def test_fk4_no_e_fk5():
t = Table.read(os.path.join(ROOT, 'fk4_no_e_fk4.csv'), format='ascii')
# FK4 to FK5
c1 = FK4(t['ra_in'], t['dec_in'],
unit=(u.degree, u.degree),
obstime=Time(t['obstime'], scale='utc'))
c2 = c1.transform_to(FK4NoETerms)
# Find difference
diff = angular_separation(c2.ra.radian, c2.dec.radian,
np.radians(t['ra_fk4ne']), np.radians(t['dec_fk4ne']))
assert np.all(np.degrees(diff) * 3600. < TOLERANCE)
# FK5 to FK4
c1 = FK4NoETerms(t['ra_in'], t['dec_in'],
unit=(u.degree, u.degree),
obstime=Time(t['obstime'], scale='utc'))
c2 = c1.transform_to(FK4)
# Find difference
diff = angular_separation(c2.ra.radian, c2.dec.radian,
np.radians(t['ra_fk4']), np.radians(t['dec_fk4']))
assert np.all(np.degrees(diff) * 3600. < TOLERANCE)
def deformation_theta(theta = -math.pi/2., plot = False):
"""Return lists of deformation os SMA actuator per theta"""
theta_list = np.linspace(-theta, theta)
eps_s_list = []
eps_l_list = []
for theta in theta_list:
s.theta = theta
l.theta = theta
s.update()
l.update()
l.calculate_force()
eps_s_list.append(s.eps)
eps_l_list.append(l.eps)
if plot:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(np.degrees(theta_list), eps_s_list, 'r', np.degrees(theta_list), eps_l_list, 'b')
plt.xlabel('$\\theta (degrees)$')
plt.ylabel('$\epsilon$')
return eps_s_list, theta_list
def j2000tob1950(ra, dec):
"""
Convert J2000 to B1950 coordinates.
This routine was derived by taking the inverse of the b1950toj2000 routine
"""
# Convert to radians
ra = np.radians(ra)
dec = np.radians(dec)
# Convert RA, Dec to rectangular coordinates
x = np.cos(ra) * np.cos(dec)
y = np.sin(ra) * np.cos(dec)
z = np.sin(dec)
# Apply the precession matrix
x2 = P2[0, 0] * x + P2[1, 0] * y + P2[2, 0] * z
y2 = P2[0, 1] * x + P2[1, 1] * y + P2[2, 1] * z
z2 = P2[0, 2] * x + P2[1, 2] * y + P2[2, 2] * z
# Convert the new rectangular coordinates back to RA, Dec
ra = np.arctan2(y2, x2)
dec = np.arcsin(z2)
# Convert to degrees
ra = np.degrees(ra)
dec = np.degrees(dec)
# Make sure ra is between 0. and 360.
ra = np.mod(ra, 360.0)
dec = np.mod(dec + 90.0, 180.0) - 90.0
return ra, dec
def get_center(self):
"""
Return the RA, Dec of the center of the circle bounding
this trixel (RA, Dec both in degrees)
"""
ra, dec = sphericalFromCartesian(self.bounding_circle[0])
return np.degrees(ra), np.degrees(dec)
def b1950toj2000(ra, dec):
"""
Convert B1950 to J2000 coordinates.
This routine is based on the technique described at
http://www.stargazing.net/kepler/b1950.html
"""
# Convert to radians
ra = np.radians(ra)
dec = np.radians(dec)
# Convert RA, Dec to rectangular coordinates
x = np.cos(ra) * np.cos(dec)
y = np.sin(ra) * np.cos(dec)
z = np.sin(dec)
# Apply the precession matrix
x2 = P1[0, 0] * x + P1[1, 0] * y + P1[2, 0] * z
y2 = P1[0, 1] * x + P1[1, 1] * y + P1[2, 1] * z
z2 = P1[0, 2] * x + P1[1, 2] * y + P1[2, 2] * z
# Convert the new rectangular coordinates back to RA, Dec
ra = np.arctan2(y2, x2)
dec = np.arcsin(z2)
# Convert to degrees
ra = np.degrees(ra)
dec = np.degrees(dec)
# Make sure ra is between 0. and 360.
ra = np.mod(ra, 360.0)
dec = np.mod(dec + 90.0, 180.0) - 90.0
return ra, dec
def lambet(self):
"""Ecliptic longitude and latitude."""
from astropy.coordinates import Angle
lam = np.arctan2(self._rot.T[1], self._rot.T[0])
bet = np.arctan2(self._rot.T[2],
np.sqrt(self._rot.T[0]**2 + self._rot.T[1]**2))
return Angle(np.degrees(lam) * u.deg), Angle(np.degrees(bet) * u.deg)
def star(a,b,c,alpha,beta,gamma):
"Calculate unit cell volume, reciprocal cell volume, reciprocal lattice parameters"
alpha=np.radians(alpha)
beta=np.radians(beta)
gamma=np.radians(gamma)
V=2*a*b*c*\
np.sqrt(np.sin((alpha+beta+gamma)/2)*\
np.sin((-alpha+beta+gamma)/2)*\
np.sin((alpha-beta+gamma)/2)*\
np.sin((alpha+beta-gamma)/2))
Vstar=(2*np.pi)**3/V;
astar=2*np.pi*b*c*np.sin(alpha)/V
bstar=2*np.pi*a*c*np.sin(beta)/V
cstar=2*np.pi*b*a*np.sin(gamma)/V
alphastar=np.arccos((np.cos(beta)*np.cos(gamma)-\
np.cos(alpha))/ \
(np.sin(beta)*np.sin(gamma)))
betastar= np.arccos((np.cos(alpha)*np.cos(gamma)-\
np.cos(beta))/ \
(np.sin(alpha)*np.sin(gamma)))
gammastar=np.arccos((np.cos(alpha)*np.cos(beta)-\
np.cos(gamma))/ \
(np.sin(alpha)*np.sin(beta)))
V=V
alphastar=np.degrees(alphastar)
betastar=np.degrees(betastar)
gammastar=np.degrees(gammastar)
return astar,bstar,cstar,alphastar,betastar,gammastar
def azalt_to_lb(az, alt, lat, lon, unixtime=None):
"""
Converts az/alt coordiantes to galactic coordinates. All inputs are
expected to be in degrees.
"""
# Get the of-date ra/dec
ra, dec = azalt_to_radec(az, alt, lat, lon, unixtime)
# Convert of-date ra/dec to J2000
ra, dec = get_radec_j2000(ra, dec, unixtime)
# Convert degrees to radians
ra = _np.radians(ra)
dec = _np.radians(dec)
# Location on unit sphere
theta = _np.pi / 2 - dec
phi = ra
x = _np.sin(theta) * _np.cos(phi)
y = _np.sin(theta) * _np.sin(phi)
z = _np.cos(theta)
cartesian = _np.array([x, y, z])
# Perform the final matrix multilication to get l/b
lb_cart = _np.dot(matrix_radec_lb(), cartesian)
x, y, z = lb_cart
r = _np.sqrt(x**2 + y**2 + z**2) # should be 1
theta = _np.arccos(z / r)
phi = _np.arctan2(y, x)
gal_l = _np.degrees(phi)
gal_b = _np.degrees(_np.pi / 2 - theta)
if gal_l < 0:
gal_l += 360.0
return (gal_l, gal_b)
def _lambet(self):
"""Lower overhead ecliptic longitude and latitude. [deg]"""
from astropy.coordinates import Angle
lam = np.arctan2(self._rot.T[1], self._rot.T[0])
bet = np.arctan2(self._rot.T[2],
np.sqrt(self._rot.T[0]**2 + self._rot.T[1]**2))
return np.degrees(lam), np.degrees(bet)
def __init__(self, raCol='fieldRA', decCol='fieldDec', mjdCol='expMJD', latRad=None,
lonRad=None, height=None, tempCentigrade=None, lapseRate=None,
humidity=None, pressure=None):
self.raCol = raCol
self.decCol = decCol
self.mjdCol = mjdCol
if latRad is None:
latDeg = None
else:
latDeg = np.degrees(latRad)
if lonRad is None:
lonDeg = None
else:
lonDeg = np.degrees(lonRad)
self.site = Site(longitude=lonDeg, latitude=latDeg,
temperature=tempCentigrade,
height=height, humidity=humidity,
pressure=pressure, lapseRate=lapseRate,
name='LSST')
self.units = ['radians']
self.colsAdded = ['PA']
self.colsReq = [self.raCol, self.decCol, self.mjdCol]
def lb_to_azalt(gal_l, gal_b, lat, lon, unixtime=None):
"""
Converts galactic coordiantes to az/alt coordinates. The input
angles are expected to be in degrees.
"""
# Convert degrees to radians
gal_l = _np.radians(gal_l)
gal_b = _np.radians(gal_b)
# Location on unit sphere
theta = _np.pi / 2 - gal_b
phi = gal_l
x = _np.sin(theta) * _np.cos(phi)
y = _np.sin(theta) * _np.sin(phi)
z = _np.cos(theta)
cartesian = _np.array([x, y, z])
# Get the of-date ra/dec to convert to az/alt
radec_cart = _np.dot(matrix_lb_radec(), cartesian)
x, y, z = radec_cart
r = _np.sqrt(x**2 + y**2 + z**2) # should be 1
theta = _np.arccos(z / r)
phi = _np.arctan2(y, x)
ra = _np.degrees(phi)
dec = _np.degrees(_np.pi / 2 - theta)
if ra < 0:
ra += 360.0
ra, dec = get_radec_ofdate(ra, dec, unixtime)
# Convert ra/dec to az/alt (in degrees)
return radec_to_azalt(ra, dec, lat, lon, unixtime)
def great_circle(**kwargs):
"""
Named arguments:
distance = distance to travel, or numpy array of distances
azimuth = angle, in DEGREES of HEADING from NORTH, or numpy array of azimuths
latitude = latitude, in DECIMAL DEGREES, or numpy array of latitudes
longitude = longitude, in DECIMAL DEGREES, or numpy array of longitudes
rmajor = radius of earth's major axis. default=6378137.0 (WGS84)
rminor = radius of earth's minor axis. default=6356752.3142 (WGS84)
Returns a dictionary with:
'latitude' in decimal degrees
'longitude' in decimal degrees
'reverse_azimuth' in decimal degrees
"""
distance = kwargs.pop('distance')
azimuth = np.radians(kwargs.pop('azimuth'))
latitude = np.radians(kwargs.pop('latitude'))
longitude = np.radians(kwargs.pop('longitude'))
rmajor = kwargs.pop('rmajor', 6378137.0)
rminor = kwargs.pop('rminor', 6356752.3142)
f = (rmajor - rminor) / rmajor
vector_pt = np.vectorize(vinc_pt)
lat_result, lon_result, angle_result = vector_pt(f, rmajor,
latitude,
longitude,
azimuth,
distance)
return {'latitude': np.degrees(lat_result),
'longitude': np.degrees(lon_result),
'reverse_azimuth': np.degrees(angle_result)}
def waypoints_to_commands(coords): #cmd = [[vx,az,time],etc] #Convert waypoints to value in stage lin_vel = 0.2 ang_vel = math.radians(45) #45 deg/s in rad/s init_ang = 0; move_ang = [0] move_dist = [0] for i in range(len(coords)-1): p1 = coords[i] p2 = coords[i+1] move_ang.append(math.atan2(p2[1]-p1[1],p2[0]-p1[0])) move_dist.append(math.sqrt((p2[1]-p1[1])**2+(p2[0]-p1[0])**2)) print np.degrees(move_ang) print len(move_dist) move_cmd = [] for i in range(len(move_ang)-1): ang_cmd = (move_ang[i+1]-move_ang[i]) ang_time = ang_cmd/ang_vel dist_cmd =move_dist[i+1]-move_dist[i] dist_time = dist_cmd/lin_vel move_cmd.append([0,np.sign(ang_cmd),math.fabs(ang_time)]) move_cmd.append([np.sign(dist_cmd),0,math.fabs(dist_time)]) print move_cmd print len(move_cmd) return move_cmd
def proj(lons, lats, reverse=False):
if not reverse:
lambdas, phis = numpy.radians(lons), numpy.radians(lats)
cos_phis = numpy.cos(phis)
lambdas -= lambda0
# calculate the sine of the distance between projection center
# and each of the points to project
sin_dist = numpy.sqrt(
numpy.sin((phi0 - phis) / 2.0) ** 2.0
+ cos_phi0 * cos_phis * numpy.sin(lambdas / 2.0) ** 2.0
)
if (sin_dist > sin_pi_over_4).any():
raise ValueError('some points are too far from the projection '
'center lon=%s lat=%s' %
(numpy.degrees(lambda0), numpy.degrees(phi0)))
xx = numpy.cos(phis) * numpy.sin(lambdas)
yy = cos_phi0 * numpy.sin(phis) \
- sin_phi0 * cos_phis * numpy.cos(lambdas)
return xx * EARTH_RADIUS, yy * EARTH_RADIUS
else:
# "reverse" mode, arguments are actually abscissae
# and ordinates in 2d space
xx, yy = lons / EARTH_RADIUS, lats / EARTH_RADIUS
cos_c = numpy.sqrt(1 - (xx ** 2 + yy ** 2))
phis = numpy.arcsin(cos_c * sin_phi0 + yy * cos_phi0)
lambdas = numpy.arctan2(xx, cos_phi0 * cos_c - yy * sin_phi0)
return numpy.degrees(lambda0 + lambdas), numpy.degrees(phis)
def test_degrees(self):
# the following doesn't make much sense in terms of the name of the
# routine, but we check it gives the correct result.
q1 = np.rad2deg(60. * u.degree)
assert_allclose(q1.value, 60.)
assert q1.unit == u.degree
q2 = np.degrees(60. * u.degree)
assert_allclose(q2.value, 60.)
assert q2.unit == u.degree
q3 = np.rad2deg(np.pi * u.radian)
assert_allclose(q3.value, 180.)
assert q3.unit == u.degree
q4 = np.degrees(np.pi * u.radian)
assert_allclose(q4.value, 180.)
assert q4.unit == u.degree
with pytest.raises(TypeError):
np.rad2deg(3. * u.m)
with pytest.raises(TypeError):
np.degrees(3. * u.m)
def testObservedFromICRS(self):
obs = ObservationMetaData(pointingRA=35.0, pointingDec=-45.0, mjd=43572.0)
for pmRaList in [self.pm_raList, None]:
for pmDecList in [self.pm_decList, None]:
for pxList in [self.pxList, None]:
for vRadList in [self.v_radList, None]:
for includeRefraction in [True, False]:
raRad, decRad = utils._observedFromICRS(self.raList, self.decList,
pm_ra=pmRaList, pm_dec=pmDecList,
parallax=pxList, v_rad=vRadList,
obs_metadata=obs, epoch=2000.0,
includeRefraction=includeRefraction)
raDeg, decDeg = utils.observedFromICRS(np.degrees(self.raList),
np.degrees(self.decList),
pm_ra=utils.arcsecFromRadians(pmRaList),
pm_dec=utils.arcsecFromRadians(pmDecList),
parallax=utils.arcsecFromRadians(pxList),
v_rad=vRadList,
obs_metadata=obs, epoch=2000.0,
includeRefraction=includeRefraction)
dRa = utils.arcsecFromRadians(raRad - np.radians(raDeg))
np.testing.assert_array_almost_equal(dRa, np.zeros(self.nStars), 9)
dDec = utils.arcsecFromRadians(decRad - np.radians(decDeg))
np.testing.assert_array_almost_equal(dDec, np.zeros(self.nStars), 9)
def pitch_roll(self, px, pz):
"""works out the pitch (rx) and roll (rz) to apply to an object
on the surface of the map at this point
* returns a tuple (pitch, roll) in degrees
Arguments:
*px*
x location
*pz*
z location
"""
px -= self.unif[0]
pz -= self.unif[2]
halfw = self.width/2.0
halfd = self.depth/2.0
dx = self.width/self.ix
dz = self.depth/self.iy
x0 = int(math.floor((halfw + px)/dx + 0.5))
if x0 < 0: x0 = 0
if x0 > self.ix-1: x0 = self.ix-1
z0 = int(math.floor((halfd + pz)/dz + 0.5))
if z0 < 0: z0 = 0
if z0 > self.iy-1: z0 = self.iy-1
normp = array(self.buf[0].normals[z0*self.ix + x0])
# slight simplification to working out cross products as dirctn always 0,0,1
#sidev = cross(normp, dirctn)
sidev = array([normp[1], -normp[0], 0.0])
sidev = sidev / sqrt(sidev.dot(sidev))
#forwd = cross(sidev, normp)
forwd = array([-normp[2]*normp[0], -normp[2]*normp[1],
normp[0]*normp[0] + normp[1]*normp[1]])
forwd = forwd / sqrt(forwd.dot(forwd))
return (degrees(arcsin(-forwd[1])), degrees(arctan2(sidev[1], normp[1])))
def place_label(x,y,label,indice=None,cotan=False,color='k'):
""" Routine qui se débrouille pour mettre un label semi-transparent au
niveau de la courbe données par ses coordonnées x et y. Si on sait que le
label sera presque vertical avec possibilité de dépasser 90°, on peut
utiliser cotan=True pour corriger (considération purement esthétique).
