"""
A module for converting numbers or color arguments to *RGB* or *RGBA*

*RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the
range 0-1.

This module includes functions and classes for color specification
conversions, and for mapping numbers to colors in a 1-D array of
colors called a colormap. Colormapping typically involves two steps:
a data array is first mapped onto the range 0-1 using an instance
of :class:`Normalize` or of a subclass; then this number in the 0-1
range is mapped to a color using an instance of a subclass of
:class:`Colormap`.  Two are provided here:
:class:`LinearSegmentedColormap`, which is used to generate all
the built-in colormap instances, but is also useful for making
custom colormaps, and :class:`ListedColormap`, which is used for
generating a custom colormap from a list of color specifications.

The module also provides a single instance, *colorConverter*, of the
:class:`ColorConverter` class providing methods for converting single
color specifications or sequences of them to *RGB* or *RGBA*.

Commands which take color arguments can use several formats to specify
the colors.  For the basic builtin colors, you can use a single letter

    - b  : blue
    - g  : green
    - r  : red
    - c  : cyan
    - m  : magenta
    - y  : yellow
    - k  : black
    - w  : white

Gray shades can be given as a string encoding a float in the 0-1
range, e.g.::

    color = '0.75'

For a greater range of colors, you have two options.  You can specify
the color using an html hex string, as in::

      color = '#eeefff'

or you can pass an *R* , *G* , *B* tuple, where each of *R* , *G* , *B*
are in the range [0,1].

Finally, legal html names for colors, like 'red', 'burlywood' and
'chartreuse' are supported.
"""
import re
import numpy as np
from numpy import ma
import matplotlib.cbook as cbook

parts = np.__version__.split('.')
NP_MAJOR, NP_MINOR = map(int, parts[:2])
# true if clip supports the out kwarg
NP_CLIP_OUT = NP_MAJOR>=1 and NP_MINOR>=2

cnames = {
    'aliceblue'            : '#F0F8FF',
    'antiquewhite'         : '#FAEBD7',
    'aqua'                 : '#00FFFF',
    'aquamarine'           : '#7FFFD4',
    'azure'                : '#F0FFFF',
    'beige'                : '#F5F5DC',
    'bisque'               : '#FFE4C4',
    'black'                : '#000000',
    'blanchedalmond'       : '#FFEBCD',
    'blue'                 : '#0000FF',
    'blueviolet'           : '#8A2BE2',
    'brown'                : '#A52A2A',
    'burlywood'            : '#DEB887',
    'cadetblue'            : '#5F9EA0',
    'chartreuse'           : '#7FFF00',
    'chocolate'            : '#D2691E',
    'coral'                : '#FF7F50',
    'cornflowerblue'       : '#6495ED',
    'cornsilk'             : '#FFF8DC',
    'crimson'              : '#DC143C',
    'cyan'                 : '#00FFFF',
    'darkblue'             : '#00008B',
    'darkcyan'             : '#008B8B',
    'darkgoldenrod'        : '#B8860B',
    'darkgray'             : '#A9A9A9',
    'darkgreen'            : '#006400',
    'darkkhaki'            : '#BDB76B',
    'darkmagenta'          : '#8B008B',
    'darkolivegreen'       : '#556B2F',
    'darkorange'           : '#FF8C00',
    'darkorchid'           : '#9932CC',
    'darkred'              : '#8B0000',
    'darksalmon'           : '#E9967A',
    'darkseagreen'         : '#8FBC8F',
    'darkslateblue'        : '#483D8B',
    'darkslategray'        : '#2F4F4F',
    'darkturquoise'        : '#00CED1',
    'darkviolet'           : '#9400D3',
    'deeppink'             : '#FF1493',
    'deepskyblue'          : '#00BFFF',
    'dimgray'              : '#696969',
    'dodgerblue'           : '#1E90FF',
    'firebrick'            : '#B22222',
    'floralwhite'          : '#FFFAF0',
    'forestgreen'          : '#228B22',
    'fuchsia'              : '#FF00FF',
    'gainsboro'            : '#DCDCDC',
    'ghostwhite'           : '#F8F8FF',
    'gold'                 : '#FFD700',
    'goldenrod'            : '#DAA520',
    'gray'                 : '#808080',
    'green'                : '#008000',
    'greenyellow'          : '#ADFF2F',
    'honeydew'             : '#F0FFF0',
    'hotpink'              : '#FF69B4',
    'indianred'            : '#CD5C5C',
    'indigo'               : '#4B0082',
    'ivory'                : '#FFFFF0',
    'khaki'                : '#F0E68C',
    'lavender'             : '#E6E6FA',
    'lavenderblush'        : '#FFF0F5',
    'lawngreen'            : '#7CFC00',
    'lemonchiffon'         : '#FFFACD',
    'lightblue'            : '#ADD8E6',
    'lightcoral'           : '#F08080',
    'lightcyan'            : '#E0FFFF',
    'lightgoldenrodyellow' : '#FAFAD2',
    'lightgreen'           : '#90EE90',
    'lightgrey'            : '#D3D3D3',
    'lightpink'            : '#FFB6C1',
    'lightsalmon'          : '#FFA07A',
    'lightseagreen'        : '#20B2AA',
    'lightskyblue'         : '#87CEFA',
    'lightslategray'       : '#778899',
    'lightsteelblue'       : '#B0C4DE',
    'lightyellow'          : '#FFFFE0',
    'lime'                 : '#00FF00',
    'limegreen'            : '#32CD32',
    'linen'                : '#FAF0E6',
    'magenta'              : '#FF00FF',
    'maroon'               : '#800000',
    'mediumaquamarine'     : '#66CDAA',
    'mediumblue'           : '#0000CD',
    'mediumorchid'         : '#BA55D3',
    'mediumpurple'         : '#9370DB',
    'mediumseagreen'       : '#3CB371',
    'mediumslateblue'      : '#7B68EE',
    'mediumspringgreen'    : '#00FA9A',
    'mediumturquoise'      : '#48D1CC',
    'mediumvioletred'      : '#C71585',
    'midnightblue'         : '#191970',
    'mintcream'            : '#F5FFFA',
    'mistyrose'            : '#FFE4E1',
    'moccasin'             : '#FFE4B5',
    'navajowhite'          : '#FFDEAD',
    'navy'                 : '#000080',
    'oldlace'              : '#FDF5E6',
    'olive'                : '#808000',
    'olivedrab'            : '#6B8E23',
    'orange'               : '#FFA500',
    'orangered'            : '#FF4500',
    'orchid'               : '#DA70D6',
    'palegoldenrod'        : '#EEE8AA',
    'palegreen'            : '#98FB98',
    'palevioletred'        : '#AFEEEE',
    'papayawhip'           : '#FFEFD5',
    'peachpuff'            : '#FFDAB9',
    'peru'                 : '#CD853F',
    'pink'                 : '#FFC0CB',
    'plum'                 : '#DDA0DD',
    'powderblue'           : '#B0E0E6',
    'purple'               : '#800080',
    'red'                  : '#FF0000',
    'rosybrown'            : '#BC8F8F',
    'royalblue'            : '#4169E1',
    'saddlebrown'          : '#8B4513',
    'salmon'               : '#FA8072',
    'sandybrown'           : '#FAA460',
    'seagreen'             : '#2E8B57',
    'seashell'             : '#FFF5EE',
    'sienna'               : '#A0522D',
    'silver'               : '#C0C0C0',
    'skyblue'              : '#87CEEB',
    'slateblue'            : '#6A5ACD',
    'slategray'            : '#708090',
    'snow'                 : '#FFFAFA',
    'springgreen'          : '#00FF7F',
    'steelblue'            : '#4682B4',
    'tan'                  : '#D2B48C',
    'teal'                 : '#008080',
    'thistle'              : '#D8BFD8',
    'tomato'               : '#FF6347',
    'turquoise'            : '#40E0D0',
    'violet'               : '#EE82EE',
    'wheat'                : '#F5DEB3',
    'white'                : '#FFFFFF',
    'whitesmoke'           : '#F5F5F5',
    'yellow'               : '#FFFF00',
    'yellowgreen'          : '#9ACD32',
    }

