, 8, 4, 7],
                [2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4],
                [8, 4, 7, 3, 11, 2],
                [11, 4, 7, 11, 2, 4, 2, 0, 4],
                [9, 0, 1, 8, 4, 7, 2, 3, 11],
                [4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1],
                [3, 10, 1, 3, 11, 10, 7, 8, 4],
                [1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4],
                [4, 7, 8, 9, 0, 11, 9, 11, 10, 11, 0, 3],
                [4, 7, 11, 4, 11, 9, 9, 11, 10],
                [9, 5, 4],
                [9, 5, 4, 0, 8, 3],
                [0, 5, 4, 1, 5, 0],
                [8, 5, 4, 8, 3, 5, 3, 1, 5],
                [1, 2, 10, 9, 5, 4],
                [3, 0, 8, 1, 2, 10, 4, 9, 5],
                [5, 2, 10, 5, 4, 2, 4, 0, 2],
                [2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8],
                [9, 5, 4, 2, 3, 11],
                [0, 11, 2, 0, 8, 11, 4, 9, 5],
                [0, 5, 4, 0, 1, 5, 2, 3, 11],
                [2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5],
                [10, 3, 11, 10, 1, 3, 9, 5, 4],
                [4, 9, 5, 0, 8, 1, 8, 10, 1, 8, 11, 10],
                [5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3],
                [5, 4, 8, 5, 8, 10, 10, 8, 11],
                [9, 7, 8, 5, 7, 9],
                [9, 3, 0, 9, 5, 3, 5, 7, 3],
                [0, 7, 8, 0, 1, 7, 1, 5, 7],
                [1, 5, 3, 3, 5, 7],
                [9, 7, 8, 9, 5, 7, 10, 1, 2],
                [10, 1, 2, 9, 5, 0, 5, 3, 0, 5, 7, 3],
                [8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2],
                [2, 10, 5, 2, 5, 3, 3, 5, 7],
                [7, 9, 5, 7, 8, 9, 3, 11, 2],
                [9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11],
                [2, 3, 11, 0, 1, 8, 1, 7, 8, 1, 5, 7],
                [11, 2, 1, 11, 1, 7, 7, 1, 5],
                [9, 5, 8, 8, 5, 7, 10, 1, 3, 10, 3, 11],
                [5, 7, 0, 5, 0, 9, 7, 11, 0, 1, 0, 10, 11, 10, 0],
                [11, 10, 0, 11, 0, 3, 10, 5, 0, 8, 0, 7, 5, 7, 0],
                [11, 10, 5, 7, 11, 5],
                [10, 6, 5],
                [0, 8, 3, 5, 10, 6],
                [9, 0, 1, 5, 10, 6],
                [1, 8, 3, 1, 9, 8, 5, 10, 6],
                [1, 6, 5, 2, 6, 1],
                [1, 6, 5, 1, 2, 6, 3, 0, 8],
                [9, 6, 5, 9, 0, 6, 0, 2, 6],
                [5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8],
                [2, 3, 11, 10, 6, 5],
                [11, 0, 8, 11, 2, 0, 10, 6, 5],
                [0, 1, 9, 2, 3, 11, 5, 10, 6],
                [5, 10, 6, 1, 9, 2, 9, 11, 2, 9, 8, 11],
                [6, 3, 11, 6, 5, 3, 5, 1, 3],
                [0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6],
                [3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9],
                [6, 5, 9, 6, 9, 11, 11, 9, 8],
                [5, 10, 6, 4, 7, 8],
                [4, 3, 0, 4, 7, 3, 6, 5, 10],
                [1, 9, 0, 5, 10, 6, 8, 4, 7],
                [10, 6, 5, 1, 9, 7, 1, 7, 3, 7, 9, 4],
                [6, 1, 2, 6, 5, 1, 4, 7, 8],
                [1, 2, 5, 5, 2, 6, 3, 0, 4, 3, 4, 7],
                [8, 4, 7, 9, 0, 5, 0, 6, 5, 0, 2, 6],
                [7, 3, 9, 7, 9, 4, 3, 2, 9, 5, 9, 6, 2, 6, 9],
                [3, 11, 2, 7, 8, 4, 10, 6, 5],