'indice' correspond à la position dans les tableaux x et y où devra
s'afficher le label demandé. """
print(x[0],y[0],label) # un peu de feedback pour savoir ce qu'on calcule
N = len(x)//2 # Emplacement par défaut
if indice: N=indice # sauf si l'utilisateur impose la valeur
xi,xf = plt.xlim() # Les limites en x du graphe
yi,yf = plt.ylim() # Pareil en y
Xsize = xf - xi # La largeur
# Pour la hauteur et la pente, cela dépend si les ordonnées sont en repère
# logarithmique ou non.
if Plogscale:
Ysize = np.log10(yf) - np.log10(yi)
a = (np.log10(y[N+1])-np.log10(y[N-1]))/(x[N+1]-x[N-1]) * Xsize/Ysize
else:
Ysize = yf - yi
a = (y[N+1]-y[N-1])/(x[N+1]-x[N-1]) * Xsize/Ysize
bbox = plt.gca().get_window_extent() # Récupération de la taille de la figure
a *= bbox.height / bbox.width # Correction de la pente avec la taille
rot = np.degrees(np.arctan(a)) # Calcul de l'angle de rotation
if cotan: # Si on dépasse la verticale
rot = 90 - np.degrees(np.arctan(1/a))
t = plt.text(x[N],y[N],label, # On met le texte au bon endroit
ha='center',va='center',color=color,rotation = str(rot)) # Avec la bonne rotation
# On se débrouille pour que la "boîte" d'écriture soit semi-transparente
#t.set_bbox(dict(facecolor='w',edgecolor='None',alpha=0.8))
t.set_bbox(dict(boxstyle="round",facecolor='w',edgecolor='None',alpha=0.85))
def testAppGeoFromICRS(self):
mjd = 42350.0
for pmRaList in [self.pm_raList, None]:
for pmDecList in [self.pm_decList, None]:
for pxList in [self.pxList, None]:
for vRadList in [self.v_radList, None]:
raRad, decRad = utils._appGeoFromICRS(self.raList, self.decList,
pmRaList, pmDecList,
pxList, vRadList,
mjd=ModifiedJulianDate(TAI=mjd))
raDeg, decDeg = utils.appGeoFromICRS(np.degrees(self.raList),
np.degrees(self.decList),
utils.arcsecFromRadians(pmRaList),
utils.arcsecFromRadians(pmDecList),
utils.arcsecFromRadians(pxList),
vRadList,
mjd=ModifiedJulianDate(TAI=mjd))
dRa = utils.arcsecFromRadians(
raRad - np.radians(raDeg))
np.testing.assert_array_almost_equal(
dRa, np.zeros(self.nStars), 9)
dDec = utils.arcsecFromRadians(
raRad - np.radians(raDeg))
np.testing.assert_array_almost_equal(
dDec, np.zeros(self.nStars), 9)
def raDecFromVec(v):
"""
Taken from
http://www.math.montana.edu/frankw/ccp/multiworld/multipleIVP/spherical/learn.htm
Search for "convert from Cartestion to spherical coordinates"
Adapted because I'm dealing with declination which is defined
with 90degrees at zenith
"""
# Ensure v is a normal vector
v /= np.linalg.norm(v)
ra_deg = 0 # otherwise not in namespace0
dec_rad = np.arcsin(v[2])
s = np.hypot(v[0], v[1])
if s == 0:
ra_rad = 0
else:
ra_rad = np.arcsin(v[1] / s)
ra_deg = np.degrees(ra_rad)
if v[0] >= 0:
if v[1] >= 0:
pass
else:
ra_deg = 360 + ra_deg
else:
if v[1] > 0:
ra_deg = 180 - ra_deg
else:
ra_deg = 180 - ra_deg
raDec = ra_deg, np.degrees(dec_rad)
return np.array(raDec)
def makeAlignment(lX,lX_orient,lX_centerx,lX_centery,maxPixel,crop=2):
imageSize = maxPixel * maxPixel
#cropped_size = (maxPixel-2*crop)*(maxPixel-2*crop)
names = lX.columns
lX_new = np.zeros((lX.shape[0],lX.shape[1]),dtype=np.float32)
t0 = time()
for i,img in enumerate(lX.values):
img = np.reshape(img, (maxPixel, maxPixel)).astype('float32')
#crop border
img[0:crop,:]=1.0
img[-crop:,:]=1.0
img[:,0:crop]=1.0
img[:,-crop:]=1.0
img = 1.0 - img
#print "Orientation:",lX_orient[i], " Degree:",np.degrees(lX_orient[i])
#showImage(img,maxPixel,fac=10,matrix=True)
#
#
angle = np.degrees(lX_orient[i])
#angle = 0.0
M1 = cv2.getRotationMatrix2D((maxPixel/2,maxPixel/2),np.degrees(angle),1)
img_new = cv2.warpAffine(img,M1,(maxPixel,maxPixel))
img_new = 1.0 - img_new
#img_new = rotate(img, angle, reshape=False)
#img_new = 255 - img_new
#showImage(img_new,maxPixel,fac=10,matrix=True)
lX_new[i]=img_new.ravel().astype('float32')
print("Alignment done in %0.3fs" % (time() - t0))
lX_new = pd.DataFrame(lX_new,columns = names,dtype=np.float32)
print lX_new.shape
print lX_new.describe()
return lX_new
def setUp(self):
self.metadata={}
#below are metadata values that need to be set in order for
#get_getFocalPlaneCoordinates to work. If we had been querying the database,
#these would be set to meaningful values. Because we are generating
#an artificial set of inputs that must comport to the baseline SLALIB
#inputs, these are set arbitrarily by hand
self.metadata['pointingRA'] = (numpy.radians(200.0), float)
self.metadata['pointingDec'] = (numpy.radians(-30.0), float)
self.metadata['Opsim_rotskypos'] = (1.0, float)
# these were the LSST site parameters as coded when this unit test was written
self.test_site=Site(longitude=numpy.degrees(-1.2320792),
latitude=numpy.degrees(-0.517781017),
height=2650.0,
temperature=11.505,
pressure=749.3,
lapseRate=0.0065,
humidity=0.4)
self.obs_metadata=ObservationMetaData(mjd=50984.371741,
boundType='circle',
boundLength=0.05,
phoSimMetaData=self.metadata,
site=self.test_site)
self.tol=1.0e-5
def sendSearchRequest(matches):
ra=list(np.degrees(matches[matches['isinobs']==False]['can_ra']))
dec=list(np.degrees(matches[matches['isinobs']==False]['can_dec']))
bands='[g,r,i,z]'
req='http://desdev3.cosmology.illinois.edu:8000/api?username=lzullo&password=lzu70chips&ra=%s&dec=%s&bands=%s' % (ra,dec,bands)
submit = requests.get(req)
return submit.json()['job']
def GetHealPixRectangles(nside, dbrange, nest):
hpindex = np.arange(hp.nside2npix(nside))
vec_corners = hp.boundaries(nside, hpindex, nest=nest)
vec_corners = np.transpose(vec_corners, (0,2,1))
vec_corners = np.reshape(vec_corners, (vec_corners.shape[0]*vec_corners.shape[1], vec_corners.shape[2]))
theta_corners, phi_corners = hp.vec2ang(vec_corners)
theta_corners = np.reshape(theta_corners, (theta_corners.shape[0]/4, 4))
phi_corners = np.reshape(phi_corners, (phi_corners.shape[0]/4, 4))
ra_corners = np.degrees(phi_corners)
dec_corners = 90.0 - np.degrees(theta_corners)
rainside = ( (ra_corners > dbrange[0]) & (ra_corners < dbrange[1]) )
rakeep = np.sum(rainside, axis=-1)
decinside = ( (dec_corners > dbrange[2]) & (dec_corners < dbrange[3]) )
deckeep = np.sum(decinside, axis=-1)
keep = ( (rakeep > 0) & (deckeep > 0) )
ra_corners, dec_corners, hpindex = Cut(ra_corners, dec_corners, hpindex, cut=keep)
ramin = np.amin(ra_corners, axis=-1)
ramax = np.amax(ra_corners, axis=-1)
decmin = np.amin(dec_corners, axis=-1)
decmax = np.amax(dec_corners, axis=-1)
return ramin, ramax, decmin, decmax, hpindex
def Jacobsen(h1, Xm_1, h2, Xm_2):
alp = np.degrees(np.arccos(np.dot(h1, h2) /
(np.linalg.norm(h1) * np.linalg.norm(h2))))
bet = np.degrees(np.arccos(np.dot(Xm_1, Xm_2) /
(np.linalg.norm(Xm_1) * np.linalg.norm(Xm_2))))
if ((alp - bet)**2) > 1:
print('check your indexing!')
a = 3.567 # diamond lattice parameter
# recip lattice par(note this is the mantid convention: no 2 pi)
ast = (2 * np.pi) / a
B = np.array([[ast, 0, 0], [0, ast, 0], [0, 0, ast]])
Xm_g = np.cross(Xm_1, Xm_2)
Xm = np.column_stack([Xm_1, Xm_2, Xm_g])
# Vector Q1 is described in reciprocal space by its coordinate matrix h1
Xa_1 = B.dot(h1)
Xa_2 = B.dot(h2)
Xa_g = np.cross(Xa_1, Xa_2)
Xa = np.column_stack((Xa_1, Xa_2, Xa_g))
R = Xa.dot(np.linalg.inv(Xm))
U = np.linalg.inv(R)
UB = U.dot(B)
return UB
def run(self, stars, visits, **kwargs):
# XXX-Double check extinction is close to the Opsim transparency
extinc_mags = visits['transparency']
if extinc_mags != 0.:
# need to decide on how to get extinc_mags from Opsim
# Maybe push some of these params up to be setable?
SFtheta, SFsf = self.SF.CloudSf(500., 300., 5., extinc_mags, .55)
# Call the Clouds
self.cloud.makeCloudImage(SFtheta,SFsf,extinc_mags, fov=self.fov)
# Interpolate clouds to correct position. Nearest neighbor for speed?
nim = self.cloud.cloudimage[0,:].size
# calc position in cloud image of each star
starx_interp = (np.degrees(stars['x']) + self.fov/2.)*3600./ self.cloud.pixscale
stary_interp = (np.degrees(stars['y']) + self.fov/2.)*3600./ self.cloud.pixscale
# Round off position and make it an int
starx_interp = np.round(starx_interp).astype(int)
stary_interp = np.round(stary_interp).astype(int)
# Handle any stars that are out of the field for some reason
starx_interp[np.where(starx_interp < 0)] = 0
starx_interp[np.where(starx_interp > nim-1)] = nim-1
stary_interp[np.where(stary_interp < 0)] = 0
stary_interp[np.where(stary_interp > nim-1)] = nim-1
dmag = self.cloud.cloudimage[starx_interp,stary_interp]
else:
dmag = np.zeros(stars.size)
return dmag
def cone(plunge, bearing, angle, segments=100):
"""
Calculates the longitude and latitude of the small circle (i.e. a cone)
centered at the given *plunge* and *bearing* with an apical angle of
*angle*, all in degrees.
Parameters
----------
plunge : number or sequence of numbers
The plunge of the center of the cone(s) in degrees. The plunge is
measured in degrees downward from the end of the feature specified by
the bearing.
bearing : number or sequence of numbers
The bearing (azimuth) of the center of the cone(s) in degrees.
angle : number or sequence of numbers
The apical angle (i.e. radius) of the cone(s) in degrees.
segments : int, optional
The number of vertices in the small circle.
Returns
-------
lon, lat : arrays
`num_measurements` x `num_segments` arrays of longitude and latitude in
radians.
"""
plunges, bearings, angles = np.atleast_1d(plunge, bearing, angle)
lons, lats = [], []
for plunge, bearing, angle in zip(plunges, bearings, angles):
lat = (90 - angle) * np.ones(segments, dtype=float)
lon = np.linspace(-180, 180, segments)
lon, lat = _rotate(lon, lat, -plunge, axis='y')
lon, lat = _rotate(np.degrees(lon), np.degrees(lat), bearing, axis='x')
lons.append(lon)
lats.append(lat)
return np.vstack(lons), np.vstack(lats)
def draw(self):
gL.glPushMatrix()
if self.b:
self.draw_rod(self.b)
gL.glTranslatef(0, 0, self.b)
gL.glRotatef(degrees(self.gamma), 0, 0, 1)
if self.d:
gL.glPushMatrix()
gL.glRotatef(90, 0, 1, 0)
self.draw_rod(self.d)
gL.glPopMatrix()
gL.glTranslatef(self.d, 0, 0)
gL.glRotatef(degrees(self.alpha), 1, 0, 0)
if self.r:
self.draw_rod(self.r)
gL.glTranslatef(0, 0, self.r)
gL.glRotatef(degrees(self.theta), 0, 0, 1)
if self.shift:
gL.glPushMatrix()
shift = self.shift * self.length
self.draw_rod(shift)
gL.glTranslatef(0, 0, shift)
self.draw_joint()
gL.glPopMatrix()
else:
self.draw_joint()
for child in self.children:
child.draw()
gL.glPopMatrix()
def plot_lm(d, snrs, l1s, m1s, outroot):
""" Plot the lm coordinates (relative to phase center) for all candidates.
"""
outname = os.path.join(d["workdir"], "plot_" + outroot + "_impeak.png")
snrmin = 0.8 * min(d["sigma_image1"], d["sigma_image2"])
fig4 = plt.Figure(figsize=(10, 10))
ax4 = fig4.add_subplot(111)
# plot positive
good = n.where(snrs > 0)
sizes = (snrs[good] - snrmin) ** 5 # set scaling to give nice visual sense of SNR
xarr = 60 * n.degrees(l1s[good])
yarr = 60 * n.degrees(m1s[good])
ax4.scatter(xarr, yarr, s=sizes, facecolor="none", alpha=0.5, clip_on=False)
# plot negative
good = n.where(snrs < 0)
sizes = (n.abs(snrs[good]) - snrmin) ** 5 # set scaling to give nice visual sense of SNR
xarr = 60 * n.degrees(l1s[good])
yarr = 60 * n.degrees(m1s[good])
ax4.scatter(xarr, yarr, s=sizes, marker="x", edgecolors="k", alpha=0.5, clip_on=False)
ax4.set_xlabel("Dec Offset (amin)")
ax4.set_ylabel("RA Offset (amin)")
fov = n.degrees(1.0 / d["uvres"]) * 60.0
ax4.set_xlim(fov / 2, -fov / 2)
ax4.set_ylim(-fov / 2, fov / 2)
canvas4 = FigureCanvasAgg(fig4)
canvas4.print_figure(outname)
def makeObservationMetaData():
#create a cartoon ObservationMetaData object
mjd = 52000.0
alt = numpy.pi/2.0
az = 0.0
band = 'r'
testSite = Site(latitude=numpy.degrees(0.5), longitude=numpy.degrees(1.1), height=3000,
temperature=260.0, pressure=725.0, lapseRate=0.005, humidity=0.4)
obsTemp = ObservationMetaData(site=testSite, mjd=mjd)
centerRA, centerDec = _raDecFromAltAz(alt, az, obsTemp)
rotTel = _getRotTelPos(centerRA, centerDec, obsTemp, 0.0)
obsDict = calcObsDefaults(centerRA, centerDec, alt, az, rotTel, mjd, band,
testSite.longitude_rad, testSite.latitude_rad)
obsDict['Opsim_expmjd'] = mjd
radius = 0.1
phoSimMetaData = OrderedDict([
(k, (obsDict[k],numpy.dtype(type(obsDict[k])))) for k in obsDict])
obs_metadata = ObservationMetaData(boundType='circle', boundLength=2.0*radius,
phoSimMetaData=phoSimMetaData, site=testSite)
return obs_metadata
print "Pixel scale = {0} [arcseconds]".format(options.scale)
print "Pointing centre RA = {0} [deg]".format(np.round(PC_RA),3)
print "Pointing centre DEC = {0} [deg]".format(np.round(PC_DEC),3)
# Creating the Alt, Az, Zenith and radial orthographic vectors.
Alt, Az, Zen = mwa_alt_az_za(options.obsid, RA, DEC, degrees=True)
r = np.cos(np.radians(Alt))
#################################################################################
# Specifying circle parameters:
#################################################################################
theta_PA = np.radians(metadata['azimuth'])
d = np.cos(np.radians(metadata['altitude']))
print "Pointing centre azmiuth = {0} [deg]".format(np.degrees(theta_PA))
print "Pointing centre altitude = {0} [deg]".format(np.round(metadata['altitude']),5)
print "Max apparent int flux = {0} [Jy]".format(np.round(np.max(App_int_flux)),3)
print "Total apparent flux = {0} [Jy]".format(np.round(np.sum(App_int_flux)),3)
#################################################################################
# Subsetting the pb.
#################################################################################
print "########################################################################"
print "# Finding and subtracting the number of sources in the primary beam pb.#"
print "########################################################################"
centre,circ_ind,dRA,dDEC = lobe_subset(RA,DEC,App_int_flux,options.obsid,d,theta_PA,Az=Az,r=r,verb_cond=verbcond,plot_cond=plotcond)
# Determining the pixel dimensions:
def azel2radec(az_deg: float, el_deg: float,
lat_deg: float, lon_deg: float,
time: datetime) -> Tuple[float, float]:
"""
convert azimuth, elevation to right ascension, declination
Inputs
az_deg
Numpy ndarray of azimuth to point [degrees]
el_deg
Numpy ndarray of elevation to point [degrees]
lat_deg
scalar observer WGS84 latitude [degrees]
lon_deg
scalar observer WGS84 longitude [degrees]
time
time of observation
Outputs
ra_deg
Numpy ndarray of right ascension values [degrees]
dec_deg
Numpy ndarray of declination values [degrees]
from D.Vallado Fundamentals of Astrodynamics and Applications
p.258-259
"""
az = atleast_1d(az_deg)
el = atleast_1d(el_deg)
lat = atleast_1d(lat_deg)
lon = atleast_1d(lon_deg)
if az.shape != el.shape:
raise ValueError('az and el must be same shape ndarray')
if not(lat.size == 1 and lon.size == 1):
raise ValueError('need one observer and one or more (az,el).')
