Revision: 7311
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7311&view=rev
Author: efiring
Date: 2009-07-31 02:48:18 +0000 (Fri, 31 Jul 2009)
Log Message:
-----------
Cache the color data to reduce startup time.
Also, cmap kwargs now accept colormap names as strings.
Modified Paths:
--------------
trunk/matplotlib/CHANGELOG
trunk/matplotlib/lib/matplotlib/_cm.py
trunk/matplotlib/lib/matplotlib/axes.py
trunk/matplotlib/lib/matplotlib/cm.py
Modified: trunk/matplotlib/CHANGELOG
===================================================================
--- trunk/matplotlib/CHANGELOG 2009-07-30 21:50:10 UTC (rev 7310)
+++ trunk/matplotlib/CHANGELOG 2009-07-31 02:48:18 UTC (rev 7311)
@@ -1,6 +1,7 @@
2009-07-30 Add set_cmap and register_cmap, and improve get_cmap,
to provide convenient handling of user-generated
- colormaps. - EF
+ colormaps. Reorganized _cm and cm modules, and added
+ caching of the color data to reduce startup time. - EF
2009-07-28 Quiver speed improved, thanks to tip by Ray Speth. -EF
Modified: trunk/matplotlib/lib/matplotlib/_cm.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/_cm.py 2009-07-30 21:50:10 UTC (rev
7310)
+++ trunk/matplotlib/lib/matplotlib/_cm.py 2009-07-31 02:48:18 UTC (rev
7311)
@@ -1,25 +1,15 @@
"""
-Color data and pre-defined cmap objects.
+Nothing here but dictionaries for generating LinearSegmentedColormaps,
+and a dictionary of these dictionaries.
-This is a helper for cm.py, originally part of that file.
-Separating the data (this file) from cm.py makes both easier
-to deal with.
-
-Objects visible in cm.py are the individual cmap objects ('autumn',
-etc.) and a dictionary, 'datad', including all of these objects.
"""
-import matplotlib as mpl
-import matplotlib.colors as colors
-LUTSIZE = mpl.rcParams['image.lut']
-
_binary_data = {
'red' : ((0., 1., 1.), (1., 0., 0.)),
'green': ((0., 1., 1.), (1., 0., 0.)),
'blue' : ((0., 1., 1.), (1., 0., 0.))
}
-
_bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))}
@@ -379,44 +369,6 @@
(1.0, 0.80, 0.80)]}
-autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE)
-bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE)
-binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE)
-cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE)
-copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE)
-flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE)
-gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE)
-hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE)
-hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE)
-jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE)
-pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE)
-prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE)
-spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE)
-summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE)
-winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE)
-spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE)
