def hillshade(data,scale=10.0,azdeg=165.0,altdeg=45.0):
'''
This code thanks to Ran Novitsky Nof
http://rnovitsky.blogspot.co.uk/2010/04/using-hillshade-image-as-intensity.html
Repeated here to make my cyclopean uk_map code prettier.
convert data to hillshade based on matplotlib.colors.LightSource class.
input:
data - a 2-d array of data
scale - scaling value of the data. higher number = lower gradient
azdeg - where the light comes from: 0 south ; 90 east ; 180 north ;
270 west
altdeg - where the light comes from: 0 horison ; 90 zenith
output: a 2-d array of normalized hilshade
'''
from pylab import pi, gradient, arctan, hypot, arctan2, sin, cos
# convert alt, az to radians
az = azdeg*pi/180.0
alt = altdeg*pi/180.0
# gradient in x and y directions
dx, dy = gradient(data/float(scale))
slope = 0.5*pi - arctan(hypot(dx, dy))
aspect = arctan2(dx, dy)
intensity = sin(alt)*sin(slope) + cos(alt)*cos(slope)*cos(-az - aspect - 0.5*pi)
intensity = (intensity - intensity.min())/(intensity.max() - intensity.min())
return intensity
def hillshade(data, scale=10.0, azdeg=165.0, altdeg=45.0):
'''
Convert data to hillshade based on matplotlib.colors.LightSource class.
Args:
data - a 2-d array of data
scale - scaling value of the data. higher number = lower gradient
azdeg - where the light comes from: 0 south ; 90 east ; 180 north ;
270 west
altdeg - where the light comes from: 0 horison ; 90 zenith
Returns:
a 2-d array of normalized hilshade
'''
# convert alt, az to radians
az = azdeg*pi/180.0
alt = altdeg*pi/180.0
# gradient in x and y directions
dx, dy = gradient(data/float(scale))
slope = 0.5*pi - arctan(hypot(dx, dy))
aspect = arctan2(dx, dy)
az = -az - aspect - 0.5*pi
intensity = sin(alt)*sin(slope) + cos(alt)*cos(slope)*cos(az)
mi, ma = intensity.min(), intensity.max()
intensity = (intensity - mi)/(ma - mi)
return intensity
def compareVelocity(rootDir='./', align = 'align/align_d_rms1000t',
poly='polyfit_d/fit', points='points_d/'):
"""
Compare velocities from the 2D and 3D polynomial fits.
rootDir -- An align root directory such as '06_14_02/' (def='./')
align -- Align root name (def = 'align/align_all1000_t')
poly -- Polyfit root name (def = 'polyfit/fit')
points -- Points directory (def = 'points/')
"""
s = starset.StarSet(rootDir + align)
s.loadPolyfit(rootDir + poly, accel=0, arcsec=1)
s.loadPolyfit(rootDir + poly, accel=1, arcsec=1)
names = s.getArray('name')
x = s.getArray('x')
y = s.getArray('y')
r = hypot(x, y)
vx_vel = s.getArray('fitXv.v')
vy_vel = s.getArray('fitYv.v')
vxe_vel = s.getArray('fitXv.verr')
vye_vel = s.getArray('fitYv.verr')
vx_acc = s.getArray('fitXa.v')
vy_acc = s.getArray('fitYa.v')
vxe_acc = s.getArray('fitXa.verr')
vye_acc = s.getArray('fitYa.verr')
# Calculate the residuals
diffx = vx_vel - vx_acc
diffxErr = np.sqrt(vxe_vel**2 + vxe_acc**2)
diffy = vy_vel - vy_acc
diffyErr = np.sqrt(vye_vel**2 + vye_acc**2)
diff = py.hypot(diffx, diffy)
diffErr = np.sqrt((diffx*diffxErr)**2 + (diffy*diffyErr)**2) / diff
yngNames = young.youngStarNames()
idx = (np.where((r > 0.8) & ((diff/diffErr) > 2.0)))[0]
print '** Stars with large velocity discrepencies: **'
print '%15s %5s %5s %5s %3s' % ('Name', 'Sigma', 'X', 'Y', 'YNG?')
for i in idx:
try:
foo = yngNames.index(names[i])
yngString = 'yng'
except ValueError, e:
yngString = ''
print '%15s %5.1f %5.2f %5.2f %3s' % \
(names[i], diff[i]/diffErr[i], x[i], y[i], yngString)
def exportTrajectoriesFigToPNG(self, filename):
# values = linspace(0.3, 0.9, 5) # position of X0 between X_f0 and X_f1
# vcolors = p.cm.autumn_r(linspace(0.3, 1., len(values))) # colors for each trajectory
f2 = p.figure()
# -------------------------------------------------------
# plot trajectories
#for v, col in zip(values, vcolors):
X0 = self.X_f1 # starting point
X = odeint(self.dX_dt, self.initialCondition,
self.time) # we don't need infodict here
p.plot(X[:, 0],
X[:, 1],
lw=3.5,
label='X0=(%.f, %.f)' %
(self.initialCondition[0], self.initialCondition[1]))
p.plot(X0[0], X0[1], 'b.')
p.plot(self.X_f1[0], self.X_f1[1], 'r.')
# -------------------------------------------------------
# define a grid and compute direction at each point
ymax = p.ylim(ymin=0)[1] # get axis limits
xmax = p.xlim(xmin=0)[1]
nb_points = 30
x = np.linspace(0, xmax, nb_points)
y = np.linspace(0, ymax, nb_points)
X1, Y1 = p.meshgrid(x, y) # create a grid
DX1, DY1 = self.dX_dt([X1, Y1]) # compute growth rate on the gridt
M = (p.hypot(DX1, DY1)) # Norm of the growth rate
M[M == 0] = 1. # Avoid zero division errors
DX1 /= M # Normalize each arrows
DY1 /= M
p.title('Trajectories and direction fields')
p.quiver(X1, Y1, DX1, DY1, M, pivot='mid')
p.xlabel('Number of rabbits')
p.ylabel('Number of foxes')
p.legend()
p.grid()
p.xlim(0, xmax)
p.ylim(0, ymax)
f2.savefig(filename)
def intPlot(v0, w0, i):
pl.figure()
x0 = np.array([v0, w0])
ps = integrate(diff, x0, 0, 1000, 0.1, i)
pl.plot(ps[:,0], ps[:,1], 'm', linewidth=3)
x = pl.linspace(-2.5, 2.5, 30)
y = pl.linspace(-2.5, 2.5, 30)
x1,y1 = pl.meshgrid(x,y)
dx1, dy1 = diff(0, [x1,y1], i)
m = (pl.hypot(dx1, dy1))
m[m == 0] = 1.
dx1 /= m
dy1 /= m
v0 = x - (x**3)/3.0 + i
w0 = (x + 0.7)/0.8
# Desenha grade e vetores de velocidade
pl.grid()
pl.quiver(x1, y1, dx1, dy1, m, pivot='mid', cmap=pl.cm.jet)
pl.rcParams['figure.figsize'] = 13,9
bd = 0.8
pl.ylim(ymax=max(ps[:,1]) + bd, ymin=min(ps[:,1]) - bd)
pl.xlim(xmax=max(ps[:,0]) + bd, xmin=min(ps[:,0]) - bd)
pl.colorbar()
pl.plot(x, w0, 'g--', linewidth=3)
pl.plot(x, v0, 'r--', linewidth=3)
pl.ylabel("W")
pl.xlabel("V")
pl.savefig('/usr/share/nginx/www/neuron/graph.png')
def exportTrajectoriesFigToPNG(self, filename: str):
f2 = p.figure()
X0 = self.X_f1
X = odeint(self.dX_dt, self.initialCondition, self.time)
p.plot(X[:, 0],
X[:, 1],
lw=3.5,
label='X0=(%.f, %.f)' %
(self.initialCondition[0], self.initialCondition[1]))
p.plot(X0[0], X0[1], 'b.')
p.plot(self.X_f1[0], self.X_f1[1], 'r.')
ymax = p.ylim(ymin=0)[1]
xmax = p.xlim(xmin=0)[1]
nb_points = 30
x = np.linspace(0, xmax, nb_points)
y = np.linspace(0, ymax, nb_points)
X1, Y1 = p.meshgrid(x, y)
DX1, DY1 = self.dX_dt([X1, Y1])
M = (p.hypot(DX1, DY1))
M[M == 0] = 1.