if ((lat < -90) | (lat > 90)).any():
raise ValueError('-90 <= lat <= 90')
if ((lon < -180) | (lon > 360)).any():
raise ValueError('-180 <= lat <= 360')
az = radians(az)
el = radians(el)
lat = radians(lat)
lon = radians(lon)
# %% Vallado "algorithm 28" p 268
dec = arcsin(sin(el) * sin(lat) + cos(el) * cos(lat) * cos(az))
lha = arctan2(-(sin(az) * cos(el)) / cos(dec),
(sin(el) - sin(lat) * sin(dec)) / (cos(dec) * cos(lat)))
lst = datetime2sidereal(time, lon) # lon, ra in RADIANS
""" by definition right ascension [0, 360) degrees """
return degrees(lst - lha) % 360, degrees(dec)
def listener_callback(self, msg):
# create numpy array
occdata = np.array(msg.data)
# compute histogram to identify bins with -1, values between 0 and below 50,
# and values between 50 and 100. The binned_statistic function will also
# return the bin numbers so we can use that easily to create the image
occ_counts, edges, binnum = stats.binned_statistic(occdata,
np.nan,
statistic='count',
bins=occ_bins)
# get width and height of map
iwidth = msg.info.width
iheight = msg.info.height
# calculate total number of bins
total_bins = iwidth * iheight
# log the info
# self.get_logger().info('Unmapped: %i Unoccupied: %i Occupied: %i Total: %i' % (occ_counts[0], occ_counts[1], occ_counts[2], total_bins))
# find transform to obtain base_link coordinates in the map frame
# lookup_transform(target_frame, source_frame, time)
try:
trans = self.tfBuffer.lookup_transform('map', 'base_link',
rclpy.time.Time())
except (LookupException, ConnectivityException,
ExtrapolationException) as e:
self.get_logger().info('No transformation found')
return
print(trans)
cur_pos = trans.transform.translation
cur_rot = trans.transform.rotation
self.get_logger().info('Trans: %f, %f' % (cur_pos.x, cur_pos.y))
# convert quaternion to Euler angles
roll, pitch, yaw = euler_from_quaternion(cur_rot.x, cur_rot.y,
cur_rot.z, cur_rot.w)
self.get_logger().info('Rot-Yaw: Rad: %f Deg: %f' %
(yaw, np.degrees(yaw)))
# get map resolution
map_res = msg.info.resolution
# get map origin struct has fields of scripts, y, and z
map_origin = msg.info.origin.position
# get map grid positions for scripts, y position
grid_x = round((cur_pos.x - map_origin.x) / map_res)
grid_y = round(((cur_pos.y - map_origin.y) / map_res))
self.get_logger().info('Grid Y: %i Grid X: %i' % (grid_y, grid_x))
# binnum go from 1 to 3 so we can use uint8
# convert into 2D array using column order
odata = np.uint8(binnum.reshape(msg.info.height, msg.info.width))
# set current robot location to 0
odata[grid_y][grid_x] = 0
# create image from 2D array using PIL
img = Image.fromarray(odata)
# find center of image
i_centerx = iwidth / 2
i_centery = iheight / 2
# find how much to shift the image to move grid_x and grid_y to center of image
shift_x = round(grid_x - i_centerx)
shift_y = round(grid_y - i_centery)
self.get_logger().info('Shift Y: %i Shift X: %i' % (shift_y, shift_x))
# pad image to move robot position to the center
# adapted from https://note.nkmk.me/en/python-pillow-add-margin-expand-canvas/
left = 0
right = 0
top = 0
bottom = 0
if shift_x > 0:
# pad right margin
right = 2 * shift_x
else:
# pad left margin
left = 2 * (-shift_x)
if shift_y > 0:
# pad bottom margin
bottom = 2 * shift_y
else:
# pad top margin
top = 2 * (-shift_y)
# create new image
new_width = iwidth + right + left
new_height = iheight + top + bottom
img_transformed = Image.new(img.mode, (new_width, new_height),
map_bg_color)
img_transformed.paste(img, (left, top))
# rotate by 90 degrees so that the forward direction is at the top of the image
rotated = img_transformed.rotate(np.degrees(yaw) - 90,
expand=True,
fillcolor=map_bg_color)
# show the image using grayscale map
# plt.imshow(img, cmap='gray', origin='lower')
# plt.imshow(img_transformed, cmap='gray', origin='lower')
plt.imshow(rotated, cmap='gray', origin='lower')
plt.draw_all()
# pause to make sure the plot gets created
plt.pause(0.00000000001)
plt.imshow(warpedimage,vmin=warpedimage.min(),vmax=warpedimage.max(),cmap='gray')
plt.show()
if __name__ == "__main__":
# Read image
ref = cv2.imread('rot1.png',0)
cmp = cv2.imread('rot2.png',0)
# plt.imshow(ref,cmap="gray")
ref = cv2.resize(ref,(360,360))
cmp = cv2.resize(cmp,(360,360))
# # reference parameter (you can change this)
match = imregpoc(ref,cmp)
# print(match.peak,match.param)
print(np.degrees(match.getParam()[2]))
print((match.getParam()[0], match.getParam()[1]))
# match_para = imregpoc(ref,cmp,fitting='Parabola')
# print(match_para.peak,match_para.param)
# match_cog = imregpoc(ref,cmp,fitting='COG')
# print(match_cog.peak,match_cog.param)
match.showRotatePeak()
match.showTranslationPeak()
# match.stitching()
# match_para.stitching()
# match_cog.stitching()
# center = np.array(ref.shape)/2
# persp = match.poc2warp(center,[-5.40E+01,-2.00E+00,9.72E+01/180*math.pi,6.03E-01])
# match.stitching(persp)
def quadrant_check(RA,DEC,weights=None,gcd_cond=False): """ This function is designed to deal with fringe case sources, when the RA values are both less than pi/2 and greater than 3pi/2. This corresponds to sources in quadrants 4 and 1, where the angle wraps back around again. To properly determine the angular distances between sources, and the centre of mass (com) sources need to be shifted into two other neighbouring quadrants that don't wrap. This function flips the RA values then calculates the new com. It then shifts the com RA value back to it's appropriate quadrant. This function also can alternatively return the angular distance between each source and the com, if gcd_cond=True. This is useful for determining the size of the images required map the lobes. Args: RA : vector_like; Vector of Right Ascention values in radians. DEC : vector_like; Vector of Declination values in radians. weights : vector_like; (default=None) Vector of weights. gcd_cond : boolean; If True, the function determines the angular distance to every given point relative to the centre of mass. It does this in the shifted frame since the distance from the com is only relative. """ if (np.any(RA > 0.0) and np.any(RA <= pi/2.0)) and (np.any(RA <= 2*pi) and np.any(RA >= (3.0/2.0)*pi)): # shifting the RA values into neighbouring unwrapped quadrants. RA[RA > (3.0/2.0)*pi] = pi + (2*pi - RA[RA > (3.0/2.0)*pi]) RA[RA < pi/2.0] = pi - RA[RA < pi/2.0] # Calculating the weighted or unweighted com. #RA_com,DEC_com = com(RA,DEC,weights) RA_com,DEC_com = com(RA,DEC)#,weights) if gcd_cond == True: # The angular separtation between the com and every source. sep_vec = gcd(np.degrees(RA_com),np.degrees(DEC_com),np.degrees(RA),np.degrees(DEC)) return sep_vec else: pass # Shifting the com back to the original quadrant. if RA_com >= pi: RA_com = 2*pi - (RA_com - pi) elif RA_com < pi: RA_com = pi - RA_com return np.degrees(RA_com), np.degrees(DEC_com) #return RA_com, DEC_com else: # Calculating the weighted or unweighted com. RA_com,DEC_com = com(RA,DEC)#,weights) #RA_com,DEC_com = com(RA,DEC,weights) if gcd_cond == True: # The angular separation between the com and every source. sep_vec = gcd(np.degrees(RA_com),np.degrees(DEC_com),np.degrees(RA),np.degrees(DEC)) return sep_vec else: return np.degrees(RA_com), np.degrees(DEC_com)
def sidelobe_finder(r,Az,weights=None,plot_cond=False,verb_cond=False):
"""
This function is run after the pb has been subtracted from the dataset. This function takes an
input radius and azimuth vecotr for an orthographic poar projection. It then seperates the data
into azimuthal slices that are 2pi/8 in width. It counts the number of sources in each slice,
selecting the slice with the maximum number of sources. This will likely correspond to a grating
lobe. It then finds the weighted average and position angle of the slice, which should be close
to the centre of the grating lobe. This function then outputs the weighted average radius and
position angle.
Args:
r : vector_like; Vector of radius values for the orthographic polar projection.
Az : vector_like; Vector of azimuthal values for the orthographic polar projection.
Weights : vector_like; (default = None) Vector of weights with the same dimensions as radius and
azimuth vectors. This vector is used to find the weighted average radius and position angle for
the azimuthal slice with the most number of sources.
plot_cond : boolean; (default = False) Option to plot the orthographic projection in polar coordinates,
with the defined subsetting regions.
verb_cond : boolean; (default = False) Option to give verbose output, this prints additional information
to the command line about the operations of the function. This is useful for diagnostics tests.
"""
phi_slice = np.linspace(0,2*pi,9)
N_sources_per_slice = []
for i in range(len(phi_slice)):
if i==0:
pass
else:
# Creating a temporary phi slice radius vector.
r_tpm = r[np.logical_and(Az >= np.degrees(phi_slice[i-1]),Az <= np.degrees(phi_slice[i]))]
# Determining the number of sources per slice.
N_sources_per_slice.append(len(r_tpm))
if verb_cond == True:
print "Azimuthal slice: [{0},{1}] [deg]".format(np.round(np.degrees(phi_slice[i-1]),3),np.round(np.degrees(phi_slice[i]),3))
print "Mean radius = {0} [sin(theta)]".format(np.round(np.mean(r_tpm),3))
print "Number of sources in slice = {0}\n".format(len(r_tpm))
if plot_cond == True and np.any(weights) != None:
# Creating a phi slice apparent flux vector.
App_flux_slice = weights[np.logical_and(Az >= np.degrees(phi_slice[i-1]),Az <= np.degrees(phi_slice[i]))]
# Plotting the histogram of every slice.
plt.hist(np.degrees(np.arcsin(r_tpm)),bins=25,edgecolor='k',label='radius',weights=App_flux_slice)
plt.title(r"$\theta \in [{0},{1}] $".format(np.round(np.degrees(phi_slice[i-1]),3),np.round(np.degrees(phi_slice[i]),3)))
plt.xlabel(r'$\rm{radius [\sin(\theta)]}$',fontsize=14)
plt.show()
plt.close()
else:
pass
if np.max(N_sources_per_slice) <= 10:
# Case when there is no cler sidelobe, return none.
return None
else:
pass
# Index for the slice with the max number of sources.
Max_slice_ind = np.argmax(N_sources_per_slice)
sub_set_ind = np.logical_and(Az >= np.degrees(phi_slice[Max_slice_ind]),Az <= np.degrees(phi_slice[Max_slice_ind+1]))
# Subset of the orthographic radius values for sources in slice.
r_max_slice = r[sub_set_ind]
# Subset of azimuth values for sources in slice.
Az_max_slice = Az[sub_set_ind]
if np.any(weights) == None:
# Determining the position angle of the maximum slice.
PA_max_slice = np.radians(np.mean(Az_max_slice))
# Determining the mean radius for the maximum slice.
r_mean_max_slice = np.mean(r_max_slice)
else:
# weights will need to be subsetted too.
weights = weights[sub_set_ind]
# This option is for determining the weighted average.
PA_max_slice = np.radians(np.sum(Az_max_slice*weights)/np.sum(weights))
r_mean_max_slice = np.sum(r_max_slice*weights)/np.sum(weights)
return r_mean_max_slice, PA_max_slice
def lobe_subset(RA,DEC,Flux,OBSID,d,PA,R=0.1,Az=None,r=None,plot_cond=False,output_cond=False,verb_cond=False):
"""
This function takes an input RA and DEC vector, transforms them into their corresponding Alt and Az vectors for a given
OBSID. It then uses the position angle of the pointing centre in azimuthal cooridinates, as well as the distance to the
poitning centre from zenith to define the centre of the primary beam (pb). It then subsets the sources in the pb by
drawing a circle in polar coordinates using the functions 'draw_circle()' and 'circle_subset()'. It then iterates this
process by increasing the input radius 'R' by i*0.01. It repeats this process until the same number of sources are found
in subsequent iterations. The function the determines the weighted centre of mass, using the integrated flux densities
of sources, and writes the string of the weighted RA and DEC in 'hms', 'dms' format. This process can be repeated for
the grating lobes, or sidelobes of the tile beam pattern, so long as the position angle 'PA', and distance to the
supposed centre 'd' is defined.
Args:
RA : vector_like [deg]; Vector of right ascension positions in degrees.
DEC : vector_like [deg]; Vector of declination positions in degrees.
Flux : Vector_like; Vector of the integrated apparent flux densities for the given sources, corresponds to the RA and
OBSID : scalar; GPS time of the obsid.
DEC positions.
d : scalar; Radial distance in the orthographic projection from zenith to the supposed centre of the lobe.
PA : scalar; Position angle of the supposed centre of the lobe.
R : scalar; Initial radius of arbitrary circle, (default = 0.1).
Az : Vector_like; (default = None) Optional to include the azimuth vector for each source, computed from the OBSID
r : Vector_like; (default = None) Optional to include the rorthographic radius vector for each source, computed from the OBSID
plot_cond : boolean; (default = False) Option to plot the orthographic projection in polar coordinates, with the defined
subsetting regions.
output_cond : boolean; (default = False) Option to give addition output, this provides the subsetted RA, DEC, Az, r vectors.
The non-verbose output gives only the circle indices.
verb_cond : boolean; (default = False) Option to give verbose output, this prints additional information to the command
line about the operations of the function. This is useful for diagnostics tests.
"""
if plot_cond == True:
fig = plt.figure(figsize = (12.5,9.5), dpi=90)
if verb_cond == True:
print "Radius of search circle = {0}".format(R)
print "Distance to circle centre = {0}".format(np.round(d,3))
if np.any(Az) == None or np.any(r) == None:
# Case when the altitude and Azimuth are not provided.
Alt, Az, Zen = mwa_alt_az_za(OBSID, RA, DEC, degrees=True)
r = np.cos(np.radians(Alt))
else:
pass
# Circle growing condition.
lobe_cond = True
dR = (2.0/100.0)
for i in range(100):
if i == 0:
# Getting the index of sources in the circle.
circ_ind = circle_subset(R,d,PA,r,np.radians(Az))
# The number of sources in the circle
N_circ_sources = len(circ_ind)
else:
# Getting the index of sources in the circle.
circ_ind = circle_subset(R + i*dR,d,PA,r,np.radians(Az))
if len(circ_ind) != N_circ_sources:
# Update the number of souces in the circle.
N_circ_sources = len(circ_ind)
elif len(circ_ind) == N_circ_sources:
# Else if the number of sources doesn't change then subset out pb sources.
r_nu = np.delete(r,circ_ind)
Az_nu = np.delete(Az,circ_ind)
# Specifying the primary beam RA and DEC subsets.
# Can use these and the com function to determine the centre of the image, as well as the size.
RA_sub = RA[circ_ind]
DEC_sub = DEC[circ_ind]
Flux_sub = Flux[circ_ind]
# Defining the centre of mass RA and DEC.
#RA_sub_wcent, DEC_sub_wcent = com(RA_sub,DEC_sub,weights=Flux_sub)
RA_sub_wcent, DEC_sub_wcent = quadrant_check(np.radians(RA_sub),np.radians(DEC_sub),weights=Flux_sub,gcd_cond=False)
print "RA min, RA max",np.min(RA_sub),np.max(RA_sub)
print "RA com",RA_sub_wcent
# Getting the hmsdms format of the pointing centre.
Cent_hmsdms_string = SkyCoord(ra=RA_sub_wcent*u.degree, dec=DEC_sub_wcent*u.degree).to_string('hmsdms')
# Condition to break the for loop.
lobe_cond = False
if plot_cond == True:
ax1 = fig.add_subplot(111,projection="polar")
pcm1 = ax1.scatter(np.radians(np.delete(Az,circ_ind)), np.delete(r,circ_ind), \
c=np.log10(np.delete(Flux,circ_ind)), cmap='viridis')
radius, phi_vec = draw_circle(R + i*dR,d,PA)
ax1.plot(phi_vec,radius)
ax1.set_rmin(0.0)
ax1.set_rmax(1.2)
ax1.set_theta_zero_location('N')
fig.colorbar(pcm1, ax=ax1, label='Apparent flux')
plt.show(block=False)
plt.pause(0.5)
plt.clf()
#plt.close()
if lobe_cond == False:
# Exit the for loop.
break
# Returning the angular disance from the com to each point.
dtheta = quadrant_check(np.radians(RA_sub),np.radians(DEC_sub),weights=Flux_sub,gcd_cond=True)
# This is used to determine the scale of the images.
dDEC = np.abs(np.max(DEC[circ_ind]) - np.min(DEC[circ_ind]))
#dRA = np.abs(np.max(RA[circ_ind]) - np.min(RA[circ_ind]))
dRA = np.degrees(np.arccos((np.cos(2*np.max(np.radians(dtheta)))/np.cos(np.radians(dDEC)))))
if verb_cond == True:
print "Final circle radius = {0}".format(R + i*(1.0/100.0))
print "Number of sources in lobe = {0}".format(len(circ_ind))
print "Max(RA-pb) = {0} [deg], Min(RA-pb) = {1} [deg]".format(np.round(np.float(np.max(RA[circ_ind])),3),np.round(np.float(np.min(RA[circ_ind])),3))
print "Max(DEC-pb) = {0} [deg], Min(DEC-pb) = {1} [deg]".format(np.round(np.float(np.max(DEC[circ_ind])),3),np.round(np.float(np.min(DEC[circ_ind])),3))
print "Weighted centre of mass {0}".format(Cent_hmsdms_string)
if output_cond == True:
# Verbose output condition.
return Cent_hmsdms_string, RA_pb, DEC_pb, r_nu, Az_nu, dRA, dDEC
else:
# Non-verbose output.
return Cent_hmsdms_string, circ_ind, dRA, dDEC
def _extract_angle(self, dataset, cfg, src_meta):
"""Extract data for vector direction from dataset.
Angles are expected to be counter-clockwise from east in degrees.
Angles must be recomputed to match the output projection."""
# Get metadata
u_channel = src_meta['u_channel']
v_channel = src_meta['v_channel']
uv_offset = src_meta['uv_offset']
uv_scale = src_meta['uv_scale']
uv_nodatavalue = src_meta['uv_nodatavalue']
angle_channel = src_meta['angle_channel']
angle_offset = src_meta['angle_offset']
angle_scale = src_meta['angle_scale']
# Get projection settings
x_origin, pixel_col_width, pixel_row_width, y_origin, \
pixel_col_height, pixel_row_height = dataset.GetGeoTransform()
# Get u/v and projected x/y
u_band = dataset.GetRasterBand(u_channel)
v_band = dataset.GetRasterBand(v_channel)
u_array = u_band.ReadAsArray()
v_array = v_band.ReadAsArray()
output_shape = u_array.shape
x_size, y_size = output_shape
if uv_nodatavalue is not None:
valid = numpy.where(u_array != uv_nodatavalue)
u = u_array[valid]
v = v_array[valid]
x1 = valid[1] * pixel_col_width + valid[0] * pixel_row_width
y1 = valid[1] * pixel_col_height + valid[0] * pixel_row_height
else:
u = u_array
v = v_array
col = numpy.arange(y_size)
row = numpy.arange(x_size)
x1 = numpy.tile(col * pixel_col_width, x_size) + \
numpy.repeat(row * pixel_row_width, y_size)
y1 = numpy.tile(col * pixel_col_height, x_size) + \
numpy.repeat(row * pixel_row_height, y_size)
u = u * uv_scale + uv_offset
v = v * uv_scale + uv_offset
x1 = x_origin + x1
y1 = y_origin + y1
# Compute angle from u/v
real_angle = numpy.degrees(numpy.arctan2(v, u))
del u, v, u_array, v_array
# Reverse projection to get (lon,lat) for each (x,y)
output_proj = get_proj4(cfg['output_proj'], src_meta)
proj = pyproj.Proj(output_proj)
# Get lon/lat for each x/y
lons1, lats1 = proj(x1, y1, inverse=True)
# Use an arbitrary distance (1km)
dists = numpy.ndarray(shape=lons1.shape)
dists.fill(1000.0)
# pyproj.Geod.fwd expects bearings to be clockwise angles from north
# (in degrees).
fixed_angles = 90.0 - real_angle
# Interpolate a destination from grid point and data direction
geod = pyproj.Geod(ellps='WGS84')
lons2, lats2, bearings2 = geod.fwd(lons1, lats1, fixed_angles, dists)
del bearings2, lons1, lats1, real_angle, fixed_angles, dists
# Warp destination to output projection
x2, y2 = proj(lons2, lats2)
del lons2, lats2
# Fix issue when an interpolated point is not reprojected in the same
# longitude range as it origin (applies to cylindric projections only)
## NEW metadata.py
## Before
#if int(cfg['output_proj']) in (4326, 3857, 900913):
## Now
if cfg['output_proj_type'] == 'cylindric':
## \NEW metadata.py
extent = [float(x) for x in cfg['extent'].split(' ')]
vport_bottom, vport_left, vport_top, vport_right = extent
vport_x_extent = vport_right - vport_left
x2 = numpy.mod(x2 - (x1 + vport_left), vport_x_extent) \
+ (x1 + vport_left)
# Compute angle in output projection between [0, 360] degrees
projected_angles = numpy.arctan2(y2 - y1, x2 - x1)
ranged_angles = numpy.mod(360 + numpy.degrees(projected_angles), 360)
del x1, y1, x2, y2
# Rescale angle
scaled_angles = (ranged_angles - angle_offset) / angle_scale
scaled_angles = numpy.round(scaled_angles).astype('uint8')
# Rebuild matrix from flattened data
if uv_nodatavalue is not None:
nodatavalue = src_meta['nodatavalues'][angle_channel - 1]
angle = numpy.empty(output_shape, dtype='uint8')
angle.fill(nodatavalue)
angle[valid] = scaled_angles
return angle
else:
scaled_angles.shape = output_shape
return scaled_angles
def crosscorrelate(results, low, high, rotor, debug=False):
'''obtains the difference in phase between the accelerometer an ir signal
The function has been tested and produces reproducable results.