# add british equivs
for k, v in cnames.items():
    if k.find('gray')>=0:
        k = k.replace('gray', 'grey')
        cnames[k] = v

def is_color_like(c):
    'Return *True* if *c* can be converted to *RGB*'
    try:
        colorConverter.to_rgb(c)
        return True
    except ValueError:
        return False

def rgb2hex(rgb):
    'Given a len 3 rgb tuple of 0-1 floats, return the hex string'
    return '#{0:02x}{1:02x}{2:02x}'.format(*tuple([round(val*255) for val in rgb]))

hexColorPattern = re.compile("\A#[a-fA-F0-9]{6}\Z")

def hex2color(s):
    """
    Take a hex string *s* and return the corresponding rgb 3-tuple
    Example: #efefef -> (0.93725, 0.93725, 0.93725)
    """
    if not isinstance(s, basestring):
        raise TypeError('hex2color requires a string argument')
    if hexColorPattern.match(s) is None:
        raise ValueError('invalid hex color string "{0!s}"'.format(s))
    return tuple([int(n, 16)/255.0 for n in (s[1:3], s[3:5], s[5:7])])

class ColorConverter:
    """
    Provides methods for converting color specifications to *RGB* or *RGBA*

    Caching is used for more efficient conversion upon repeated calls
    with the same argument.

    Ordinarily only the single instance instantiated in this module,
    *colorConverter*, is needed.
    """
    colors = {
        'b' : (0.0, 0.0, 1.0),
        'g' : (0.0, 0.5, 0.0),
        'r' : (1.0, 0.0, 0.0),
        'c' : (0.0, 0.75, 0.75),
        'm' : (0.75, 0, 0.75),
        'y' : (0.75, 0.75, 0),
        'k' : (0.0, 0.0, 0.0),
        'w' : (1.0, 1.0, 1.0),
        }

    cache = {}
    def to_rgb(self, arg):
        """
        Returns an *RGB* tuple of three floats from 0-1.

