On Thursday, November 22, 2012 23:51:08 TP wrote:
> Thus it seems to me that my dummy example given in the previous post covers
> exactly the problem encountered in my real-world imshow function.
>
> Is there a memory-efficient workaround in my dummy example (instead of
> increasing N)?
I have modified LinearSegmentedColormap so as to solve my problem. The
difference is that I do not create an huge array in my test case, but instead I
interpolate linearly in the colormap. This is a quick and dirty code that
does work in my case, but which does not deal with all cases (no management of
transparency, no discontinuity in the colormap, etc.)
#####################
from __future__ import division
from pylab import *
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.collections import CircleCollection
from scipy.interpolate import interp1d
class ContinuousLinearSegmentedColormap( LinearSegmentedColormap ):
def __init__(self, name, segmentdata, gamma=1.0):
LinearSegmentedColormap.__init__( self
, name, segmentdata, gamma = gamma )
def _init(self):
self.N = len( self._segmentdata['red'] )
self._lut = np.ones((self.N, 5), np.float)
for i in range( self.N ):
self._lut[i, 0] = self._segmentdata['red'][i][0]
# 2 because I do not manage discontinuities in color
self._lut[i, 1] = self._segmentdata['red'][i][2]
self._lut[i, 2] = self._segmentdata['green'][i][2]
self._lut[i, 3] = self._segmentdata['blue'][i][2]
self._isinit = True
def __call__(self, X, alpha=None, bytes=False):
if not self._isinit: self._init()
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
xa = np.array([X])
else:
vtype = 'array'
xma = ma.array(X, copy=False)
mask_bad = xma.mask
xa = xma.data.copy() # Copy here to avoid side effects.
del xma
lut = self._lut.copy()
rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
# We construct interpolation functions.
fred = interp1d( lut[:,0], lut[:,1])
fgreen = interp1d( lut[:,0], lut[:,2])
fblue = interp1d( lut[:,0], lut[:,3])
rgba[:,3] = 1 # alpha=1 for the time being
for i in range( xa.shape[0] ):
rgba[i,0] = fred( xa[i] )
rgba[i,1] = fgreen( xa[i] )
rgba[i,2] = fblue( xa[i] )
if vtype == 'scalar':
rgba = tuple(rgba[0,:])
return rgba
ioff()
large_value = 257 # blue above this value
large_value = 258 # black above this value
large_value = 1e8
cdict = { 'blue': [(0.0, 0.0, 0.0)
, (2*1/large_value, 1, 1)
, (1.0, 1.0, 1.0)]
, 'green': [(0.0, 0.0, 0.0)
, (2*1/large_value, 0, 0)
, (1.0, 1.0, 1.0)]
, 'red': [(0.0, 0.0, 0.0)
, (2*1/large_value, 0, 0)
, (1.0, 1.0, 1.0)] }
measures= array( [[ 0.2, 0.3, 0],
[ 0.3, 0.4, 2],
[ 0.5, 0.6, large_value]] )
cmap = ContinuousLinearSegmentedColormap( "cmap foobar"
, cdict
)
fig = figure()
axes = fig.add_subplot(111)
ec = CircleCollection( [80]
, offsets = measures[:,:2]
, transOffset = axes.transData
)
ec.set_array( measures[:,2] )
ec.set_cmap( cmap )
axes.add_collection( ec )
show()
#####################
------------------------------------------------------------------------------
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