def potential_plot(env,
model,
obj_index,
src_index,
with_colorbar=True,
with_contours=False):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
grid = obj.basis.potential_grid(data)
levs = obj.basis.potential_contour_levels(data)
# pl.matshow(grid, fignum=False, extent=[-R,R,-R,R], interpolation='nearest')
pl.matshow(grid,
fignum=False,
cmap=cm.bone,
extent=[-R, R, -R, R],
interpolation='nearest')
if with_colorbar: glspl.colorbar()
# pl.contour(grid, extent=[-R,R,-R,R], origin='upper')
#print levs
if with_contours:
for i, lev in enumerate(levs):
pl.over(contour,
grid,
lev,
colors=system_color(i),
extent=[-R, R, -R, R],
origin='upper',
extend='both')
pl.xlabel('arcsec')
pl.ylabel('arcsec')
def grad_kappa_plot(env, model, obj_index, which='x', with_contours=False, only_contours=False, clevels=30, with_colorbar=True):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
grid = obj.basis.kappa_grid(data)
grid = grid.copy()
kw = default_kw(R)
kw['vmin'] = -1
kw['vmax'] = 2
if not only_contours:
print '!!!!!!', grid.shape
if which == 'x': grid = np.diff(grid, axis=1)
if which == 'y': grid = np.diff(grid, axis=0)
print '!!!!!!', grid.shape
pl.matshow(grid, **kw)
if with_colorbar:
glspl.colorbar()
if with_contours:
kw.pop('cmap')
pl.over(contour, grid, clevels, extend='both', colors='k', alpha=0.7, **kw)
pl.xlabel('arcsec')
pl.ylabel('arcsec')
def plot(model, si, hilite=False):
obj, data = model
xs = []
ys = []
cs = []
for i, sys in enumerate(obj.sources):
if src_index is not None and i != src_index: continue
xs.append(data['src'][i].real)
ys.append(data['src'][i].imag)
lw, ls, c = si.next()
cs.append(c) #system_color(i))
if hilite:
pl.over(scatter,
xs,
ys,
s=80,
c=hilite_color,
zorder=2000,
marker='x',
alpha=1.0,
**kwargs)
else:
pl.scatter(xs,
ys,
s=80,
c=cs,
zorder=1000,
marker='d',
alpha=0.5,
facecolor='none',
linewidths=1,
**kwargs)
def img_plot(
env, **kwargs
): #src_index=None, with_maximum=True, color=None, with_guide=False, tight=False):
obj_index = kwargs.pop('obj_index', 0)
src_index = kwargs.pop('src_index', None)
tight = kwargs.pop('tight', False)
with_guide = kwargs.pop('with_guide', False)
color = kwargs.pop('color', None)
with_maximum = kwargs.pop('with_maximum', True)
#src_index = np.atleast_1d(src_index)
obj = env.objects[obj_index]
oxlim, oylim = pl.xlim(), pl.ylim()
rmax = 0
si = style_iterator()
for i, src in enumerate(obj.sources):
lw, ls, c = si.next()
if src_index:
if i not in np.atleast_1d(src_index): continue
xs, ys, cs = [], [], []
for img in src.images:
#print img.pos
if not with_maximum and img.parity_name == 'max': continue
xs.append(img.pos.real)
ys.append(img.pos.imag)
if not color:
if img.parity_name == 'unk':
cs.append('red')
else:
cs.append(c)
else:
cs.append(color)
if xs and ys:
pl.over(pl.scatter, xs, ys, s=80, c=cs, zorder=1000, alpha=1.0)
if with_guide or tight:
a = pl.gca()
for x, y in zip(xs, ys):
r = np.sqrt(x**2 + y**2)
rmax = np.amax([r, rmax])
if with_guide:
a.add_artist(
Circle((0, 0), r, fill=False, color='lightgrey'))
pl.xlim(oxlim)
pl.ylim(oylim)
if tight and rmax > 0:
#rmax *= 1.01
pl.gca().set_xlim(-rmax, rmax)
pl.gca().set_ylim(-rmax, rmax)
def img_plot(env, **kwargs): #src_index=None, with_maximum=True, color=None, with_guide=False, tight=False):
obj_index = kwargs.pop('obj_index', 0)
src_index = kwargs.pop('src_index', None)
tight = kwargs.pop('tight', False)
with_guide = kwargs.pop('with_guide', False)
color = kwargs.pop('color', None)
with_maximum = kwargs.pop('with_maximum', True)
#src_index = np.atleast_1d(src_index)
obj = env.objects[obj_index]
oxlim, oylim = pl.xlim(), pl.ylim()
rmax = 0
si = style_iterator()
for i,src in enumerate(obj.sources):
lw,ls,c = si.next()
if src_index:
if i not in np.atleast_1d(src_index): continue
xs,ys,cs = [], [], []
for img in src.images:
#print img.pos
if not with_maximum and img.parity_name == 'max': continue
xs.append(img.pos.real)
ys.append(img.pos.imag)
if not color:
if img.parity_name == 'unk':
cs.append('red')
else:
cs.append(c)
else:
cs.append(color)
if xs and ys:
pl.over(pl.scatter,xs, ys, s=80, c=cs, zorder=1000, alpha=1.0)
if with_guide or tight:
a = pl.gca()
for x,y in zip(xs,ys):
r = np.sqrt(x**2 + y**2)
rmax = np.amax([r,rmax])
if with_guide:
a.add_artist(Circle((0,0),r, fill=False,color='lightgrey'))
pl.xlim(oxlim); pl.ylim(oylim)
if tight and rmax > 0:
#rmax *= 1.01