DX1 /= M
DY1 /= M
p.title('Trajectories and direction fields')
p.quiver(X1, Y1, DX1, DY1, M, pivot='mid')
p.xlabel('Number of rabbits')
p.ylabel('Number of foxes')
p.legend()
p.grid()
p.xlim(0, xmax)
p.ylim(0, ymax)
f2.savefig(filename)
def plotcomp(compdict, showplot=True, wantdict=False, symb=',',
include0amp=False, include0bl=False, blunit='', bl0flux=0.0):
"""
Given a dict including
{'clist': component list,
'objname': objname,
'epoch': epoch,
'shape': component shape dict, including direction.
'freqs (GHz)': pl.array of frequencies,
'antennalist': An array configuration file as used by simdata,
'savedfig': False or, if specified, the filename to save the plot to,
'standard': setjy fluxstandard type},
and symb: One of matplotlib's codes for plot symbols: .:,o^v<>s+xDd234hH|_
default: ',': The smallest points I could find,
make a plot of visibility amplitude vs. baseline length for clist at epoch.
If antennalist is not found as is, it will look for antennalist in
os.getenv('CASAPATH').split(' ')[0] + '/data/alma/simmos/'.
showplot: Whether or not to show the plot on screen.
If wantdict is True, it returns a dictionary with the amplitudes and
baselines on success. Otherwise, it returns True or False as its estimated
success value.
include0amp: Force the lower limit of the amplitude axis to 0.
include0bl: Force the lower limit of the baseline length axis to 0.
blunit: unit of the baseline length (='' used the unit in the data or klambda)
bl0flux: Zero baseline flux
"""
def failval():
"""
Returns an appropriate failure value.
Note that mydict.update(plotcomp(wantdict=True, ...)) would give a
confusing error message if plotcomp returned False.
"""
retval = False
if wantdict:
retval = {}
return retval
retval = failval() # Default
try:
clist = compdict['clist']
objname = compdict['objname']
epoch = compdict['epoch']
epstr = mepoch_to_str(epoch)
antennalist = compdict['antennalist']
# Read the configuration info.
if not antennalist:
print "compdict['antennalist'] must be set!"
print "Try something in", os.getenv("CASAPATH").split(' ')[0] + "/data/alma/simmos/"
return failval()
# Try repodir if raw antennalist doesn't work.
if not os.path.exists(antennalist):
repodir = os.getenv("CASAPATH").split(' ')[0] + "/data/alma/simmos/"
antennalist = repodir + antennalist
su = simutil("")
stnx, stny, stnz, diam, padnames, telescopename, obsmeas = su.readantenna(antennalist)
#print "telescopename:", telescopename
# Check that the source is up.
myme = metool()
posobs = myme.observatory(telescopename)
#print "posobs:", posobs
myme.doframe(epoch)
myme.doframe(posobs)
azel = myme.measure(compdict['shape']['direction'], 'azel')
azeldegs = tuple([qa.convert(azel[m], 'deg')['value'] for m in ('m0', 'm1')])
casalog.post("(az, el): (%.2f, %.2f) degrees" % azeldegs)
# riseset blabs to the logger, so introduce it now.
casalog.post('Rise and set times of ' + objname + " from " + telescopename + ':')
approx = ''
if 'JPL' in compdict.get('standard', 'JPL'):
# The object is in the Solar System or not known to be extragalactic.
approx = "APPROXIMATE. The times do not account for the apparent motion of "\
+ objname + "."
casalog.post(" (" + approx + ")")
riset = myme.riseset(compdict['shape']['direction'])
msg = ''
if riset['rise'] == 'above':
msg = objname + " is circumpolar"
elif riset['rise'] == 'below':
msg = objname + ' is not visible from ' + telescopename
if msg:
if approx:
msg += ' around ' + mepoch_to_str(epoch)
casalog.post(msg)
else:
for t in riset:
riset[t]['str'] = mepoch_to_str(riset[t]['utc'])
casalog.post(objname + " rises at %s and sets at %s." % (riset['rise']['str'],
riset['set']['str']))
tmeridian=(riset['rise']['utc']['m0']['value']+riset['set']['utc']['m0']['value'])/2.
casalog.post(objname + ': meridian passage at ' + qa.time(str(tmeridian)+'d')[0])
if approx:
riset['NOTE'] = approx
if not azel['m1']['value'] > 0.0:
casalog.post(objname + " is not visible from " + telescopename + " at " + epstr,
'SEVERE')
if wantdict:
return riset
else:
return False
# Start a temp MS.
workingdir = os.path.abspath(os.path.dirname(clist.rstrip('/')))
tempms = tempfile.mkdtemp(prefix=objname, dir=workingdir)
mysm = smtool()
mysm.open(tempms)
su.setcfg(mysm, telescopename, stnx, stny, stnz, diam,
padnames, posobs)
#print "cfg set"
# Only 1 polarization is wanted for now.
stokes, feeds = su.polsettings(telescopename, 'RR')
casalog.post("stokes, feeds: %s, %s" % (stokes, feeds))
fband = su.bandname(compdict['freqs (GHz)'][0])
chaninc = 1.0
nchan = len(compdict['freqs (GHz)'])
if nchan > 1:
chaninc = (compdict['freqs (GHz)'][-1] - compdict['freqs (GHz)'][0]) / (nchan - 1)
mysm.setspwindow(spwname=fband,
freq=str(compdict['freqs (GHz)'][0]) + 'GHz',
deltafreq=str(chaninc) + 'GHz',
freqresolution='1Hz',
nchannels=nchan, refcode="LSRK",
stokes=stokes)
mysm.setfeed(mode=feeds, pol=[''])
mysm.setlimits(shadowlimit=0.01, elevationlimit='10deg')
mysm.setauto(0.0)
mysm.setfield(sourcename=objname,
sourcedirection=compdict['shape']['direction'],
calcode="OBJ", distance='0m')
mysm.settimes(integrationtime="1s", usehourangle=False,
referencetime=epoch)
# this only creates blank uv entries
mysm.observe(sourcename=objname, spwname=fband,
starttime="-0.5s", stoptime="0.5s", project=objname)
mysm.setdata(fieldid=[0])
mysm.setvp()
casalog.post("done setting up simulation parameters")
mysm.predict(complist=clist) # do actual calculation of visibilities:
mysm.close()
casalog.post("Simulation finished.")
mytb = tbtool()
mytb.open(tempms)
data = mytb.getcol('DATA')[0] # Again, only 1 polarization for now.
data = abs(data)
baselines = mytb.getcol('UVW')[:2,:] # Drop w.
datablunit = mytb.getcolkeywords('UVW')['QuantumUnits']
mytb.close()
#print "Got the data and baselines"
shutil.rmtree(tempms)
if datablunit[1] != datablunit[0]:
casalog.post('The baseline units are mismatched!: %s' % datablunit,
'SEVERE')
return failval()
datablunit = datablunit[0]
# uv dist unit in klambda or m
if datablunit == 'm' and blunit=='klambda':
kl = qa.constants('C')['value']/(compdict['freqs (GHz)'][0]*1e6)
blunit = 'k$\lambda$'
else:
blunit = datablunit
kl = 1.0
pl.ioff()
#baselines = pl.hypot(baselines[0]/kl, baselines[1]/kl)
baselines = pl.hypot(baselines[0], baselines[1])
#if not showplot:
# casalog.post('Sorry, not showing the plot is not yet implemented',
# 'WARN')
if showplot:
pl.ion()
pl.clf()
pl.ioff()
nfreqs = len(compdict['freqs (GHz)'])
for freqnum in xrange(nfreqs):
freq = compdict['freqs (GHz)'][freqnum]
casalog.post("Plotting " + str(freq) + " GHz.")