The result is not very accurate.
With a sample frequency of 952 Hertz and a rotor frequency of
100 Hertz, rouglhy 9.5 measurements are available per period so the outcome
in degrees is not accurate
Therefore the function get details was developped
and this is used as alternative.
In the code there is also the option to use a fake signal.
Code was copied from;
https://stackoverflow.com/questions/6157791/find-phase-difference-between-two-inharmonic-
Keyword arguments:
results -- dictionary resulting from the main.main function
low -- low frequency cut in Hz, used to filter noise,
should be lower than rotor frequency
high -- high frequency cut in Hz, used to filter noise,
should higher than rotor frequency
rotor -- frequency at which the rotor is thought to rotate
debug -- makes fake ac_meas and ir_meas signals, used to test code
'''
ac_meas = results['ac_meas']
ir_meas = results['ir_meas']
freq = rotor # hertz, used to calculate phase shift
N = len(ac_meas)
T = 1/results['sample_freq']
if debug:
print("debug activated")
phasediff = np.pi*debug
# timeshift = round(debug/T)
x = np.linspace(0.0, N*T, N)
ir_meas = np.sin(freq*2*np.pi*x+phasediff)
# ir_meas = np.roll(ir_meas, timeshift)
ac_meas = np.sin(freq*2*np.pi*x)
# band pass filter
ac_meas = butter_bandpass_filter(ac_meas,
low,
high,
results['sample_freq'],
order=6)
ir_meas = butter_bandpass_filter(ir_meas,
low,
high,
results['sample_freq'],
order=6)
# mean centering, SDV scaling
ac_meas = ac_meas-np.mean(ac_meas)
ac_meas = ac_meas/np.std(ac_meas)
ir_meas = ir_meas-np.mean(ir_meas)
ir_meas = ir_meas/np.std(ir_meas)
# calculate cross correlation of the two signal
xcorr = correlate(ir_meas, ac_meas)
# delta time array to match xcorrr
t = np.linspace(0.0, N*T, N, endpoint=False)
dt = np.linspace(-t[-1], t[-1], 2*N-1)
# dt = np.arange(1-N, N)
recovered_time_shift = dt[xcorr.argmax()]
# force the phase shift to be in [-pi:pi]
recovered_phase_shift = (
2*np.pi*(((0.5 + recovered_time_shift/(1/freq) % 1.0) - 0.5)))
print("Recovered time shift {}".format(recovered_time_shift))
print("Recovered phase shift {} radian".format(recovered_phase_shift))
print("Recovered phase shift {} degrees"
.format(np.degrees(recovered_phase_shift)))
def add_geo_to_arrivals(arrivals,
source_latitude_in_deg,
source_longitude_in_deg,
receiver_latitude_in_deg,
receiver_longitude_in_deg,
radius_of_planet_in_km,
flattening_of_planet,
resample=False,
sampleds=5.0):
"""
Add geographical information to arrivals.
:param arrivals: Set of taup arrivals
:type: :class:`Arrivals`
:param source_latitude_in_deg: Source location latitude in degrees
:type source_latitude_in_deg: float
:param source_longitude_in_deg: Source location longitude in degrees
:type source_longitude_in_deg: float
:param receiver_latitude_in_deg: Receiver location latitude in degrees
:type receiver_latitude_in_deg: float
:param receiver_longitude_in_deg: Receiver location longitude in degrees
:type receiver_longitude_in_deg: float
:param radius_of_planet_in_km: Radius of the planet in km
:type radius_of_planet_in_km: float
:param flattening_of_planet: Flattening of planet (0 for a sphere)
:type receiver_longitude_in_deg: float
:param resample: adds sample points to allow for easy cartesian
interpolation. This is especially useful for phases
like Pdiff.
:type resample: boolean
:return: List of ``Arrival`` objects, each of which has the time,
corresponding phase name, ray parameter, takeoff angle, etc. as
attributes.
:rtype: :class:`Arrivals`
"""
if geodetics.HAS_GEOGRAPHICLIB:
if not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34:
# geographiclib is not installed ...
# and obspy/geodetics does not help much
msg = ("This functionality needs the Python module "
"'geographiclib' in version 1.34 or higher.")
raise ImportError(msg)
ellipsoid = Geodesic(a=radius_of_planet_in_km * 1000.0,
f=flattening_of_planet)
g = ellipsoid.Inverse(source_latitude_in_deg, source_longitude_in_deg,
receiver_latitude_in_deg,
receiver_longitude_in_deg)
azimuth = g['azi1']
line = ellipsoid.Line(source_latitude_in_deg, source_longitude_in_deg,
azimuth)
# We may need to update many arrival objects
# and each could have pierce points and a
# path
for arrival in arrivals:
# check if we go in minor or major arc direction
distance = arrival.purist_distance % 360.
if distance > 180.:
sign = -1
az_arr = (azimuth + 180.) % 360.
else:
sign = 1
az_arr = azimuth
arrival.azimuth = az_arr
if arrival.pierce is not None:
geo_pierce = np.empty(arrival.pierce.shape, dtype=TimeDistGeo)
for i, pierce_point in enumerate(arrival.pierce):
signed_dist = np.degrees(sign * pierce_point['dist'])
pos = line.ArcPosition(signed_dist)
geo_pierce[i] = (pierce_point['p'], pierce_point['time'],
pierce_point['dist'],
pierce_point['depth'], pos['lat2'],
pos['lon2'])
arrival.pierce = geo_pierce
# choose whether we need to resample the trace
if arrival.path is not None:
if resample:
rplanet = radius_of_planet_in_km
# compute approximate distance between sampling points
# mindist = 200 # km
mindist = sampleds
radii = rplanet - arrival.path['depth']
rmean = np.sqrt(radii[1:] * radii[:-1])
diff_dists = rmean * np.diff(arrival.path['dist'])
npts_extra = np.floor(diff_dists / mindist).astype(np.int)
# count number of extra points and initialize array
npts_old = len(arrival.path)
npts_new = int(npts_old + np.sum(npts_extra))
geo_path = np.empty(npts_new, dtype=TimeDistGeo)
# now loop through path, adding extra points
i_new = 0
for i_old, path_point in enumerate(arrival.path):
# first add the original point at the new index
dist = np.degrees(sign * path_point['dist'])
pos = line.ArcPosition(dist)
geo_path[i_new] = (path_point['p'], path_point['time'],
path_point['dist'],
path_point['depth'], pos['lat2'],
pos['lon2'])
i_new += 1
if i_old > npts_old - 2:
continue
# now check if we need to add new points
npts_new = npts_extra[i_old]
if npts_new > 0:
# if yes, distribute them linearly between the old
# and the next point
next_point = arrival.path[i_old + 1]
dist_next = np.degrees(sign * next_point['dist'])
dists_new = np.linspace(dist, dist_next,
npts_new + 2)[1:-1]
# now get all interpolated parameters
xs = [dist, dist_next]
ys = [path_point['p'], next_point['p']]
p_interp = np.interp(dists_new, xs, ys)
ys = [path_point['time'], next_point['time']]
time_interp = np.interp(dists_new, xs, ys)
ys = [path_point['depth'], next_point['depth']]
depth_interp = np.interp(dists_new, xs, ys)
pos_interp = [
line.ArcPosition(dist_new)
for dist_new in dists_new
]
lat_interp = [
point['lat2'] for point in pos_interp
]
lon_interp = [
point['lon2'] for point in pos_interp
]
# add them to geo_path
# dists_new --> np.radians(dists_new), modified by Hongjian Fang
for i_extra in range(npts_new):
geo_path[i_new] = (p_interp[i_extra],
time_interp[i_extra],
np.radians(
dists_new[i_extra]),
depth_interp[i_extra],
lat_interp[i_extra],
lon_interp[i_extra])
i_new += 1
arrival.path = geo_path
else:
geo_path = np.empty(arrival.path.shape, dtype=TimeDistGeo)
for i, path_point in enumerate(arrival.path):
signed_dist = np.degrees(sign * path_point['dist'])
pos = line.ArcPosition(signed_dist)
geo_path[i] = (path_point['p'], path_point['time'],
path_point['dist'], path_point['depth'],
pos['lat2'], pos['lon2'])
arrival.path = geo_path
else:
# geographiclib is not installed ...
# and obspy/geodetics does not help much
msg = "You need to install the Python module 'geographiclib' in " + \
"order to add geographical information to arrivals."
raise ImportError(msg)
return arrivals
def mindist(a):
d = map(a.difference, B.skydir.values)
n = np.argmin(d)
return (B.index[n], np.degrees(d[n]))
def get_thresholded_rotated(im_path):
#read image
img = cv2.imread(im_path)
img = cv2.resize(img, (600, 800), interpolation=Image.BILINEAR)
sat = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:, :, 1]
val = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:, :, 2]
sat = cv2.medianBlur(sat, 11)
val = cv2.medianBlur(val, 11)
#create threshold
thresh_S = cv2.adaptiveThreshold(sat, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 401, 10)
thresh_V = cv2.adaptiveThreshold(val, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 401, 10)
#mean, std
mean_S, stdev_S = cv2.meanStdDev(img, mask=255 - thresh_S)
mean_S = mean_S.ravel().flatten()
stdev_S = stdev_S.ravel()
#chromacity
chrom_S = chromacity_distortion(img, mean_S, stdev_S)
chrom255_S = cv2.normalize(chrom_S,
chrom_S,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX).astype(
np.uint8)[:, :, None]
mean_V, stdev_V = cv2.meanStdDev(img, mask=255 - thresh_V)
mean_V = mean_V.ravel().flatten()
stdev_V = stdev_V.ravel()
chrom_V = chromacity_distortion(img, mean_V, stdev_V)
chrom255_V = cv2.normalize(chrom_V,
chrom_V,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX).astype(
np.uint8)[:, :, None]
#create different thresholds
thresh2_S = cv2.adaptiveThreshold(chrom255_S, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 401, 10)
thresh2_V = cv2.adaptiveThreshold(chrom255_V, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 401, 10)
#thresholded image
thresh = cv2.bitwise_and(thresh2_S, cv2.bitwise_not(thresh2_V))
#find countours and keep max
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# fit ellipse to leaf contours
ellipse = cv2.fitEllipse(big_contour)
(xc, yc), (d1, d2), angle = ellipse
print('thresh shape: ', thresh.shape)
#print(xc,yc,d1,d2,angle)
rmajor = max(d1, d2) / 2
rminor = min(d1, d2) / 2
origi_angle = angle
if angle > 90:
angle = angle - 90
else:
angle = angle + 90
#calc major axis line
xtop = xc + math.cos(math.radians(angle)) * rmajor
ytop = yc + math.sin(math.radians(angle)) * rmajor
xbot = xc + math.cos(math.radians(angle + 180)) * rmajor
ybot = yc + math.sin(math.radians(angle + 180)) * rmajor
#calc minor axis line
xtop_m = xc + math.cos(math.radians(origi_angle)) * rminor
ytop_m = yc + math.sin(math.radians(origi_angle)) * rminor
xbot_m = xc + math.cos(math.radians(origi_angle + 180)) * rminor
ybot_m = yc + math.sin(math.radians(origi_angle + 180)) * rminor
#determine which region is up and which is down
if max(xtop, xbot) == xtop:
x_tij = xtop
y_tij = ytop
x_b_tij = xbot
y_b_tij = ybot
else:
x_tij = xbot
y_tij = ybot
x_b_tij = xtop
y_b_tij = ytop
if max(xtop_m, xbot_m) == xtop_m:
x_tij_m = xtop_m
y_tij_m = ytop_m
x_b_tij_m = xbot_m
y_b_tij_m = ybot_m
else:
x_tij_m = xbot_m
y_tij_m = ybot_m
x_b_tij_m = xtop_m
y_b_tij_m = ytop_m
print('-----')
print(x_tij, y_tij)
rect_size = 100
"""
calculate regions of edges of major axis of ellipse
this is done by creating a squared region of rect_size x rect_size, being the edge the center of the square
"""
x_min_tij = int(0 if x_tij - rect_size < 0 else x_tij - rect_size)
x_max_tij = int(thresh.shape[1] -
1 if x_tij + rect_size > thresh.shape[1] else x_tij +
rect_size)
y_min_tij = int(0 if y_tij - rect_size < 0 else y_tij - rect_size)
y_max_tij = int(thresh.shape[0] -
1 if y_tij + rect_size > thresh.shape[0] else y_tij +
rect_size)
x_b_min_tij = int(0 if x_b_tij - rect_size < 0 else x_b_tij - rect_size)
x_b_max_tij = int(thresh.shape[1] -
1 if x_b_tij + rect_size > thresh.shape[1] else x_b_tij +
rect_size)
y_b_min_tij = int(0 if y_b_tij - rect_size < 0 else y_b_tij - rect_size)
y_b_max_tij = int(thresh.shape[0] -
1 if y_b_tij + rect_size > thresh.shape[0] else y_b_tij +
rect_size)
sum_red_region = np.sum(thresh[y_min_tij:y_max_tij, x_min_tij:x_max_tij])
sum_yellow_region = np.sum(thresh[y_b_min_tij:y_b_max_tij,
x_b_min_tij:x_b_max_tij])
"""
calculate regions of edges of minor axis of ellipse
this is done by creating a squared region of rect_size x rect_size, being the edge the center of the square
"""
x_min_tij_m = int(0 if x_tij_m - rect_size < 0 else x_tij_m - rect_size)
x_max_tij_m = int(thresh.shape[1] -
1 if x_tij_m + rect_size > thresh.shape[1] else x_tij_m +
rect_size)
y_min_tij_m = int(0 if y_tij_m - rect_size < 0 else y_tij_m - rect_size)
y_max_tij_m = int(thresh.shape[0] -
1 if y_tij_m + rect_size > thresh.shape[0] else y_tij_m +
rect_size)
x_b_min_tij_m = int(0 if x_b_tij_m - rect_size < 0 else x_b_tij_m -
rect_size)
x_b_max_tij_m = int(thresh.shape[1] - 1 if x_b_tij_m +
rect_size > thresh.shape[1] else x_b_tij_m + rect_size)
y_b_min_tij_m = int(0 if y_b_tij_m - rect_size < 0 else y_b_tij_m -
rect_size)
y_b_max_tij_m = int(thresh.shape[0] - 1 if y_b_tij_m +
rect_size > thresh.shape[0] else y_b_tij_m + rect_size)
#value of the regions, the names of the variables are related to the color of the rectangles drawn at the end of the function
sum_red_region_m = np.sum(thresh[y_min_tij_m:y_max_tij_m,
x_min_tij_m:x_max_tij_m])
sum_yellow_region_m = np.sum(thresh[y_b_min_tij_m:y_b_max_tij_m,
x_b_min_tij_m:x_b_max_tij_m])
#print(sum_red_region, sum_yellow_region, sum_red_region_m, sum_yellow_region_m)
min_arg = np.argmin(
np.array([
sum_red_region, sum_yellow_region, sum_red_region_m,
sum_yellow_region_m
]))
print('min: ', min_arg)
if min_arg == 1: #sum_yellow_region < sum_red_region :
left_quartile = x_b_tij < thresh.shape[0] / 2
upper_quartile = y_b_tij < thresh.shape[1] / 2
center_x = x_b_min_tij + ((x_b_max_tij - x_b_min_tij) / 2)
center_y = y_b_min_tij + (y_b_max_tij - y_b_min_tij / 2)
center_x = x_b_min_tij + np.argmax(
thresh[y_b_min_tij:y_b_max_tij,
x_b_min_tij:x_b_max_tij].mean(axis=0))
center_y = y_b_min_tij + np.argmax(
thresh[y_b_min_tij:y_b_max_tij,
x_b_min_tij:x_b_max_tij].mean(axis=1))
elif min_arg == 0:
left_quartile = x_tij < thresh.shape[0] / 2
upper_quartile = y_tij < thresh.shape[1] / 2
center_x = x_min_tij + ((x_b_max_tij - x_b_min_tij) / 2)
center_y = y_min_tij + ((y_b_max_tij - y_b_min_tij) / 2)
center_x = x_min_tij + np.argmax(
thresh[y_min_tij:y_max_tij, x_min_tij:x_max_tij].mean(axis=0))
center_y = y_min_tij + np.argmax(
thresh[y_min_tij:y_max_tij, x_min_tij:x_max_tij].mean(axis=1))
elif min_arg == 3:
left_quartile = x_b_tij_m < thresh.shape[0] / 2
upper_quartile = y_b_tij_m < thresh.shape[1] / 2
center_x = x_b_min_tij_m + ((x_b_max_tij_m - x_b_min_tij_m) / 2)
center_y = y_b_min_tij_m + (y_b_max_tij_m - y_b_min_tij_m / 2)
center_x = x_b_min_tij_m + np.argmax(
thresh[y_b_min_tij_m:y_b_max_tij_m,
x_b_min_tij_m:x_b_max_tij_m].mean(axis=0))
center_y = y_b_min_tij_m + np.argmax(
thresh[y_b_min_tij_m:y_b_max_tij_m,
x_b_min_tij_m:x_b_max_tij_m].mean(axis=1))
else:
left_quartile = x_tij_m < thresh.shape[0] / 2
upper_quartile = y_tij_m < thresh.shape[1] / 2
center_x = x_min_tij_m + ((x_b_max_tij_m - x_b_min_tij_m) / 2)
center_y = y_min_tij_m + ((y_b_max_tij_m - y_b_min_tij_m) / 2)
center_x = x_min_tij_m + np.argmax(
thresh[y_min_tij_m:y_max_tij_m,
x_min_tij_m:x_max_tij_m].mean(axis=0))
center_y = y_min_tij_m + np.argmax(
thresh[y_min_tij_m:y_max_tij_m,
x_min_tij_m:x_max_tij_m].mean(axis=1))
# draw ellipse on copy of input
result = img.copy()
cv2.ellipse(result, ellipse, (0, 0, 255), 1)
cv2.line(result, (int(xtop), int(ytop)), (int(xbot), int(ybot)),
(255, 0, 0), 1)
cv2.circle(result, (int(xc), int(yc)), 10, (255, 255, 255), -1)
cv2.circle(result, (int(center_x), int(center_y)), 10, (255, 0, 255), 5)
cv2.circle(result, (int(thresh.shape[1] / 2), int(thresh.shape[0] - 1)),
10, (255, 0, 0), 5)
cv2.rectangle(result, (x_min_tij, y_min_tij), (x_max_tij, y_max_tij),
(255, 0, 0), 3)
cv2.rectangle(result, (x_b_min_tij, y_b_min_tij),
(x_b_max_tij, y_b_max_tij), (255, 255, 0), 3)
cv2.rectangle(result, (x_min_tij_m, y_min_tij_m),
(x_max_tij_m, y_max_tij_m), (255, 0, 0), 3)
cv2.rectangle(result, (x_b_min_tij_m, y_b_min_tij_m),
(x_b_max_tij_m, y_b_max_tij_m), (255, 255, 0), 3)
plt.imshow(result)
plt.figure()
#rotate the image
rot_img = Image.fromarray(thresh)
#180
bot_point_x = int(thresh.shape[1] / 2)
bot_point_y = int(thresh.shape[0] - 1)
#poi
poi_x = int(center_x)
poi_y = int(center_y)
#image_center
im_center_x = int(thresh.shape[1] / 2)
im_center_y = int(thresh.shape[0] - 1) / 2
#a - adalt, b - abaix, c - dreta
#ba = a - b
#bc = c - a(b en realitat)
ba = np.array([im_center_x, im_center_y]) - np.array(
[bot_point_x, bot_point_y])
bc = np.array([poi_x, poi_y]) - np.array([im_center_x, im_center_y])
#angle 3 punts
cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
cos_angle = np.arccos(cosine_angle)
cos_angle = np.degrees(cos_angle)
print('cos angle: ', cos_angle)
print('print: ', abs(poi_x - bot_point_x))
m = (int(thresh.shape[1] / 2) - int(center_x) / int(thresh.shape[0] - 1) -
int(center_y))
ttan = math.tan(m)
theta = math.atan(ttan)
print('theta: ', theta)
result = Image.fromarray(result)
result = result.rotate(cos_angle)
plt.imshow(result)
plt.figure()
#rot_img = rot_img.rotate(origi_angle)
rot_img = rot_img.rotate(cos_angle)
return rot_img
def analyse():
"""
This function goes to the folder of the previously run simulation, and averages over
all the runs (which, because of thermal noise, are different). It writes the mean
squared separation and mean separation of each constant simulated in a file. It also
creates a files with the maximum separation of every constant and at which time it occured
"""
os.chdir(
"Shear_Wi:{}-{}_chi:{}-{}_hydro:{}_steps:{}_ts:{}_ra{}_noise{}".format(
constant[0], constant[-1], chi[0], chi[-1], hydro, steps,
time_step, ar_ratio, noise))
max_file = open(
"AMax_File_con"
":{}-{}_numc:{}_h{}_s{}_ts{}_ra{}_n{}".format(constant[0],
constant[-1],
len(constant), hydro,
steps, time_step,
ar_ratio, noise), "w")
max_file.write(
"#Constant, Maxseparation over initial separation, time of max separation\n"
)
for v in constant:
#Rfile = open(os.getcwd() + "/Results.out", "a")
out = open(
"MSS_{}_{}_{}_{}_{}_{}.out".format(v, hydro, steps, time_step,
ar_ratio, noise), "w")
nout = open(
"MS_{}_{}_{}_{}_{}_{}.out".format(v, hydro, steps, time_step,
ar_ratio, noise), "w")
#The following way of reading files is because of a limitation
#in the number of files a computer can have open at the same time
thousands_of_runs = int(math.ceil(runs / 1000))
ms_list = []
mss_list = []
# Reads every thousand runs of a simulation
for k in range(thousands_of_runs):
