def moments(self, unit="km/s", **kwargs):
"""
Return the moments of the spectrum. In order to assure that the 1st
and 2nd moments are meaningful, a 'default' unit is set. If unit is
not set, will use current unit.
*Documentation imported from the moments module:*
"""
if unit is False or unit is None:
return moments_module.moments(self.xarr, self.data, **kwargs)
else:
return moments_module.moments(self.xarr.as_unit(unit), self.data, **kwargs)
def moments(self, unit='km/s', **kwargs):
"""
Return the moments of the spectrum. In order to assure that the 1st
and 2nd moments are meaningful, a 'default' unit is set. If unit is
not set, will use current unit.
*Documentation imported from the moments module:*
"""
if unit is False or unit is None:
return moments_module.moments(self.xarr, self.data, **kwargs)
else:
return moments_module.moments(self.xarr.as_unit(unit), self.data,
**kwargs)
def __init__(self, x, size, selectTrain, sess, toTarget=None, ts=0.001):
self.sess = sess
self.mean_x_train, self.variance_x_train = moments(x, [0])
#self.mean_x_ma, self.variance_x_ma = moments(self.x_splh, [0])
self.mean_x_ma = tf.Variable(tf.zeros([size]))
self.variance_x_ma = tf.Variable(tf.ones([size]))
self.update = tf.tuple(
[
self.variance_x_ma.assign(0.95 * self.variance_x_ma +
0.05 * self.variance_x_train)
],
control_inputs=[
self.mean_x_ma.assign(0.95 * self.mean_x_ma +
0.05 * self.mean_x_train)
])[0]
self.mean_x_ma_update = tf.tuple([self.mean_x_train],
control_inputs=[])[0]
self.printUp = tf.Print(self.mean_x_ma_update, [selectTrain],
message="selectTrain value : ")
self.variance_x_ma_update = tf.tuple([self.variance_x_train],
control_inputs=[])[0]
def getxmau():
return self.mean_x_ma_update
def getxma():
return self.mean_x_ma
def getvxmau():
return self.variance_x_ma_update
def getvxma():
return self.variance_x_ma
self.mean_x = tf.cond(selectTrain, getxmau, getxma)
self.variance_x = tf.cond(selectTrain, getvxmau, getvxma)
self.beta = tf.Variable(tf.zeros([size]))
self.gamma = tf.Variable(tf.ones([size]))
#tfs.tfs.session.run(tf.initialize_variables([self.beta, self.gamma]))#, self.mean_x_ma, self.variance_x_ma]))
self.xNorm = tf.reshape(
tf.nn.batch_norm_with_global_normalization(
tf.reshape(x, [-1, 1, 1, size]), self.mean_x, self.variance_x,
self.beta, self.gamma, 0.01, True), [-1, size])
if toTarget != None:
self.isTracking = toTarget
self.updateBeta = self.beta.assign(self.beta * (1 - ts) +
self.isTracking.beta * ts)
self.updateGamma = self.gamma.assign(self.gamma * (1 - ts) +
self.isTracking.gamma * ts)
self.updateTarget = tf.group(self.updateBeta, self.updateGamma)
def get_focus_score(self, image):
"""Determine how well the image is focussed, and award a score"""
hdul = HDUList(image)
hdr = hdul[0].header
data = hdul[0].data
nrows = hdr['NAXIS1']
ncols = hdr['NAXIS2']
ns = 1
back = 400
imx = 512
jmx = 512
spix = 50
background = 360
from moments import moments
fwhm = moments(data, imx, jmx, spix, nrows, ncols, background)
print("FWHM = {}".format(fwhm))
return fwhm
def __init__(self, x, size, selectTrain, sess, toTarget=None, ts=0.001):
self.sess = sess
self.mean_x_train, self.variance_x_train = moments(x, [0])
#self.mean_x_ma, self.variance_x_ma = moments(self.x_splh, [0])
self.mean_x_ma = tf.Variable(tf.zeros([size]))
self.variance_x_ma = tf.Variable(tf.ones([size]))
self.update = tf.tuple([self.variance_x_ma.assign(0.95*self.variance_x_ma+ 0.05*self.variance_x_train)] , control_inputs=[self.mean_x_ma.assign(0.95*self.mean_x_ma+ 0.05*self.mean_x_train)])[0]
self.mean_x_ma_update = tf.tuple([self.mean_x_train] , control_inputs=[])[0]
self.printUp = tf.Print(self.mean_x_ma_update, [selectTrain], message="selectTrain value : ")
self.variance_x_ma_update = tf.tuple([self.variance_x_train], control_inputs=[])[0]
def getxmau(): return self.mean_x_ma_update
def getxma(): return self.mean_x_ma
def getvxmau(): return self.variance_x_ma_update
def getvxma(): return self.variance_x_ma
self.mean_x = tf.cond(selectTrain, getxmau, getxma)
self.variance_x = tf.cond(selectTrain, getvxmau, getvxma)
self.beta = tf.Variable(tf.zeros([size]))
self.gamma = tf.Variable(tf.ones([size]))
#tfs.tfs.session.run(tf.initialize_variables([self.beta, self.gamma]))#, self.mean_x_ma, self.variance_x_ma]))
self.xNorm = tf.reshape(tf.nn.batch_norm_with_global_normalization(tf.reshape(x, [-1, 1, 1, size]), self.mean_x, self.variance_x, self.beta, self.gamma, 0.01, True), [-1, size])
if toTarget!=None:
self.isTracking = toTarget
self.updateBeta = self.beta.assign(self.beta*(1-ts)+self.isTracking.beta*ts)
self.updateGamma = self.gamma.assign(self.gamma*(1-ts)+self.isTracking.gamma*ts)
self.updateTarget = tf.group(self.updateBeta, self.updateGamma)
#print("map_A:\t", g.map_A)
#print("p1:\t", g.p1)
#print("p2:\t", g.p2)
#print("q1:\t", g.q1)
#print("q2:\t", g.q2)
#print("idx2:\t", g.idx2)
#print("idx2s:\t", g.idx2s)
#print("d:\t", g.d)
#print(check_measure_r(g, g.m_r, epsilon))
#print(check_measure_r(g, g.m_r1, epsilon))
#print(check_measure_s(g, g.m_s, epsilon))
#print(check_measure_s(g, g.m_s1, epsilon))
#print(check_measure(g, g.m_rs, epsilon))
# exp = ep.E_D(g)
exp0 = mm.moments(0, g)
exp1 = mm.moments(1, g)
exp2 = mm.moments(2, g)
var = exp2 - exp1**2
print(foldr(concat('\t'), ['total', exp0, exp1, exp2, var], ''))
pLst = [0, 0.2, 0.4, 0.6, 0.85, 0.87, 0.89, 0.9, 0.92, 0.94, 0.96]
# pLst = [0]
e = list(g.edges())[0]
for p in pLst:
expc0 = mc.momentsCond(0, g, e, p)
expc1 = mc.momentsCond(1, g, e, p)
expc2 = mc.momentsCond(2, g, e, p)
varc = expc2 - expc1**2