        *arg* can be an *RGB* or *RGBA* sequence or a string in any of
        several forms:

            1) a letter from the set 'rgbcmykw'
            2) a hex color string, like '#00FFFF'
            3) a standard name, like 'aqua'
            4) a float, like '0.4', indicating gray on a 0-1 scale

        if *arg* is *RGBA*, the *A* will simply be discarded.
        """
        try: return self.cache[arg]
        except KeyError: pass
        except TypeError: # could be unhashable rgb seq
            arg = tuple(arg)
            try: return self.cache[arg]
            except KeyError: pass
            except TypeError:
                raise ValueError(
                      'to_rgb: arg "{0!s}" is unhashable even inside a tuple'.format(str(arg)))

        try:
            if cbook.is_string_like(arg):
                color = self.colors.get(arg, None)
                if color is None:
                    str1 = cnames.get(arg, arg)
                    if str1.startswith('#'):
                        color = hex2color(str1)
                    else:
                        fl = float(arg)
                        if fl < 0 or fl > 1:
                            raise ValueError(
                                   'gray (string) must be in range 0-1')
                        color = tuple([fl]*3)
            elif cbook.iterable(arg):
                if len(arg) > 4 or len(arg) < 3:
                    raise ValueError(
                           'sequence length is {0:d}; must be 3 or 4'.format(len(arg)))
                color = tuple(arg[:3])
                if [x for x in color if (float(x) < 0) or  (x > 1)]:
                    # This will raise TypeError if x is not a number.
                    raise ValueError('number in rbg sequence outside 0-1 range')
            else:
                raise ValueError('cannot convert argument to rgb sequence')

            self.cache[arg] = color

        except (KeyError, ValueError, TypeError), exc:
            raise ValueError('to_rgb: Invalid rgb arg "{0!s}"\n{1!s}'.format(str(arg), exc))
            # Error messages could be improved by handling TypeError
            # separately; but this should be rare and not too hard
            # for the user to figure out as-is.
        return color

    def to_rgba(self, arg, alpha=None):
        """
        Returns an *RGBA* tuple of four floats from 0-1.

        For acceptable values of *arg*, see :meth:`to_rgb`.
        If *arg* is an *RGBA* sequence and *alpha* is not *None*,
        *alpha* will replace the original *A*.
        """
        try:
            if not cbook.is_string_like(arg) and cbook.iterable(arg):
                if len(arg) == 4:
                    if [x for x in arg if (float(x) < 0) or  (x > 1)]:
                        # This will raise TypeError if x is not a number.
                        raise ValueError('number in rbga sequence outside 0-1 range')
                    if alpha is None:
                        return tuple(arg)
                    if alpha < 0.0 or alpha > 1.0:
                        raise ValueError("alpha must be in range 0-1")
                    return arg[0], arg[1], arg[2], arg[3] * alpha
                r,g,b = arg[:3]
                if [x for x in (r,g,b) if (float(x) < 0) or  (x > 1)]:
                    raise ValueError('number in rbg sequence outside 0-1 range')
            else:
                r,g,b = self.to_rgb(arg)
            if alpha is None:
                alpha = 1.0
            return r,g,b,alpha
        except (TypeError, ValueError), exc:
            raise ValueError('to_rgba: Invalid rgba arg "{0!s}"\n{1!s}'.format(str(arg), exc))

    def to_rgba_array(self, c, alpha=None):
        """
        Returns a numpy array of *RGBA* tuples.

        Accepts a single mpl color spec or a sequence of specs.

        Special case to handle "no color": if *c* is "none" (case-insensitive),
        then an empty array will be returned.  Same for an empty list.
        """
        try:
            if c.lower() == 'none':
                return np.zeros((0,4), dtype=np.float_)
        except AttributeError:
            pass
        if len(c) == 0:
            return np.zeros((0,4), dtype=np.float_)
        try:
            result = np.array([self.to_rgba(c, alpha)], dtype=np.float_)
        except ValueError:
            if isinstance(c, np.ndarray):
                if c.ndim != 2 and c.dtype.kind not in 'SU':
                    raise ValueError("Color array must be two-dimensional")

            result = np.zeros((len(c), 4))
            for i, cc in enumerate(c):
                result[i] = self.to_rgba(cc, alpha)  # change in place
        return np.asarray(result, np.float_)

colorConverter = ColorConverter()

def makeMappingArray(N, data):
    """Create an *N* -element 1-d lookup table

    *data* represented by a list of x,y0,y1 mapping correspondences.
    Each element in this list represents how a value between 0 and 1
    (inclusive) represented by x is mapped to a corresponding value
    between 0 and 1 (inclusive). The two values of y are to allow
    for discontinuous mapping functions (say as might be found in a
    sawtooth) where y0 represents the value of y for values of x
    <= to that given, and y1 is the value to be used for x > than
    that given). The list must start with x=0, end with x=1, and
    all values of x must be in increasing order. Values between
    the given mapping points are determined by simple linear interpolation.

