def recalculate_position(self):
"""
takes all the positions of the associated observation list and recalculated a position
"""
global blah
if self.stype == "real":
print "! not supposed to do this with a real source.."
return
if len(self.associated_obs) == 0:
return
pos_array = numarray.fromlist(
[[x.pos[0], x.pos[1], x.assumed_err[0], x.assumed_err[1]]
for x in self.associated_obs])
raa = numarray.fromlist([x[0] for x in pos_array])
raerra = numarray.fromlist([x[2] for x in pos_array])
deca = numarray.fromlist([x[1] for x in pos_array])
decerra = numarray.fromlist([x[3] for x in pos_array])
ra = numarray.sum(raa / raerra**2) / numarray.sum(1.0 / raerra**2)
dec = numarray.sum(deca / decerra**2) / numarray.sum(1.0 / decerra**2)
raerr = math.sqrt(1.0 / numarray.sum(1.0 / raerra**2))
decerr = math.sqrt(1.0 / numarray.sum(1.0 / decerra**2))
self.current_pos = [ra, dec]
self.current_err = [raerr, decerr]
def plot_source_info(self,event):
ra = event.xdata
dec = event.ydata
#print (event.key,ra,dec)
if event.key == 's':
#print self.constructed_pos[:,0]
#print self.constructed_pos[:,1]
dist = numarray.sqrt( (self.constructed_pos[:,0] - ra)**2 + (self.constructed_pos[:,1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.constructed_source_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.constructed_source_list[the_source_ind[0]]
print the_source
dist = numarray.sqrt( (the_source.current_pos[0] - self.real_pos[:,0])**2 + (the_source.current_pos[1] - self.real_pos[:,1])**2)
print "min distances to nearest real source = %f arcsec" % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list))))
the_source = self.real_list[the_source_ind[0]]
print "That real source is at ra=%f dec=%f" % (the_source.start_pos[0],the_source.start_pos[1])
if event.key == 'r':
dist = numarray.sqrt( (self.real_pos[:,0] - ra)**2 + (self.real_pos[:,1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.real_list[the_source_ind[0]]
print the_source
def plot_source_info(self, event):
ra = event.xdata
dec = event.ydata
#print (event.key,ra,dec)
if event.key == 's':
#print self.constructed_pos[:,0]
#print self.constructed_pos[:,1]
dist = numarray.sqrt((self.constructed_pos[:, 0] - ra)**2 +
(self.constructed_pos[:, 1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.constructed_source_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.constructed_source_list[the_source_ind[0]]
print the_source
dist = numarray.sqrt(
(the_source.current_pos[0] - self.real_pos[:, 0])**2 +
(the_source.current_pos[1] - self.real_pos[:, 1])**2)
print "min distances to nearest real source = %f arcsec" % min(
dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.real_list))))
the_source = self.real_list[the_source_ind[0]]
print "That real source is at ra=%f dec=%f" % (
the_source.start_pos[0], the_source.start_pos[1])
if event.key == 'r':
dist = numarray.sqrt((self.real_pos[:, 0] - ra)**2 +
(self.real_pos[:, 1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.real_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.real_list[the_source_ind[0]]
print the_source
def Matrix(data, typecode=None, copy=1, savespace=0):
"""Matrix constructs new matrices from 2D nested lists of numbers"""
if isinstance(data, type("")):
raise TypeError("numerix Matrix does not support Numeric matrix string notation. Use nested lists.")
a = fromlist(data, type=typecode)
if a.rank == 0:
a.shape = (1,1)
elif a.rank == 1:
a.shape = (1,) + a.shape
a.__class__ = _Matrix
return a
def Matrix(data, typecode=None, copy=1, savespace=0):
"""Matrix constructs new matrices from 2D nested lists of numbers"""
if isinstance(data, type("")):
raise TypeError("numerix Matrix does not support Numeric matrix string notation. Use nested lists.")
a = fromlist(data, type=typecode)
if a.rank == 0:
a.shape = (1,1)
elif a.rank == 1:
a.shape = (1,) + a.shape
a.__class__ = _Matrix
return a
def recalculate_position(self):
"""
takes all the positions of the associated observation list and recalculated a position
"""
global blah
if self.stype=="real":
print "! not supposed to do this with a real source.."