# Opens the first 1000 runs in a dictionary, then opens the next 1000 and so on.
filedata = {
i: open("Run{}_Wi{}_chi{}".format(i, v, chi[0]), "r")
for i in xrange(k * 1000, min(runs, (k + 1) * 1000))
}
# Mean separation and Mean square separation lists that contain temporary files
# with the respective values for every thousand runs. They are deleted afterwards
ms_list.append(open("ms_{}th_thousand.tmp".format(k), "w"))
mss_list.append(open("mss_{}th_thousand.tmp".format(k), "w"))
# Adding squared separation and separation together
# to average noise
for lines in xrange(steps + 1):
s = 0
ssq = 0
totangle1 = 0
for file in filedata.values():
token = str.split(file.readline())
# This convenion will most likely change in the 3rd version of the program
t = float(token[0])
x = float(token[1])
y = float(token[2])
z = float(token[3])
rsepparation = x * x + y * y + z * z
angle1 = np.degrees(
np.arccos(
np.clip(
np.dot(np.array([x, y, z]), np.array(
[1, 0, 0])) / math.sqrt(rsepparation),
-1.0, 1.0)))
totangle1 += angle1
s += rsepparation
ssq += math.sqrt(rsepparation)
mss_list[k].write("{} {}\n".format(t, s / runs))
ms_list[k].write("{} {} {}\n".format(t, (ssq / runs),
totangle1 / runs))
update_progress(lines / (steps))
for fruns in filedata.values():
fruns.close()
ms_list[k].close()
mss_list[k].close()
ms_list[k] = open("ms_{}th_thousand.tmp".format(k), "r")
mss_list[k] = open("mss_{}th_thousand.tmp".format(k), "r")
# This loop goes through the temporary file in ms_list and mss_list and finds the
# largest sepparation. It also finds the mean separation and separation squared if
# the number of runs was more than 1000. If its under 1000 runs then this loop will
# slow down the computation by a bit.
# ~~~~~~~~~ NOTE: If computation time is an issue then modify this ~~~~~~~~~~~~~~~~~~~~~~~~~~
print "~~~~~~~Merging and finding Max value~~~~~~~~~"
maxr = 0
tmax = 0
for j in xrange(steps + 1):
mean_mss = 0
mean_ms = 0
mean_angle = 0
for k in range(thousands_of_runs):
mstoken = str.split(ms_list[k].readline())
msstoken = str.split(mss_list[k].readline())
t = float(mstoken[0])
angle = float(mstoken[2])
mean_angle += angle
mssn = float(msstoken[1])
msn = float(mstoken[1])
mean_mss += mssn
mean_ms += msn
out.write("{} {}\n".format(t, mean_mss))
nout.write("{} {} {}\n".format(t, mean_ms, mean_angle))
# if maxr <= mean_mss:
# maxr = mean_mss
# tmax = t
# Max separation squared over initial separation squared is stored in a max file for
# every constant
# The loop deletes the unnecessary temporary files
#max_file.write("{} {} {}\n".format(v, maxr / (init_separation ** 2), tmax))
for k in range(thousands_of_runs):
os.remove(mss_list[k].name)
os.remove(ms_list[k].name)
out.close()
nout.close()
#meansqsep = float(str.split(linecache.getline(out.name, steps + 1))[1])
#meansep = float(str.split(linecache.getline(nout.name, steps + 1))[1])
#print("Mean squared separation over {} runs: {} ".format(runs, meansqsep))
#print("Root Mean squared separation over {} runs: {} ".format(runs, math.sqrt(meansqsep)))
#print ("Mean separation over {} runs : {}".format(runs, meansep))
# Appending the results at the end of the Results.out file
# Rfile.write("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R E S U L T S~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ \n")
# Rfile.write(
# "Time-Step:{} Steps:{} runs:{} constant:{} Initial separation:{} hydro: {} time&date: {} \n".format(time_step,
# steps, runs,
# constant[j],
# init_separation,
# hydro,
# time.strftime(
# "%c")))
# Rfile.write("Mean Squared separation {}\n".format(meansqsep))
# Rfile.write("Root Mean squared separation {}\n".format(math.sqrt(meansqsep)))
# Rfile.write("Mean separation {}\n".format(meansep))
# Rfile.close()
# Mean squared displacement. Each row has a colour of the rainbow.
# if args.walk:
# os.chdir("Max_Separation_constants:{}-{}_numc:{}".format(constant[0],constant[-1],len(constant)))
# max_file = open("Max_Separation_constants:{}-{}_numc:{}".format(constant[0],constant[-1],len(constant)),"w")
# for n,j in enumerate(constant):
os.chdir("..")
def setup_cat(self):
ft = self.ft
fieldnames = ft.dtype.fields.keys()
id_check = map(lambda n: n.startswith('ID_Prob'), fieldnames)
if sum(id_check) == 0:
print 'warning: ID_Probability field not found'
id_prob = [np.nan] * len(ft)
else:
id_name = fieldnames[range(len(fieldnames))[id_check][0]]
print 'Using prob from field {}'.format(id_name)
id_prob = ft[id - name]
cat_skydirs = map(lambda x, y: SkyDir(float(x), float(y)), ft.RAJ2000,
ft.DEJ2000)
glat = [s.b() for s in cat_skydirs]
glon = [s.l() for s in cat_skydirs]
truncnames = [n.replace(' ', '') for n in self.df.index]
not_found = []
def nickfix(n):
# put blanks into index
if n in truncnames:
return self.df.index[truncnames.index(n)]
not_found.append(n)
return n
nicknames = ft.NickName_3FGL if 'NickName_3FGL' in ft.dtype.fields else ft.NickName
sourcenames = ft.Source_Name if 'Source_Name' in ft.dtype.fields else nicknames
index = map(nickfix, nicknames) #Source_Name
if len(not_found) > 0:
print '{} entries not found in {}, names starting with {}'.format(
len(not_found), self.skymodel,
list(set(map(lambda n: n[:4], not_found))))
self.not_found = not_found
self.gtlike_info['missing'] = len(not_found)
flags = np.asarray(
ft.Flags_3FGL,
int) if 'Flags_3FGL' in ft.dtype.fields else [0] * len(ft)
self.cat = pd.DataFrame(
dict(
namec=sourcenames, #_3FGL_1,
#nickname=nicknames, #map(nickfix, nicknames),
ra=ft.RAJ2000,
dec=ft.DEJ2000,
ts=ft.Test_Statistic,
pindex=ft.PL_Index,
unc_pindex=ft.Unc_PL_Index,
beta=ft.LP_beta,
pivot=ft.Pivot_Energy,
flux=ft.Flux_Density,
unc_flux=ft.Unc_Flux_Density,
#skydir=cat_skydirs,
glat=glat,
glon=glon,
#pivot=ft.Pivot_Energy, flux=ft.Flux_Density,
#modelname=ft.SpectrumType,
id_prob=id_prob,
a95=ft.Conf_95_SemiMajor,
b95=ft.Conf_95_SemiMinor,
ang95=ft.Conf_95_PosAng,
#ROI_num = ft.ROI_num,
#ROI_dist = ft.ROI_dist,
),
index=index,
)
self.cat.index.name = 'name'
self.cat['eflux'] = self.cat.flux * self.cat['pivot']**2 * 1e6
# set values for corresponding source model
self.cat['pt_ts'] = self.df.ts
self.cat['pt_ra'] = self.df.ra
self.cat['pt_dec'] = self.df.dec
self.cat['pt_beta'] = self.df.beta
self.cat['pt_index'] = self.df.pindex
self.cat['pt_index_unc'] = self.df.pindex_unc
self.cat['pt_eflux'] = self.df.eflux
try:
self.cat['pt_eflux_unc'] = self.df.eflux_unc
except:
print 'No eflux_unc in input.'
self.cat['pt_eflux_unc'] = np.nan
self.cat['pt_pivot'] = self.df.pivot_energy
self.cat['ispsr'] = map(lambda name: name.startswith('PSR'),
self.cat.index)
self.cat['ismissing'] = [name in not_found for name in self.cat.index]
extended_names = self.df.query('isextended==True').index
self.cat['isextended'] = [
name in extended_names for name in self.cat.index
]
self.gtlike_info['pulsars'] = sum(self.cat.ispsr)
self.gtlike_info['extended'] = sum(self.cat.isextended)
print 'Found {} extended sources, {} pulsars'.format(
sum(self.cat.isextended), sum(self.cat.ispsr))
def find_close(A, B):
""" helper function: make a DataFrame with A index containg
columns of the
name of the closest entry in B, and its distance
A, B : DataFrame objects each with a skydir column
"""
def mindist(a):
d = map(a.difference, B.skydir.values)
n = np.argmin(d)
return (B.index[n], np.degrees(d[n]))
return pd.DataFrame(map(mindist, A.skydir.values),
index=A.index,
columns=('otherid', 'distance'))
if self.catname == '2FGL' or self.catname == '3FGL':
print 'generating closest distance to catalog "%s"' % self.catname
closedf = find_close(self.df, self.cat)
self.df['closest'] = closedf['distance']
self.df['close_name'] = closedf.otherid
closedf.to_csv(
os.path.join('plots', self.plotfolder,
'comparison_%s.csv' % self.catname))
closest2 = np.degrees(
np.array([
min(map(sdir.difference, self.df.skydir.values))
for sdir in cat_skydirs
]))
self.cat['closest'] = closest2
elif self.catname == 'psc8':
self.cat['closest'] = 0
def plot_error(year):
#The following currently devoted to making energy and angular error plots for a year of data.
# init likelihood class
if year == 40:
mc = cache.load(filename_pickle + "sirin_IC40/mc.pickle")
extra = cache.load(filename_pickle + "sirin_IC40/dpsi.pickle")
if year == 59:
mc = cache.load(filename_pickle + "sirin_IC59/mc.pickle")
extra = cache.load(filename_pickle + "sirin_IC59/dpsi.pickle")
if year == 79:
mc = cache.load(filename_pickle + "sirin_IC79/mc.pickle")
extra = cache.load(filename_pickle + "sirin_IC79/dpsi.pickle")
elif year == 86:
mc = cache.load(filename_pickle + "sirin_IC86I/mc.pickle")
extra = cache.load(filename_pickle + "sirin_IC86I/dpsi.pickle")
dpsi = extra['dpsi']
# datatest
#Currently don't need to remake these plots. but DONT DELETE
colors = ['b', 'g', 'y', 'r']
gamma = np.linspace(1., 2.7, 4)
# fig_energy, (ax1, ax2) = plt.subplots(ncols=2)
# ax1.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Pseudo-Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# ax1.legend(loc="best")
# ax1.set_title("Reconstructed Energy - IC{}".format(str(year)))
# ax1.set_xlabel("logE")
# ax1.set_ylabel("Relative Abundance")
# ax1.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# ax2.set_title("Zoomed In")
# ax2.set_xlabel("logE")
# ax2.set_xlim(4,10)
# ax2.set_ylim(1e-5,1)
# ax2.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Pseudo-Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# fig_energy.savefig(filename_plots + 'energy_hists_IC{}.pdf'.format(str(year)))
fig_angular_error, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
dec = np.arcsin(mc["sinDec"])
angdist = np.degrees(dpsi)
ax1.hist([np.log10(np.degrees(mc["sigma"])) for i in gamma],
label=[r"$\sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma],
linestyle='dashed',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.hist(
[np.log10(angdist) for i in gamma],
label=[r"$\Delta \psi$ - $\gamma={0:.1f}$".format(g) for g in gamma],
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
linestyle='solid',
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.set_title("Reco MC Angular Error Check - IC{}".format(str(year)))
ax1.set_xlabel(r"log$\sigma_{ang}$ (degrees)")
ax1.set_ylabel("Relative Abundance")
ax1.set_ylim(0, 1.5)
ax2.hist([(np.degrees(mc["sigma"])) for i in gamma],
label=[r"$\sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma],
linestyle='dashed',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.hist(
[(angdist) for i in gamma],
label=[r"$\Delta \psi$ - $\gamma={0:.1f}$".format(g) for g in gamma],
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
linestyle='solid',
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.legend(loc="upper right")
ax2.set_xlim(0, 5)
ax2.set_ylim(0, 3.5)
ax2.set_xlabel(r"$\sigma_{ang}$ (degrees)")
fig_angular_error.savefig(
filename_plots +
'angular_error_hists_sirin_IC{}.pdf'.format(str(year)))
def starPos(degFitX=2, sTitl=''):
jdAll = np.array([])
xShiftAll = np.array([])
yShiftAll = np.array([])
print('Reading in files...')
files = glob.glob('aligned*.fits')
#files = glob.glob('*.fits')
#Data=np.genfromtxt
#fileNames = sorted(files)
#fileNames = fileNames[0:]
for f in files:
#Data=np.genfromtxt(fname=f)
thisHdr = fits.getheader(f)
thisJD = thisHdr['JD']
jdAll = np.hstack((jdAll, thisJD))
thisX = thisHdr['X_SHIFT']
thisY = thisHdr['Y_SHIFT']
xShiftAll = np.hstack((thisX, xShiftAll))
yShiftAll = np.hstack((thisY, yShiftAll))
#xyShift = np.vstack((thisX, thisY))
#print jdAll, xShiftAll, yShiftAll
#print jdAll
# update the time vector in-place to convert from days --> seconds
#print jdAll
jdAll = (jdAll - np.min(jdAll))*86400.
# fit low-order polynomial to the shifts
parsX = np.polyfit(jdAll, xShiftAll, deg=degFitX)
parsY = np.polyfit(jdAll, yShiftAll, deg=degFitX)
tFine = np.linspace(np.min(jdAll)-0.01, np.max(jdAll)+0.01, 1000, endpoint=True)
#----------------- FIGURE 1 AND 2 -------------------#
#Plot data
figx = plt.figure(1)
figx.clf()
axx = figx.add_subplot(211)
dum1 = axx.scatter(jdAll, xShiftAll, marker='^', s=9, c='b', \
label=r"xShift %s" % (sTitl))
if np.abs(degFitX - 1) < 1e-3:
axx.set_title(r'$\Delta x = (%.2e)t + (%.2e$)' % (parsX[0], parsX[1]))
#axx.set_xlabel(r"$\Delta t$, seconds")
# overplot the fit
dum2 = axx.plot(tFine, np.polyval(parsX, tFine), 'k-')
#plt.show(block=False)
#figy = plt.figure(2)
#figy.clf()
axy = figx.add_subplot(212, sharex=axx)
dum2=axy.scatter(jdAll, yShiftAll, marker='s', s=9, c='r', \
label=r"yShift %s" % (sTitl))
#plt.show(block=False)
dum3 = axy.plot(tFine, np.polyval(parsY, tFine), 'k-')
#axy.set_xlabel('JD - min(JD), seconds')
sLabelT =r"$\Delta t$, seconds"
axy.set_xlabel(sLabelT)
#axy.set_xlabel(r"$\Delta t$, seconds")
# for ax in [axx, axy]:
# ax.grid(which='both')
# leg=ax.legend()
axx.set_ylabel('xShift (pixels)')
axy.set_ylabel('yShift (pixels)')
plt.suptitle('X-Shift and Y-Shift vs. Time')
#---------------- FIGURE 3 -----------------------#
figxy = plt.figure(3)
figxy.clf()
axxy = figxy.add_subplot(111)
dum = axxy.scatter(xShiftAll, yShiftAll, c=jdAll, marker='o', label='path %s' % (sTitl), \
edgecolor='0.5')
axxy.set_xlabel('xShift (pixels)')
axxy.set_ylabel('yShift (pixels)')
plt.title('X-Shift vs. Y-Shift')
cbar = figxy.colorbar(dum)
# let's get the residuals from the straight line fit in X and the fit in Y
residX = xShiftAll - np.polyval(parsX, jdAll)
residY = yShiftAll - np.polyval(parsY, jdAll)
#--------------- FIGURE 4 -----------------------#
fig4 = plt.figure(4)
fig4.clf()
ax4 = fig4.add_subplot(211)
dum4 = ax4.plot(jdAll, residX, color='b', ms=3, label='X Residual %s' %(sTitl), marker='^')
#ax4.set_xlabel(r"JD (seconds)")
ax4.set_ylabel(r"$\Delta x$, pix")
ax5 = fig4.add_subplot(212, sharex=ax4)
dum5 = ax5.plot(jdAll, residY, color='r', ms=3, label='Y Residual', marker='s')
#ax5.set_xlabel(r"JD(seconds)")
ax5.set_xlabel(sLabelT)
ax5.set_ylabel(r"$\Delta y$, pix")
plt.suptitle('X and Y Residuals vs. Time')
#-------------- FIGURE 5 ------------------------#
# Creating Lomb-Scargle Periodograms
# imported from astropy.stats import LombScargle
fig5 = plt.figure(5)
fig5.clf()
ax6 = fig5.add_subplot(212)
# fig.suptitle() (could also use plt.suptitle()) displays a tilte above both subplots
plt.suptitle('Lomb-Scargle Power Spectrum for X and Y Residuals')
# let's generate the frequencies we want
pDesired = np.linspace(500., 1500., 1000, endpoint=True)
freq = 1.0/pDesired
power = LombScargle(jdAll, residY).power(freq)
# let's get the maximium value
iMax = np.argmax(power)
periodMax = pDesired[iMax]
sPeakY = 'Peak period = %.2fs' % (periodMax)
#freq, power = LombScargle(jdAll,residY).autopower()
ax6.plot(1.0/freq, power, 'ro', ls='-', ms=2, label=sPeakY)
#ax6.set_xlabel('Period (s)')
ax6.set_xlim(500., 1500.)