pl.plot(baselines/kl, data[freqnum], symb, label="%.3g GHz" % freq)
#pl.plot(baselines, data[freqnum], symb, label="%.3g GHz" % freq)
pl.xlabel("Baseline length (" + blunit + ")")
pl.ylabel("Visibility amplitude (Jy)")
if include0amp:
pl.ylim(ymin=0.0)
if include0bl:
pl.xlim(xmin=0.0)
pl.suptitle(objname + " (predicted by %s)" % compdict['standard'], fontsize=14)
#pl.suptitle(objname + " (predicted)", fontsize=14)
# Unlike compdict['antennalist'], antennalist might have had repodir
# prefixed to it.
pl.title('at ' + epstr + ' for ' + os.path.basename(compdict['antennalist']), fontsize=10)
titletxt='($%.0f^\circ$ az, $%.0f^\circ$ el)' % azeldegs
# for comparison of old and new models - omit azeldegs as all in az~0
if bl0flux > 0.0:
if len(compdict['freqs (GHz)']) == 1:
titletxt+='\n bl0 flux:%.3f Jy' % bl0flux
else:
titletxt+='\n bl0 flux:%.3f Jy @ %s GHz' % (bl0flux, compdict['freqs (GHz)'][0])
pl.legend(loc='best', title=titletxt)
#pl.legend(loc='best', title='($%.0f^\circ$ az, $%.0f^\circ$ el)' % azeldegs)
y_formatter=matplotlib.ticker.ScalarFormatter(useOffset=False)
pl.axes().yaxis.set_major_formatter(y_formatter)
if showplot:
pl.ion()
pl.draw()
if compdict.get('savedfig'):
pl.savefig(compdict.get('savedfig'))
casalog.post("Saved plot to " + str(compdict.get('savedfig')))
if wantdict:
retval = {'amps': data,
'antennalist': antennalist, # Absolute path, now.
'azel': azel,
'baselines': baselines,
'blunit': blunit,
'riseset': riset,
'savedfig': compdict.get('savedfig')}
else:
retval = True
except Exception, instance:
casalog.post(str(instance), 'SEVERE')
if os.path.isdir(tempms):
shutil.rmtree(tempms)
def nonPhysical(rootDir='./', align='align/align_d_rms_1000_abs_t',
poly='polyfit_d/fit', points='points_d/', sigma=5,
plotChiSq=False, plotPrefix=''):
"""
Print out a list of stars that have non-physical accelerations.
Return: returns a list of star names that are unphysical
"""
# Load GC constants
cc = objects.Constants()
# Load up positional information from align.
s = starset.StarSet(rootDir + align)
s.loadPolyfit(rootDir + poly, arcsec=1)
s.loadPolyfit(rootDir + poly, arcsec=1, accel=1)
names = s.getArray('name')
mag = s.getArray('mag')*1.0
print len(mag)
print '%7.4f mag' % mag[0]
# In mas/yr^2
x = s.getArray('x')
y = s.getArray('y')
# pdb.set_trace()
vx = s.getArray('fitXa.v')
vy = s.getArray('fitYa.v')
ax = s.getArray('fitXa.a') * 1000.0
ay = s.getArray('fitYa.a') * 1000.0
axe = s.getArray('fitXa.aerr') * 1000.0
aye = s.getArray('fitYa.aerr') * 1000.0
r2d = s.getArray('r2d')
cnt = s.getArray('velCnt')
# chi2x = s.getArray('fitXa.chi2red')
# chi2y = s.getArray('fitYa.chi2red')
chi2x = s.getArray('fitXa.chi2')
chi2y = s.getArray('fitYa.chi2')
chi2xv = s.getArray('fitXv.chi2')
chi2yv = s.getArray('fitYv.chi2')
nEpochs = s.getArray('velCnt')
# T0 for each of the acceleration fits
epoch = s.getArray('fitXa.t0')
# All epochs
allEpochs = np.array(s.stars[0].years)
# Lets do radial/tangential
r = np.sqrt(x**2 + y**2)
if ('Radial' in poly):
at = ax
ar = ay
ate = axe
are = aye
else:
ar = ((ax*x) + (ay*y)) / r
at = ((ax*y) - (ay*x)) / r
are = np.sqrt((axe*x)**2 + (aye*y)**2) / r
ate = np.sqrt((axe*y)**2 + (aye*x)**2) / r
# Total acceleration
atot = py.hypot(ax, ay)
atoterr = np.sqrt((ax*axe)**2 + (ay*aye)**2) / atot
# Calculate the acceleration limit set by the projected radius
# Convert into cm
r2d = r * cc.dist * cc.cm_in_au
rarc =np.arange(0.01,10.0,0.01)
rsim = rarc * cc.dist * cc.cm_in_au
# acc1 in cm/s^2
a2d = -cc.G * cc.mass * cc.msun / r2d**2
a2dsim = -cc.G * cc.mass * cc.msun / rsim**2
# acc1 in km/s/yr
a2d *= cc.sec_in_yr / 1.0e5
a2d *= 1000.0 / cc.asy_to_kms
a2dsim *= cc.sec_in_yr / 1.0e5
a2dsim *= 1000.0 / cc.asy_to_kms
##########
#
# Non-physical accelerations.
#
##########
# 1. Look for signficant non-zero tangential accelerations.
bad1 = (np.where( abs(at/ate) > sigma))[0]
print 'Found %d stars with tangential accelerations > %4.1f sigma' % \
(len(bad1), sigma)
# 2. Look for significant positive radial accelerations
bad2 = (np.where( ((ar - (sigma*are)) > 0) ))[0]
print 'Found %d stars with positive radial accelerations > %4.1f sigma' % \
(len(bad2), sigma)