                [5, 10, 6, 4, 7, 2, 4, 2, 0, 2, 7, 11],
                [0, 1, 9, 4, 7, 8, 2, 3, 11, 5, 10, 6],
                [9, 2, 1, 9, 11, 2, 9, 4, 11, 7, 11, 4, 5, 10, 6],
                [8, 4, 7, 3, 11, 5, 3, 5, 1, 5, 11, 6],
                [5, 1, 11, 5, 11, 6, 1, 0, 11, 7, 11, 4, 0, 4, 11],
                [0, 5, 9, 0, 6, 5, 0, 3, 6, 11, 6, 3, 8, 4, 7],
                [6, 5, 9, 6, 9, 11, 4, 7, 9, 7, 11, 9],
                [10, 4, 9, 6, 4, 10],
                [4, 10, 6, 4, 9, 10, 0, 8, 3],
                [10, 0, 1, 10, 6, 0, 6, 4, 0],
                [8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10],
                [1, 4, 9, 1, 2, 4, 2, 6, 4],
                [3, 0, 8, 1, 2, 9, 2, 4, 9, 2, 6, 4],
                [0, 2, 4, 4, 2, 6],
                [8, 3, 2, 8, 2, 4, 4, 2, 6],
                [10, 4, 9, 10, 6, 4, 11, 2, 3],
                [0, 8, 2, 2, 8, 11, 4, 9, 10, 4, 10, 6],
                [3, 11, 2, 0, 1, 6, 0, 6, 4, 6, 1, 10],
                [6, 4, 1, 6, 1, 10, 4, 8, 1, 2, 1, 11, 8, 11, 1],
                [9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3],
                [8, 11, 1, 8, 1, 0, 11, 6, 1, 9, 1, 4, 6, 4, 1],
                [3, 11, 6, 3, 6, 0, 0, 6, 4],
                [6, 4, 8, 11, 6, 8],
                [7, 10, 6, 7, 8, 10, 8, 9, 10],
                [0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10],
                [10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0],
                [10, 6, 7, 10, 7, 1, 1, 7, 3],
                [1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7],
                [2, 6, 9, 2, 9, 1, 6, 7, 9, 0, 9, 3, 7, 3, 9],
                [7, 8, 0, 7, 0, 6, 6, 0, 2],
                [7, 3, 2, 6, 7, 2],
                [2, 3, 11, 10, 6, 8, 10, 8, 9, 8, 6, 7],
                [2, 0, 7, 2, 7, 11, 0, 9, 7, 6, 7, 10, 9, 10, 7],
                [1, 8, 0, 1, 7, 8, 1, 10, 7, 6, 7, 10, 2, 3, 11],
                [11, 2, 1, 11, 1, 7, 10, 6, 1, 6, 7, 1],
                [8, 9, 6, 8, 6, 7, 9, 1, 6, 11, 6, 3, 1, 3, 6],
                [0, 9, 1, 11, 6, 7],
                [7, 8, 0, 7, 0, 6, 3, 11, 0, 11, 6, 0],
                [7, 11, 6],
                [7, 6, 11],
                [3, 0, 8, 11, 7, 6],
                [0, 1, 9, 11, 7, 6],
                [8, 1, 9, 8, 3, 1, 11, 7, 6],
                [10, 1, 2, 6, 11, 7],
                [1, 2, 10, 3, 0, 8, 6, 11, 7],
                [2, 9, 0, 2, 10, 9, 6, 11, 7],
                [6, 11, 7, 2, 10, 3, 10, 8, 3, 10, 9, 8],
                [7, 2, 3, 6, 2, 7],
                [7, 0, 8, 7, 6, 0, 6, 2, 0],
                [2, 7, 6, 2, 3, 7, 0, 1, 9],
                [1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6],
                [10, 7, 6, 10, 1, 7, 1, 3, 7],
                [10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8],
                [0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7],
                [7, 6, 10, 7, 10, 8, 8, 10, 9],
                [6, 8, 4, 11, 8, 6],
                [3, 6, 11, 3, 0, 6, 0, 4, 6],
                [8, 6, 11, 8, 4, 6, 9, 0, 1],
                [9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6],
                [6, 8, 4, 6, 11, 8, 2, 10, 1],
                [1, 2, 10, 3, 0, 11, 0, 6, 11, 0, 4, 6],
                [4, 11, 8, 4, 6, 11, 0, 2, 9, 2, 10, 9],