-
-
-
-datad = {
- 'autumn': _autumn_data,
- 'bone': _bone_data,
- 'binary': _binary_data,
- 'cool': _cool_data,
- 'copper': _copper_data,
- 'flag': _flag_data,
- 'gray' : _gray_data,
- 'hot': _hot_data,
- 'hsv': _hsv_data,
- 'jet' : _jet_data,
- 'pink': _pink_data,
- 'prism': _prism_data,
- 'spring': _spring_data,
- 'summer': _summer_data,
- 'winter': _winter_data,
- 'spectral': _spectral_data
- }
-
# 34 colormaps based on color specifications and designs
# developed by Cynthia Brewer (http://colorbrewer.org).
# The ColorBrewer palettes have been included under the terms
@@ -5859,48 +5811,26 @@
0.0078431377187371254, 0.0078431377187371254), (1.0,
0.0039215688593685627, 0.0039215688593685627)]}
-Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE)
-Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE)
-BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE)
-BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE)
-BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE)
-Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE)
-GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE)
-Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE)
-Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE)
-Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE)
-OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE)
-Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE)
-Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE)
-Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE)
-PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE)
-PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE)
-PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE)
-PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE)
-PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE)
-PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE)
-Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE)
-RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE)
-RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE)
-RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE)
-RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE)
-RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE)
-Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE)
-Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE)
-Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE)
-Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE)
-Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE)
-YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE)
-YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE)
-YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE)
-YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE)
-gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data,
LUTSIZE)
-gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data,
LUTSIZE)
-gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data,
LUTSIZE)
-gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data,
LUTSIZE)
-gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow',
_gist_rainbow_data, LUTSIZE)
-gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data,
LUTSIZE)
-gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data,
LUTSIZE)
+datad = {
+ 'autumn': _autumn_data,
+ 'bone': _bone_data,
+ 'binary': _binary_data,
+ 'cool': _cool_data,
+ 'copper': _copper_data,
+ 'flag': _flag_data,
+ 'gray' : _gray_data,
+ 'hot': _hot_data,
+ 'hsv': _hsv_data,
+ 'jet' : _jet_data,
+ 'pink': _pink_data,
+ 'prism': _prism_data,
+ 'spring': _spring_data,
+ 'summer': _summer_data,
+ 'winter': _winter_data,
+ 'spectral': _spectral_data
+ }
+
+
datad['Accent']=_Accent_data
datad['Blues']=_Blues_data
datad['BrBG']=_BrBG_data
@@ -5944,19 +5874,7 @@
datad['gist_stern']=_gist_stern_data
datad['gist_yarg']=_gist_yarg_data
-# reverse all the colormaps.
-# reversed colormaps have '_r' appended to the name.
-def revcmap(data):
- data_r = {}
- for key, val in data.iteritems():
- valnew = [(1.-a, b, c) for a, b, c in reversed(val)]
- data_r[key] = valnew
- return data_r
-cmapnames = datad.keys()
-for cmapname in cmapnames:
- cmapname_r = cmapname+'_r'
- cmapdat_r = revcmap(datad[cmapname])
- datad[cmapname_r] = cmapdat_r
- locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r,
cmapdat_r, LUTSIZE)
+
+
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2009-07-30 21:50:10 UTC (rev
7310)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2009-07-31 02:48:18 UTC (rev
7311)
@@ -3911,12 +3911,12 @@
*bbox_to_anchor* keyword argument. bbox_to_anchor can be an instance
of BboxBase(or its derivatives) or a tuple of 2 or 4 floats.
For example,
-
+
loc = 'upper right', bbox_to_anchor = (0.5, 0.5)
will place the legend so that the upper right corner of the legend at
the center of the axes.
-
+
The legend location can be specified in other coordinate, by using the
*bbox_transform* keyword.
@@ -3946,7 +3946,7 @@
*fancybox*: [ None | False | True ]
if True, draw a frame with a round fancybox. If None, use rc
-
+
*shadow*: [ None | False | True ]
If *True*, draw a shadow behind legend. If *None*, use rc settings.
@@ -5172,9 +5172,9 @@
arguments will be used only if *c* is an array of floats.
*cmap*: [ None | Colormap ]
- A :class:`matplotlib.colors.Colormap` instance. If *None*,
- defaults to rc ``image.cmap``. *cmap* is only used if *c*
- is an array of floats.
+ A :class:`matplotlib.colors.Colormap` instance or registered
+ name. If *None*, defaults to rc ``image.cmap``. *cmap* is
+ only used if *c* is an array of floats.