pl.gca().set_xlim(-rmax, rmax)
pl.gca().set_ylim(-rmax, rmax)
def external_mass_plot(env,
obj_index=0,
with_maximum=True,
color=None,
with_guide=False,
tight=False):
#obj,_ = model['obj,data'][obj_index]
#obj,_ = model['obj,data'][obj_index]
si = style_iterator()
for i in xrange(obj_index + 1):
lw, ls, c = si.next()
obj = env.objects[obj_index]
#print obj.external_masses
# if isinstance(model, (list, tuple)):
# obj,_ = model
# else:
# obj = model
oxlim, oylim = pl.xlim(), pl.ylim()
rmax = 0
xs, ys, cs = [], [], []
for i, m in enumerate(obj.extra_potentials):
if isinstance(m, Shear): continue
xs.append(m.r.real)
ys.append(m.r.imag)
if not color:
cs.append(c)
else:
cs.append(color)
rmax = np.amax([np.abs(m.r), rmax])
if xs and ys:
pl.over(pl.scatter,
xs,
ys,
s=160,
c=cs,
zorder=1000,
alpha=1.0,
marker='s')
pl.xlim(oxlim)
pl.ylim(oylim)
if tight and rmax > 0:
pl.pl.gca().set_pl.xlim(-rmax, rmax)
pl.pl.gca().set_pl.ylim(-rmax, rmax)
def plot(model, si, hilite=False):
obj, data = model
xs = []
ys = []
cs = []
for i,sys in enumerate(obj.sources):
if src_index is not None and i != src_index: continue
xs.append(data['src'][i].real)
ys.append(data['src'][i].imag)
lw,ls,c = si.next()
cs.append(c) #system_color(i))
if hilite:
pl.over(scatter,xs, ys, s=80, c=hilite_color, zorder=2000, marker='x', alpha=1.0, **kwargs)
else:
pl.scatter(xs, ys, s=80, c=cs, zorder=1000, marker='d', alpha=0.5, facecolor='none', linewidths=1, **kwargs)
def critical_curve_plot(env, model, obj_index, src_index):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
g = obj.basis.maginv_grid(data)[src_index]
pl.matshow(g,
fignum=False,
cmap=cm.bone,
extent=[-R, R, -R, R],
interpolation='nearest')
pl.over(contour,
g, [0],
colors='g',
linewidths=1,
extent=[-R, R, -R, R],
origin='upper')
def potential_plot(env, model, obj_index, src_index, with_colorbar=True, with_contours=False):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
grid = obj.basis.potential_grid(data)
levs = obj.basis.potential_contour_levels(data)
# pl.matshow(grid, fignum=False, extent=[-R,R,-R,R], interpolation='nearest')
pl.matshow(grid, fignum=False, cmap=cm.bone, extent=[-R,R,-R,R], interpolation='nearest')
if with_colorbar: glspl.colorbar()
# pl.contour(grid, extent=[-R,R,-R,R], origin='upper')
#print levs
if with_contours:
for i,lev in enumerate(levs):
pl.over(contour, grid, lev, colors = system_color(i),
extent=[-R,R,-R,R], origin='upper', extend='both')
pl.xlabel('arcsec')
pl.ylabel('arcsec')
def external_mass_plot(env, obj_index=0, with_maximum=True, color=None, with_guide=False, tight=False):
#obj,_ = model['obj,data'][obj_index]
#obj,_ = model['obj,data'][obj_index]
si = style_iterator()
for i in xrange(obj_index+1):
lw,ls,c = si.next()
obj = env.objects[obj_index]
#print obj.external_masses
# if isinstance(model, (list, tuple)):
# obj,_ = model
# else:
# obj = model
oxlim, oylim = pl.xlim(), pl.ylim()
rmax = 0
xs,ys,cs = [], [], []
for i,m in enumerate(obj.extra_potentials):
if isinstance(m, Shear): continue
xs.append(m.r.real)
ys.append(m.r.imag)
if not color:
cs.append(c)
else:
cs.append(color)
rmax = np.amax([np.abs(m.r),rmax])
if xs and ys:
pl.over(pl.scatter,xs, ys, s=160, c=cs, zorder=1000, alpha=1.0, marker='s')
pl.xlim(oxlim); pl.ylim(oylim)
if tight and rmax > 0:
pl.pl.gca().set_pl.xlim(-rmax, rmax)
pl.pl.gca().set_pl.ylim(-rmax, rmax)
def grad_kappa_plot(env,
model,
obj_index,
which='x',
with_contours=False,
only_contours=False,
clevels=30,
with_colorbar=True):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
grid = obj.basis.kappa_grid(data)
grid = grid.copy()
kw = default_kw(R)
kw['vmin'] = -1
kw['vmax'] = 2
if not only_contours:
print '!!!!!!', grid.shape
if which == 'x': grid = np.diff(grid, axis=1)
if which == 'y': grid = np.diff(grid, axis=0)
print '!!!!!!', grid.shape
pl.matshow(grid, **kw)
if with_colorbar:
glspl.colorbar()
if with_contours:
kw.pop('cmap')
pl.over(contour,
grid,
clevels,
extend='both',
colors='k',
alpha=0.7,
**kw)
pl.xlabel('arcsec')
pl.ylabel('arcsec')
def critical_curve_plot(env, model, obj_index, src_index):
obj, data = model['obj,data'][obj_index]
R = obj.basis.mapextent
g = obj.basis.maginv_grid(data)[src_index]
pl.matshow(g, fignum=False, cmap=cm.bone, extent=[-R,R,-R,R], interpolation='nearest')
pl.over(contour, g, [0], colors='g', linewidths=1, extent=[-R,R,-R,R], origin='upper')