    The function returns an array "result" where ``result[x*(N-1)]``
    gives the closest value for values of x between 0 and 1.
    """
    try:
        adata = np.array(data)
    except:
        raise TypeError("data must be convertable to an array")
    shape = adata.shape
    if len(shape) != 2 and shape[1] != 3:
        raise ValueError("data must be nx3 format")

    x  = adata[:,0]
    y0 = adata[:,1]
    y1 = adata[:,2]

    if x[0] != 0. or x[-1] != 1.0:
        raise ValueError(
           "data mapping points must start with x=0. and end with x=1")
    if np.sometrue(np.sort(x)-x):
        raise ValueError(
           "data mapping points must have x in increasing order")
    # begin generation of lookup table
    x = x * (N-1)
    lut = np.zeros((N,), np.float)
    xind = np.arange(float(N))
    ind = np.searchsorted(x, xind)[1:-1]

    lut[1:-1] = ( ((xind[1:-1] - x[ind-1]) / (x[ind] - x[ind-1]))
                  * (y0[ind] - y1[ind-1]) + y1[ind-1])
    lut[0] = y1[0]
    lut[-1] = y0[-1]
    # ensure that the lut is confined to values between 0 and 1 by clipping it
    np.clip(lut, 0.0, 1.0)
    #lut = where(lut > 1., 1., lut)
    #lut = where(lut < 0., 0., lut)
    return lut

class Colormap:
    """Base class for all scalar to rgb mappings

        Important methods:

            * :meth:`set_bad`
            * :meth:`set_under`
            * :meth:`set_over`
    """
    def __init__(self, name, N=256):
        """
        Public class attributes:
            :attr:`N` : number of rgb quantization levels
            :attr:`name` : name of colormap

        """
        self.name = name
        self.N = N
        self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything.
        self._rgba_under = None
        self._rgba_over = None
        self._i_under = N
        self._i_over = N+1
        self._i_bad = N+2
        self._isinit = False

    def __call__(self, X, alpha=1.0, bytes=False):
        """
        *X* is either a scalar or an array (of any dimension).
        If scalar, a tuple of rgba values is returned, otherwise
        an array with the new shape = oldshape+(4,). If the X-values
        are integers, then they are used as indices into the array.
        If they are floating point, then they must be in the
        interval (0.0, 1.0).
        Alpha must be a scalar.
        If bytes is False, the rgba values will be floats on a
        0-1 scale; if True, they will be uint8, 0-255.
        """

        if not self._isinit: self._init()
        alpha = min(alpha, 1.0) # alpha must be between 0 and 1
        alpha = max(alpha, 0.0)
        self._lut[:-3, -1] = alpha
        mask_bad = None
        if not cbook.iterable(X):
            vtype = 'scalar'
            xa = np.array([X])
        else:
            vtype = 'array'
            xma = ma.asarray(X)
            xa = xma.filled(0)
            mask_bad = ma.getmask(xma)
        if xa.dtype.char in np.typecodes['Float']:
            np.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
            # The following clip is fast, and prevents possible
            # conversion of large positive values to negative integers.

            if NP_CLIP_OUT:
                np.clip(xa * self.N, -1, self.N, out=xa)
            else:
                xa = np.clip(xa * self.N, -1, self.N)
            xa = xa.astype(int)
        # Set the over-range indices before the under-range;
        # otherwise the under-range values get converted to over-range.
        np.putmask(xa, xa>self.N-1, self._i_over)
        np.putmask(xa, xa<0, self._i_under)
        if mask_bad is not None and mask_bad.shape == xa.shape:
            np.putmask(xa, mask_bad, self._i_bad)
        if bytes:
            lut = (self._lut * 255).astype(np.uint8)
        else:
            lut = self._lut
        rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
        lut.take(xa, axis=0, mode='clip', out=rgba)
                    #  twice as fast as lut[xa];
                    #  using the clip or wrap mode and providing an
                    #  output array speeds it up a little more.
        if vtype == 'scalar':
            rgba = tuple(rgba[0,:])
        return rgba

    def set_bad(self, color = 'k', alpha = 1.0):
        '''Set color to be used for masked values.
        '''
        self._rgba_bad = colorConverter.to_rgba(color, alpha)
        if self._isinit: self._set_extremes()

    def set_under(self, color = 'k', alpha = 1.0):
        '''Set color to be used for low out-of-range values.
           Requires norm.clip = False
        '''
        self._rgba_under = colorConverter.to_rgba(color, alpha)
        if self._isinit: self._set_extremes()

    def set_over(self, color = 'k', alpha = 1.0):
        '''Set color to be used for high out-of-range values.
           Requires norm.clip = False
        '''
        self._rgba_over = colorConverter.to_rgba(color, alpha)
        if self._isinit: self._set_extremes()

    def _set_extremes(self):
        if self._rgba_under:
            self._lut[self._i_under] = self._rgba_under
        else:
            self._lut[self._i_under] = self._lut[0]
        if self._rgba_over:
            self._lut[self._i_over] = self._rgba_over
        else:
            self._lut[self._i_over] = self._lut[self.N-1]
        self._lut[self._i_bad] = self._rgba_bad

    def _init():
        '''Generate the lookup table, self._lut'''
        raise NotImplementedError("Abstract class only")

    def is_gray(self):
        if not self._isinit: self._init()
        return (np.alltrue(self._lut[:,0] == self._lut[:,1])
                    and np.alltrue(self._lut[:,0] == self._lut[:,2]))

class LinearSegmentedColormap(Colormap):
    """Colormap objects based on lookup tables using linear segments.

