def shape_chi(env, models, model0, frac='1sigma'):
ns, ds = [[], []], [[], []]
for m in models:
nT, dT = 0, 0
nP, dP = 0, 0
for m1, m2 in izip(m['obj,data'], model0['obj,data']):
obj, data = m1
obj0, data0 = m2
rs = [abs(img.pos) for src in obj.sources for img in src.images]
rmin, rmax = np.amin(rs), np.amax(rs)
w = (abs(obj.basis.ploc) >= obj.basis.top_level_cell_size *
0.9) * (abs(obj.basis.ploc) <=
(rmax + obj.basis.top_level_cell_size * 0.5))
t0, p0 = _shape(data0['kappa'][w], obj0.basis.ploc[w])
t, p = _shape(data['kappa'][w], obj.basis.ploc[w])
#dTheta = lambda t0,t1 = (t0-t1) if np.abs(t0-t1) < np.pi else 2*np.pi-(t0-t1)
nT += (t - t0)**2
nT = (t - t0)
dT += t0**2
nP += (p - p0)**2
nP = (p - p0)
dP += p0**2
ns[0].append(nT)
ds[0].append(dT)
ns[1].append(nP)
ds[1].append(dP)
return dist_range(np.array(ns[0]), frac), dist_range(np.array(ns[1]), frac)
def shape_chi(env, models, model0, frac='1sigma'):
ns, ds = [[],[]], [[],[]]
for m in models:
nT,dT = 0,0
nP,dP = 0,0
for m1,m2 in izip(m['obj,data'], model0['obj,data']):
obj,data = m1
obj0,data0 = m2
rs = [ abs(img.pos) for src in obj.sources for img in src.images]
rmin, rmax = np.amin(rs), np.amax(rs)
w = (abs(obj.basis.ploc) >= obj.basis.top_level_cell_size * 0.9) * (abs(obj.basis.ploc) <= (rmax+ obj.basis.top_level_cell_size * 0.5))
t0,p0 = _shape(data0['kappa'][w], obj0.basis.ploc[w])
t,p = _shape(data['kappa'][w], obj.basis.ploc[w])
#dTheta = lambda t0,t1 = (t0-t1) if np.abs(t0-t1) < np.pi else 2*np.pi-(t0-t1)
nT += (t-t0)**2
nT = (t-t0)
dT += t0**2
nP += (p-p0)**2
nP = (p-p0)
dP += p0**2
ns[0].append(nT)
ds[0].append(dT)
ns[1].append(nP)
ds[1].append(dP)
return dist_range(np.array(ns[0]), frac), dist_range(np.array(ns[1]), frac)
def kappa_profile_chi2(env, models, model0, frac='1sigma'):
n_max,d_max=0,0
n_min,d_min=np.inf,np.inf
ns, ds = [], []
for m in models:
n,d = 0,0
for m1,m2 in izip(m['obj,data'], model0['obj,data']):
obj,data = m1
obj0,data0 = m2
rs = [ abs(img.pos) for src in obj.sources for img in src.images]
#rs = [ abs(img.pos) for src in obj.sources for img in src.images if img.parity_name != 'max']
rmin, rmax = np.amin(rs), np.amax(rs)
if 0:
b = 0
else:
rmin = obj.basis.top_level_cell_size * 1.6
b = np.argmin(abs(data['R'] - rmin))
e = np.argmin(abs(data['R'] - rmax))
v0 = data0['kappa(R)'][b:e+1]
v1 = data['kappa(R)'][b:e+1]
n += np.sum((v1 - v0)**2)
d += np.sum(v0**2)
#d += len(v0) #np.sum(v0**2)
ns.append(n)
ds.append(d)
nd = array(ns) / array(ds)
return dist_range(nd, frac)
def kappa_chi2(env, models, model0, frac='1sigma'):
n_max,d_max=0,0
n_min,d_min=np.inf,np.inf
ns, ds = [], []
for m in models:
n,d = 0,0
for m1,m2 in izip(m['obj,data'], model0['obj,data']):
obj,data = m1
obj0,data0 = m2
rs = [ abs(img.pos) for src in obj.sources for img in src.images if img.parity_name != 'max']
rmin, rmax = np.amin(rs), np.amax(rs)
#w = (abs(obj.basis.rs) >= rmin) * (abs(obj.basis.rs) <= rmax)
#w = abs(obj.basis.rs) <= rmax