# 3. Look for negative radial accelerations that are higher
# than physically allowed.
bad3 = (np.where( ar + (sigma*are) < a2d))[0]
print 'Found %d stars with too large negative radial accelerations' % \
(len(bad3))
bad = np.unique( np.concatenate((bad1, bad2, bad3)) )
# chose only bright stars
nbad = len(bad)
bright = (np.where(mag[bad] < 16.0))[0]
bad = bad[bright]
# bad = bad2
# Sort
rdx = r2d[bad].argsort()
bad = bad[rdx]
######### look for physical accelerations
goodAccel = np.where(ar + sigma*are < 0)[0]
# loop through the bad points and look at the closest stars
nearestStar = np.zeros(len(x))
nearestStarBad = np.zeros(len(x))
# look for the minium distance between all stars according to the fit
for ii in arange(0,len(x)):
distances = sqrt((x[ii] - x)**2 + (y[ii] - y)**2)
srt = distances.argsort()
distances = distances[srt]
#nearestStar[ii] = distances[1]
#loop over the 5 closest sources to see which one is closest
#print names[ii]
fitMin = np.zeros(5)
for rr in arange(1,6):
#print names[srt[rr]]
xfit1 = [x[ii],vx[ii],ax[ii]/1000.0]
xfit2 = [x[srt[rr]],vx[srt[rr]],ax[srt[rr]]/1000.0]
yfit1 = [y[ii],vy[ii],ay[ii]/1000.0]
yfit2 = [y[srt[rr]],vy[srt[rr]],ay[srt[rr]]/1000.0]
fitMin[rr-1] = minDistance(xfit1,yfit1,xfit2,yfit2,epoch[ii],[np.amin(allEpochs),np.amax(allEpochs)])
#print 'fitMin: %f' % fitMin[rr-1]
## if np.amin(fitMin) < 0.1:
## indMin = np.argmin(fitMin)
## print names[ii] +' ' +names[srt[indMin+1]]+ ' minDist: %f fitMin: %f' % (distances[1], np.amin(fitMin))
# pdb.set_trace()
nearestStar[ii] = np.amin(fitMin)
# check only the unphysical accelerations
for jj in bad:
distances = sqrt((x[jj] - x)**2 + (y[jj] - y)**2)
srt = distances.argsort()
distances = distances[srt]
nearestStarBad[jj] = distances[1]
fitMin = np.zeros(5)
for rr in arange(1,6):
#print names[srt[rr]]
xfit1 = [x[jj],vx[jj],ax[jj]/1000.0]
xfit2 = [x[srt[rr]],vx[srt[rr]],ax[srt[rr]]/1000.0]
yfit1 = [y[jj],vy[jj],ay[jj]/1000.0]
yfit2 = [y[srt[rr]],vy[srt[rr]],ay[srt[rr]]/1000.0]
## if names[jj] == 'S1-10':
## print names[srt[rr]]
## pause = 1
## else:
## pause = 0
## fitMin[rr-1] = minDistance(xfit1,yfit1,xfit2,yfit2,epoch[jj],[np.amin(allEpochs),np.amax(allEpochs)], pause = pause)
fitMin[rr-1] = minDistance(xfit1,yfit1,xfit2,yfit2,epoch[jj],[np.amin(allEpochs),np.amax(allEpochs)])
#print 'fitMin: %f' % fitMin[rr-1]
if np.amin(fitMin) < 0.1:
indMin = np.argmin(fitMin)
print names[jj] +' ' +names[srt[indMin+1]]+ ' minDist: %f fitMin: %f' % (distances[1], np.amin(fitMin))
# pdb.set_trace()
nearestStar[jj] = np.amin(fitMin)
print '%d faint stars with non-physical accelerations' % (nbad - len(bright))
print '*** Found %d stars with non-physical accelerations ***' % len(bad)
returnNames = []
print chi2x
# print 'Name Mag x y chi2x chi2y ar are at ate nearest'
print '|Name |Mag |x |y |chi2x |chi2y |ar |are |at |ate |nearest|'
for bb in bad:
returnNames = np.concatenate([returnNames,[names[bb]]])
# print '%6s %7.3f %7.3f %7.3f %7.3f %7.3f %7.3f %7.3f %7.3f %7.3f %7.3f' % (names[bb], mag[bb], x[bb], y[bb],chi2x[bb],chi2y[bb],ar[bb]*cc.asy_to_kms/1000.0,are[bb]*cc.asy_to_kms/1000.0,at[bb]*cc.asy_to_kms/1000.0,ate[bb]*cc.asy_to_kms/1000.0,nearestStar[bb])
print '|%6s |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f |%7.3f|' % (names[bb], mag[bb], x[bb], y[bb],chi2x[bb],chi2y[bb],ar[bb]*cc.asy_to_kms/1000.0,are[bb]*cc.asy_to_kms/1000.0,at[bb]*cc.asy_to_kms/1000.0,ate[bb]*cc.asy_to_kms/1000.0,nearestStar[bb])
if plotChiSq:
# plot some chi-square info
clf()
subplot(231)
# look at the distribution of chi-squares for stars brighter than 16 magnitude and have maximum degree of freedom
maxEpoch = np.amax(nEpochs)
dof = maxEpoch - 3
good = np.where((mag < 16.0) & (nEpochs == maxEpoch))[0]
# filter out the accelerations that do not have the max number of epochs measured
print shape(good)
print shape(bad)
bad = np.intersect1d(good, bad)
goodAccel = np.intersect1d(goodAccel, good)
# use mean degree of freedom:
#dof = np.floor(np.mean(nEpochs)-3)
print 'Degree of freedom %f' % dof
maxChi=30
n, bins, patches1 = hist(chi2x[good], bins = np.arange(0, maxChi, 0.5),normed=1,label='x',alpha=0.6,color='blue')
n, bins, patches2 = hist(chi2y[good], bins = bins, normed=1, label='y',alpha=0.6,color='green')
xlabel('Chi-Sq Acc')
title('All stars')
chiInd = np.arange(0.01,100,0.01)
chi2Theory = stats.chi2.pdf(chiInd,dof)
plot(chiInd, chi2Theory)
legend()
subplot(232)
n, bins, patches1 = hist(chi2x[bad], bins = np.arange(0,maxChi,0.5), normed=1,label='x',alpha=0.6)
n, bins, patches2 = hist(chi2y[bad], bins = bins,normed=1,label='y',alpha=0.6)
title('Non-physical Accelerations')
xlabel('Chi-Sq Acc')
plot(chiInd, chi2Theory)
legend()
# chi-sq distribution of physical accelerations
subplot(233)
n, bins, patches1 = hist(chi2x[goodAccel], bins = np.arange(0,maxChi,0.5), normed=1,label='x',alpha=0.6)
n, bins, patches2 = hist(chi2y[goodAccel], bins = bins,normed=1,label='y',alpha=0.6)
title('Physical Accelerations')
xlabel('Chi-Sq Acc')
plot(chiInd, chi2Theory)
legend()
#savefig('./chiSq_dist_accel.png')
#clf()
# plot the velocities
velDof = dof + 1
subplot(234)
n, bins, patches1 = hist(chi2xv[good], bins = np.arange(0, maxChi, 0.5),normed=1,label='x',alpha=0.6,color='blue')
n, bins, patches2 = hist(chi2yv[good], bins = bins, normed=1, label='y',alpha=0.6,color='green')
xlabel('Chi-Sq Vel')
title('All stars')
chiInd = np.arange(0.01,100,0.01)
chi2Theory = stats.chi2.pdf(chiInd,velDof)
plot(chiInd, chi2Theory)
legend()
subplot(235)
n, bins, patches1 = hist(chi2xv[bad], bins = np.arange(0,maxChi,0.5), normed=1,label='x',alpha=0.6)
n, bins, patches2 = hist(chi2yv[bad], bins = bins,normed=1,label='y',alpha=0.6)
title('Non-physical Accelerations')
xlabel('Chi-Sq Vel')
plot(chiInd, chi2Theory)
legend()
subplot(236)
n, bins, patches1 = hist(chi2xv[goodAccel], bins = np.arange(0,maxChi,0.5), normed=1,label='x',alpha=0.6)
n, bins, patches2 = hist(chi2yv[goodAccel], bins = bins,normed=1,label='y',alpha=0.6)
title('Physical Accelerations')
xlabel('Chi-Sq Vel')
plot(chiInd, chi2Theory)
legend()
savefig(plotPrefix+'chiSq_dist_accel_vel.pdf')
clf()
subplot(131)
loglog(chi2xv[good]/(dof+1.0),chi2x[good]/dof,'bo',label='X',alpha=0.6)
plot(chi2yv[good]/(dof+1.0),chi2y[good]/dof,'go',label='Y',alpha=0.6)
plot([0.001,100],[0.001,100])
xlim(0.01,maxChi)
ylim(0.01,maxChi)
xlabel('Vel. Fit Reduced Chi-Sq')
ylabel('Acc. Fit Reduced Chi-Sq')
title('All Stars')
legend(loc=2)
subplot(132)
loglog(chi2xv[bad]/(nEpochs[bad]-2.0),chi2x[bad]/(nEpochs[bad]-3.0),'bo',label='X',alpha=0.6)