    The lookup table is generated using linear interpolation for each
    primary color, with the 0-1 domain divided into any number of
    segments.
    """
    def __init__(self, name, segmentdata, N=256):
        """Create color map from linear mapping segments

        segmentdata argument is a dictionary with a red, green and blue
        entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
        forming rows in a table.

        Example: suppose you want red to increase from 0 to 1 over
        the bottom half, green to do the same over the middle half,
        and blue over the top half.  Then you would use::

            cdict = {'red':   [(0.0,  0.0, 0.0),
                               (0.5,  1.0, 1.0),
                               (1.0,  1.0, 1.0)],

                     'green': [(0.0,  0.0, 0.0),
                               (0.25, 0.0, 0.0),
                               (0.75, 1.0, 1.0),
                               (1.0,  1.0, 1.0)],

                     'blue':  [(0.0,  0.0, 0.0),
                               (0.5,  0.0, 0.0),
                               (1.0,  1.0, 1.0)]}

        Each row in the table for a given color is a sequence of
        *x*, *y0*, *y1* tuples.  In each sequence, *x* must increase
        monotonically from 0 to 1.  For any input value *z* falling
        between *x[i]* and *x[i+1]*, the output value of a given color
        will be linearly interpolated between *y1[i]* and *y0[i+1]*::

            row i:   x  y0  y1
                           /
                          /
            row i+1: x  y0  y1

        Hence y0 in the first row and y1 in the last row are never used.

        .. seealso::
            :func:`makeMappingArray`
        """
        self.monochrome = False  # True only if all colors in map are identical;
                                 # needed for contouring.
        Colormap.__init__(self, name, N)
        self._segmentdata = segmentdata

    def _init(self):
        self._lut = np.ones((self.N + 3, 4), np.float)
        self._lut[:-3, 0] = makeMappingArray(self.N, self._segmentdata['red'])
        self._lut[:-3, 1] = makeMappingArray(self.N, self._segmentdata['green'])
        self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue'])
        self._isinit = True
        self._set_extremes()

class ListedColormap(Colormap):
    """Colormap object generated from a list of colors.

    This may be most useful when indexing directly into a colormap,
    but it can also be used to generate special colormaps for ordinary
    mapping.
    """
    def __init__(self, colors, name = 'from_list', N = None):
        """
        Make a colormap from a list of colors.

        *colors*
            a list of matplotlib color specifications,
            or an equivalent Nx3 floating point array (*N* rgb values)
        *name*
            a string to identify the colormap
        *N*
            the number of entries in the map.  The default is *None*,
            in which case there is one colormap entry for each
            element in the list of colors.  If::

                N < len(colors)

            the list will be truncated at *N*.  If::

                N > len(colors)

            the list will be extended by repetition.
        """
        self.colors = colors
        self.monochrome = False  # True only if all colors in map are identical;
                                 # needed for contouring.
        if N is None:
            N = len(self.colors)
        else:
            if cbook.is_string_like(self.colors):
                self.colors = [self.colors] * N
                self.monochrome = True
            elif cbook.iterable(self.colors):
                self.colors = list(self.colors) # in case it was a tuple
                if len(self.colors) == 1:
                    self.monochrome = True
                if len(self.colors) < N:
                    self.colors = list(self.colors) * N
                del(self.colors[N:])
            else:
                try: gray = float(self.colors)
                except TypeError: pass
                else:  self.colors = [gray] * N
                self.monochrome = True
        Colormap.__init__(self, name, N)

    def _init(self):
        rgb = np.array([colorConverter.to_rgb(c)
                    for c in self.colors], np.float)
        self._lut = np.zeros((self.N + 3, 4), np.float)
        self._lut[:-3, :-1] = rgb
        self._lut[:-3, -1] = 1
        self._isinit = True
        self._set_extremes()

class Normalize:
    """
    Normalize a given value to the 0-1 range
    """
    def __init__(self, vmin=None, vmax=None, clip=False):
        """
        If *vmin* or *vmax* is not given, they are taken from the input's
        minimum and maximum value respectively.  If *clip* is *True* and
        the given value falls outside the range, the returned value
        will be 0 or 1, whichever is closer. Returns 0 if::

            vmin==vmax

        Works with scalars or arrays, including masked arrays.  If
        *clip* is *True*, masked values are set to 1; otherwise they
        remain masked.  Clipping silently defeats the purpose of setting
        the over, under, and masked colors in the colormap, so it is
        likely to lead to surprises; therefore the default is
        *clip* = *False*.
        """
        self.vmin = vmin
        self.vmax = vmax
        self.clip = clip

    def __call__(self, value, clip=None):
        if clip is None:
            clip = self.clip

        if cbook.iterable(value):
            vtype = 'array'
            val = ma.asarray(value).astype(np.float)
        else:
            vtype = 'scalar'
            val = ma.array([value]).astype(np.float)

        self.autoscale_None(val)
        vmin, vmax = self.vmin, self.vmax
        if vmin > vmax:
            raise ValueError("minvalue must be less than or equal to maxvalue")
        elif vmin==vmax:
            return 0.0 * val
        else:
            if clip:
                mask = ma.getmask(val)
                val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
                                mask=mask)
            result = (val-vmin) * (1.0/(vmax-vmin))
        if vtype == 'scalar':
            result = result[0]
        return result