                [10, 9, 3, 10, 3, 2, 9, 4, 3, 11, 3, 6, 4, 6, 3],
                [8, 2, 3, 8, 4, 2, 4, 6, 2],
                [0, 4, 2, 4, 6, 2],
                [1, 9, 0, 2, 3, 4, 2, 4, 6, 4, 3, 8],
                [1, 9, 4, 1, 4, 2, 2, 4, 6],
                [8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1],
                [10, 1, 0, 10, 0, 6, 6, 0, 4],
                [4, 6, 3, 4, 3, 8, 6, 10, 3, 0, 3, 9, 10, 9, 3],
                [10, 9, 4, 6, 10, 4],
                [4, 9, 5, 7, 6, 11],
                [0, 8, 3, 4, 9, 5, 11, 7, 6],
                [5, 0, 1, 5, 4, 0, 7, 6, 11],
                [11, 7, 6, 8, 3, 4, 3, 5, 4, 3, 1, 5],
                [9, 5, 4, 10, 1, 2, 7, 6, 11],
                [6, 11, 7, 1, 2, 10, 0, 8, 3, 4, 9, 5],
                [7, 6, 11, 5, 4, 10, 4, 2, 10, 4, 0, 2],
                [3, 4, 8, 3, 5, 4, 3, 2, 5, 10, 5, 2, 11, 7, 6],
                [7, 2, 3, 7, 6, 2, 5, 4, 9],
                [9, 5, 4, 0, 8, 6, 0, 6, 2, 6, 8, 7],
                [3, 6, 2, 3, 7, 6, 1, 5, 0, 5, 4, 0],
                [6, 2, 8, 6, 8, 7, 2, 1, 8, 4, 8, 5, 1, 5, 8],
                [9, 5, 4, 10, 1, 6, 1, 7, 6, 1, 3, 7],
                [1, 6, 10, 1, 7, 6, 1, 0, 7, 8, 7, 0, 9, 5, 4],
                [4, 0, 10, 4, 10, 5, 0, 3, 10, 6, 10, 7, 3, 7, 10],
                [7, 6, 10, 7, 10, 8, 5, 4, 10, 4, 8, 10],
                [6, 9, 5, 6, 11, 9, 11, 8, 9],
                [3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5],
                [0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11],
                [6, 11, 3, 6, 3, 5, 5, 3, 1],
                [1, 2, 10, 9, 5, 11, 9, 11, 8, 11, 5, 6],
                [0, 11, 3, 0, 6, 11, 0, 9, 6, 5, 6, 9, 1, 2, 10],
                [11, 8, 5, 11, 5, 6, 8, 0, 5, 10, 5, 2, 0, 2, 5],
                [6, 11, 3, 6, 3, 5, 2, 10, 3, 10, 5, 3],
                [5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2],
                [9, 5, 6, 9, 6, 0, 0, 6, 2],
                [1, 5, 8, 1, 8, 0, 5, 6, 8, 3, 8, 2, 6, 2, 8],
                [1, 5, 6, 2, 1, 6],
                [1, 3, 6, 1, 6, 10, 3, 8, 6, 5, 6, 9, 8, 9, 6],
                [10, 1, 0, 10, 0, 6, 9, 5, 0, 5, 6, 0],
                [0, 3, 8, 5, 6, 10],
                [10, 5, 6],
                [11, 5, 10, 7, 5, 11],
                [11, 5, 10, 11, 7, 5, 8, 3, 0],
                [5, 11, 7, 5, 10, 11, 1, 9, 0],
                [10, 7, 5, 10, 11, 7, 9, 8, 1, 8, 3, 1],
                [11, 1, 2, 11, 7, 1, 7, 5, 1],
                [0, 8, 3, 1, 2, 7, 1, 7, 5, 7, 2, 11],
                [9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7],
                [7, 5, 2, 7, 2, 11, 5, 9, 2, 3, 2, 8, 9, 8, 2],
                [2, 5, 10, 2, 3, 5, 3, 7, 5],
                [8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5],
                [9, 0, 1, 5, 10, 3, 5, 3, 7, 3, 10, 2],
                [9, 8, 2, 9, 2, 1, 8, 7, 2, 10, 2, 5, 7, 5, 2],
                [1, 3, 5, 3, 7, 5],
                [0, 8, 7, 0, 7, 1, 1, 7, 5],
                [9, 0, 3, 9, 3, 5, 5, 3, 7],
                [9, 8, 7, 5, 9, 7],
                [5, 8, 4, 5, 10, 8, 10, 11, 8],
                [5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0],
                [0, 1, 9, 8, 4, 10, 8, 10, 11, 10, 4, 5],
                [10, 11, 4, 10, 4, 5, 11, 3, 4, 9, 4, 1, 3, 1, 4],
                [2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8],