return
if len(self.associated_obs) == 0:
return
pos_array = numarray.fromlist([[x.pos[0],x.pos[1],x.assumed_err[0],x.assumed_err[1]] for x in self.associated_obs])
raa = numarray.fromlist([x[0] for x in pos_array])
raerra = numarray.fromlist([x[2] for x in pos_array])
deca = numarray.fromlist([x[1] for x in pos_array])
decerra = numarray.fromlist([x[3] for x in pos_array])
ra = numarray.sum(raa/raerra**2)/numarray.sum(1.0/raerra**2)
dec = numarray.sum(deca/decerra**2)/numarray.sum(1.0/decerra**2)
raerr = math.sqrt(1.0/numarray.sum(1.0/raerra**2))
decerr = math.sqrt(1.0/numarray.sum(1.0/decerra**2))
self.current_pos = [ra,dec]
self.current_err = [raerr,decerr]
def run(self, shuffle=True):
constructed_source_list = []
observation_list = []
obslist = self.obj_dict.keys()
if shuffle:
random.seed()
random.shuffle(obslist)
random.shuffle(obslist)
random.shuffle(obslist)
print "shuffled"
for theo in obslist:
# choose a real source to draw an observation from
o = obs(initial_pos=[theo[0], theo[1]],
assumed_err=[theo[2], theo[3]])
o.pos = [theo[0], theo[1]]
o.plot()
tmp = o.is_associated_with_source(constructed_source_list)
# print tmp
if tmp['answer'] == True:
# o.associate_with_source(tmp['sources'])
for s in tmp['best_source']:
# print (len(tmp['best_source']),o.pos,s.current_pos)
s.add_associated_obs(copy.deepcopy(o))
s.recalculate_position()
else:
## make a new source
s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\
current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)])
print "1 new source"
#print s
#print s.associated_obs
constructed_source_list.append(copy.deepcopy(s))
observation_list.append(copy.deepcopy(o))
for s in constructed_source_list:
# print s
s.plot('g^')
self.constructed_source_list = constructed_source_list
self.constructed_pos = (numarray.fromlist(
[x.current_pos for x in self.constructed_source_list]))
def run(self,shuffle=True):
constructed_source_list = []
observation_list = []
obslist = self.obj_dict.keys()
if shuffle:
random.seed()
random.shuffle(obslist)
random.shuffle(obslist)
random.shuffle(obslist)
print "shuffled"
for theo in obslist:
# choose a real source to draw an observation from
o = obs(initial_pos=[theo[0],theo[1]],assumed_err=[theo[2],theo[3]])
o.pos = [theo[0],theo[1]]
o.plot()
tmp = o.is_associated_with_source(constructed_source_list)
# print tmp
if tmp['answer'] == True:
# o.associate_with_source(tmp['sources'])
for s in tmp['best_source']:
# print (len(tmp['best_source']),o.pos,s.current_pos)
s.add_associated_obs(copy.deepcopy(o))
s.recalculate_position()
else:
## make a new source
s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\
current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)])
print "1 new source"
#print s
#print s.associated_obs
constructed_source_list.append(copy.deepcopy(s))
observation_list.append(copy.deepcopy(o))
for s in constructed_source_list:
# print s
s.plot('g^')
self.constructed_source_list = constructed_source_list
self.constructed_pos = (numarray.fromlist([x.current_pos for x in self.constructed_source_list]))
def Matrix(data, typecode=None, copy=1, savespace=0):
if type(data) is types.StringType:
data = _convert_from_string(data)
a = numarray.fromlist(data, type=typecode)
a.__class__ = _Matrix
return a
def Matrix(data, typecode=None, copy=1, savespace=0):
if type(data) is types.StringType:
data = _convert_from_string(data)
a = numarray.fromlist(data, type=typecode)
a.__class__ = _Matrix
return a
def run(self,n_sources = 3, n_observations = 21, ra_range = [-20.0,20.0],dec_range=[-20.0,20.0],typical_err=0.3,reuse=True):
global real_list
clf()
# make the real sources
if reuse:
try:
type(real_list) == type([])
except NameError:
reuse = False
real_list = []
for ns in range(n_sources):
