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Cache the color data to reduce startup time.
Also, cmap kwargs now accept colormap names as strings. svn path=/trunk/matplotlib/; revision=7311
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‎CHANGELOG

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
11
2009-07-30 Add set_cmap and register_cmap, and improve get_cmap,
22
to provide convenient handling of user-generated
3-
colormaps. - EF
3+
colormaps. Reorganized _cm and cm modules, and added
4+
caching of the color data to reduce startup time. - EF
45

56
2009-07-28 Quiver speed improved, thanks to tip by Ray Speth. -EF
67

‎lib/matplotlib/_cm.py

Lines changed: 26 additions & 108 deletions
Original file line numberDiff line numberDiff line change
@@ -1,25 +1,15 @@
11
"""
2-
Color data and pre-defined cmap objects.
2+
Nothing here but dictionaries for generating LinearSegmentedColormaps,
3+
and a dictionary of these dictionaries.
34
4-
This is a helper for cm.py, originally part of that file.
5-
Separating the data (this file) from cm.py makes both easier
6-
to deal with.
7-
8-
Objects visible in cm.py are the individual cmap objects ('autumn',
9-
etc.) and a dictionary, 'datad', including all of these objects.
105
"""
116

12-
import matplotlib as mpl
13-
import matplotlib.colors as colors
14-
LUTSIZE = mpl.rcParams['image.lut']
15-
167
_binary_data = {
178
'red' : ((0., 1., 1.), (1., 0., 0.)),
189
'green': ((0., 1., 1.), (1., 0., 0.)),
1910
'blue' : ((0., 1., 1.), (1., 0., 0.))
2011
}
2112

22-
2313
_bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
2414
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
2515
'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))}
@@ -379,44 +369,6 @@
379369
(1.0, 0.80, 0.80)]}
380370

381371

382-
autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE)
383-
bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE)
384-
binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE)
385-
cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE)
386-
copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE)
387-
flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE)
388-
gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE)
389-
hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE)
390-
hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE)
391-
jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE)
392-
pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE)
393-
prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE)
394-
spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE)
395-
summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE)
396-
winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE)
397-
spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE)
398-
399-
400-
401-
datad = {
402-
'autumn': _autumn_data,
403-
'bone': _bone_data,
404-
'binary': _binary_data,
405-
'cool': _cool_data,
406-
'copper': _copper_data,
407-
'flag': _flag_data,
408-
'gray' : _gray_data,
409-
'hot': _hot_data,
410-
'hsv': _hsv_data,
411-
'jet' : _jet_data,
412-
'pink': _pink_data,
413-
'prism': _prism_data,
414-
'spring': _spring_data,
415-
'summer': _summer_data,
416-
'winter': _winter_data,
417-
'spectral': _spectral_data
418-
}
419-
420372
# 34 colormaps based on color specifications and designs
421373
# developed by Cynthia Brewer (http://colorbrewer.org).
422374
# The ColorBrewer palettes have been included under the terms
@@ -5859,48 +5811,26 @@
58595811
0.0078431377187371254, 0.0078431377187371254), (1.0,
58605812
0.0039215688593685627, 0.0039215688593685627)]}
58615813

5862-
Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE)
5863-
Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE)
5864-
BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE)
5865-
BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE)
5866-
BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE)
5867-
Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE)
5868-
GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE)
5869-
Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE)
5870-
Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE)
5871-
Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE)
5872-
OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE)
5873-
Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE)
5874-
Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE)
5875-
Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE)
5876-
PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE)
5877-
PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE)
5878-
PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE)
5879-
PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE)
5880-
PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE)
5881-
PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE)
5882-
Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE)
5883-
RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE)
5884-
RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE)
5885-
RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE)
5886-
RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE)
5887-
RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE)
5888-
Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE)
5889-
Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE)
5890-
Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE)
5891-
Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE)
5892-
Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE)
5893-
YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE)
5894-
YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE)
5895-
YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE)
5896-
YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE)
5897-
gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data, LUTSIZE)
5898-
gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data, LUTSIZE)
5899-
gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data, LUTSIZE)
5900-
gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data, LUTSIZE)
5901-
gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow', _gist_rainbow_data, LUTSIZE)
5902-
gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data, LUTSIZE)
5903-
gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data, LUTSIZE)
5814+
datad = {
5815+
'autumn': _autumn_data,
5816+
'bone': _bone_data,
5817+
'binary': _binary_data,
5818+
'cool': _cool_data,
5819+
'copper': _copper_data,
5820+
'flag': _flag_data,
5821+
'gray' : _gray_data,
5822+
'hot': _hot_data,
5823+
'hsv': _hsv_data,
5824+
'jet' : _jet_data,
5825+
'pink': _pink_data,
5826+
'prism': _prism_data,
5827+
'spring': _spring_data,
5828+
'summer': _summer_data,
5829+
'winter': _winter_data,
5830+
'spectral': _spectral_data
5831+
}
5832+
5833+
59045834
datad['Accent']=_Accent_data
59055835
datad['Blues']=_Blues_data
59065836
datad['BrBG']=_BrBG_data
@@ -5944,19 +5874,7 @@
59445874
datad['gist_stern']=_gist_stern_data
59455875
datad['gist_yarg']=_gist_yarg_data
59465876