                [0, 4, 11, 0, 11, 3, 4, 5, 11, 2, 11, 1, 5, 1, 11],
                [0, 2, 5, 0, 5, 9, 2, 11, 5, 4, 5, 8, 11, 8, 5],
                [9, 4, 5, 2, 11, 3],
                [2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4],
                [5, 10, 2, 5, 2, 4, 4, 2, 0],
                [3, 10, 2, 3, 5, 10, 3, 8, 5, 4, 5, 8, 0, 1, 9],
                [5, 10, 2, 5, 2, 4, 1, 9, 2, 9, 4, 2],
                [8, 4, 5, 8, 5, 3, 3, 5, 1],
                [0, 4, 5, 1, 0, 5],
                [8, 4, 5, 8, 5, 3, 9, 0, 5, 0, 3, 5],
                [9, 4, 5],
                [4, 11, 7, 4, 9, 11, 9, 10, 11],
                [0, 8, 3, 4, 9, 7, 9, 11, 7, 9, 10, 11],
                [1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11],
                [3, 1, 4, 3, 4, 8, 1, 10, 4, 7, 4, 11, 10, 11, 4],
                [4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2],
                [9, 7, 4, 9, 11, 7, 9, 1, 11, 2, 11, 1, 0, 8, 3],
                [11, 7, 4, 11, 4, 2, 2, 4, 0],
                [11, 7, 4, 11, 4, 2, 8, 3, 4, 3, 2, 4],
                [2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9],
                [9, 10, 7, 9, 7, 4, 10, 2, 7, 8, 7, 0, 2, 0, 7],
                [3, 7, 10, 3, 10, 2, 7, 4, 10, 1, 10, 0, 4, 0, 10],
                [1, 10, 2, 8, 7, 4],
                [4, 9, 1, 4, 1, 7, 7, 1, 3],
                [4, 9, 1, 4, 1, 7, 0, 8, 1, 8, 7, 1],
                [4, 0, 3, 7, 4, 3],
                [4, 8, 7],
                [9, 10, 8, 10, 11, 8],
                [3, 0, 9, 3, 9, 11, 11, 9, 10],
                [0, 1, 10, 0, 10, 8, 8, 10, 11],
                [3, 1, 10, 11, 3, 10],
                [1, 2, 11, 1, 11, 9, 9, 11, 8],
                [3, 0, 9, 3, 9, 11, 1, 2, 9, 2, 11, 9],
                [0, 2, 11, 8, 0, 11],
                [3, 2, 11],
                [2, 3, 8, 2, 8, 10, 10, 8, 9],
                [9, 10, 2, 0, 9, 2],
                [2, 3, 8, 2, 8, 10, 0, 1, 8, 1, 10, 8],
                [1, 10, 2],
                [1, 3, 8, 9, 1, 8],
                [0, 9, 1],
                [0, 3, 8],
                []
            ]
            edgeShifts = np.array([  ## maps edge ID (0-11) to (x,y,z) cell offset and edge ID (0-2)
                [0, 0, 0, 0],
                [1, 0, 0, 1],
                [0, 1, 0, 0],
                [0, 0, 0, 1],
                [0, 0, 1, 0],
                [1, 0, 1, 1],
                [0, 1, 1, 0],
                [0, 0, 1, 1],
                [0, 0, 0, 2],
                [1, 0, 0, 2],
                [1, 1, 0, 2],
                [0, 1, 0, 2],
                #[9, 9, 9, 9]  ## fake
            ], dtype=np.uint16) # don't use ubyte here! This value gets added to cell index later; will need the extra precision.
            nTableFaces = np.array([len(f)/3 for f in triTable], dtype=np.ubyte)
            faceShiftTables = [None]
            for i in range(1,6):
                ## compute lookup table of index: vertexes mapping
                faceTableI = np.zeros((len(triTable), i*3), dtype=np.ubyte)
                faceTableInds = np.argwhere(nTableFaces == i)
                faceTableI[faceTableInds[:,0]] = np.array([triTable[j] for j in faceTableInds])
                faceTableI = faceTableI.reshape((len(triTable), i, 3))
                faceShiftTables.append(edgeShifts[faceTableI])