w = (abs(obj.basis.ploc) >= obj.basis.top_level_cell_size * 0.9) * (abs(obj.basis.ploc) <= (rmax+ obj.basis.top_level_cell_size * 0.5))
#b = np.argmin(abs(data['R'] - rmin))
#e = np.argmin(abs(data['R'] - rmax))
#v0 = data0['kappa'][b:e+1]
#v1 = data['kappa'][b:e+1]
v0 = data0['kappa'][w]
v1 = data['kappa'][w]
n += np.sum((v1 - v0)**2)
d += np.sum(v0**2)
ns.append(n)
ds.append(d)
#n_max,d_max = np.amax([n,n_max]), np.amax([d,d_max])
#n_min,d_min = np.amin([n,n_min]), np.amin([d,d_min])
nd = array(ns) / array(ds)
return dist_range(nd, frac)
def kappa_profile_chi2(env, models, model0, frac='1sigma'):
n_max, d_max = 0, 0
n_min, d_min = np.inf, np.inf
ns, ds = [], []
for m in models:
n, d = 0, 0
for m1, m2 in izip(m['obj,data'], model0['obj,data']):
obj, data = m1
obj0, data0 = m2
rs = [abs(img.pos) for src in obj.sources for img in src.images]
#rs = [ abs(img.pos) for src in obj.sources for img in src.images if img.parity_name != 'max']
rmin, rmax = np.amin(rs), np.amax(rs)
if 0:
b = 0
else:
rmin = obj.basis.top_level_cell_size * 1.6
b = np.argmin(abs(data['R'] - rmin))
e = np.argmin(abs(data['R'] - rmax))
v0 = data0['kappa(R)'][b:e + 1]
v1 = data['kappa(R)'][b:e + 1]
n += np.sum((v1 - v0)**2)
d += np.sum(v0**2)
#d += len(v0) #np.sum(v0**2)
ns.append(n)
ds.append(d)
nd = array(ns) / array(ds)
return dist_range(nd, frac)
def kappa_chi2(env, models, model0, frac='1sigma'):
n_max, d_max = 0, 0
n_min, d_min = np.inf, np.inf
ns, ds = [], []
for m in models:
n, d = 0, 0
for m1, m2 in izip(m['obj,data'], model0['obj,data']):
obj, data = m1
obj0, data0 = m2
rs = [
abs(img.pos) for src in obj.sources for img in src.images
if img.parity_name != 'max'
]
rmin, rmax = np.amin(rs), np.amax(rs)
#w = (abs(obj.basis.rs) >= rmin) * (abs(obj.basis.rs) <= rmax)
#w = abs(obj.basis.rs) <= rmax
w = (abs(obj.basis.ploc) >= obj.basis.top_level_cell_size *
0.9) * (abs(obj.basis.ploc) <=
(rmax + obj.basis.top_level_cell_size * 0.5))
#b = np.argmin(abs(data['R'] - rmin))
#e = np.argmin(abs(data['R'] - rmax))
#v0 = data0['kappa'][b:e+1]
#v1 = data['kappa'][b:e+1]
v0 = data0['kappa'][w]
v1 = data['kappa'][w]
n += np.sum((v1 - v0)**2)
d += np.sum(v0**2)
ns.append(n)
ds.append(d)
#n_max,d_max = np.amax([n,n_max]), np.amax([d,d_max])
#n_min,d_min = np.amin([n,n_min]), np.amin([d,d_min])
nd = array(ns) / array(ds)
return dist_range(nd, frac)
def _data_error_plot(models, X,Y, **kwargs):
with_legend = False
use = [0,0,0]
if isinstance(X, basestring): X = [X,None]
if isinstance(Y, basestring): Y = [Y,None]
x_prop, x_units = X
y_prop, y_units = Y
ret_list = []
every = kwargs.pop('every', 1)
upto = kwargs.pop('upto', len(models))
mark_images = kwargs.pop('mark_images', True)
hilite_model = kwargs.pop('hilite_model', None)
hilite_color = kwargs.pop('hilite_color', 'm')
yscale = kwargs.pop('yscale', 'log')
xscale = kwargs.pop('xscale', 'linear')
xlabel = kwargs.pop('xlabel', None)
ylabel = kwargs.pop('ylabel', None)
sigma = kwargs.pop('sigma', '1sigma')
kwargs.setdefault('color', 'k')
kwargs.setdefault('marker', '.')