plot(chi2yv[bad]/(nEpochs[bad]-2.0),chi2y[bad]/(nEpochs[bad]-3.0),'go',label='Y',alpha=0.6)
plot([0.001,100],[0.001,100])
xlim(0.01,maxChi)
ylim(0.01,maxChi)
xlabel('Vel. Fit Reduced Chi-Sq')
ylabel('Acc. Fit Reduced Chi-Sq')
title('Non-physical Accelerations')
legend(loc=2)
subplot(133)
loglog(chi2xv[goodAccel]/(nEpochs[goodAccel]-2.0),chi2x[goodAccel]/(nEpochs[goodAccel]-3.0),'bo',label='X',alpha=0.6)
plot(chi2yv[goodAccel]/(nEpochs[goodAccel]-2.0),chi2y[goodAccel]/(nEpochs[goodAccel]-3.0),'go',label='Y',alpha=0.6)
plot([0.001,100],[0.001,100])
xlim(0.01,maxChi)
ylim(0.01,maxChi)
xlabel('Vel. Fit Reduced Chi-Sq')
ylabel('Acc. Fit Reduced Chi-Sq')
title('Physical Accelerations')
legend(loc=2)
savefig(plotPrefix+'chi2_vel_vs_accel.pdf')
# plot chi-sq as a function of magnitude
clf()
subplot(231)
semilogy(mag[good],chi2x[good]/(nEpochs[good]-3.0),'bo',label='X',alpha=0.6)
plot(mag[good],chi2y[good]/(nEpochs[good]-3.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Accel. Fit Chi-Sq')
title('All Stars')
legend(loc=3)
subplot(232)
semilogy(mag[bad],chi2x[bad]/(nEpochs[bad]-3.0),'bo',label='X',alpha=0.6)
plot(mag[bad],chi2y[bad]/(nEpochs[bad]-3.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Accel. Fit Chi-Sq')
title('Non-physical Accelerations')
legend(loc=3)
subplot(233)
semilogy(mag[goodAccel],chi2x[goodAccel]/(nEpochs[goodAccel]-3.0),'bo',label='X',alpha=0.6)
plot(mag[goodAccel],chi2y[goodAccel]/(nEpochs[goodAccel]-3.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Accel. Fit Chi-Sq')
title('Physical Accelerations')
legend(loc=3)
subplot(234)
semilogy(mag[good],chi2xv[good]/(nEpochs[good]-2.0),'bo',label='X',alpha=0.6)
plot(mag[good],chi2yv[good]/(nEpochs[good]-2.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Vel. Fit Chi-Sq')
title('All Stars')
legend(loc=3)
subplot(235)
semilogy(mag[bad],chi2xv[bad]/(nEpochs[bad]-2.0),'bo',label='X',alpha=0.6)
plot(mag[bad],chi2yv[bad]/(nEpochs[bad]-2.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Vel. Fit Chi-Sq')
title('Non-physical Accelerations')
legend(loc=3)
subplot(236)
semilogy(mag[goodAccel],chi2xv[goodAccel]/(nEpochs[goodAccel]-2.0),'bo',label='X',alpha=0.6)
plot(mag[goodAccel],chi2yv[goodAccel]/(nEpochs[goodAccel]-2.0),'go',label='Y',alpha=0.6)
ylim(0.01,maxChi)
xlabel('K mag')
ylabel('Vel. Fit Chi-Sq')
title('Physical Acceleration')
legend(loc=3)
savefig(plotPrefix+'mag_vs_chi2.pdf')
else:
# plot the histogram
clf()
subplot(221)
nAll, allbins, patches = hist(nearestStar,bins=50)
#nBad, badbins, patches2 = hist(nearestStarBad[bad],bins=allbins)
nBad, badbins, patches2 = hist(nearestStarBad[bad],bins=10)
setp(patches2, 'facecolor','r')
xlabel('Nearest Neighbor (arcsec)')
ylabel('N Stars')
# do a ks test of the two distributions
print 'len(allbins) %f' % len(allbins)
print 'len(badbins) %f' % len(badbins)
print nAll
print nBad
ksTest = stats.ks_2samp(nAll, nBad)
print ksTest
subplot(222)
plot(rarc,a2dsim)
plot([0,10],[0,0],hold=True)
# plot(r[bad],ar[bad],are[bb],'o')
py.errorbar(r[bad],ar[bad],are[bad],fmt='o')
xlim(0,5)
ylim(2,-2)
xlabel('Distance from Sgr A* (arcsec)')
ylabel('Acceleration (km/s/yr)')
subplot(223)
# look at the distribution of chi-squares for stars brighter than 16 magnitude
good = np.where(mag < 16.0)
# use mean degree of freedom:
dof = np.floor(np.mean(nEpochs)-3)
print 'Mean degree of freedom %f' % dof
n, bins, patches1 = hist(chi2x[good], bins = np.arange(0, 20, 0.1),normed=1,label='x',alpha=0.6,color='blue')
n, bins, patches2 = hist(chi2y[good], bins = bins, normed=1, label='y',alpha=0.6,color='green')
xlabel('Reduced Chi^2')
chiInd = np.arange(0.01,100,0.01)
chi2Theory = stats.chi2.pdf(chiInd,dof)
plot(chiInd, chi2Theory)
legend()
subplot(224)
n, bins, patches1 = hist(chi2x[bad], bins = np.arange(0,20,0.5), normed=1,label='x',alpha=0.6)
n, bins, patches2 = hist(chi2y[bad], bins = bins,normed=1,label='y',alpha=0.6)
xlabel('Reduced Chi^2')
plot(chiInd, chi2Theory)
legend()
savefig('./unphysicalAccel_stats.png')
return returnNames
def plotLimits(rootDir='./', align='align/align_d_rms1000_t',
poly='polyfit_d_points/fit', points='points_d/',
youngOnly=False, sigma=3):
"""
Find the 4 sigma radial acceleration limits for each star.
"""
# Load GC constants
cc = objects.Constants()
# Load up positional information from align.
s = starset.StarSet(rootDir + align)
s.loadPolyfit(rootDir + poly, arcsec=1)
s.loadPolyfit(rootDir + poly, arcsec=1, accel=1)
names = s.getArray('name')
# In mas/yr^2
x = s.getArray('x')
y = s.getArray('y')
vx = s.getArray('fitXa.v')
vy = s.getArray('fitYa.v')
ax = s.getArray('fitXa.a') * 1000.0
ay = s.getArray('fitYa.a') * 1000.0
axe = s.getArray('fitXa.aerr') * 1000.0
aye = s.getArray('fitYa.aerr') * 1000.0
r2d = s.getArray('r2d')
cnt = s.getArray('velCnt')
if (youngOnly == True):
yngNames = young.youngStarNames()
idx = []
for ii in range(len(names)):
if (r2d[ii] > 0.8 and
names[ii] in yngNames and
cnt[ii] >= 24):
idx.append(ii)
names = [names[i] for i in idx]
x = x[idx]
y = y[idx]
vx = vx[idx]
vy = vy[idx]
ax = ax[idx]
ay = ay[idx]
axe = axe[idx]
aye = aye[idx]
r2d = r2d[idx]
print 'Found %d young stars' % len(names)
# Lets do radial/tangential
r = np.sqrt(x**2 + y**2)
if ('Radial' in poly):
at = ax
ar = ay
ate = axe
are = aye
else:
ar = ((ax*x) + (ay*y)) / r
at = ((ax*y) - (ay*x)) / r
are = np.sqrt((axe*x)**2 + (aye*y)**2) / r
ate = np.sqrt((axe*y)**2 + (aye*x)**2) / r
# Total acceleration
atot = py.hypot(ax, ay)
atoterr = np.sqrt((ax*axe)**2 + (ay*aye)**2) / atot
accLim = ar - (sigma * are)
# Calculate the acceleration limit set by the projected radius
# Convert into cm
r2d = r * cc.dist * cc.cm_in_au
# acc1 in cm/s^2
a2d = -cc.G * cc.mass * cc.msun / r2d**2
# acc1 in km/s/yr
a2d *= cc.sec_in_yr / 1.0e5
a2d *= 1000.0 / cc.asy_to_kms
accLimR2d = a2d
_f = open(rootDir + 'tables/accel_limits.txt', 'w')
_f.write('### Radial Acceleration Limits for Young Stars ###\n')
_f.write('%13s %7s - (%d * %5s) = %9s %21s Constrained?\n' % \
('Name', 'a_rad', sigma, 'aerr', 'a_obs_lim', 'a_proj_lim'))
fmt = '%13s %7.3f - (%d * %5.3f) = %7.3f '
fmt += 'vs %10.3f (mas/yr^2) %s'
fmt2 = fmt + '\n'
for i in range(len(ar)):
constrained = ''
if (accLim[i] > accLimR2d[i]):
constrained = '**'
print fmt % (names[i], ar[i], sigma, are[i], accLim[i],
accLimR2d[i], constrained)
_f.write(fmt2 % (names[i], ar[i], sigma, are[i], accLim[i],
accLimR2d[i], constrained))
_f.close()
def plotLimitProps():
"""Make a table of the observed properties of those stars that
have significant orbital acceleration limits.
Currently hard coded to 06_02_14 and efit results.