# leg6 = ax6.legend()
# Plot for X residuals
# sPeakX = 'Peak period = %.2f s' % (periodMax)
powerX = LombScargle(jdAll, residX).power(freq)
iMaxX = np.argmax(powerX)
periodMaxX = pDesired[iMaxX]
sPeakX = 'Peak period = %.2fs %s' % (periodMaxX, sTitl)
ax7=fig5.add_subplot(211)
ax7.plot(1.0/freq, powerX, 'bo', ls='-', ms=2, label=sPeakX)
ax6.set_xlabel('Period (s)')
#leg7 = ax7.legend()
avgPeriod = np.average([periodMax,periodMaxX])
phase = jdAll/np.float(avgPeriod)
print np.shape(phase)
phase = phase - np.floor(phase)
# let's fit the residuals against each other
parsXY = np.polyfit(residX, residY, 1)
# ------------------ FIGURE 6 --------------- #
fig6 = plt.figure(6)
fig6.clf()
axdxdy = fig6.add_subplot(111)
# let's try ordering by phase
iSort = np.argsort(phase)
# increase the plot symbol size with phase
sPhs = 25.0 + 50.*phase**2
#sPhs = np.repeat(25., np.size(phase))
ax8 = axdxdy.scatter(residX[iSort], residY[iSort], c=phase[iSort], \
marker='o', label='path %s' % (sTitl), edgecolor='0.5', s=sPhs, \
vmin=0., vmax=1., zorder=2)
#dumLine = axdxdy.plot(residX[iSort], residY[iSort], ls='-.', lw=1, color='0.8')
axdxdy.set_xlabel('residX (pixels)')
axdxdy.set_ylabel('residY (pixels)')
cbar1 = fig6.colorbar(ax8)
#axdxdy.set_xlim(500., 1500.)
#a = np.array([residX])
# b = np.array([residY])
c = np.hstack((residX, residY))
cMax = np.max(np.abs(c))
# let's overplot the trend HERE
xFine = np.linspace(-cMax, cMax, 1000)
dumTrend = axdxdy.plot(xFine, np.polyval(parsXY, xFine), color='k', ls='--', zorder=1, \
label=r'Trend angle: %.2f$^{\circ}$' % (np.degrees(np.arctan(parsXY[0]))) )
axdxdy.set_xlim(-cMax,cMax)
axdxdy.set_ylim(-cMax,cMax)
plt.title('Residuals in X vs. Residuals in Y')
# axdxdy.set_xlim(-np.max(np.abs(c)), np.max(np.abs(c)))
# axdxdy.set_ylim(-np.max(np.abs(c)), np.max(np.abs(c)))
#axdxdy.set_xlim(
for ax in [ax6, ax7]:
ax.set_ylabel('Lomb-Scargle power')
for ax in [axx, axy, axxy, ax4, ax5, ax6, ax7, axdxdy]:
ax.grid(which='both')
leg=ax.legend()
figList = [figx, figxy, fig4, fig6, fig5 ]
figTails = ['shiftVsTime', 'shiftVsShift', 'residVsTime', 'residVsResid','powspec']
# clean up the string to make it filename-appropriate
sFil=''
if len(sTitl) > 0:
sFil = sTitl[:].replace("(","").replace(")","")
sFil = sFil.replace(",","_").replace(" ","")
sFil = sFil.replace("/","").replace("\\","")
sFil = "%s_" % (sFil)
#print "SFIL INFO: %s" % (sFil)
print '---------Date---file name--'
print ''
for iFig in range(len(figList)):
fileName = 'fig_%s%s.pdf' % (sFil, figTails[iFig])
figList[iFig].savefig(fileName)
print fileName, 'SAVED'
def main():
# Initialize and parse command-line arguments.
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--verbose', action = 'store_true',
help = 'Provide verbose output.')
descwl.output.Reader.add_args(parser)
parser.add_argument('--no-display', action = 'store_true',
help = 'Do not display the image on screen.')
parser.add_argument('-o','--output-name',type = str, default = None, metavar = 'FILE',
help = 'Name of the output file to write.')
select_group = parser.add_argument_group('Object selection options')
select_group.add_argument('--galaxy', type = int, action = 'append',
default = [ ], metavar = 'ID',
help = 'Select the galaxy with this database ID (can be repeated).')
select_group.add_argument('--group', type = int, action = 'append',
default = [ ], metavar = 'ID',
help = 'Select galaxies belonging to the group with this group ID (can be repeated).')
select_group.add_argument('--select', type = str, action = 'append',
default = [ ], metavar = 'CUT',
help = 'Select objects passing the specified cut (can be repeated).')
select_group.add_argument('--select-region', type = str,
default = None, metavar = '[XMIN,XMAX,YMIN,YMAX]',
help = 'Select objects within this region relative to the image center (arcsecs).')
select_group.add_argument('--save-selected', type = str, default = None,
help = 'Name of FITS file for saving table of selected objects')
match_group = parser.add_argument_group('Detection catalog matching options')
match_group.add_argument('--match-catalog', type = str,
default = None, metavar = 'FILE',
help = 'Name of SExtractor-compatible detection catalog to read.')
match_group.add_argument('--match-color', type = str,
default = 'black', metavar = 'COL',
help = 'Matplotlib color name to use for displaying detection catalog matches.')
match_group.add_argument('--match-info', type = str,
default = None, metavar = 'FMT',
help = 'String interpolation format to generate matched object annotations.')
view_group = parser.add_argument_group('Viewing options')
view_group.add_argument('--magnification', type = float,
default = 1, metavar = 'MAG',
help = 'Magnification factor to use for display.')
view_group.add_argument('--crop', action = 'store_true',
help = 'Crop the displayed pixels around the selected objects.')
view_group.add_argument('--view-region', type = str,
default = None, metavar = '[XMIN,XMAX,YMIN,YMAX]',
help = 'Viewing region in arcsecs relative to the image center (overrides crop if set).')
view_group.add_argument('--draw-moments', action = 'store_true',
help = 'Draw ellipses to represent the 50%% iosophote second moments of selected objects.')
view_group.add_argument('--info', type = str,
default = None, metavar = 'FMT',
help = 'String interpolation format to generate annotation labels.')
view_group.add_argument('--no-crosshair', action = 'store_true',
help = 'Do not draw a crosshair at the centroid of each selected object.')
view_group.add_argument('--clip-lo-noise-fraction', type = float,
default = 0.05, metavar = 'FRAC',
help = 'Clip pixels with values below this fraction of the mean sky noise.')
view_group.add_argument('--clip-hi-percentile', type = float,
default = 90.0, metavar = 'PCT',
help = 'Clip pixels with non-zero values above this percentile for the selected image.')
view_group.add_argument('--hide-background', action = 'store_true',
help = 'Do not display background pixels.')
view_group.add_argument('--hide-selected', action = 'store_true',
help = 'Do not overlay any selected pixels.')
view_group.add_argument('--add-noise',type = int,default = None,metavar = 'SEED',
help = 'Add Poisson noise using the seed provided (no noise is added unless this is set).')
view_group.add_argument('--clip-noise',type = float,default = -1.,metavar = 'SIGMAS',
help = 'Clip background images at this many sigmas when noise is added.')
view_group.add_argument('--zscale-all', action='store_true',
help = 'Set zscale using all displayed objects instead of only selected ones')
format_group = parser.add_argument_group('Formatting options')
format_group.add_argument('--info-size', type = str,
default = 'large', metavar = 'SIZE',
help = 'Matplotlib font size specification in points or relative (small,large,...)')
format_group.add_argument('--dpi', type = float, default = 64.,
help = 'Number of pixels per inch to use for display.')
format_group.add_argument('--max-view-size', type = int,
default = 2048, metavar = 'SIZE',
help = 'Maximum allowed pixel dimensions of displayed image.')
format_group.add_argument('--colormap', type = str,
default = 'viridis', metavar = 'CMAP',
help = 'Matplotlib colormap name to use for background pixel values.')
format_group.add_argument('--highlight', type = str,
default = 'red', metavar = 'COL',
help = 'Matplotlib color name to use for highlighted pixel values.')
format_group.add_argument('--crosshair-color', type = str,
default = 'greenyellow', metavar = 'COL',
help = 'Matplotlib color name to use for crosshairs.')
format_group.add_argument('--ellipse-color', type = str,
default = 'greenyellow', metavar = 'COL',
help = 'Matplotlib color name to use for second-moment ellipses.')
format_group.add_argument('--info-color', type = str,
default = 'green', metavar = 'COL',
help = 'Matplotlib color name to use for info text.')
format_group.add_argument('--outline-color', type = str,
default = None, metavar = 'COL',
help = 'Matplotlib color name to use for outlining text.')
args = parser.parse_args()
if args.no_display and not args.output_name:
print('No display our output requested.')
return 0
if args.hide_background and args.hide_selected:
print('No pixels visible with --hide-background and --hide-selected.')
return 0
# Load the analysis results file we will display from.
try:
reader = descwl.output.Reader.from_args(defer_stamp_loading = True,args = args)
results = reader.results
if args.verbose:
print(results.survey.description())
except RuntimeError as e:
print(str(e))
return -1
# Add noise, if requested.
if args.add_noise is not None:
results.add_noise(args.add_noise)
# Match detected objects to simulated objects, if requested.
if args.match_catalog:
detected,matched,matched_indices,matched_distance = (
results.match_sextractor(args.match_catalog))
if args.verbose:
print('Matched %d of %d detected objects (median sep. = %.2f arcsecs).' % (
np.count_nonzero(matched),len(matched),np.median(matched_distance)))
# Create region selectors.
if args.select_region:
try:
assert args.select_region[0] == '[' and args.select_region[-1] == ']'
xmin,xmax,ymin,ymax = [ float(token) for token in args.select_region[1:-1].split(',') ]
assert xmin < xmax and ymin < ymax
except (ValueError,AssertionError):
print('Invalid select-region xmin,xmax,ymin,ymax = %s.' % args.select_region)
return -1
args.select.extend(['dx>=%f'%xmin,'dx<%f'%xmax,'dy>=%f'%ymin,'dy<%f'%ymax])
# Perform object selection.
if args.select:
# Combine select clauses with logical AND.
selection = results.select(*args.select,mode='and',format='mask')
else:
# Nothing is selected by default.
selection = results.select('NONE',format='mask')
# Add any specified groups to the selection with logical OR.
for identifier in args.group:
selected = results.select('grp_id==%d' % identifier,format='mask')
if not np.any(selected):
print('WARNING: no group found with ID %d.' % identifier)
selection = np.logical_or(selection,selected)
# Add any specified galaxies to the selection with logical OR.
for identifier in args.galaxy:
selected = results.select('db_id==%d' % identifier,format='mask')
if not np.any(selected):
print('WARNING: no galaxy found with ID %d.' % identifier)
selection = np.logical_or(selection,selected)
selected_indices = np.arange(results.num_objects)[selection]
if args.verbose:
print('Selected IDs:\n%s' % np.array(results.table['db_id'][selected_indices]))
groups = np.unique(results.table[selected_indices]['grp_id'])
print('Selected group IDs:\n%s' % np.array(groups))
# Do we have individual objects available for selection in the output file?
if np.any(selection) and not results.stamps:
print('Cannot display selected objects without any stamps available.')
return -1
# Save table of selected objects if requested.
if args.save_selected:
if args.verbose:
print('Saving selected objects to %s.' % args.save_selected)
results.table[selected_indices].write(args.save_selected, overwrite=True)
# Build the image of selected objects (might be None).
selected_image = results.get_subimage(selected_indices)
# Calculate our viewing bounds as (xmin,xmax,ymin,ymax) in floating-point pixels
# relative to the image bottom-left corner. Also calculate view_bounds with
# integer values that determine how to extract sub-images to display.
scale = results.survey.pixel_scale
if args.view_region is not None:
try:
assert args.view_region[0] == '[' and args.view_region[-1] == ']'
xmin,xmax,ymin,ymax = [ float(token) for token in args.view_region[1:-1].split(',') ]
assert xmin < xmax and ymin < ymax
except (ValueError,AssertionError):
print('Invalid view-window xmin,xmax,ymin,ymax = %s.' % args.view_region)
return -1
# Convert to pixels relative to bottom-left corner.
xmin = xmin/scale + 0.5*results.survey.image_width
xmax = xmax/scale + 0.5*results.survey.image_width
ymin = ymin/scale + 0.5*results.survey.image_height
ymax = ymax/scale + 0.5*results.survey.image_height
# Calculate integer pixel bounds that cover the view window.
view_bounds = galsim.BoundsI(
int(math.floor(xmin)),int(math.ceil(xmax))-1,
int(math.floor(ymin)),int(math.ceil(ymax))-1)
elif args.crop and selected_image is not None:
view_bounds = selected_image.bounds
xmin,xmax,ymin,ymax = (
view_bounds.xmin,view_bounds.xmax+1,view_bounds.ymin,view_bounds.ymax+1)
else:
view_bounds = results.survey.image.bounds
xmin,xmax,ymin,ymax = 0,results.survey.image_width,0,results.survey.image_height
if args.verbose:
vxmin = (xmin - 0.5*results.survey.image_width)*scale
vxmax = (xmax - 0.5*results.survey.image_width)*scale
vymin = (ymin - 0.5*results.survey.image_height)*scale
vymax = (ymax - 0.5*results.survey.image_height)*scale
print('View window is [xmin,xmax,ymin,ymax] = [%.2f,%.2f,%.2f,%.2f] arcsecs' % (
vxmin,vxmax,vymin,vymax))
print('View pixels in %r' % view_bounds)
# Initialize a matplotlib figure to display our view bounds.
view_width = float(xmax - xmin)
view_height = float(ymax - ymin)
if (view_width*args.magnification > args.max_view_size or
view_height*args.magnification > args.max_view_size):
print('Requested view dimensions %d x %d too big. Increase --max-view-size if necessary.' % (
view_width*args.magnification,view_height*args.magnification))
return -1
fig_height = args.magnification*(view_height/args.dpi)
fig_width = args.magnification*(view_width/args.dpi)
figure = plt.figure(figsize = (fig_width,fig_height),frameon = False,dpi = args.dpi)
axes = plt.Axes(figure, [0., 0., 1., 1.])
axes.axis(xmin = xmin,xmax = xmax,ymin = ymin,ymax = ymax)
axes.set_axis_off()
figure.add_axes(axes)
# Get the background and highlighted images to display, sized to our view.
background = galsim.Image(bounds = view_bounds,dtype = np.float32,scale = scale)
highlighted = background.copy()
if not args.hide_background:
overlap = results.survey.image.bounds & view_bounds
if overlap.area() > 0:
background[overlap] = results.survey.image[overlap]
if not args.hide_selected and selected_image is not None:
overlap = selected_image.bounds & view_bounds
if overlap.area() > 0:
highlighted[overlap] = selected_image[overlap]
if np.count_nonzero(highlighted.array) == 0:
if args.hide_background or np.count_nonzero(background.array) == 0:
print('There are no non-zero pixel values in the view window.')