5947-
# reverse all the colormaps.
5948-
# reversed colormaps have '_r' appended to the name.
5949-
5950-
def revcmap(data):
5951-
data_r = {}
5952-
for key, val in data.iteritems():
5953-
valnew = [(1.-a, b, c) for a, b, c in reversed(val)]
5954-
data_r[key] = valnew
5955-
return data_r
5956-
5957-
cmapnames = datad.keys()
5958-
for cmapname in cmapnames:
5959-
cmapname_r = cmapname+'_r'
5960-
cmapdat_r = revcmap(datad[cmapname])
5961-
datad[cmapname_r] = cmapdat_r
5962-
locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r, cmapdat_r, LUTSIZE)
5877+
5878+
5879+
5880+

‎lib/matplotlib/axes.py

Lines changed: 6 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -3911,12 +3911,12 @@ def legend(self, *args, **kwargs):
39113911
*bbox_to_anchor* keyword argument. bbox_to_anchor can be an instance
39123912
of BboxBase(or its derivatives) or a tuple of 2 or 4 floats.
39133913
For example,
3914-
3914+
39153915
loc = 'upper right', bbox_to_anchor = (0.5, 0.5)
39163916
39173917
will place the legend so that the upper right corner of the legend at
39183918
the center of the axes.
3919-
3919+
39203920
The legend location can be specified in other coordinate, by using the
39213921
*bbox_transform* keyword.
39223922
@@ -3946,7 +3946,7 @@ def legend(self, *args, **kwargs):
39463946
39473947
*fancybox*: [ None | False | True ]
39483948
if True, draw a frame with a round fancybox. If None, use rc
3949-
3949+
39503950
*shadow*: [ None | False | True ]
39513951
If *True*, draw a shadow behind legend. If *None*, use rc settings.
39523952
@@ -5172,9 +5172,9 @@ def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
51725172
arguments will be used only if *c* is an array of floats.
51735173
51745174
*cmap*: [ None | Colormap ]
5175-
A :class:`matplotlib.colors.Colormap` instance. If *None*,
5176-
defaults to rc ``image.cmap``. *cmap* is only used if *c*
5177-
is an array of floats.
5175+
A :class:`matplotlib.colors.Colormap` instance or registered
5176+
name. If *None*, defaults to rc ``image.cmap``. *cmap* is
5177+
only used if *c* is an array of floats.
51785178
51795179
*norm*: [ None | Normalize ]
51805180
A :class:`matplotlib.colors.Normalize` instance is used to
@@ -5370,7 +5370,6 @@ def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
53705370

53715371
if colors is None:
53725372
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
5373-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
53745373
collection.set_array(np.asarray(c))
53755374
collection.set_cmap(cmap)
53765375
collection.set_norm(norm)
@@ -5712,7 +5711,6 @@ def hexbin(self, x, y, C = None, gridsize = 100, bins = None,
57125711
accum = bins.searchsorted(accum)
57135712

57145713
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
5715-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
57165714
collection.set_array(accum)
57175715
collection.set_cmap(cmap)
57185716
collection.set_norm(norm)
@@ -6245,7 +6243,6 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
62456243
if not self._hold: self.cla()
62466244

62476245
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
6248-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
62496246
if aspect is None: aspect = rcParams['image.aspect']
62506247
self.set_aspect(aspect)
62516248
im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,
@@ -6490,7 +6487,6 @@ def pcolor(self, *args, **kwargs):
64906487
collection.set_alpha(alpha)
64916488
collection.set_array(C)
64926489
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
6493-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
64946490
collection.set_cmap(cmap)
64956491
collection.set_norm(norm)
64966492
if vmin is not None or vmax is not None:
@@ -6612,7 +6608,6 @@ def pcolormesh(self, *args, **kwargs):
66126608
collection.set_alpha(alpha)
66136609
collection.set_array(C)
66146610
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
6615-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
66166611
collection.set_cmap(cmap)
66176612
collection.set_norm(norm)
66186613
if vmin is not None or vmax is not None:
@@ -6719,7 +6714,6 @@ def pcolorfast(self, *args, **kwargs):
67196714
vmin = kwargs.pop('vmin', None)
67206715
vmax = kwargs.pop('vmax', None)
67216716
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
6722-
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
67236717