    def inverse(self, value):
        if not self.scaled():
            raise ValueError("Not invertible until scaled")
        vmin, vmax = self.vmin, self.vmax

        if cbook.iterable(value):
            val = ma.asarray(value)
            return vmin + val * (vmax - vmin)
        else:
            return vmin + value * (vmax - vmin)

    def autoscale(self, A):
        '''
        Set *vmin*, *vmax* to min, max of *A*.
        '''
        self.vmin = ma.minimum(A)
        self.vmax = ma.maximum(A)

    def autoscale_None(self, A):
        ' autoscale only None-valued vmin or vmax'
        if self.vmin is None: self.vmin = ma.minimum(A)
        if self.vmax is None: self.vmax = ma.maximum(A)

    def scaled(self):
        'return true if vmin and vmax set'
        return (self.vmin is not None and self.vmax is not None)

class LogNorm(Normalize):
    """
    Normalize a given value to the 0-1 range on a log scale
    """
    def __call__(self, value, clip=None):
        if clip is None:
            clip = self.clip

        if cbook.iterable(value):
            vtype = 'array'
            val = ma.asarray(value).astype(np.float)
        else:
            vtype = 'scalar'
            val = ma.array([value]).astype(np.float)

        self.autoscale_None(val)
        vmin, vmax = self.vmin, self.vmax
        if vmin > vmax:
            raise ValueError("minvalue must be less than or equal to maxvalue")
        elif vmin<=0:
            raise ValueError("values must all be positive")
        elif vmin==vmax:
            return 0.0 * val
        else:
            if clip:
                mask = ma.getmask(val)
                val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
                                mask=mask)
            result = (ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
        if vtype == 'scalar':
            result = result[0]
        return result

    def inverse(self, value):
        if not self.scaled():
            raise ValueError("Not invertible until scaled")
        vmin, vmax = self.vmin, self.vmax

        if cbook.iterable(value):
            val = ma.asarray(value)
            return vmin * ma.power((vmax/vmin), val)
        else:
            return vmin * pow((vmax/vmin), value)

class BoundaryNorm(Normalize):
    '''
    Generate a colormap index based on discrete intervals.

    Unlike :class:`Normalize` or :class:`LogNorm`,
    :class:`BoundaryNorm` maps values to integers instead of to the
    interval 0-1.

    Mapping to the 0-1 interval could have been done via
    piece-wise linear interpolation, but using integers seems
    simpler, and reduces the number of conversions back and forth
    between integer and floating point.
    '''
    def __init__(self, boundaries, ncolors, clip=False):
        '''
        *boundaries*
            a monotonically increasing sequence
        *ncolors*
            number of colors in the colormap to be used

        If::

            b[i] <= v < b[i+1]

        then v is mapped to color j;
        as i varies from 0 to len(boundaries)-2,
        j goes from 0 to ncolors-1.

        Out-of-range values are mapped to -1 if low and ncolors
        if high; these are converted to valid indices by
        :meth:`Colormap.__call__` .
        '''
        self.clip = clip
        self.vmin = boundaries[0]
        self.vmax = boundaries[-1]
        self.boundaries = np.asarray(boundaries)
        self.N = len(self.boundaries)
        self.Ncmap = ncolors
        if self.N-1 == self.Ncmap:
            self._interp = False
        else:
            self._interp = True

    def __call__(self, x, clip=None):
        if clip is None:
            clip = self.clip
        x = ma.asarray(x)
        mask = ma.getmaskarray(x)
        xx = x.filled(self.vmax+1)
        if clip:
            np.clip(xx, self.vmin, self.vmax)
        iret = np.zeros(x.shape, dtype=np.int16)
        for i, b in enumerate(self.boundaries):
            iret[xx>=b] = i
        if self._interp:
            iret = (iret * (float(self.Ncmap-1)/(self.N-2))).astype(np.int16)
        iret[xx<self.vmin] = -1
        iret[xx>=self.vmax] = self.Ncmap
        ret = ma.array(iret, mask=mask)
        if ret.shape == () and not mask:
            ret = int(ret)  # assume python scalar
        return ret

    def inverse(self, value):
        return ValueError("BoundaryNorm is not invertible")

class NoNorm(Normalize):
    '''
    Dummy replacement for Normalize, for the case where we
    want to use indices directly in a
    :class:`~matplotlib.cm.ScalarMappable` .
    '''
    def __call__(self, value, clip=None):
        return value

    def inverse(self, value):
        return value

# compatibility with earlier class names that violated convention:
normalize = Normalize
no_norm = NoNorm


import dragonfly
import dragonfly.pandahive
import bee
from bee import connect

import math, functools

from panda3d.core import NodePath

import dragonfly.scene.unbound, dragonfly.scene.bound
import dragonfly.std
import dragonfly.io
import dragonfly.canvas

import Spyder

# ## random matrix generator
from random import random

def random_matrix_generator():
    while 1:
        a = Spyder.AxisSystem()
        a.rotateZ(360 * random())
        a.origin = Spyder.Coordinate(15 * random() - 7.5, 15 * random() - 7.5, 0)
        yield dragonfly.scene.matrix(a, "AxisSystem")

