""" 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 '#%02x%02x%02x' % 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 "%s"' % 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 "%s" is unhashable even inside a tuple' % (str(arg),)) try: if cbook.is_string_like(arg): argl = arg.lower() color = self.colors.get(argl, None) if color is None: str1 = cnames.get(argl, argl) if str1.startswith('#'): color = hex2color(str1) else: fl = float(argl) 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 %d; must be 3 or 4'%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 "%s"\n%s' % (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`. In addition, if *arg* is "none" (case-insensitive), then (0,0,0,0) will be returned. If *arg* is an *RGBA* sequence and *alpha* is not *None*, *alpha* will replace the original *A*. """ try: if arg.lower() == 'none': return (0.0, 0.0, 0.0, 0.0) except AttributeError: pass 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], 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 "%s"\n%s' % (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: nc = len(c) except TypeError: raise ValueError( "Cannot convert argument type %s to rgba array" % type(c)) try: if nc == 0 or c.lower() == 'none': return np.zeros((0,4), dtype=np.float) except AttributeError: pass try: # Single value? Put it in an array with a single row. return 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") if (c.ndim == 2 and c.shape[1] == 4 and c.dtype.kind == 'f'): if (c.ravel() > 1).any() or (c.ravel() < 0).any(): raise ValueError( "number in rgba sequence is outside 0-1 range") # looks like rgba already, nothing to be done; do # we want to apply alpha here if # (c[:,3]==1).all() ? return np.asarray(c, np.float) # It must be some other sequence of color specs. result = np.zeros((nc, 4), dtype=np.float) for i, cc in enumerate(c): result[i] = self.to_rgba(cc, alpha) return result 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[:-1,-1] = alpha # Don't assign global alpha to i_bad; # it would defeat the purpose of the # default behavior, which is to not # show anything where data are missing. mask_bad = None if not cbook.iterable(X): vtype = 'scalar' xa = np.array([X]) else: vtype = 'array' # force a copy here -- the ma.array and filled functions # do force a cop of the data by default - JDH xma = ma.array(X, copy=True) 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` For information about making a mapping array. """ 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() @staticmethod def from_list(name, colors, N=256): """ Make a linear segmented colormap with *name* from a sequence of *colors* which evenly transitions from colors[0] at val=1 to colors[-1] at val=1. N is the number of rgb quantization levels. """ ncolors = len(colors) vals = np.linspace(0., 1., ncolors) cdict = dict(red=[], green=[], blue=[]) for val, color in zip(vals, colors): r,g,b = colorConverter.to_rgb(color) cdict['red'].append((val, r, r)) cdict['green'].append((val, g, g)) cdict['blue'].append((val, b, b)) return LinearSegmentedColormap(name, cdict, N) 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.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 def rgb_to_hsv(arr): """ convert rgb values in a numpy array to hsv values input and output arrays should have shape (M,N,3) """ out = np.empty_like(arr) arr_max = arr.max(-1) delta = arr.ptp(-1) s = delta / arr_max s[delta==0] = 0 # red is max idx = (arr[:,:,0] == arr_max) out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx] # green is max idx = (arr[:,:,1] == arr_max) out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0] ) / delta[idx] # blue is max idx = (arr[:,:,2] == arr_max) out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1] ) / delta[idx] out[:,:,0] = (out[:,:,0]/6.0) % 1.0 out[:,:,1] = s out[:,:,2] = arr_max return out def hsv_to_rgb(hsv): """ convert hsv values in a numpy array to rgb values both input and output arrays have shape (M,N,3) """ h = hsv[:,:,0]; s = hsv[:,:,1]; v = hsv[:,:,2] r = np.empty_like(h); g = np.empty_like(h); b = np.empty_like(h) i = (h*6.0).astype(np.int) f = (h*6.0) - i p = v*(1.0 - s) q = v*(1.0 - s*f) t = v*(1.0 - s*(1.0-f)) idx = i%6 == 0 r[idx] = v[idx]; g[idx] = t[idx]; b[idx] = p[idx] idx = i == 1 r[idx] = q[idx]; g[idx] = v[idx]; b[idx] = p[idx] idx = i == 2 r[idx] = p[idx]; g[idx] = v[idx]; b[idx] = t[idx] idx = i == 3 r[idx] = p[idx]; g[idx] = q[idx]; b[idx] = v[idx] idx = i == 4 r[idx] = t[idx]; g[idx] = p[idx]; b[idx] = v[idx] idx = i == 5 r[idx] = v[idx]; g[idx] = p[idx]; b[idx] = q[idx] idx = s == 0 r[idx] = v[idx]; g[idx] = v[idx]; b[idx] = v[idx] rgb = np.empty_like(hsv) rgb[:,:,0]=r; rgb[:,:,1]=g; rgb[:,:,2]=b return rgb class LightSource(object): """ Create a light source coming from the specified azimuth and elevation. Angles are in degrees, with the azimuth measured clockwise from north and elevation up from the zero plane of the surface. The :meth:`shade` is used to produce rgb values for a shaded relief image given a data array. """ def __init__(self,azdeg=315,altdeg=45,\ hsv_min_val=0,hsv_max_val=1,hsv_min_sat=1,hsv_max_sat=0): """ Specify the azimuth (measured clockwise from south) and altitude (measured up from the plane of the surface) of the light source in degrees. The color of the resulting image will be darkened by moving the (s,v) values (in hsv colorspace) toward (hsv_min_sat, hsv_min_val) in the shaded regions, or lightened by sliding (s,v) toward (hsv_max_sat hsv_max_val) in regions that are illuminated. The default extremes are chose so that completely shaded points are nearly black (s = 1, v = 0) and completely illuminated points are nearly white (s = 0, v = 1). """ self.azdeg = azdeg self.altdeg = altdeg self.hsv_min_val = hsv_min_val self.hsv_max_val = hsv_max_val self.hsv_min_sat = hsv_min_sat self.hsv_max_sat = hsv_max_sat def shade(self,data,cmap): """ Take the input data array, convert to HSV values in the given colormap, then adjust those color values to given the impression of a shaded relief map with a specified light source. RGBA values are returned, which can then be used to plot the shaded image with imshow. """ # imagine an artificial sun placed at infinity in # some azimuth and elevation position illuminating our surface. The parts of # the surface that slope toward the sun should brighten while those sides # facing away should become darker. # convert alt, az to radians az = self.azdeg*np.pi/180.0 alt = self.altdeg*np.pi/180.0 # gradient in x and y directions dx, dy = np.gradient(data) slope = 0.5*np.pi - np.arctan(np.hypot(dx, dy)) aspect = np.arctan2(dx, dy) intensity = np.sin(alt)*np.sin(slope) + np.cos(alt)*np.cos(slope)*np.cos(-az -\ aspect - 0.5*np.pi) # rescale to interval -1,1 # +1 means maximum sun exposure and -1 means complete shade. intensity = (intensity - intensity.min())/(intensity.max() - intensity.min()) intensity = 2.*intensity - 1. # convert to rgb, then rgb to hsv rgb = cmap((data-data.min())/(data.max()-data.min())) hsv = rgb_to_hsv(rgb[:,:,0:3]) # modify hsv values to simulate illumination. hsv[:,:,1] = np.where(np.logical_and(np.abs(hsv[:,:,1])>1.e-10,intensity>0),\ (1.-intensity)*hsv[:,:,1]+intensity*self.hsv_max_sat, hsv[:,:,1]) hsv[:,:,2] = np.where(intensity > 0, (1.-intensity)*hsv[:,:,2] +\ intensity*self.hsv_max_val, hsv[:,:,2]) hsv[:,:,1] = np.where(np.logical_and(np.abs(hsv[:,:,1])>1.e-10,intensity<0),\ (1.+intensity)*hsv[:,:,1]-intensity*self.hsv_min_sat, hsv[:,:,1]) hsv[:,:,2] = np.where(intensity < 0, (1.+intensity)*hsv[:,:,2] -\ intensity*self.hsv_min_val, hsv[:,:,2]) hsv[:,:,1:] = np.where(hsv[:,:,1:]<0.,0,hsv[:,:,1:]) hsv[:,:,1:] = np.where(hsv[:,:,1:]>1.,1,hsv[:,:,1:]) # convert modified hsv back to rgb. rgb[:,:,0:3] = hsv_to_rgb(hsv) return rgb