"""
# Load GC constants
cc = objects.Constants()
root = '/u/jlu/work/gc/proper_motion/align/06_02_14/'
efitResults = root + 'efit/results/outer3'
# Load up positional information from align.
s = starset.StarSet(root + 'align/align_all1000_t')
s.loadPolyfit(root + 'polyfit/fit', arcsec=1)
s.loadPolyfit(root + 'polyfit/fit', arcsec=1, accel=1)
s.loadEfitResults(efitResults, trimStars=1)
# Calculate acceleration limit from the 2D position
x = s.getArray('fitXalign.p')
y = s.getArray('fitYalign.p')
xerr = s.getArray('fitXalign.perr')
yerr = s.getArray('fitYalign.perr')
r2d = py.hypot(x, y)
# Convert into cm
r2d *= cc.dist * cc.cm_in_au
# acc1 in cm/s^2
a2d = cc.G * cc.mass * cc.msun / r2d**2
# acc1 in km/s/yr
a2d *= cc.sec_in_yr / 1e5
a1 = s.getArray('efit.at_lo')
a2 = s.getArray('efit.at_hi')
alim = [max(abs(a1[ii]), abs(a2[ii])) for ii in range(len(a1))]
name = s.getArray('name')
# The properties of interest
cnt = s.getArray('velCnt')
mag = s.getArray('mag')
vx = s.getArray('fitXv.v') * 1000.0
vxerr = s.getArray('fitXv.verr') * 1000.0
vy = s.getArray('fitYv.v') * 1000.0
vyerr = s.getArray('fitYv.verr') * 1000.0
v2d = py.hypot(vx, vy)
v2derr = np.sqrt((vx*vxerr)**2 + (vy*vyerr)**2) / v2d
x = s.getArray('x')
y = s.getArray('y')
# Lets also read in definities only (no 05jullgs) as comparison
root2 = root + '../06_02_14a/'
s2 = starset.StarSet(root2 + 'align/align_d_rms_t')
names2 = s2.getArray('name')
idx = []
stars2 = []
for i in range(len(alim)):
if (alim[i] < a2d[i]):
idx.append(i)
# Find this star in the new analysis
new = names2.index(name[i])
stars2.append(s2.stars[new])
s2.stars = stars2
cnt2 = s2.getArray('velCnt')
mag2 = s2.getArray('mag')
# Clear plot region
py.figure(2, figsize=(7,6))
py.clf()
# Make a histogram of the number of epochs
py.subplot(2, 2, 1)
n, bins, patches1 = py.hist(cnt, bins=range(0, 32, 1))
py.setp(patches1, 'facecolor', 'r')
n, bins, patches2 = py.hist(cnt[idx], bins=range(0, 32, 1))
py.setp(patches2, 'facecolor', 'b')
py.legend((patches1[0], patches2[0]),
('All Young Stars', 'Young Stars With Acc. Limits'),
loc='upper left', prop=FontProperties(size=8))
py.xlabel('Number of Epochs')
py.subplot(2, 2, 2)
n, bins, patches = py.hist(mag, bins=range(8, 16, 1))
py.setp(patches, 'facecolor', 'r')
n, bins, patches = py.hist(mag[idx], bins=range(8, 16, 1))
py.setp(patches, 'facecolor', 'b')
py.xlabel('Magnitude')
py.subplot(2, 2, 3)
n, bins, patches1 = py.hist(v2d, bins=range(0,16,1))
py.setp(patches1, 'facecolor', 'r')
n, bins, patches2 = py.hist(v2d[idx], bins=range(0,16,1))
py.setp(patches1, 'facecolor', 'r')
py.xlabel('Velocity (mas/yr)')
py.subplot(2, 2, 4)
n, bins, patches1 = py.hist(v2derr, bins=np.arange(0,0.25,0.02))
py.setp(patches1, 'facecolor', 'r')
n, bins, patches2 = py.hist(v2derr[idx], bins=np.arange(0,0.25,0.02))
py.setp(patches1, 'facecolor', 'r')
py.xlabel('Velocity Error (mas/yr)')
py.savefig(root + 'plots/acc_limits_properties.ps')
py.clf()
py.plot(x, y, 'k^')
py.plot(x[idx], y[idx], 'r^')
for i in range(len(x)):
py.text(x[i], y[i], name[i])
py.axis('equal')
py.axis([4, -4, -4, 4])
def isPhysical(x, y, ax, axe, ay, aye, arcsec = None, sigma = 3):
"""
Return a True or False depending on whether the acceleration
measurement is physical. Unphysical accelerations are defined as
either: 1. Significant tangential acceleration 2. Significant
positive radial acceleration 3. Negative acceleration greater than
the maximum allowed at the 2D position.
RETURN: status array where 1 is true [tangential, pos. radial, > max radial]
"""
status = np.zeros(3)
# Lets do radial/tangential
r = np.sqrt(x**2 + y**2)
ar = ((ax*x) + (ay*y)) / r
at = ((ax*y) - (ay*x)) / r
are = np.sqrt((axe*x)**2 + (aye*y)**2) / r
ate = np.sqrt((axe*y)**2 + (aye*x)**2) / r
# Total acceleration
atot = py.hypot(ax, ay)
atoterr = np.sqrt((ax*axe)**2 + (ay*aye)**2) / atot
# Calculate the acceleration limit set by the projected radius
if arcsec:
#convert to mks
cc = objects.Constants()
# Convert into cm
r2d = r * cc.dist * cc.cm_in_au
rarc =np.arange(0.01,10.0,0.01)
rsim = rarc * cc.dist * cc.cm_in_au
# acc1 in cm/s^2
a2d = -cc.G * cc.mass * cc.msun / r2d**2
a2dsim = -cc.G * cc.mass * cc.msun / rsim**2
# acc1 in km/s/yr
a2d *= cc.sec_in_yr / 1.0e5
#a2d *= 1000.0 / cc.asy_to_kms
# convert between arcsec/yr^2 to km/s/yr
ar *= cc.asy_to_kms
are *= cc.asy_to_kms
at *= cc.asy_to_kms
ate *= cc.asy_to_kms
a2dsim *= cc.sec_in_yr / 1.0e5
#a2dsim *= 1000.0 / cc.asy_to_kms
# tests to see if the accelerations are physical
if (abs(at/ate) > sigma):
#print 'significant tangential acceleration'
status[0] = 1
#print 'radial acceleration %f +- %f' % (ar, are)
#print 'tangential acceleration %f +- %f' % (at, ate)
if ((ar - (sigma*are)) > 0):
#print 'positive radial acceleration'
status[1] = 1
if (ar + (sigma*are) < a2d):
#print 'too large radial acceleration'
status[2] = 1
return status
def histAccel(rootDir='./', align = 'align/align_d_rms_1000_abs_t',
poly='polyfit_d/fit', points='points_d/',
youngOnly=False):
"""
Make a histogram of the accelerations in the radial/tangential,
X/Y, and inline/perp. direction of motion. Also, the large outliers
(> 3 sigma) are also printed out.
Inputs:
rootDir = The root directory of an astrometry analysis
(e.g. '07_05_18/' or './' if you are in the directory).
align = The align root file name (including the directory relative
to rootDir). Make sure that polyfit was run on this align
output.
poly = The polyfit root file name (including the directory relative
to rootDir). This should be run on the same align as above.
points = The points directory.
youngOnly = Only plot the known young stars. This does not include
the central arcsecond sources.
Output:
plots/polyfit_hist_accel.eps (and png)
-- Contains the histograms of the accelerations.
plots/polyfit_hist_accel_nepochs.eps (ang png)
-- Contains a plot of number of stars vs. acceleration significance.