def id_generator():
    n = 0
    while 1:
        n += 1
        yield "spawnedpanda" + str(n)

from dragonfly.canvas import box2d
from bee.mstr import mstr

class parameters: pass

class myscene(dragonfly.pandahive.spyderframe):
    a = Spyder.AxisSystem()
    a *= 0.25
    a.origin += (-8, 42, 0)
    env = Spyder.Model3D("models/environment", "egg", a)

    a = Spyder.AxisSystem()
    a *= 0.005
    mypanda = Spyder.Actor3D("models/panda-model", "egg", [("walk", "models/panda-walk4", "egg")], a,
                             entityname="mypanda")

    a = Spyder.AxisSystem()
    a *= 0.005
    pandaclass = Spyder.ActorClass3D("models/panda-model", "egg", [("walk", "models/panda-walk4", "egg")], a,
                                     actorclassname="pandaclass")

    box = Spyder.Box2D(50, 470, 96, 96)
    icon = Spyder.Icon("pandaicon.png", "pandaicon", box, transparency=True)

    camcenter = Spyder.Entity3D(
        "camcenter",
        (
            Spyder.NewMaterial("white", color=(255, 255, 255)),
            Spyder.Block3D((1, 1, 1), material="white"),
        )
    )

    del a, box

class pandawalkhive(bee.inithive):
    animation = dragonfly.scene.bound.animation()
    walk = dragonfly.std.variable("str")("walk")
    connect(walk, animation.animation_name)

    key_w = dragonfly.io.keyboardsensor_trigger("W")
    connect(key_w, animation.loop)
    key_s = dragonfly.io.keyboardsensor_trigger("S")
    connect(key_s, animation.stop)

    setPos = dragonfly.scene.bound.setPos()
    setHpr = dragonfly.scene.bound.setHpr()

    interval = dragonfly.time.interval_time(18)
    connect(key_w, interval.start)
    connect(key_s, interval.pause)
    sequence = dragonfly.time.sequence(4)(8, 1, 8, 1)
    connect(interval.value, sequence.inp)

    ip1 = dragonfly.time.interpolation("Coordinate")((0, 0, 0), (0, -10, 0))
    connect(sequence.outp1, ip1)
    connect(ip1, setPos)
    connect(key_w, ip1.start)
    connect(key_s, ip1.stop)

    ip2 = dragonfly.time.interpolation("Coordinate")((0, 0, 0), (180, 0, 0))
    connect(sequence.outp2, ip2)
    connect(ip2, setHpr)
    connect(key_w, ip2.start)
    connect(key_s, ip2.stop)

    ip3 = dragonfly.time.interpolation("Coordinate")((0, -10, 0), (0, 0, 0))
    connect(sequence.outp3, ip3)
    connect(ip3, setPos)
    connect(key_w, ip3.start)
    connect(key_s, ip3.stop)

    ip4 = dragonfly.time.interpolation("Coordinate")((180, 0, 0), (0, 0, 0))
    connect(sequence.outp4, ip4)
    connect(ip4, setHpr)
    connect(key_w, ip4.start)
    connect(key_s, ip4.stop)

    connect(ip4.reach_end, interval.start)

from bee.staticbind import staticbind_baseclass

class pandawalkbind(staticbind_baseclass,
                    dragonfly.event.bind,
                    dragonfly.io.bind,
                    dragonfly.sys.bind,
                    dragonfly.scene.bind,
                    dragonfly.time.bind):
    hive = pandawalkhive

class camerabindhive(bee.inithive):
    interval = dragonfly.time.interval_time(30)
    sequence = dragonfly.time.sequence(2)(1, 1)
    connect(interval.value, sequence.inp)
    startsensor = dragonfly.sys.startsensor()

    ip1 = dragonfly.time.interpolation("Coordinate")((180, -20, 0), (360, -20, 0))
    ip2 = dragonfly.time.interpolation("Coordinate")((0, -20, 0), (180, -20, 0))
    connect(sequence.outp1, ip1.inp)
    connect(sequence.outp2, ip2.inp)

    connect(startsensor, interval.start)
    connect(startsensor, ip1.start)

    connect(ip1.reach_end, ip1.stop)
    connect(ip1.reach_end, ip2.start)
    connect(ip2.reach_end, ip2.stop)
    connect(ip2.reach_end, ip1.start)

    connect(ip2.reach_end, interval.start)

    sethpr = dragonfly.scene.bound.setHpr()
    connect(ip1, sethpr)
    connect(ip2, sethpr)

class camerabind(staticbind_baseclass,
                 dragonfly.event.bind,
                 dragonfly.io.bind,
                 dragonfly.sys.bind,
                 dragonfly.scene.bind,
                 dragonfly.time.bind):
    hive = camerabindhive

class myhive(dragonfly.pandahive.pandahive):
    pandaname = "mypanda"
    pandaname_ = bee.attribute("pandaname")
    pandaclassname = "pandaclass"
    pandaclassname_ = bee.attribute("pandaclassname")

    canvas = dragonfly.pandahive.pandacanvas()
    mousearea = dragonfly.canvas.mousearea()
    raiser = bee.raiser()
    connect("evexc", raiser)