return -1
# Prepare the z scaling.
zscale_pixels = results.survey.image.array
if selected_image and not args.zscale_all:
if selected_image.bounds.area() < 16:
print('WARNING: using full image for z-scaling since only %d pixel(s) selected.' % (
selected_image.bounds.area()))
else:
zscale_pixels = selected_image.array
# Clip large fluxes to a fixed percentile of the non-zero selected pixel values.
non_zero_pixels = (zscale_pixels != 0)
vmax = np.percentile(zscale_pixels[non_zero_pixels],q = (args.clip_hi_percentile))
# Clip small fluxes to a fixed fraction of the mean sky noise.
vmin = args.clip_lo_noise_fraction*np.sqrt(results.survey.mean_sky_level)
if args.verbose:
print('Clipping pixel values to [%.1f,%.1f] detected electrons.' % (vmin,vmax))
# Define the z scaling function. See http://ds9.si.edu/ref/how.html#Scales
def zscale(pixels):
return np.sqrt(pixels)
# Calculate the clipped and scaled pixel values to display.
highlighted_z = zscale((np.clip(highlighted.array,vmin,vmax) - vmin)/(vmax-vmin))
if args.add_noise:
vmin = args.clip_noise*np.sqrt(results.survey.mean_sky_level)
if args.verbose:
print('Background pixels with noise clipped to [%.1f,%.1f].' % (vmin,vmax))
background_z = zscale((np.clip(background.array,vmin,vmax) - vmin)/(vmax-vmin))
# Convert the background image to RGB using the requested colormap.
# Drop the alpha channel [3], which is all ones anyway.
cmap = matplotlib.cm.get_cmap(args.colormap)
background_rgb = cmap(background_z)[:,:,:3]
# Overlay the highlighted image using alpha blending.
# http://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
if args.highlight and args.highlight != 'none':
alpha = highlighted_z[:,:,np.newaxis]
color = np.array(matplotlib.colors.colorConverter.to_rgb(args.highlight))
final_rgb = alpha*color + background_rgb*(1.-alpha)
else:
final_rgb = background_rgb
# Draw the composite image.
extent = (view_bounds.xmin,view_bounds.xmax+1,view_bounds.ymin,view_bounds.ymax+1)
axes.imshow(final_rgb,extent = extent,aspect = 'equal',origin = 'lower',
interpolation = 'nearest')
# The argparse module escapes any \n or \t in string args, but we need these
# to be unescaped in the annotation format string.
if args.info:
args.info = binary_type(args.info, 'utf-8').decode('unicode-escape')
if args.match_info:
args.match_info = binary_type(args.match_info, 'utf-8').decode('string-escape')
num_selected = len(selected_indices)
ellipse_centers = np.empty((num_selected,2))
ellipse_widths = np.empty(num_selected)
ellipse_heights = np.empty(num_selected)
ellipse_angles = np.empty(num_selected)
match_ellipse_centers = np.empty((num_selected,2))
match_ellipse_widths = np.empty(num_selected)
match_ellipse_heights = np.empty(num_selected)
match_ellipse_angles = np.empty(num_selected)
num_match_ellipses = 0
for index,selected in enumerate(selected_indices):
info = results.table[selected]
# Do we have a detected object matched to this simulated source?
match_info = None
if args.match_catalog and info['match'] >= 0:
match_info = detected[info['match']]
# Calculate the selected object's centroid position in user display coordinates.
x_center = (0.5*results.survey.image_width + info['dx']/scale)
y_center = (0.5*results.survey.image_height + info['dy']/scale)
if match_info is not None:
x_match_center = match_info['X_IMAGE']-0.5
y_match_center = match_info['Y_IMAGE']-0.5
# Draw a crosshair at the centroid of selected objects.
if not args.no_crosshair:
axes.plot(x_center,y_center,'+',color = args.crosshair_color,
markeredgewidth = 2,markersize = 24)
if match_info:
axes.plot(x_match_center,y_match_center,'x',color = args.match_color,
markeredgewidth = 2,markersize = 24)
# Add annotation text if requested.
if args.info:
path_effects = None if args.outline_color is None else [
matplotlib.patheffects.withStroke(linewidth = 2,
foreground = args.outline_color)]
try:
annotation = args.info % info
except IndexError:
print('Invalid annotate-format %r' % args.info)
return -1
axes.annotate(annotation,xy = (x_center,y_center),xytext = (4,4),
textcoords = 'offset points',color = args.info_color,
fontsize = args.info_size,path_effects = path_effects)
if match_info and args.match_info:
path_effects = None if args.outline_color is None else [
matplotlib.patheffects.withStroke(linewidth = 2,
foreground = args.outline_color)]
try:
annotation = args.match_info % match_info
except IndexError:
print('Invalid match-format %r' % args.match_info)
return -1
axes.annotate(annotation,xy = (x_match_center,y_match_center),
xytext = (4,4),textcoords = 'offset points',
color = args.info_color,fontsize = args.info_size,
path_effects = path_effects)
# Add a second-moments ellipse if requested.
if args.draw_moments:
ellipse_centers[index] = (x_center,y_center)
ellipse_widths[index] = info['a']/scale
ellipse_heights[index] = info['b']/scale
ellipse_angles[index] = np.degrees(info['beta'])
if match_info:
# This will only work if we have the necessary additional fields in the match catalog.
try:
match_ellipse_centers[num_match_ellipses] = (x_match_center,y_match_center)
match_ellipse_widths[num_match_ellipses] = match_info['A_IMAGE']
match_ellipse_heights[num_match_ellipses] = match_info['B_IMAGE']
match_ellipse_angles[num_match_ellipses] = match_info['THETA_IMAGE']
num_match_ellipses += 1
except IndexError:
pass
# Draw any ellipses.
if args.draw_moments:
ellipses = matplotlib.collections.EllipseCollection(units = 'x',
widths = ellipse_widths,heights = ellipse_heights,angles = ellipse_angles,
offsets = ellipse_centers, transOffset = axes.transData)
ellipses.set_facecolor('none')
ellipses.set_edgecolor(args.ellipse_color)
axes.add_collection(ellipses,autolim = True)
if num_match_ellipses > 0:
ellipses = matplotlib.collections.EllipseCollection(units = 'x',
widths = match_ellipse_widths,heights = match_ellipse_heights,
angles = match_ellipse_angles,offsets = match_ellipse_centers,
transOffset = axes.transData)
ellipses.set_facecolor('none')
ellipses.set_edgecolor(args.match_color)
#ellipses.set_linestyle('dashed')
axes.add_collection(ellipses,autolim = True)
if args.output_name:
figure.savefig(args.output_name,dpi = args.dpi)
if not args.no_display:
plt.show()
def process_file(pcap_file_in, pcap_dir_in, shm_name, shm_shp, shm_dtp,
txt_dir_in, fn_keyword):
loc_shm = SharedMemory(shm_name)
loc_apx_arr = np.recarray(shape=shm_shp, dtype=shm_dtp, buf=loc_shm.buf)
this_os = platform.system()
if this_os == "Linux":
concatenate_cmd = "cat"
wine = "wine "
elif this_os == "Windows":
concatenate_cmd = "type"
wine = ""
else:
print("Unknown OS. Terminating.")
sys.exit(0)
print(
f"Running on {this_os}. To concatenate we wil use '{concatenate_cmd}'")
print(f"Calling lastools with e.g. '{wine}lastool'")
print(f"Processing {pcap_file_in}")
logging.info(f"Processing {pcap_file_in}")
# ### Read entire file only once (takes most time)
start = time.time()
packets = rdpcap(os.path.join(pcap_dir_in, pcap_file_in))
packets_read = len(packets)
end = time.time()
print(F"Read {packets_read} packets in {end-start:.2f} seconds.")
logging.info(F"Read {packets_read} packets in {end-start:.2f} seconds.")
# ### Make sure all packets have length == 1206!
start = time.time()
wrong_lengths = 0
for p in packets:
if len(p.load) != DATA_PACKET_LENGTH:
wrong_lengths += 1
end = time.time()
logging.info(F"Checked {packets_read} packets in {end-start:.2f} seconds.")
logging.info('All have same length (' + str(DATA_PACKET_LENGTH) +
').' if wrong_lengths == 0 else str(wrong_lengths) +
' packets have a different length.')
logging.info('This is GOOD!' if wrong_lengths == 0 else 'This is BAD!')
# ### Read all packets into 1 numpy array
start = time.time()
raw_pack_data = np.zeros((packets_read, DATA_PACKET_LENGTH),
dtype=np.uint8)
for i, p in enumerate(packets):
raw_pack_data[i, :] = np.frombuffer(p.load, dtype=np.uint8)
if i % 1e5 == 0:
print(
f"Packet {i} out of {packets_read} in {time.time()-start:.2f} seconds."
)
end = time.time()
logging.info(
F"Copied data from {packets_read} packets into a numpy array of shape {raw_pack_data.shape} in {end-start:.2f} seconds."
)
# ### Make sure all packets are captured in the same mode (last, strongest, dual)
mode_hypothesis = raw_pack_data[0, RETURN_MODE_OFFSET]
logging.info(
f"First packet reports {RETURN_MODE_NAME[mode_hypothesis]} capture mode."
)
diff_ret_mode = (raw_pack_data[:, RETURN_MODE_OFFSET] !=
mode_hypothesis).sum()
logging.info(f"{diff_ret_mode} packets disagree.")
logging.info(
f"{'This is GOOD!' if diff_ret_mode == 0 else 'This is BAD!'}")
# ### Make sure all packets are captured with the same sensor (only VLP16 expected)
sensor_hypothesis = raw_pack_data[0, PRODUCT_MODEL_OFFSET]
logging.info(
f"First packet reports {PRODUCT_MODEL_NAME[sensor_hypothesis]} sensor model."
)
diff_sensor = (raw_pack_data[:, PRODUCT_MODEL_OFFSET] !=
sensor_hypothesis).sum()
logging.info(f"{diff_sensor} packets disagree.")
logging.info(f"{'This is GOOD!' if diff_sensor == 0 else 'This is BAD!'}")
# ### Get µs timestamp from packets and transform to UNIX timestamp
#
# I found that Ethernet timestamp agrees with GNSS timestamp very well.
#
# Can be problematic if very close ho full hour and I am not careful.
#
# Let's look at 1st Ethernet timestamp.
#
# * if it is far enough from a full hour (>=1 minute), then we continue
# * ~if it is too close (<1 minute), then we look at last one~ _not implemented_
# * ~if last one is also too close (recorded for 1 entire hour, not likely), we find an optimal one in the middle~ _not implemented_
ts_1st_pack = datetime.datetime.fromtimestamp(int(packets[0].time))
if ts_1st_pack.minute > 1 and ts_1st_pack.minute < 59:
logging.info(
f"Far enough from full hour (~{ts_1st_pack.minute} minutes).")
logging.info("This is GOOD!\nContinue!")
else:
logging.info(
f"Too close to full hour (~{ts_1st_pack.minute} minutes).")
logging.info(
"That is not great, but the code below should deal with it.")
# #### Take Ethernet timestamp of (1st) packet, discard sub-hour info and add replace it with that from GNSS µs timestamp
#
# What happens when the capture rolls over a full hour?
#
# **Need to deal with this when such data is captured!**
#
# # Solution below!
start = time.time()
micros = np.zeros((packets_read, ), dtype=np.int64)
micro_bytes = micros.view(dtype=np.uint8)
micro_bytes[0::8] = raw_pack_data[:, DATA_PACK_TIMESTAMP_OFFSET + 0]
micro_bytes[1::8] = raw_pack_data[:, DATA_PACK_TIMESTAMP_OFFSET + 1]
micro_bytes[2::8] = raw_pack_data[:, DATA_PACK_TIMESTAMP_OFFSET + 2]
micro_bytes[3::8] = raw_pack_data[:, DATA_PACK_TIMESTAMP_OFFSET + 3]
plt.plot(micros)
end = time.time()
logging.info(
f"Extracted time stamp from {packets_read} packets in {end-start:.2f} seconds."
)
logging.info(
"If the line jumps, a full hour occurs. Need to deal with it!")
# #### Another problem could be that the UDP packets are not guaranteed to arrive in order.
#
# An assumption that is made for the following calculations is that this does not happen.
#
# **Need to deal with this when such data is captured!**
while (micros[1:] < micros[:-1]).sum() > 0:
jump_position = np.where((micros[1:] < micros[:-1]))[0][0] + 1
micros[jump_position:] += int(3.6e9)
logging.info(
f"Added another hour to micros at position {jump_position}")
plt.plot(micros)
if (micros[1:] - micros[:-1]).min() > 0: #all chronological
logging.info("Packets seem to be in right order. Continue!")
else:
logging.info("Not all packets are in order. Handle somehow!")
print("Not all packets are in order. Handle somehow!")
sys.exit(0)
eth_ts_hour = remove_min_sec(packets[0].time)
puck_timestamps = micros / 1e6 + eth_ts_hour * 1.0
# ### Get range and intensity info for all packets
start = time.time()
# the following contains only channel data (i.e. no timestamp, factory bytes or azimuth)
channel_data = raw_pack_data[:, :-6].reshape(
(packets_read, DATA_BLOCKS, 100))[:, :, 4:]
channel_data = channel_data.reshape(
(packets_read, DATA_BLOCKS * LASERS_PER_DATA_BLOCK * 3))
#puck ranges in mm
puck_ranges = np.zeros((packets_read, DATA_BLOCKS * LASERS_PER_DATA_BLOCK),
dtype=np.uint32)
puck_range_bytes = puck_ranges.view(dtype=np.uint8)
puck_range_bytes[:, 0::4] = channel_data[:, 0::3]
puck_range_bytes[:, 1::4] = channel_data[:, 1::3]
puck_ranges *= 2
#intensities as 1 byte
puck_intens = np.zeros((packets_read, DATA_BLOCKS * LASERS_PER_DATA_BLOCK),
dtype=np.uint8)
puck_intens[:, :] = channel_data[:, 2::3]
end = time.time()
logging.info(
f"Extracted range and intensity for {packets_read * DATA_BLOCKS * LASERS_PER_DATA_BLOCK} laser pulses in {end-start:.2f} seconds."
)
# ### Get all given azimuths
#
# Think how to treat them for dual / single cases later.
#
# For now assume it is always DUAL!
# Changed azimuths data type to signed 32-bit integer to support in-place substraction
start = time.time()
# the following contains only azimuth data (i.e. no timestamp, factory bytes or channel data)
azimuths = np.zeros((packets_read, DATA_BLOCKS, 1), dtype=np.int32)
azim_data = azimuths.view(dtype=np.uint8)
azim_data[:, :,
0:2] = raw_pack_data[:, :-6].reshape(packets_read, DATA_BLOCKS,
100)[:, :, 2:4]
azim_data = azim_data.reshape((packets_read, DATA_BLOCKS * 4))
#azimuth
azimuths = azim_data.view(dtype=np.int32)
end = time.time()
logging.info(
f"Extracted azimuths for {packets_read * DATA_BLOCKS} firing sequences in {end-start:.2f} seconds."
)
# ### All packets are in dual return mode, so the azimuths are expected to repeat (VLP-16 User Manual, Figure 9-3)
#
# The following checks this assumption again:
az_repeat = ((azimuths[:, 0::2] != azimuths[:, 1::2]).sum() == 0)
if az_repeat:
logging.info("All azimuths repeat. This is good.")
else:
logging.info("Not all azimuths repeat. Investigate before continuing.")
azimuths_gap = get_azim_gap(azimuths)
micros_pulses = get_micros_pulses(micros)
nanos_pulses = get_nanos_pulses(micros)
# timestamp for each laser pulse
puck_pulse_time = micros_pulses / 1e6 + eth_ts_hour * 1.0
# ### Calculate the azimutzhs for each datapoint
#Use the following simplified array if in dual mode
#Otherwise can still refer to it, but it's just the original array
if mode_hypothesis == RETURN_MODE_DUAL:
az_simple = azimuths[:, 0::2]
else:
az_simple = azimuths
prec_az = get_precision_azimuth(az_simple, azimuths_gap, True, True)
#cut the big dataframe to only what the puck data covers
interv = np.where(
np.logical_and(loc_apx_arr.timestamp > puck_timestamps[0],
loc_apx_arr.timestamp < puck_timestamps[-1]))[0]
mid_apx_arr = loc_apx_arr[max(interv[0] -
1, 0):min(interv[-1] +
1, loc_apx_arr.shape[0])]
# ### process puck data...
concat_files = []
MAXIMUM_POINTS_PER_RUN = 2000 # * DATA_BLOCKS * LASERS_PER_DATA_BLOCK # iterate over puck_timestamps
max_laps = int(np.ceil(puck_timestamps.size / MAXIMUM_POINTS_PER_RUN))
for run_count in range(max_laps):
print(f'Running slice {run_count} out of {max_laps}')
current_range = np.arange(0, min(MAXIMUM_POINTS_PER_RUN,
puck_timestamps.size - MAXIMUM_POINTS_PER_RUN * run_count)) +\
MAXIMUM_POINTS_PER_RUN * run_count #a slice that hopefully fits in RAM
#time in seconds
min_time = puck_timestamps[current_range][0]
max_time = puck_timestamps[current_range][-1]
print(
f"{pcap_file_in}: Processing {(max_time - min_time):.2f} seconds")
interv = np.where(
np.logical_and(mid_apx_arr.timestamp > min_time,
mid_apx_arr.timestamp < max_time))[0]
if interv.size < 1:
continue
sml_apx_arr = mid_apx_arr[max(interv[0] -
1, 0):min(interv[-1] +
2, mid_apx_arr.shape[0])]
relevant_times = puck_pulse_time[current_range, :]
relevant_nanos = nanos_pulses[current_range, :]
strongest_return_ranges = puck_ranges[current_range].reshape(
(-1, DATA_BLOCKS, LASERS_PER_DATA_BLOCK))[:, 1::2].flatten() / 1000
strongest_return_intensities = puck_intens[current_range].reshape(
(-1, DATA_BLOCKS, LASERS_PER_DATA_BLOCK))[:, 1::2].flatten()
last_return_ranges = puck_ranges[current_range].reshape(
(-1, DATA_BLOCKS, LASERS_PER_DATA_BLOCK))[:, 0::2].flatten() / 1000
last_return_intensities = puck_intens[current_range].reshape(
(-1, DATA_BLOCKS, LASERS_PER_DATA_BLOCK))[:, 0::2].flatten()
azimuth = prec_az[current_range]
vert_elev_angle = np.tile(elevation_and_vert_corr_by_laser_id[:, 0],
(1, 12))
vert_elev_angle = np.tile(vert_elev_angle, (azimuth.shape[0], 1))
global_laser_id = np.tile(np.arange(16, dtype=np.uint8), (1, 12))
global_laser_id = np.tile(global_laser_id, (azimuth.shape[0], 1))
azimuth = np.deg2rad(azimuth / 100).flatten()
vert_elev_angle = vert_elev_angle.flatten()
#not enough points to interpolate
if sml_apx_arr.shape[0] < 4:
continue
f_lat = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["lat_EPSG32632"],
kind='cubic',
fill_value="extrapolate")
f_lon = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["lon_EPSG32632"],
kind='cubic',
fill_value="extrapolate")
f_ele = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["elevation"],
kind='cubic',
fill_value="extrapolate")
f_yaw = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["heading_continuous"],
kind='cubic',
fill_value="extrapolate")
f_rol = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["roll"],
kind='cubic',
fill_value="extrapolate")
f_pit = interp1d(sml_apx_arr.timestamp,
sml_apx_arr["pitch"],
kind='cubic',
fill_value="extrapolate")
MIN_RANGE = 2 #metres
MIN_INTENSITY = 100
for return_counter in range(1, 3):
if return_counter == 1:
condition = np.logical_and(
strongest_return_ranges > MIN_RANGE,
strongest_return_intensities > MIN_INTENSITY)
condition_double = np.logical_and(
last_return_ranges > MIN_RANGE,
last_return_ranges != strongest_return_ranges)
return_ranges = strongest_return_ranges
return_intensities = strongest_return_intensities
elif return_counter == 2:
condition = np.logical_and(
np.logical_and(
last_return_ranges > MIN_RANGE,
last_return_ranges != strongest_return_ranges),
last_return_intensities > MIN_INTENSITY)
condition_double = np.ones_like(last_return_ranges,
dtype=np.bool8)
return_ranges = last_return_ranges
return_intensities = last_return_intensities
lat = f_lat(relevant_times).flatten()
lon = f_lon(relevant_times).flatten()
ele = f_ele(relevant_times).flatten()
yaw = f_yaw(relevant_times).flatten() % 360
rol = f_rol(relevant_times).flatten()
pit = f_pit(relevant_times).flatten()
X_puck = np.ones_like(return_intensities) * np.nan
Y_puck = np.ones_like(return_intensities) * np.nan
Z_puck = np.ones_like(return_intensities) * np.nan
#VLP manual p.53
X_puck[condition] = return_ranges[condition] * np.cos(
vert_elev_angle)[condition] * np.sin(azimuth)[condition]
Y_puck[condition] = return_ranges[condition] * np.cos(
vert_elev_angle)[condition] * np.cos(azimuth)[condition]
Z_puck[condition] = return_ranges[condition] * np.sin(
vert_elev_angle)[condition]
# first rotate into XYZ of the drone!
# x_roll = -90 #degrees
# y_pitch = 0
# z_yaw = -90
#rotation from puck to uav coordinates:
R_01 = np.array([[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]])
#get rid of invalid entries
X_puck = X_puck[condition]
Y_puck = Y_puck[condition]
Z_puck = Z_puck[condition]
XYZ_puck = np.vstack((X_puck, Y_puck, Z_puck)).T
XYZ_puck = XYZ_puck[:, np.newaxis, :]
XYZ_uav = np.matmul(XYZ_puck, R_01)
#now rotate to real world...
yaw_correction, pit_correction, rol_correction = -np.radians(
yaw[condition]), -np.radians(pit[condition]), -np.radians(
rol[condition])
cos_gamma = np.cos(rol_correction)
sin_gamma = np.sin(rol_correction)
cos_beta = np.cos(pit_correction)
sin_beta = np.sin(pit_correction)
cos_alpha = np.cos(yaw_correction)
sin_alpha = np.sin(yaw_correction)
R_gamma = np.array([[
np.ones_like(cos_gamma),
np.zeros_like(cos_gamma),
np.zeros_like(cos_gamma)
], [np.zeros_like(cos_gamma), cos_gamma,
-sin_gamma], [np.zeros_like(cos_gamma), sin_gamma, cos_gamma]])
R_gamma = np.transpose(R_gamma, (2, 0, 1))
R_beta = np.array([[cos_beta,
np.zeros_like(cos_beta), sin_beta],
[
np.zeros_like(cos_beta),
np.ones_like(cos_beta),
np.zeros_like(cos_beta)
],
[-sin_beta,
np.zeros_like(cos_beta), cos_beta]])
R_beta = np.transpose(R_beta, (2, 0, 1))
R_alpha = np.array(
[[cos_alpha, -sin_alpha,
np.zeros_like(cos_alpha)],
[sin_alpha, cos_alpha,
np.zeros_like(cos_alpha)],
[
np.zeros_like(cos_alpha),
np.zeros_like(cos_alpha),
np.ones_like(cos_alpha)
]])
R_alpha = np.transpose(R_alpha, (2, 0, 1))
XYZ_rotated = np.matmul(XYZ_uav, R_gamma)
XYZ_rotated = np.matmul(XYZ_rotated, R_beta)
XYZ_rotated = np.matmul(XYZ_rotated, R_alpha)
#bring it into East, North, Up system (+90° around z, then +180° around new x)
R_last = np.array([[0., 1., 0.], [1., 0., 0.], [0., 0., -1.]])