            ## Let's try something different:
            #faceTable = np.empty((256, 5, 3, 4), dtype=np.ubyte)   # (grid cell index, faces, vertexes, edge lookup)
            #for i,f in enumerate(triTable):
                #f = np.array(f + [12] * (15-len(f))).reshape(5,3)
                #faceTable[i] = edgeShifts[f]

            IsosurfaceDataCache = (faceShiftTables, edgeShifts, edgeTable, nTableFaces)
        else:
            faceShiftTables, edgeShifts, edgeTable, nTableFaces = IsosurfaceDataCache

        # We use strides below, which means we need contiguous array input.
        # Ideally we can fix this just by removing the dependency on strides.
        if not data.flags['C_CONTIGUOUS']:
            raise TypeError("isosurface input data must be c-contiguous.")

        ## mark everything below the isosurface level
        mask = data < level

        ### make eight sub-fields and compute indexes for grid cells
        index = np.zeros([x-1 for x in data.shape], dtype=np.ubyte)
        fields = np.empty((2,2,2), dtype=object)
        slices = [slice(0,-1), slice(1,None)]
        for i in [0,1]:
            for j in [0,1]:
                for k in [0,1]:
                    fields[i,j,k] = mask[slices[i], slices[j], slices[k]]
                    vertIndex = i - 2*j*i + 3*j + 4*k  ## this is just to match Bourk's vertex numbering scheme
                    np.add(index, fields[i,j,k] * 2**vertIndex, out=index, casting='unsafe')

        ### Generate table of edges that have been cut
        cutEdges = np.zeros([x+1 for x in index.shape]+[3], dtype=np.uint32)
        edges = edgeTable[index]
        for i, shift in enumerate(edgeShifts[:12]):
            slices = [slice(shift[j],cutEdges.shape[j]+(shift[j]-1)) for j in range(3)]
            cutEdges[slices[0], slices[1], slices[2], shift[3]] += edges & 2**i

