def get_bounds(self):
""" Returns the minimum and maximum values of the data source's data.
Implements AbstractDataSource.
"""
if not self._bounds_cache_valid:
if self.raw_value.size == 0:
self._cached_bounds = (0, 0)
else:
# nanmin and nanmax raise an annoying RuntimeWarning when
# all raw_value entries are NaN. Use fmin.reduce and
# fmax.reduce to avoid this.
self._cached_bounds = (fmin.reduce(self.raw_value, axis=None),
fmax.reduce(self.raw_value, axis=None))
self._bounds_cache_valid = True
return self._cached_bounds
def get_bounds(self):
""" Returns the minimum and maximum values of the data source's data.
Implements AbstractDataSource.
"""
if not self._bounds_cache_valid:
if self.raw_value.size == 0:
self._cached_bounds = (0, 0)
else:
# nanmin and nanmax raise an annoying RuntimeWarning when
# all raw_value entries are NaN. Use fmin.reduce and
# fmax.reduce to avoid this.
self._cached_bounds = (
fmin.reduce(self.raw_value, axis=None),
fmax.reduce(self.raw_value, axis=None))
self._bounds_cache_valid = True
return self._cached_bounds
def render(self,
todraw,
mass,
mode=None,
vmin=None,
vmax=None,
logscale=True,
logweight=True,
cmap=pygascmap,
gamma=1.0,
mmin=None,
mmax=None,
over=None,
composite=None):
""" vmin can be a string, eg '40 dB', so the vmin=vmax/1e4 """
if mode == 'intensity' or mode == 'mean':
todraw /= mass
if logscale:
if vmin is not None and not isinstance(vmin, basestring):
vmin = 10**vmin
if vmax is not None:
vmax = 10**vmax
if vmax is None:
if mode == 'intensity' or mode == 'mean' or over is None:
vmax = fmax.reduce(todraw.flat)
else:
sort = todraw.ravel().argsort()
ind = sort[(len(sort) - 1) * (1.0 - over)]
vmax = todraw.ravel()[ind]
del sort
if vmin is None:
if logscale:
vmin = ccode.pmin.reduce(todraw.flat)
else:
vmin = fmin.reduce(todraw.flat)
elif isinstance(vmin, basestring):
db = int(vmin[:-3])
vmin = vmax / 10**(db / 10.)
if logscale:
log10(todraw, todraw)
vmin = log10(vmin)
vmax = log10(vmax)
print 'vmax, vmin =', vmax, vmin
image = zeros(dtype=('u1', 4), shape=todraw.shape)
# gacolor is much faster then matplotlib's normalize and uses less memory(4times fewer).
if not hasattr(cmap, 'table'):
cmap = gacmap(cmap)
ccode.color(image,
todraw,
max=vmax,
min=vmin,
logscale=False,
colormap=cmap)
del todraw
if mode == 'intensity':
weight = mass
if logweight:
# in camera mode do not use logscale for mass.
weight.clip(ccode.pmin.reduce(weight.ravel()), inf, weight)
log10(weight, weight)
print 'weight', weight.mean(), weight.max(), weight.min()
if mmin is None:
mmin = weight.min()
if mmax is None:
if over is None:
mmax = weight.max()
else:
sort = weight.ravel().argsort()
ind = sort[(len(sort) - 1) * (1.0 - over)]
mmax = weight.ravel()[ind]
print 'mmax, mmin', mmax, mmin
weight -= mmin
weight /= (mmax - mmin)
weight.clip(0, 1, weight)
else:
weight = image[:, :, 3] / 255.0
weight **= gamma
if composite is not None:
alpha = composite[:, :, 3] / 255.0
multiply(1.0 - weight[:, :], alpha[:, :], alpha[:, :])
multiply(image[:, :, 0:3], weight[:, :, newaxis], image[:, :, 0:3])
multiply(composite[:, :, 0:3], alpha[:, :, newaxis],
composite[:, :, 0:3])
add(image[:, :, 0:3], composite[:, :, 0:3], image[:, :, 0:3])
image[:, :, 3] = (alpha[:, :] + weight[:, :]) * 255
else:
#multiply(image[:, :, 0:3], weight[:, :, newaxis], image[:, :, 0:3])
#image[:, :, 3] = 255
#weight **= 0.33333333333
multiply(255.9999,
weight[:, :],
out=image[:, :, 3],
casting='unsafe')
# print 'alpha', image[:, :, 3].ravel().min(), image[:,:,3].ravel().max()
return image
def render(self, todraw, mass, mode=None, vmin=None, vmax=None, logscale=True, logweight=True, cmap=pygascmap, gamma=1.0, mmin=None, mmax=None, over=None, composite=None):
""" vmin can be a string, eg '40 dB', so the vmin=vmax/1e4 """
if mode == 'intensity' or mode == 'mean':
todraw /= mass
if logscale:
if vmin is not None and not isinstance(vmin, basestring):
vmin = 10 ** vmin
if vmax is not None:
vmax = 10 ** vmax
if vmax is None:
if mode == 'intensity' or mode == 'mean' or over is None:
vmax = fmax.reduce(todraw.flat)
else:
sort = todraw.ravel().argsort()
ind = sort[(len(sort) - 1) * (1.0 - over)]
vmax = todraw.ravel()[ind]
del sort
if vmin is None:
if logscale:
vmin = ccode.pmin.reduce(todraw.flat)
else:
vmin = fmin.reduce(todraw.flat)
elif isinstance(vmin, basestring):
db = int(vmin[:-3])
vmin = vmax / 10 ** (db/10.)
if logscale:
log10(todraw, todraw)
vmin = log10(vmin)
vmax = log10(vmax)
print 'vmax, vmin =' , vmax, vmin
image = zeros(dtype = ('u1', 4), shape = todraw.shape)
# gacolor is much faster then matplotlib's normalize and uses less memory(4times fewer).
if not hasattr(cmap, 'table'):
cmap = gacmap(cmap)
ccode.color(image, todraw, max = vmax, min = vmin, logscale=False, colormap = cmap)
del todraw
if mode == 'intensity':
weight = mass
if logweight:
# in camera mode do not use logscale for mass.
weight.clip(ccode.pmin.reduce(weight.ravel()), inf, weight)
log10(weight, weight)
print 'weight', weight.mean(), weight.max(), weight.min()
if mmin is None:
mmin = weight.min()
if mmax is None:
if over is None:
mmax = weight.max()
else:
sort = weight.ravel().argsort()
ind = sort[(len(sort) - 1) * (1.0 - over)]
mmax = weight.ravel()[ind]
print 'mmax, mmin', mmax, mmin
weight -= mmin
weight /= (mmax - mmin)
weight.clip(0, 1, weight)
else:
weight = image[:, :, 3] / 255.0
weight **= gamma
if composite is not None:
alpha = composite[:, :, 3] / 255.0
multiply(1.0 - weight[:, :], alpha[:, :], alpha[:, :])
multiply(image[:, :, 0:3], weight[:, :, newaxis], image[:, :, 0:3])
multiply(composite[:, :, 0:3], alpha[:, :, newaxis], composite[:, :, 0:3])
add(image[:, :, 0:3], composite[:, :, 0:3], image[:, :, 0:3])
image[:, :, 3] = (alpha[:, :] + weight[:, :]) * 255
else:
#multiply(image[:, :, 0:3], weight[:, :, newaxis], image[:, :, 0:3])
#image[:, :, 3] = 255
#weight **= 0.33333333333
multiply(255.9999, weight[:, :], out=image[:,:,3], casting='unsafe')
# print 'alpha', image[:, :, 3].ravel().min(), image[:,:,3].ravel().max()
return image