"""
s = starset.StarSet(rootDir + align)
s.loadPolyfit(rootDir + poly, accel=0, arcsec=1)
s.loadPolyfit(rootDir + poly, accel=1, arcsec=1)
names = s.getArray('name')
# In mas/yr^2
x0 = s.getArray('fitXa.p')
y0 = s.getArray('fitYa.p')
x0e = s.getArray('fitXa.perr')
y0e = s.getArray('fitYa.perr')
vx = s.getArray('fitXa.v')
vy = s.getArray('fitYa.v')
ax = s.getArray('fitXa.a')
ay = s.getArray('fitYa.a')
axe = s.getArray('fitXa.aerr')
aye = s.getArray('fitYa.aerr')
r2d = s.getArray('r2d')
cnt = s.getArray('velCnt')
if (youngOnly == True):
yngNames = young.youngStarNames()
idx = []
for ii in range(len(names)):
if (r2d[ii] > 0.8 and
names[ii] in yngNames):# and
#cnt[ii] >= 24):
idx.append(ii)
names = [names[i] for i in idx]
x0 = x0[idx]
y0 = y0[idx]
x0e = x0e[idx]
y0e = y0e[idx]
vx = vx[idx]
vy = vy[idx]
ax = ax[idx]
ay = ay[idx]
axe = axe[idx]
aye = aye[idx]
r2d = r2d[idx]
cnt = cnt[idx]
print 'Found %d young stars' % len(names)
pntcnt = np.zeros(len(names))
for ii in range(len(names)):
pntFileName = '%s%s.points' % (rootDir+points, names[ii])
pntFile = open(pntFileName)
data = pntFile.readlines()
pntcnt[ii] = len(data)
# Lets also do radial/tangential
r = np.sqrt(x0**2 + y0**2)
ar = ((ax*x0) + (ay*y0)) / r
at = ((ax*y0) - (ay*x0)) / r
are = (axe*x0/r)**2 + (aye*y0/r)**2
are += (y0*x0e*at/r**2)**2 + (x0*y0e*at/r**2)**2
are = np.sqrt(are)
ate = (axe*y0/r)**2 + (aye*x0/r)**2
ate += (y0*x0e*ar/r**2)**2 + (x0*y0e*ar/r**2)**2
ate = np.sqrt(ate)
# Lets also do parallael/perpendicular to velocity
v = np.sqrt(vx**2 + vy**2)
am = ((ax*vx) + (ay*vy)) / v
an = ((ax*vy) - (ay*vx)) / v
ame = np.sqrt((axe*vx)**2 + (aye*vy)**2) / v
ane = np.sqrt((axe*vy)**2 + (aye*vx)**2) / v
# Total acceleration
atot = py.hypot(ax, ay)
atoterr = np.sqrt((ax*axe)**2 + (ay*aye)**2) / atot
def plotHist(val, pos, label):
py.subplot(3, 2, pos)
py.hist(val, bins=range(-8, 8, 1))
py.axis([-8, 8, 0, (len(val)/3) + 2])
#py.plot(r2d, val, 'k^')
#for ii in range(len(r2d)):
# py.text(r2d[ii], val[ii], names[ii])
#py.axis([0.5, 4.0, -7, 7])
py.title(label)
print 'Significant Values for %s' % (label)
idx = (np.where(abs(val) > 3.0))[0]
for i in idx:
print '%15s %5.2f sigma %2d epochs' % \
(names[i], val[i], pntcnt[i])
py.figure(3, figsize=(7.5,10))
py.clf()
plotHist(atot / atoterr, 1, 'Total')
py.clf()
# XY
plotHist(ax / axe, 1, 'X')
plotHist(ay / aye, 2, 'Y')
# Radial/Tangential
plotHist(ar / are, 3, 'Radial')
plotHist(at / ate, 4, 'Tangential')
# In line of Motion and out
plotHist(an / ane, 5, 'Perpendic. to v')
plotHist(am / ame, 6, 'Parallel to v')
py.savefig('plots/polyfit_hist_accel.eps')
py.savefig('plots/polyfit_hist_accel.png')
# Analyze the non-central arcsec sources for Nepochs threshold
idx = (np.where(r2d > 0.8))[0]
py.figure(1)
py.clf()
py.subplot(2, 1, 1)
py.plot(ar[idx] / are[idx], pntcnt[idx], 'k.')
py.axis([-10, 10, 0, 30])
py.xlabel('Radial Acc. Sig. (sigma)')
py.ylabel('Number of Epochs Detected')
py.subplot(2, 1, 2)
py.plot(at[idx] / ate[idx], pntcnt[idx], 'k.')
py.axis([-10, 10, 0, 30])
py.xlabel('Tangent. Acc. Sig. (sigma)')
py.ylabel('Number of Epochs Detected')
py.savefig('plots/polyfit_hist_accel_nepochs.eps')
py.savefig('plots/polyfit_hist_accel_nepochs.png')
def plotcomp(compdict, showplot=True, wantdict=False, symb=',',
include0amp=False, include0bl=False, blunit='', bl0flux=0.0):
"""
Given a dict including
{'clist': component list,
'objname': objname,
'epoch': epoch,
'shape': component shape dict, including direction.
'freqs (GHz)': pl.array of frequencies,
'antennalist': An array configuration file as used by simdata,
'savedfig': False or, if specified, the filename to save the plot to,
'standard': setjy fluxstandard type},
and symb: One of matplotlib's codes for plot symbols: .:,o^v<>s+xDd234hH|_
default: ',': The smallest points I could find,
make a plot of visibility amplitude vs. baseline length for clist at epoch.
If antennalist is not found as is, it will look for antennalist in
os.getenv('CASAPATH').split(' ')[0] + '/data/alma/simmos/'.
showplot: Whether or not to show the plot on screen.
If wantdict is True, it returns a dictionary with the amplitudes and
baselines on success. Otherwise, it returns True or False as its estimated
success value.
include0amp: Force the lower limit of the amplitude axis to 0.
include0bl: Force the lower limit of the baseline length axis to 0.
blunit: unit of the baseline length (='' used the unit in the data or klambda)
bl0flux: Zero baseline flux
"""
def failval():
"""
Returns an appropriate failure value.
Note that mydict.update(plotcomp(wantdict=True, ...)) would give a
confusing error message if plotcomp returned False.
"""
retval = False
if wantdict:
retval = {}
return retval
retval = failval() # Default
try:
clist = compdict['clist']
objname = compdict['objname']
epoch = compdict['epoch']
epstr = mepoch_to_str(epoch)
antennalist = compdict['antennalist']
# Read the configuration info.
if not antennalist:
print "compdict['antennalist'] must be set!"
print "Try something in", os.getenv("CASAPATH").split(' ')[0] + "/data/alma/simmos/"
return failval()
# Try repodir if raw antennalist doesn't work.
if not os.path.exists(antennalist):
repodir = os.getenv("CASAPATH").split(' ')[0] + "/data/alma/simmos/"
antennalist = repodir + antennalist
su = simutil("")
stnx, stny, stnz, diam, padnames, nant, telescopename = su.readantenna(antennalist)
#print "telescopename:", telescopename
# Check that the source is up.
myme = metool()
posobs = myme.observatory(telescopename)
#print "posobs:", posobs
myme.doframe(epoch)
myme.doframe(posobs)
azel = myme.measure(compdict['shape']['direction'], 'azel')
azeldegs = tuple([qa.convert(azel[m], 'deg')['value'] for m in ('m0', 'm1')])
casalog.post("(az, el): (%.2f, %.2f) degrees" % azeldegs)
# riseset blabs to the logger, so introduce it now.
casalog.post('Rise and set times of ' + objname + " from " + telescopename + ':')
approx = ''
if 'JPL' in compdict.get('standard', 'JPL'):
# The object is in the Solar System or not known to be extragalactic.
approx = "APPROXIMATE. The times do not account for the apparent motion of "\
+ objname + "."
casalog.post(" (" + approx + ")")
riset = myme.riseset(compdict['shape']['direction'])
msg = ''
if riset['rise'] == 'above':
msg = objname + " is circumpolar"
elif riset['rise'] == 'below':
msg = objname + ' is not visible from ' + telescopename
if msg:
if approx:
msg += ' around ' + mepoch_to_str(epoch)
casalog.post(msg)
else:
for t in riset:
riset[t]['str'] = mepoch_to_str(riset[t]['utc'])
casalog.post(objname + " rises at %s and sets at %s." % (riset['rise']['str'],
riset['set']['str']))
tmeridian=(riset['rise']['utc']['m0']['value']+riset['set']['utc']['m0']['value'])/2.