        ## for each cut edge, interpolate to see where exactly the edge is cut and generate vertex positions
        m = cutEdges > 0
        vertexInds = np.argwhere(m)   ## argwhere is slow!
        vertexes = vertexInds[:,:3].astype(np.float32)
        dataFlat = data.reshape(data.shape[0]*data.shape[1]*data.shape[2])

        ## re-use the cutEdges array as a lookup table for vertex IDs
        cutEdges[vertexInds[:,0], vertexInds[:,1], vertexInds[:,2], vertexInds[:,3]] = np.arange(vertexInds.shape[0])

        for i in [0,1,2]:
            vim = vertexInds[:,3] == i
            vi = vertexInds[vim, :3]
            viFlat = (vi * (np.array(data.strides[:3]) // data.itemsize)[np.newaxis,:]).sum(axis=1)
            v1 = dataFlat[viFlat]
            v2 = dataFlat[viFlat + data.strides[i]//data.itemsize]
            vertexes[vim,i] += (level-v1) / (v2-v1)

        ### compute the set of vertex indexes for each face.

        ## This works, but runs a bit slower.
        #cells = np.argwhere((index != 0) & (index != 255))  ## all cells with at least one face
        #cellInds = index[cells[:,0], cells[:,1], cells[:,2]]
        #verts = faceTable[cellInds]
        #mask = verts[...,0,0] != 9
        #verts[...,:3] += cells[:,np.newaxis,np.newaxis,:]  ## we now have indexes into cutEdges
        #verts = verts[mask]
        #faces = cutEdges[verts[...,0], verts[...,1], verts[...,2], verts[...,3]]  ## and these are the vertex indexes we want.

        ## To allow this to be vectorized efficiently, we count the number of faces in each
        ## grid cell and handle each group of cells with the same number together.
        ## determine how many faces to assign to each grid cell
        nFaces = nTableFaces[index]
        totFaces = nFaces.sum()
        faces = np.empty((totFaces, 3), dtype=np.uint32)
        ptr = 0
        #import debug
        #p = debug.Profiler()

        ## this helps speed up an indexing operation later on
        cs = np.array(cutEdges.strides)//cutEdges.itemsize
        cutEdges = cutEdges.flatten()

        ## this, strangely, does not seem to help.
        #ins = np.array(index.strides)/index.itemsize
        #index = index.flatten()

        for i in range(1,6):
            ### expensive:
            #profiler()
            cells = np.argwhere(nFaces == i)  ## all cells which require i faces  (argwhere is expensive)
            #profiler()
            if cells.shape[0] == 0:
                continue
            cellInds = index[cells[:,0], cells[:,1], cells[:,2]]   ## index values of cells to process for this round
            #profiler()

            ### expensive:
            verts = faceShiftTables[i][cellInds]
            #profiler()
            np.add(verts[...,:3], cells[:,np.newaxis,np.newaxis,:], out=verts[...,:3], casting='unsafe')  ## we now have indexes into cutEdges
            verts = verts.reshape((verts.shape[0]*i,)+verts.shape[2:])
            #profiler()

            ### expensive:
            verts = (verts * cs[np.newaxis, np.newaxis, :]).sum(axis=2)
            vertInds = cutEdges[verts]
            #profiler()
            nv = vertInds.shape[0]
            #profiler()
            faces[ptr:ptr+nv] = vertInds #.reshape((nv, 3))
            #profiler()
            ptr += nv

        return vertexes, faces

    def invertQTransform(tr):
        """Return a QTransform that is the inverse of *tr*.
        Rasises an exception if tr is not invertible.