if not reuse:
real_list.append(source(start_pos=[random_array.uniform(ra_range[0],ra_range[1]),random_array.uniform(dec_range[0],dec_range[1])],
stype='real',start_err=[0.0,0.0],associated_obs=[]))
# print real_list[-1]
real_list[ns].plot()
constructed_source_list = []
observation_list = []
## pick a vector of len n_obsevations sources to choose from from 0 --> n_source - 1
s_start_ind = random_array.randint(0,n_sources,shape=[n_observations])
for i in range(n_observations):
# choose a real source to draw an observation from
o = obs(initial_pos=real_list[s_start_ind[i]].start_pos,true_err=[[typical_err**2,0.0],[0.0,typical_err**2]],assumed_err=[1.1*typical_err,1.1*typical_err])
o.observe_pos()
o.plot()
tmp = o.is_associated_with_source(constructed_source_list)
# print tmp
if tmp['answer'] == True:
# o.associate_with_source(tmp['sources'])
for s in tmp['best_source']:
# print (len(tmp['best_source']),o.pos,s.current_pos)
s.add_associated_obs(copy.deepcopy(o))
s.recalculate_position()
else:
## make a new source
s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\
current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)])
print "new source"
#print s
#print s.associated_obs
constructed_source_list.append(copy.deepcopy(s))
observation_list.append(copy.deepcopy(o))
for s in constructed_source_list:
# print s
s.plot('g^')
## do the comparisons between real and constructed sources
for ns in range(n_sources):
#real_list[ns].plot('ys')
pass
self.real_list = real_list
self.constructed_source_list = constructed_source_list
self.real_pos = (numarray.fromlist([x.start_pos for x in self.real_list]))
self.constructed_pos = (numarray.fromlist([x.current_pos for x in self.constructed_source_list]))
def run(self,
n_sources=3,
n_observations=21,
ra_range=[-20.0, 20.0],
dec_range=[-20.0, 20.0],
typical_err=0.3,
reuse=True):
global real_list
clf()
# make the real sources
if reuse:
try:
type(real_list) == type([])
except NameError:
reuse = False
real_list = []
for ns in range(n_sources):
if not reuse:
real_list.append(
source(start_pos=[
random_array.uniform(ra_range[0], ra_range[1]),
random_array.uniform(dec_range[0], dec_range[1])
],
stype='real',
start_err=[0.0, 0.0],
associated_obs=[]))
# print real_list[-1]
real_list[ns].plot()
constructed_source_list = []
observation_list = []
## pick a vector of len n_obsevations sources to choose from from 0 --> n_source - 1
s_start_ind = random_array.randint(0,
n_sources,
shape=[n_observations])
for i in range(n_observations):
# choose a real source to draw an observation from
o = obs(initial_pos=real_list[s_start_ind[i]].start_pos,
true_err=[[typical_err**2, 0.0], [0.0, typical_err**2]],
assumed_err=[1.1 * typical_err, 1.1 * typical_err])
o.observe_pos()
o.plot()
tmp = o.is_associated_with_source(constructed_source_list)
# print tmp
if tmp['answer'] == True:
# o.associate_with_source(tmp['sources'])
for s in tmp['best_source']:
# print (len(tmp['best_source']),o.pos,s.current_pos)
s.add_associated_obs(copy.deepcopy(o))
s.recalculate_position()
else:
## make a new source
s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\
current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)])
print "new source"
#print s
#print s.associated_obs
constructed_source_list.append(copy.deepcopy(s))
observation_list.append(copy.deepcopy(o))
for s in constructed_source_list:
# print s
s.plot('g^')
## do the comparisons between real and constructed sources
for ns in range(n_sources):
#real_list[ns].plot('ys')
pass
self.real_list = real_list
self.constructed_source_list = constructed_source_list
self.real_pos = (numarray.fromlist(
[x.start_pos for x in self.real_list]))
self.constructed_pos = (numarray.fromlist(
[x.current_pos for x in self.constructed_source_list]))