kwargs.setdefault('ls', '-')
normal_kw = {'zorder':0, 'drawstyle':'steps', 'alpha':1.0}
hilite_kw = {'zorder':1000, 'drawstyle':'steps', 'alpha':1.0, 'lw':4, 'ls':'--'}
accepted_kw = {'zorder':500, 'drawstyle':'steps', 'alpha':0.5}
normal = []
hilite = []
accepted = []
#imgs = set()
imgs = defaultdict(set)
xmin, xmax = np.inf, -np.inf
ymin, ymax = np.inf, -np.inf
objplot = defaultdict(dict)
for mi in xrange(0,upto,every):
m = models[mi]
si = m.get('accepted', 2)
#print si
tag = ''
if si==False: tag = 'rejected'
if si==True: tag = 'accepted'
for [obj, data] in m['obj,data']:
try:
xs = data[x_prop][x_units]
ys = data[y_prop][y_units]
xlabel = _axis_label(xs, x_units) if not xlabel else xlabel
ylabel = _axis_label(ys, y_units) if not ylabel else ylabel
objplot[obj].setdefault(tag, {'ys':[], 'xs':None})
objplot[obj][tag]['ys'].append(ys)
objplot[obj][tag]['xs'] = xs
#objplot[obj].setdefault('%s:xs'%tag, xs)
#objplot[obj].setdefault('%s:ymax'%tag, ys)
#objplot[obj].setdefault('%s:ymin'%tag, ys)
#objplot[obj].setdefault('%s:ysum'%tag, np.zeros_like(ys))
#objplot[obj].setdefault('%s:count'%tag, 0)
#objplot[obj]['%s:ymax'%tag] = np.amax((objplot[obj]['%s:ymax'%tag], ys), axis=0)
#objplot[obj]['%s:ymin'%tag] = np.amin((objplot[obj]['%s:ymin'%tag], ys), axis=0)
#objplot[obj]['%s:ysum'%tag] += ys
#objplot[obj]['%s:count'%tag] += 1
if mark_images:
for i,src in enumerate(obj.sources):
for img in src.images:
imgs[i].add(convert('arcsec to %s' % x_units, np.abs(img.pos), obj.dL, data['nu']))
except KeyError as bad_key:
Log( "Missing information for object %s with key %s. Skipping plot." % (obj.name,bad_key) )
continue
use[si] = 1
s = _styles[si]
#xmin, xmax = min(xmin, amin(data[X])), max(xmax, amax(data[X]))
#ymin, ymax = min(ymin, amin(data[Y])), max(ymax, amax(data[Y]))
for i,tag in enumerate(['rejected', 'accepted', '']):
for k,v in objplot.iteritems():
if tag not in v: break
#if not v.has_key('%s:count'%tag): break
avg, errp, errm = dist_range(v[tag]['ys'], sigma=sigma)
errp = errp - avg
errm = avg - errm
#s = np.sort(v[tag]['ys'], axis=0)
#avg = s[len(s)//2] if len(s)%2==1 else (s[len(s)//2] + s[len(s)//2+1])/2
#print s
#avg = np.median(v[tag]['ys'], axis=0)
#print avg
#print np.median(v[tag]['ys'], axis=1)
#errp = s[len(s) * .841] - avg
#errm = avg - s[len(s) * .159]
#errp = np.amax(v[tag]['ys'], axis=0) - avg
#errm = avg - np.amin(v[tag]['ys'], axis=0)
#errp = errm = np.std(v[tag]['ys'], axis=0, dtype=np.float64)
xs = v[tag]['xs']
# print [x[1] for x in v[tag]['ys']]
# pl.hist([x[1] for x in v[tag]['ys']])
# break
#avg = v['%s:ysum'%tag] / v['%s:count'%tag]
#errp = v['%s:ymax'%tag]-avg
#errm = avg-v['%s:ymin'%tag]
#errm = errp = np.std(
#print len(v['xs'])
#print len(avg)
#assert 0
#print len(xs)
#print len(avg)
ret_list.append([xs,avg,errm,errp])
yerr = (errm,errp) if not np.all(errm == errp) else None
if tag == 'rejected':
pl.errorbar(xs, avg, yerr=yerr, c=_styles[0]['c'], zorder=_styles[0]['z'])
else:
pl.errorbar(xs, avg, yerr=yerr, **kwargs)
# return
pl.xscale(xscale)
pl.yscale(yscale)
si = style_iterator()