    z_pandawalk = pandawalkbind().worker()
    pandaid = dragonfly.std.variable("id")(pandaname_)
    connect(pandaid, z_pandawalk.bindname)

    camerabind = camerabind().worker()
    camcenter = dragonfly.std.variable("id")("camcenter")
    connect(camcenter, camerabind.bindname)

    startsensor = dragonfly.sys.startsensor()
    cam = dragonfly.scene.get_camera()
    camparent = dragonfly.scene.unbound.parent()
    connect(cam, camparent.entityname)
    connect(camcenter, camparent.entityparentname)
    connect(startsensor, camparent)
    cphide = dragonfly.scene.unbound.hide()
    connect(camcenter, cphide)
    connect(startsensor, cphide)

    pandaspawn = dragonfly.scene.spawn_actor()
    v_panda = dragonfly.std.variable("id")(pandaclassname_)
    connect(v_panda, pandaspawn)

    panda_id = dragonfly.std.generator("id", id_generator)()
    random_matrix = dragonfly.std.generator(("object", "matrix"), random_matrix_generator)()
    w_spawn = dragonfly.std.weaver(("id", ("object", "matrix")))()
    connect(panda_id, w_spawn.inp1)
    connect(random_matrix, w_spawn.inp2)

    do_spawn = dragonfly.std.transistor(("id", ("object", "matrix")))()
    connect(w_spawn, do_spawn)
    connect(do_spawn, pandaspawn.spawn_matrix)
    key_z = dragonfly.io.keyboardsensor_trigger("Z")
    connect(key_z, do_spawn)

    pandaicon_click = dragonfly.io.mouseareasensor("pandaicon")
    connect(pandaicon_click, do_spawn)

    myscene = myscene(
        scene="scene",
        canvas=canvas,
        mousearea=mousearea,
    )

    wininit = bee.init("window")
    wininit.camera.setPos(0, 45, 25)
    wininit.camera.setHpr(180, -20, 0)

main = myhive().getinstance()
main.build("main")
main.place()
main.close()
main.init()

main.run()


#!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING.  If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#

from gnuradio import gr, filter
from gnuradio import analog
from gnuradio import blocks
from gnuradio import eng_notation
from gnuradio.eng_option import eng_option
from optparse import OptionParser
import sys

try:
    import scipy
except ImportError:
    print "Error: could not import scipy (http://www.scipy.org/)"
    sys.exit(1)

try:
    import pylab
except ImportError:
    print "Error: could not import pylab (http://matplotlib.sourceforge.net/)"
    sys.exit(1)

class example_fir_filter_ccc(gr.top_block):
    def __init__(self, N, fs, bw, tw, atten, D):
        gr.top_block.__init__(self)

        self._nsamps = N
        self._fs = fs
        self._bw = bw
        self._tw = tw
        self._at = atten
        self._decim = D
        taps = filter.firdes.low_pass_2(1, self._fs, self._bw, self._tw, self._at)
        print "Num. Taps: ", len(taps)

        self.src  = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
        self.head = blocks.head(gr.sizeof_gr_complex, self._nsamps)

        self.filt0 = filter.fir_filter_ccc(self._decim, taps)

        self.vsnk_src = blocks.vector_sink_c()
        self.vsnk_out = blocks.vector_sink_c()

        self.connect(self.src, self.head, self.vsnk_src)
        self.connect(self.head, self.filt0, self.vsnk_out)

def main():
    parser = OptionParser(option_class=eng_option, conflict_handler="resolve")
    parser.add_option("-N", "--nsamples", type="int", default=10000,
                      help="Number of samples to process [default=%default]")
    parser.add_option("-s", "--samplerate", type="eng_float", default=8000,
                      help="System sample rate [default=%default]")
    parser.add_option("-B", "--bandwidth", type="eng_float", default=1000,
                      help="Filter bandwidth [default=%default]")
    parser.add_option("-T", "--transition", type="eng_float", default=100,
                      help="Transition band [default=%default]")
    parser.add_option("-A", "--attenuation", type="eng_float", default=80,
                      help="Stopband attenuation [default=%default]")
    parser.add_option("-D", "--decimation", type="int", default=1,
                      help="Decmation factor [default=%default]")
    (options, args) = parser.parse_args ()

    put = example_fir_filter_ccc(options.nsamples,
                                 options.samplerate,
                                 options.bandwidth,
                                 options.transition,
                                 options.attenuation,
                                 options.decimation)
    put.run()

    data_src = scipy.array(put.vsnk_src.data())
    data_snk = scipy.array(put.vsnk_out.data())

    # Plot the signals PSDs
    nfft = 1024
    f1 = pylab.figure(1, figsize=(12,10))
    s1 = f1.add_subplot(1,1,1)
    s1.psd(data_src, NFFT=nfft, noverlap=nfft/4,
           Fs=options.samplerate)
    s1.psd(data_snk, NFFT=nfft, noverlap=nfft/4,
           Fs=options.samplerate)

    f2 = pylab.figure(2, figsize=(12,10))
    s2 = f2.add_subplot(1,1,1)
    s2.plot(data_src)
    s2.plot(data_snk.real, 'g')

    pylab.show()

if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        pass