        Note that this function is preferred over QTransform.inverted() due to
        bugs in that method. (specifically, Qt has floating-point precision issues
        when determining whether a matrix is invertible)
        """
        try:
            import numpy.linalg
            arr = np.array([[tr.m11(), tr.m12(), tr.m13()], [tr.m21(), tr.m22(), tr.m23()], [tr.m31(), tr.m32(), tr.m33()]])
            inv = numpy.linalg.inv(arr)
            return QtGui.QTransform(inv[0,0], inv[0,1], inv[0,2], inv[1,0], inv[1,1], inv[1,2], inv[2,0], inv[2,1])
        except ImportError:
            inv = tr.inverted()
            if inv[1] is False:
                raise Exception("Transform is not invertible.")
            return inv[0]

    def pseudoScatter(data, spacing=None, shuffle=True, bidir=False):
        """
        Used for examining the distribution of values in a set. Produces scattering as in beeswarm or column scatter plots.

        Given a list of x-values, construct a set of y-values such that an x,y scatter-plot
        will not have overlapping points (it will look similar to a histogram).
        """
        inds = np.arange(len(data))
        if shuffle:
            np.random.shuffle(inds)

        data = data[inds]

        if spacing is None:
            spacing = 2.*np.std(data)/len(data)**0.5
        s2 = spacing**2

        yvals = np.empty(len(data))
        if len(data) == 0:
            return yvals
        yvals[0] = 0
        for i in range(1,len(data)):
            x = data[i]     # current x value to be placed
            x0 = data[:i]   # all x values already placed
            y0 = yvals[:i]  # all y values already placed
            y = 0

            dx = (x0-x)**2  # x-distance to each previous point
            xmask = dx < s2  # exclude anything too far away

            if xmask.sum() > 0:
                if bidir:
                    dirs = [-1, 1]
                else:
                    dirs = [1]
                yopts = []
                for direction in dirs:
                    y = 0
                    dx2 = dx[xmask]
                    dy = (s2 - dx2)**0.5
                    limits = np.empty((2,len(dy)))  # ranges of y-values to exclude
                    limits[0] = y0[xmask] - dy
                    limits[1] = y0[xmask] + dy
                    while True:
                        # ignore anything below this y-value
                        if direction > 0:
                            mask = limits[1] >= y
                        else:
                            mask = limits[0] <= y

                        limits2 = limits[:,mask]

                        # are we inside an excluded region?
                        mask = (limits2[0] < y) & (limits2[1] > y)
                        if mask.sum() == 0:
                            break

                        if direction > 0:
                            y = limits2[:,mask].max()
                        else:
                            y = limits2[:,mask].min()
                    yopts.append(y)
                if bidir:
                    y = yopts[0] if -yopts[0] < yopts[1] else yopts[1]
                else:
                    y = yopts[0]
            yvals[i] = y

        return yvals[np.argsort(inds)]  ## un-shuffle values before returning

    def toposort(deps, nodes=None, seen=None, stack=None, depth=0):
        """Topological sort. Arguments are:
          deps    dictionary describing dependencies where a:[b,c] means "a depends on b and c"
          nodes   optional, specifies list of starting nodes (these should be the nodes
                  which are not depended on by any other nodes). Other candidate starting
                  nodes will be ignored.

        Example::

            # Sort the following graph:
            #
            #   B ──┬─────> C <── D
            #       │       │
            #   E <─┴─> A <─┘
            #
            deps = {'a': ['b', 'c'], 'c': ['b', 'd'], 'e': ['b']}
            toposort(deps)
             => ['b', 'd', 'c', 'a', 'e']
        """
        # fill in empty dep lists
        deps = deps.copy()
        for k,v in list(deps.items()):
            for k in v:
                if k not in deps:
                    deps[k] = []

        if nodes is None:
            ## run through deps to find nodes that are not depended upon
            rem = set()
            for dep in deps.values():
                rem |= set(dep)
            nodes = set(deps.keys()) - rem
        if seen is None:
            seen = set()
            stack = []
        sorted = []
        for n in nodes:
            if n in stack:
                raise Exception("Cyclic dependency detected", stack + [n])
            if n in seen:
                continue
            seen.add(n)
            sorted.extend( toposort(deps, deps[n], seen, stack+[n], depth=depth+1))
            sorted.append(n)
        return sorted