for k,v in imgs.iteritems():
lw,ls,c = si.next()
for img_pos in v:
pl.axvline(img_pos, c=c, ls=ls, lw=lw, zorder=-2, alpha=0.5)
# if use[0] or use[1]:
# lines = [s['line'] for s,u in zip(_styles, use) if u]
# labels = [s['label'] for s,u in zip(_styles, use) if u]
# pl.legend(lines, labels)
if use[0]:
lines = [ _styles[0]['line'] ]
labels = [ _styles[0]['label'] ]
pl.legend(lines, labels)
#axis('scaled')
if xlabel: pl.xlabel(xlabel)
if ylabel: pl.ylabel(ylabel)
pl.xlim(xmin=pl.xlim()[0] - 0.01*(pl.xlim()[1] - pl.xlim()[0]))
#pl.ylim(0, ymax)
return ret_list
def _hist(env, data_key, **kwargs):
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', 0)
key = kwargs.pop('key', 'accepted')
label = kwargs.pop('label', None)
color = kwargs.pop('color', None)
xlabel = kwargs.pop('xlabel', data_key)
ylabel = kwargs.pop('ylabel', r'Count')
sigma = kwargs.pop('sigma', '1sigma')
mark_sigma = kwargs.pop('mark_sigma', True)
# select a list to append to based on the 'accepted' property.
l = [[], [], []]
for m in models:
obj, data = m['obj,data'][obj_index] # For H0 we only have to look at one model because the others are the same
if data.has_key(data_key):
l[m.get(key,2)].append(data[data_key])
#print 'nu', data['nu']
#l[2].append(data['kappa'][1])
#print amin(l[2]), amax(l[2])
not_accepted, accepted, notag = l
#print 'H0_plot',H0s
for d,s in zip(l, _styles):
kw = kwargs.copy()
if d:
kw.setdefault('bins', np.ptp(d)//1+1)
kw.setdefault('histtype', 'step')
#print len(d), d
#pl.hist(d, bins=20, histtype='step', edgecolor=s['c'], zorder=s['z'], label=s['label'])
pl.hist(d,
edgecolor=s['c'] if color is None else color,
zorder=s['z'],
label=s['label'] if label is None else label,
**kwargs)
if not_accepted or label:
pl.legend()
if mark_sigma:
if accepted or notag:
if accepted:
h = np.array(accepted)
else:
h = np.array(notag)
m,u,l = dist_range(h, sigma=sigma)
pl.axvline(m, c='r', ls='-', zorder = 2)
pl.axvline(u, c='g', ls='-', zorder = 2)
pl.axvline(l, c='g', ls='-', zorder = 2)
Log( '%s: %f %f %f' % (data_key, m, u, l) )
Log( '%s: %f +/- %f %f' % (data_key, m, (u-m), (m-l)) )
else:
Log( "%s: No H0 values accepted" % data_key )
#pl.axvline(72, c='k', ls=':', zorder = 2)
pl.xlabel(xlabel)
pl.ylabel(ylabel)
pl.xlim(xmax=pl.xlim()[1] + 0.01*(pl.xlim()[1] - pl.xlim()[0]))
pl.ylim(ymax=pl.ylim()[1] + 0.01*(pl.ylim()[1] - pl.ylim()[0]))
def _data_error_plot(models, X, Y, **kwargs):
with_legend = False
use = [0, 0, 0]
if isinstance(X, basestring): X = [X, None]
if isinstance(Y, basestring): Y = [Y, None]
x_prop, x_units = X
y_prop, y_units = Y
ret_list = []
every = kwargs.pop('every', 1)
upto = kwargs.pop('upto', len(models))
mark_images = kwargs.pop('mark_images', True)
hilite_model = kwargs.pop('hilite_model', None)
hilite_color = kwargs.pop('hilite_color', 'm')
yscale = kwargs.pop('yscale', 'log')
xscale = kwargs.pop('xscale', 'linear')
xlabel = kwargs.pop('xlabel', None)
ylabel = kwargs.pop('ylabel', None)
sigma = kwargs.pop('sigma', '1sigma')
kwargs.setdefault('color', 'k')
kwargs.setdefault('marker', '.')