casalog.post(objname + ': meridian passage at ' + qa.time(str(tmeridian)+'d')[0])
if approx:
riset['NOTE'] = approx
if not azel['m1']['value'] > 0.0:
casalog.post(objname + " is not visible from " + telescopename + " at " + epstr,
'SEVERE')
if wantdict:
return riset
else:
return False
# Start a temp MS.
workingdir = os.path.abspath(os.path.dirname(clist.rstrip('/')))
tempms = tempfile.mkdtemp(prefix=objname, dir=workingdir)
mysm = smtool()
mysm.open(tempms)
su.setcfg(mysm, telescopename, stnx, stny, stnz, diam,
padnames, posobs)
#print "cfg set"
# Only 1 polarization is wanted for now.
stokes, feeds = su.polsettings(telescopename, 'RR')
casalog.post("stokes, feeds: %s, %s" % (stokes, feeds))
fband = su.bandname(compdict['freqs (GHz)'][0])
chaninc = 1.0
nchan = len(compdict['freqs (GHz)'])
if nchan > 1:
chaninc = (compdict['freqs (GHz)'][-1] - compdict['freqs (GHz)'][0]) / (nchan - 1)
mysm.setspwindow(spwname=fband,
freq=str(compdict['freqs (GHz)'][0]) + 'GHz',
deltafreq=str(chaninc) + 'GHz',
freqresolution='1Hz',
nchannels=nchan, refcode="LSRK",
stokes=stokes)
mysm.setfeed(mode=feeds, pol=[''])
mysm.setlimits(shadowlimit=0.01, elevationlimit='10deg')
mysm.setauto(0.0)
mysm.setfield(sourcename=objname,
sourcedirection=compdict['shape']['direction'],
calcode="OBJ", distance='0m')
mysm.settimes(integrationtime="1s", usehourangle=False,
referencetime=epoch)
# this only creates blank uv entries
mysm.observe(sourcename=objname, spwname=fband,
starttime="-0.5s", stoptime="0.5s", project=objname)
mysm.setdata(fieldid=[0])
mysm.setvp()
casalog.post("done setting up simulation parameters")
mysm.predict(complist=clist) # do actual calculation of visibilities:
mysm.close()
casalog.post("Simulation finished.")
mytb = tbtool()
mytb.open(tempms)
data = mytb.getcol('DATA')[0] # Again, only 1 polarization for now.
data = abs(data)
baselines = mytb.getcol('UVW')[:2,:] # Drop w.
datablunit = mytb.getcolkeywords('UVW')['QuantumUnits']
mytb.close()
#print "Got the data and baselines"
shutil.rmtree(tempms)
if datablunit[1] != datablunit[0]:
casalog.post('The baseline units are mismatched!: %s' % datablunit,
'SEVERE')
return failval()
datablunit = datablunit[0]
# uv dist unit in klambda or m
if datablunit == 'm' and blunit=='klambda':
kl = qa.constants('C')['value']/(compdict['freqs (GHz)'][0]*1e6)
blunit = 'k$\lambda$'
else:
blunit = datablunit
kl = 1.0
pl.ioff()
#baselines = pl.hypot(baselines[0]/kl, baselines[1]/kl)
baselines = pl.hypot(baselines[0], baselines[1])
#if not showplot:
# casalog.post('Sorry, not showing the plot is not yet implemented',
# 'WARN')
if showplot:
pl.ion()
pl.clf()
pl.ioff()
nfreqs = len(compdict['freqs (GHz)'])
for freqnum in xrange(nfreqs):
freq = compdict['freqs (GHz)'][freqnum]
casalog.post("Plotting " + str(freq) + " GHz.")
pl.plot(baselines/kl, data[freqnum], symb, label="%.3g GHz" % freq)
#pl.plot(baselines, data[freqnum], symb, label="%.3g GHz" % freq)
pl.xlabel("Baseline length (" + blunit + ")")
pl.ylabel("Visibility amplitude (Jy)")
if include0amp:
pl.ylim(ymin=0.0)
if include0bl:
pl.xlim(xmin=0.0)
pl.suptitle(objname + " (predicted by %s)" % compdict['standard'], fontsize=14)
#pl.suptitle(objname + " (predicted)", fontsize=14)
# Unlike compdict['antennalist'], antennalist might have had repodir
# prefixed to it.
pl.title('at ' + epstr + ' for ' + os.path.basename(compdict['antennalist']), fontsize=10)
titletxt='($%.0f^\circ$ az, $%.0f^\circ$ el)' % azeldegs
# for comparison of old and new models - omit azeldegs as all in az~0
if bl0flux > 0.0:
if len(compdict['freqs (GHz)']) == 1:
titletxt+='\n bl0 flux:%.3f Jy' % bl0flux
else:
titletxt+='\n bl0 flux:%.3f Jy @ %s GHz' % (bl0flux, compdict['freqs (GHz)'][0])
pl.legend(loc='best', title=titletxt)
#pl.legend(loc='best', title='($%.0f^\circ$ az, $%.0f^\circ$ el)' % azeldegs)
y_formatter=matplotlib.ticker.ScalarFormatter(useOffset=False)
pl.axes().yaxis.set_major_formatter(y_formatter)
if showplot:
pl.ion()
pl.draw()
if compdict.get('savedfig'):
pl.savefig(compdict.get('savedfig'))
casalog.post("Saved plot to " + str(compdict.get('savedfig')))
if wantdict:
retval = {'amps': data,
'antennalist': antennalist, # Absolute path, now.
'azel': azel,
'baselines': baselines,
'blunit': blunit,
'riseset': riset,
'savedfig': compdict.get('savedfig')}
else:
retval = True
except Exception, instance:
casalog.post(str(instance), 'SEVERE')
if os.path.isdir(tempms):
shutil.rmtree(tempms)
def plot2D_fiber(self, cracking=1):
#we will build line by line the "vector field" assiciated to the results file.
if self.f.Lz > 1:
print(
"Lz != 0 impossible to plot 2d, you certainly want to plot a 3d network"
)
for j in range(0, self.f.Ly):
if (j % 2 == 0):
pair = 1
else:
pair = 0
for k in range(0, self.f.Lz):
for i in range(0, self.f.Lx):
self.X.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][0]))
self.Y.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][1]))
self.U.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][0]))
self.V.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][1]))
self.Rlength.append(self.Length[i + j * self.f.Ly, 0])
self.X.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][0]))
self.Y.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][1]))
self.U.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][2]))
self.V.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][3]))
self.Rlength.append(self.Length[i + j * self.f.Ly, 1])
self.X.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][0]))
self.Y.append(
float(self.DataNode[i + j * self.f.Ly +
k * self.f.Lz][1]))
self.U.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][4]))
self.V.append(
float(self.DataFiber[i + j * self.f.Ly +
k * self.f.Lz][5]))
self.Rlength.append(self.Length[i + j * self.f.Ly, 2])
Norm = (pylab.hypot(self.U, self.V) - self.Rlength)
pylab.quiver(self.X,
self.Y,
self.U,
self.V,
Norm,
scale=1.0,
angles='xy',
scale_units='xy',
width=0.005,
minlength=0.,
headlength=0.,
headaxislength=0.,
headwidth=0.,
alpha=1,
edgecolor='k',
cmap=cm)
#pylab.quiver(self.X,self.Y,self.U,self.V,Norm ,scale = 1.0,angles='xy',scale_units = 'xy',width = 0.005,minlength=0.,headlength=10,headaxislength=10,headwidth=10,alpha=1,edgecolor='k',cmap=cm)
#pylab.quiver(self.X,self.Y,self.U,self.V ,scale = 1.0,angles='xy',scale_units = 'xy',width = 0.005,minlength=0.,headlength=0.,headaxislength=0.,headwidth=0.,alpha=1,edgecolor='k',cmap=cm)
#pylab.clim(0.,0.5)
#pylab.clim(-0.15,0.20)
pylab.clim(-0.05, 0.05)