67246718
C = args[-1]
67256719
nr, nc = C.shape

‎lib/matplotlib/cm.py

Lines changed: 64 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -1,24 +1,67 @@
11
"""
22
This module contains the instantiations of color mapping classes
33
"""
4+
import os
5+
6+
try:
7+
import cPickle as pickle
8+
except ImportError:
9+
import pickle
410

511
import numpy as np
612
from numpy import ma
713
import matplotlib as mpl
814
import matplotlib.colors as colors
915
import matplotlib.cbook as cbook
10-
from matplotlib._cm import *
1116

12-
# Dictionary for user-registered colormaps:
17+
LUTSIZE = mpl.rcParams['image.lut']
18+
19+
_cmcache = os.path.join(mpl.get_configdir(), 'colormaps.cache')
20+
21+
loaded = False
22+
try:
23+
c = open(_cmcache)
24+
datad = pickle.load(c)
25+
c.close()
26+
mpl.verbose.report("Using colormaps from %s" % _cmcache)
27+
loaded = True
28+
except:
29+
mpl.verbose.report("Could not load colormaps from %s" % _cmcache)
30+
31+
if not loaded:
32+
from matplotlib._cm import datad
33+
34+
try:
35+
c = open(_cmcache, 'w')
36+
pickle.dump(datad, c, 2)
37+
c.close()
38+
mpl.verbose.report("New colormap cache in %s" % _cmcache)
39+
except:
40+
mpl.verbose.report("Failed to generate colormap cache")
41+
1342
cmap_d = dict()
1443

15-
# Using this second dictionary allows us to handle any
16-
# Colormap instance; the built-in datad is only for
17-
# LinearSegmentedColormaps. The advantage of keeping
18-
# datad is that it delays the generation of the Colormap
19-
# instance until it is actually needed. Generating the
20-
# instance is fast enough, and typically done few enough
21-
# times, that there is no need to cache the result.
44+
# reverse all the colormaps.
45+
# reversed colormaps have '_r' appended to the name.
46+
47+
def revcmap(data):
48+
data_r = {}
49+
for key, val in data.iteritems():
50+
valnew = [(1.0-a, b, c) for a, b, c in reversed(val)]
51+
data_r[key] = valnew
52+
return data_r
53+
54+
_cmapnames = datad.keys() # need this list because datad is changed in loop
55+
for cmapname in _cmapnames:
56+
cmapname_r = cmapname+'_r'
57+
cmapdat_r = revcmap(datad[cmapname])
58+
datad[cmapname_r] = cmapdat_r
59+
cmap_d[cmapname] = colors.LinearSegmentedColormap(
60+
cmapname, datad[cmapname], LUTSIZE)
61+
cmap_d[cmapname_r] = colors.LinearSegmentedColormap(
62+
cmapname_r, cmapdat_r, LUTSIZE)
63+
64+
locals().update(cmap_d)
2265

2366
def register_cmap(name=None, cmap=None, data=None, lut=None):
2467
"""
@@ -67,6 +110,11 @@ def get_cmap(name=None, lut=None):
67110
68111
If *name* is a :class:`colors.Colormap` instance, it will be
69112
returned.
113+
114+
If *lut* is not None it must be an integer giving the number of
115+
entries desired in the lookup table, and *name* must be a
116+
standard mpl colormap name with a corresponding data dictionary
117+
in *datad*.
70118
"""
71119
if name is None:
72120
name = mpl.rcParams['image.cmap']
@@ -75,14 +123,12 @@ def get_cmap(name=None, lut=None):
75123
return name
76124

77125
if name in cmap_d:
78-
return cmap_d[name]
79-
80-
if name not in datad:
81-
raise ValueError("%s is not a known colormap name" % name)
82-
83-
if lut is None:
84-
lut = mpl.rcParams['image.lut']
85-
return colors.LinearSegmentedColormap(name, datad[name], lut)
126+
if lut is None:
127+
return cmap_d[name]
128+
elif name in datad:
129+
return colors.LinearSegmentedColormap(name, datad[name], lut)
130+
else:
131+
raise ValueError("Colormap %s is not recognized" % name)
86132

87133
class ScalarMappable:
88134
"""
@@ -105,7 +151,7 @@ def __init__(self, norm=None, cmap=None):
105151

106152
self._A = None
107153
self.norm = norm
108-
self.cmap = cmap
154+
self.cmap = get_cmap(cmap)
109155
self.colorbar = None
110156
self.update_dict = {'array':False}
111157

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