*norm*: [ None | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
@@ -5370,7 +5370,6 @@
if colors is None:
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
collection.set_array(np.asarray(c))
collection.set_cmap(cmap)
collection.set_norm(norm)
@@ -5712,7 +5711,6 @@
accum = bins.searchsorted(accum)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
collection.set_array(accum)
collection.set_cmap(cmap)
collection.set_norm(norm)
@@ -6245,7 +6243,6 @@
if not self._hold: self.cla()
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
if aspect is None: aspect = rcParams['image.aspect']
self.set_aspect(aspect)
im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,
@@ -6490,7 +6487,6 @@
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
collection.set_cmap(cmap)
collection.set_norm(norm)
if vmin is not None or vmax is not None:
@@ -6612,7 +6608,6 @@
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
collection.set_cmap(cmap)
collection.set_norm(norm)
if vmin is not None or vmax is not None:
@@ -6719,7 +6714,6 @@
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
- if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
C = args[-1]
nr, nc = C.shape
Modified: trunk/matplotlib/lib/matplotlib/cm.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/cm.py 2009-07-30 21:50:10 UTC (rev
7310)
+++ trunk/matplotlib/lib/matplotlib/cm.py 2009-07-31 02:48:18 UTC (rev
7311)
@@ -1,25 +1,68 @@
"""
This module contains the instantiations of color mapping classes
"""
+import os
+try:
+ import cPickle as pickle
+except ImportError:
+ import pickle
+
import numpy as np
from numpy import ma
import matplotlib as mpl
import matplotlib.colors as colors
import matplotlib.cbook as cbook
-from matplotlib._cm import *
-# Dictionary for user-registered colormaps:
+LUTSIZE = mpl.rcParams['image.lut']
+
+_cmcache = os.path.join(mpl.get_configdir(), 'colormaps.cache')
+
+loaded = False
+try:
+ c = open(_cmcache)
+ datad = pickle.load(c)
+ c.close()
+ mpl.verbose.report("Using colormaps from %s" % _cmcache)
+ loaded = True
+except:
+ mpl.verbose.report("Could not load colormaps from %s" % _cmcache)
+
+if not loaded:
+ from matplotlib._cm import datad
+
+ try:
+ c = open(_cmcache, 'w')
+ pickle.dump(datad, c, 2)
+ c.close()
+ mpl.verbose.report("New colormap cache in %s" % _cmcache)
+ except:
+ mpl.verbose.report("Failed to generate colormap cache")
+
cmap_d = dict()
-# Using this second dictionary allows us to handle any
-# Colormap instance; the built-in datad is only for
-# LinearSegmentedColormaps. The advantage of keeping
-# datad is that it delays the generation of the Colormap
-# instance until it is actually needed. Generating the
-# instance is fast enough, and typically done few enough
-# times, that there is no need to cache the result.
+# reverse all the colormaps.
+# reversed colormaps have '_r' appended to the name.
+def revcmap(data):
+ data_r = {}
+ for key, val in data.iteritems():
+ valnew = [(1.0-a, b, c) for a, b, c in reversed(val)]
+ data_r[key] = valnew
+ return data_r
+
+_cmapnames = datad.keys() # need this list because datad is changed in loop
+for cmapname in _cmapnames:
+ cmapname_r = cmapname+'_r'
+ cmapdat_r = revcmap(datad[cmapname])
+ datad[cmapname_r] = cmapdat_r
+ cmap_d[cmapname] = colors.LinearSegmentedColormap(
+ cmapname, datad[cmapname], LUTSIZE)
+ cmap_d[cmapname_r] = colors.LinearSegmentedColormap(
+ cmapname_r, cmapdat_r, LUTSIZE)
+
+locals().update(cmap_d)
+
def register_cmap(name=None, cmap=None, data=None, lut=None):
"""
Add a colormap to the set recognized by :func:`get_cmap`.
@@ -67,6 +110,11 @@
If *name* is a :class:`colors.Colormap` instance, it will be
returned.
+
+ If *lut* is not None it must be an integer giving the number of
+ entries desired in the lookup table, and *name* must be a
+ standard mpl colormap name with a corresponding data dictionary
+ in *datad*.
"""
if name is None:
name = mpl.rcParams['image.cmap']
@@ -75,15 +123,13 @@
return name
if name in cmap_d:
- return cmap_d[name]
+ if lut is None:
+ return cmap_d[name]
+ elif name in datad:
+ return colors.LinearSegmentedColormap(name, datad[name], lut)
+ else:
+ raise ValueError("Colormap %s is not recognized" % name)
- if name not in datad:
- raise ValueError("%s is not a known colormap name" % name)
-
- if lut is None:
- lut = mpl.rcParams['image.lut']
- return colors.LinearSegmentedColormap(name, datad[name], lut)
-
class ScalarMappable:
"""
This is a mixin class to support scalar -> RGBA mapping. Handles
@@ -105,7 +151,7 @@
self._A = None
self.norm = norm
- self.cmap = cmap
+ self.cmap = get_cmap(cmap)
self.colorbar = None
self.update_dict = {'array':False}
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