kwargs.setdefault('ls', '-')
normal_kw = {'zorder': 0, 'drawstyle': 'steps', 'alpha': 1.0}
hilite_kw = {
'zorder': 1000,
'drawstyle': 'steps',
'alpha': 1.0,
'lw': 4,
'ls': '--'
}
accepted_kw = {'zorder': 500, 'drawstyle': 'steps', 'alpha': 0.5}
normal = []
hilite = []
accepted = []
#imgs = set()
imgs = defaultdict(set)
xmin, xmax = np.inf, -np.inf
ymin, ymax = np.inf, -np.inf
objplot = defaultdict(dict)
for mi in xrange(0, upto, every):
m = models[mi]
si = m.get('accepted', 2)
#print si
tag = ''
if si == False: tag = 'rejected'
if si == True: tag = 'accepted'
for [obj, data] in m['obj,data']:
try:
xs = data[x_prop][x_units]
ys = data[y_prop][y_units]
xlabel = _axis_label(xs, x_units) if not xlabel else xlabel
ylabel = _axis_label(ys, y_units) if not ylabel else ylabel
objplot[obj].setdefault(tag, {'ys': [], 'xs': None})
objplot[obj][tag]['ys'].append(ys)
objplot[obj][tag]['xs'] = xs
#objplot[obj].setdefault('%s:xs'%tag, xs)
#objplot[obj].setdefault('%s:ymax'%tag, ys)
#objplot[obj].setdefault('%s:ymin'%tag, ys)
#objplot[obj].setdefault('%s:ysum'%tag, np.zeros_like(ys))
#objplot[obj].setdefault('%s:count'%tag, 0)
#objplot[obj]['%s:ymax'%tag] = np.amax((objplot[obj]['%s:ymax'%tag], ys), axis=0)
#objplot[obj]['%s:ymin'%tag] = np.amin((objplot[obj]['%s:ymin'%tag], ys), axis=0)
#objplot[obj]['%s:ysum'%tag] += ys
#objplot[obj]['%s:count'%tag] += 1
if mark_images:
for i, src in enumerate(obj.sources):
for img in src.images:
imgs[i].add(
convert('arcsec to %s' % x_units,
np.abs(img.pos), obj.dL, data['nu']))
except KeyError as bad_key:
Log("Missing information for object %s with key %s. Skipping plot."
% (obj.name, bad_key))
continue
use[si] = 1
s = _styles[si]
#xmin, xmax = min(xmin, amin(data[X])), max(xmax, amax(data[X]))
#ymin, ymax = min(ymin, amin(data[Y])), max(ymax, amax(data[Y]))
for i, tag in enumerate(['rejected', 'accepted', '']):
for k, v in objplot.iteritems():
if tag not in v: break
#if not v.has_key('%s:count'%tag): break
avg, errp, errm = dist_range(v[tag]['ys'], sigma=sigma)
errp = errp - avg
errm = avg - errm
#s = np.sort(v[tag]['ys'], axis=0)
#avg = s[len(s)//2] if len(s)%2==1 else (s[len(s)//2] + s[len(s)//2+1])/2
#print s
#avg = np.median(v[tag]['ys'], axis=0)
#print avg
#print np.median(v[tag]['ys'], axis=1)
#errp = s[len(s) * .841] - avg
#errm = avg - s[len(s) * .159]
#errp = np.amax(v[tag]['ys'], axis=0) - avg
#errm = avg - np.amin(v[tag]['ys'], axis=0)
#errp = errm = np.std(v[tag]['ys'], axis=0, dtype=np.float64)
xs = v[tag]['xs']
# print [x[1] for x in v[tag]['ys']]
# pl.hist([x[1] for x in v[tag]['ys']])
# break
#avg = v['%s:ysum'%tag] / v['%s:count'%tag]
#errp = v['%s:ymax'%tag]-avg
#errm = avg-v['%s:ymin'%tag]
#errm = errp = np.std(
#print len(v['xs'])
#print len(avg)
#assert 0
#print len(xs)
#print len(avg)
ret_list.append([xs, avg, errm, errp])
yerr = (errm, errp) if not np.all(errm == errp) else None
if tag == 'rejected':
pl.errorbar(xs,
avg,
yerr=yerr,
c=_styles[0]['c'],
zorder=_styles[0]['z'])
else:
pl.errorbar(xs, avg, yerr=yerr, **kwargs)
# return
pl.xscale(xscale)
pl.yscale(yscale)
si = style_iterator()
for k, v in imgs.iteritems():
lw, ls, c = si.next()
for img_pos in v:
pl.axvline(img_pos, c=c, ls=ls, lw=lw, zorder=-2, alpha=0.5)
# if use[0] or use[1]:
# lines = [s['line'] for s,u in zip(_styles, use) if u]
# labels = [s['label'] for s,u in zip(_styles, use) if u]
# pl.legend(lines, labels)
if use[0]:
lines = [_styles[0]['line']]
labels = [_styles[0]['label']]
pl.legend(lines, labels)
#axis('scaled')
if xlabel: pl.xlabel(xlabel)
if ylabel: pl.ylabel(ylabel)
pl.xlim(xmin=pl.xlim()[0] - 0.01 * (pl.xlim()[1] - pl.xlim()[0]))
#pl.ylim(0, ymax)
return ret_list
def _hist(env, data_key, **kwargs):
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', 0)
key = kwargs.pop('key', 'accepted')
label = kwargs.pop('label', None)
color = kwargs.pop('color', None)
xlabel = kwargs.pop('xlabel', data_key)
ylabel = kwargs.pop('ylabel', r'Count')
sigma = kwargs.pop('sigma', '1sigma')
mark_sigma = kwargs.pop('mark_sigma', True)
# select a list to append to based on the 'accepted' property.
l = [[], [], []]
for m in models:
obj, data = m['obj,data'][
obj_index] # For H0 we only have to look at one model because the others are the same
if data.has_key(data_key):
l[m.get(key, 2)].append(data[data_key])
#print 'nu', data['nu']
#l[2].append(data['kappa'][1])
#print amin(l[2]), amax(l[2])
not_accepted, accepted, notag = l
#print 'H0_plot',H0s
for d, s in zip(l, _styles):
kw = kwargs.copy()
if d:
#print len(d), d, np.ptp(d), np.sqrt(len(d))
kw.setdefault('bins', int(np.ptp(d) // 1) + 1)
kw.setdefault('histtype', 'step')
#print len(d), d
#pl.hist(d, bins=20, histtype='step', edgecolor=s['c'], zorder=s['z'], label=s['label'])
pl.hist(d,
edgecolor=s['c'] if color is None else color,
zorder=s['z'],
label=s['label'] if label is None else label,
**kw)
if not_accepted or label:
pl.legend()
if mark_sigma:
if accepted or notag:
if accepted:
h = np.array(accepted)
else:
h = np.array(notag)
m, u, l = dist_range(h, sigma=sigma)
pl.axvline(m, c='r', ls='-', zorder=2)
pl.axvline(u, c='g', ls='-', zorder=2)
pl.axvline(l, c='g', ls='-', zorder=2)
Log('%s: %f %f %f' % (data_key, m, u, l))
Log('%s: %f +/- %f %f' % (data_key, m, (u - m), (m - l)))
else:
Log("%s: No H0 values accepted" % data_key)
#pl.axvline(72, c='k', ls=':', zorder = 2)
pl.xlabel(xlabel)
pl.ylabel(ylabel)
pl.xlim(xmax=pl.xlim()[1] + 0.01 * (pl.xlim()[1] - pl.xlim()[0]))
pl.ylim(ymax=pl.ylim()[1] + 0.01 * (pl.ylim()[1] - pl.ylim()[0]))