XYZ_rotated = np.matmul(XYZ_rotated, R_last)
flight_line_id = np.ones_like(
vert_elev_angle[condition], dtype=np.uint16) * int(
pcap_file_in.split(".")[0].split("_")[-1])
flight_line_id = flight_line_id[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, flight_line_id),
axis=-1)
return_id = np.ones_like(vert_elev_angle[condition],
dtype=np.uint16) * return_counter
return_id = return_id[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, return_id), axis=-1)
return_intensities = return_intensities[condition]
return_intensities = return_intensities[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, return_intensities),
axis=-1)
number_of_returns = np.ones_like(
vert_elev_angle[condition], dtype=np.uint8) + condition_double[
condition] #1 for single, 2 for double
number_of_returns = number_of_returns[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, number_of_returns),
axis=-1)
laser_times = relevant_times.flatten(
)[condition] - 1e9 #subtract 1 billion (see here https://support.geocue.com/fixing-las-global-encoding/)
laser_times = laser_times[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, laser_times), axis=-1)
#for angles
delta_pos = np.copy(XYZ_rotated[:, :, 0:3])
delta_pos = np.matmul(delta_pos, R_alpha) #or transpose?
new_scan_angle = np.degrees(
np.arctan2(delta_pos[:, 0, 0], -delta_pos[:, 0, 2])
) #take delta_z as positive when looking down (normal scan)
new_scan_angle = np.clip(
new_scan_angle, -128, +127
) #for some reason does not want to use short even though version 1.4
new_scan_angle = new_scan_angle[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, new_scan_angle),
axis=-1)
# new_along_track_angle = np.degrees(np.arctan2(delta_pos[:,0,1], - delta_pos[:,0,2])) #take delta_z as positive when looking down (normal scan)
# #new_along_track_angle %= 360; new_along_track_angle[new_along_track_angle > 180] -= 360 #normalize to +/-180
# #new_along_track_angle = np.clip(new_along_track_angle, -180, +180)
# new_along_track_angle = new_along_track_angle[:, np.newaxis, np.newaxis]
# XYZ_rotated = np.concatenate((XYZ_rotated, new_along_track_angle), axis = -1)
# put nanosecond timestamp instead of angle along track!
laser_nanos = relevant_nanos.flatten()[condition]
laser_nanos = laser_nanos[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, laser_nanos), axis=-1)
laser_id = global_laser_id.flatten()[condition]
laser_id = laser_id[:, np.newaxis, np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, laser_id), axis=-1)
XYZ_rotated[:, 0, 0] += lon[condition]
XYZ_rotated[:, 0, 1] += lat[condition]
XYZ_rotated[:, 0, 2] += ele[condition]
#to easily display height in cloudcompare
extra_elevation_field = XYZ_rotated[:, :, 2].flatten()
extra_elevation_field = extra_elevation_field[:, np.newaxis,
np.newaxis]
XYZ_rotated = np.concatenate((XYZ_rotated, extra_elevation_field),
axis=-1)
if return_counter == 1:
first_returns = np.copy(XYZ_rotated)
elif return_counter == 2:
first_returns = np.concatenate((first_returns, XYZ_rotated))
fname = f'{pcap_file_in.split(".")[0]}'
#np.savetxt(f"{fname}.xyz", np.squeeze(first_returns, axis = -2), fmt='%.4f')
np.savetxt(os.path.join(out_dir_ascii,
f"{fname}_r{run_count:03d}.xyz"),
np.squeeze(first_returns, axis=-2),
fmt=[
'%.3f', '%.3f', '%.3f', '%3d', '%1d', '%3d', '%1d',
'%.9f', '%.3f', '%d', '%d', '%.3f'
])
#fmt=['%.3f', '%.3f', '%.3f', '%3d', '%1d', '%3d', '%1d', '%.9f', '%.3f', '%.3f', '%d', '%.3f'])
#fmt=['%.3f', '%.3f', '%.3f', '%3d', '%1d', '%3d', '%1d', '%.9f', '%.3f', '%.3f', '%.3f', '%.3f', '%d'])
current_ascii_file = f"{fname}_r{run_count:03d}.xyz"
concat_files.append(os.path.join(out_dir_ascii, current_ascii_file))
merged_ascii_file = os.path.join(out_dir_ascii, f"{fname}.xyz")
print(f"Concatenating {len(concat_files)} ascii files")
start = time.time()
command = f"{concatenate_cmd} {' '.join(concat_files)} > {merged_ascii_file}"
logging.info(command)
os.system(command)
print(f"Done in {(time.time() -start):.2f} seconds.")
print(
f"{pcap_file_in}: Removing {len(concat_files)} redundant ascii files")
start = time.time()
for f in concat_files:
os.remove(f)
print(f"{pcap_file_in}: Done in {(time.time() -start):.2f} seconds.")
# for fname in concat_files.strip().split(" "):
# os.remove(fname)
print(
f"Finished processing file {os.path.join(pcap_dir_in, pcap_file_in)}")
logging.info(
f"Finished processing file {os.path.join(pcap_dir_in, pcap_file_in)}")
def merge_particles(sim_p: POINTER_REB_SIM, collision: reb_collision, ed: ExtraData):
global massloss_estimator
print("--------------")
print("colliding")
sim: Simulation = sim_p.contents
print("current time step", sim.dt)
print(f"p1 is {collision.p1}")
print(f"p2 is {collision.p2}")
# the assignment to cp1 or cp2 seems to be random
# also look at a copy instead of the original particles
# to avoid issues after they have been modified
cp1: Particle = sim.particles[collision.p1].copy()
cp2: Particle = sim.particles[collision.p2].copy()
# just calling the more massive one the target to keep its type/name
# Sun<->Protoplanet -> Sun
# and to keep collsions mostly reproducable
if cp1.m > cp2.m:
target = cp1
projectile = cp2
else: # also when masses are the same
target = cp2
projectile = cp1
if collision.p1 > collision.p2:
lower_index_particle_index = collision.p2
else:
lower_index_particle_index = collision.p1
print(f"colliding {target.hash.value} ({ed.pd(target).type}) "
f"with {projectile.hash.value} ({ed.pd(projectile).type})")
projectile_wmf = ed.pd(projectile).water_mass_fraction
projectile_cmf = ed.pd(projectile).core_mass_fraction
target_wmf = ed.pd(target).water_mass_fraction
target_cmf = ed.pd(target).core_mass_fraction
# get the velocities, velocity differences and unit vector as numpy arrays
# all units are in sytem units (so AU/year)
v1 = np.array(target.vxyz)
v2 = np.array(projectile.vxyz)
r1 = np.array(target.xyz)
r2 = np.array(projectile.xyz)
vdiff = v2 - v1
rdiff = r2 - r1
vdiff_n = linalg.norm(vdiff)
rdiff_n = linalg.norm(rdiff)
print("dt", sim.dt)
# during a collision ias15 should always be used, otherwise something weird has happend
assert sim.ri_mercurius.mode == 1
print("rdiff", rdiff)
print("vdiff", vdiff)
print("sum_radii", target.r + projectile.r)
print("rdiff_n", rdiff_n)
print("vdiff_n", vdiff_n)
ang = float(np.degrees(np.arccos(np.dot(rdiff, vdiff) / (rdiff_n * vdiff_n))))
if ang > 90:
ang = 180 - ang
print("angle_deg", ang)
print()
# get mass fraction
gamma = projectile.m / target.m
# calculate mutual escape velocity (for norming the velocities in the interpolation) in SI units
escape_velocity = sqrt(2 * G * (target.m + projectile.m) / ((target.r + projectile.r) * astronomical_unit))
print("interpolating")
if not massloss_estimator:
methods = [RbfMassloss, LeiZhouMassloss, PerfectMerging, SimpleNNMassloss]
per_name = {}
for method in methods:
per_name[method.name] = method
try:
estimator_class = per_name[ed.meta.massloss_method]
except KeyError:
print("invalid mass loss estimation method")
print("please use one of these:")
print(per_name)
raise
massloss_estimator = estimator_class()
# let interpolation calculate water and mass retention fraction
# meta is just a bunch of intermediate results that will be logged to help
# understand the collisions better
input_data = Input(
alpha=ang,
velocity_original=vdiff_n,
escape_velocity=escape_velocity,
gamma=gamma,
projectile_mass=projectile.m,
target_water_fraction=target_wmf,
projectile_water_fraction=projectile_wmf,
)
water_ret, mantle_ret, core_ret, meta = get_mass_fractions(input_data)
print("mass retentions:", water_ret, mantle_ret, core_ret)
meta.collision_velocities = (v1.tolist(), v2.tolist())
meta.collision_positions = (target.xyz, projectile.xyz)
meta.collision_radii = (target.r, projectile.r)
hash = unique_hash(ed) # hash for newly created particle
# handle loss of water and core mass
water_mass = target.m * target_wmf + projectile.m * projectile_wmf
core_mass = target.m * target_cmf + projectile.m * projectile_cmf
mantle_mass = target.m + projectile.m - water_mass - core_mass
water_mass *= water_ret
mantle_mass *= mantle_ret
core_mass *= core_ret
total_mass = water_mass + mantle_mass + core_mass
final_wmf = water_mass / total_mass
final_cmf = core_mass / total_mass
print(final_wmf)
# create new object preserving momentum
merged_planet = (target * target.m + projectile * projectile.m) / total_mass
merged_planet.m = total_mass
merged_planet.hash = hash
merged_planet.r = PlanetaryRadius(merged_planet.m, final_wmf, final_cmf).total_radius / astronomical_unit
ed.pdata[hash.value] = ParticleData(
water_mass_fraction=final_wmf,
core_mass_fraction=final_cmf,
type=ed.pd(target).type,
total_mass=total_mass
)
meta.final_wmf = final_wmf
meta.final_radius = merged_planet.r
meta.target_wmf = target_wmf
meta.projectile_wmf = projectile_wmf
meta.time = sim.t
pprint(meta)
ed.tree.add(target, projectile, merged_planet, meta)
sim.particles[lower_index_particle_index] = merged_planet
sim.move_to_com()
sim.integrator_synchronize()
sim.ri_mercurius.recalculate_coordinates_this_timestep = 1
sim.ri_mercurius.recalculate_dcrit_this_timestep = 1
print("collision finished")
print("--------------")
# from rebound docs:
# A return value of 0 indicates that both particles remain in the simulation.
# A return value of 1 (2) indicates that particle 1 (2) should be removed from the simulation.
# A return value of 3 indicates that both particles should be removed from the simulation.
# always keep lower index particle and delete other one
# this keeps the N_active working
if lower_index_particle_index == collision.p1:
print("deleting p2")
return 2
elif lower_index_particle_index == collision.p2:
print("deleting p1")
return 1
else:
raise ValueError("invalid index")
poll_interval = imu.IMUGetPollInterval()
# magnetic deviation
while True:
if imu.IMURead():
data = imu.getIMUData()
#~ print data
q = np.asarray(data["fusionQPose"])
#~ print q
#~ print data.keys()
time.sleep(poll_interval * 1.0 / 1000.0)
q_raw = pq.Quaternion(q[0], q[1], q[2], q[3])
q_x_fix = pq.Quaternion(axis=[1.0, 0.0, 0.0], degrees=-90.0)
q_corrected = q_x_fix * q_raw
#~ q_corrected[1] *= -1.0;
q_corrected[2] *= -1.0
q_corrected[3] *= -1.0
#~ look_dir = np.array([0.0, 0.0, 1.0])
#~
#~ look_dir = q_corrected.rotate(look_dir);
print 'axis:', q_corrected.axis
print 'angle:', np.degrees(q_corrected.angle)
v = v2 - v1 # [20, 3]
# Normalize v
v = v / np.linalg.norm(v, axis=1)[:, np.newaxis]
# Get angle using arcos of dot product
angle = np.arccos(
np.einsum(
'nt,nt->n', v[[
0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20
], :], v[[
1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21
], :]))
angle = np.degrees(angle) # Convert radian to degree
angle_label = np.array([angle], dtype=np.float32)
angle_label = np.append(angle_label, idx)
d = np.concatenate([joint.flatten(), angle_label])
data.append(d)
mp_drawing.draw_landmarks(img, result.face_landmarks,
mp_holistic.FACE_CONNECTIONS)
cv2.imshow('img', img)
if cv2.waitKey(1) == ord('q'):
break
def calcTheta(ra, dec, ra_c, dec_c):
deltaX, deltaY = (ra - ra_c), (dec - dec_c)
theta = np.degrees(np.arctan2(deltaY, deltaX)) + 180
return theta
def get_angle(self, p1, p2, p3):
v0 = np.array(p2) - np.array(p1)
v1 = np.array(p3) - np.array(p1)
angle = np.math.atan2(np.linalg.det([v0, v1]), np.dot(v0, v1))
return np.degrees(angle)
def make_pix_models(fname,
ra1='ra',
dec1='dec',
ra2='RAJ2000',
dec2='DEJ2000',
fitsname=None,
plots=False,
smooth=300.):
"""
Read a fits file which contains the crossmatching results for two catalogues.
Catalogue 1 is the source catalogue (positions that need to be corrected)
Catalogue 2 is the reference catalogue (correct positions)
return rbf models for the ra/dec corrections
:param fname: filename for the crossmatched catalogue
:param ra1: column name for the ra degrees in catalogue 1 (source)
:param dec1: column name for the dec degrees in catalogue 1 (source)
:param ra2: column name for the ra degrees in catalogue 2 (reference)
:param dec2: column name for the dec degrees in catalogue 2 (reference)
:param fitsname: fitsimage upon which the pixel models will be based
:param plots: True = Make plots
:param smooth: smoothing radius (in pixels) for the RBF function
:return: (dxmodel, dymodel)
"""
filename, file_extension = os.path.splitext(fname)
if file_extension == ".fits":
raw_data = fits.open(fname)[1].data
elif file_extension == ".vot":
raw_data = parse_single_table(fname).array
# get the wcs
hdr = fits.getheader(fitsname)
imwcs = wcs.WCS(hdr, naxis=2)
# filter the data to only include SNR>10 sources
flux_mask = np.where(raw_data['peak_flux'] / raw_data['local_rms'] > 10)
data = raw_data[flux_mask]
cat_xy = imwcs.all_world2pix(zip(data[ra1], data[dec1]), 1)
ref_xy = imwcs.all_world2pix(zip(data[ra2], data[dec2]), 1)
diff_xy = ref_xy - cat_xy
dxmodel = interpolate.Rbf(cat_xy[:, 0],
cat_xy[:, 1],
diff_xy[:, 0],
function='linear',
smooth=smooth)
dymodel = interpolate.Rbf(cat_xy[:, 0],
cat_xy[:, 1],
diff_xy[:, 1],
function='linear',
smooth=smooth)
if plots:
import matplotlib
# Super-computer-safe
matplotlib.use('Agg')
from matplotlib import pyplot
from matplotlib import gridspec
# Perceptually uniform cyclic color schemes
try:
import seaborn as sns
cmap = matplotlib.colors.ListedColormap(
sns.color_palette("husl", 256))
except ImportError:
print("seaborne not detected; using hsv color scheme")
cmap = 'hsv'
# Attractive serif fonts
if which("latex"):
try:
from matplotlib import rc
rc('text', usetex=True)
rc('font', **{'family': 'serif', 'serif': ['serif']})
except:
print("rc not detected; using sans serif fonts")
else:
print("latex not detected; using sans serif fonts")
xmin, xmax = 0, hdr['NAXIS1']
ymin, ymax = 0, hdr['NAXIS2']
gx, gy = np.mgrid[xmin:xmax:(xmax - xmin) / 50.,
ymin:ymax:(ymax - ymin) / 50.]
mdx = dxmodel(np.ravel(gx), np.ravel(gy))
mdy = dymodel(np.ravel(gx), np.ravel(gy))
x = cat_xy[:, 0]
y = cat_xy[:, 1]
# plot w.r.t. centre of image, in degrees
try:
delX = abs(hdr['CD1_1'])
except:
delX = abs(hdr['CDELT1'])
try:
delY = hdr['CD2_2']
except:
delY = hdr['CDELT2']
# shift all co-ordinates and put them in degrees
x -= hdr['NAXIS1'] / 2
gx -= hdr['NAXIS1'] / 2
xmin -= hdr['NAXIS1'] / 2
xmax -= hdr['NAXIS1'] / 2
x *= delX
gx *= delX
xmin *= delX
xmax *= delX
y -= hdr['NAXIS2'] / 2
gy -= hdr['NAXIS2'] / 2
ymin -= hdr['NAXIS2'] / 2
ymax -= hdr['NAXIS2'] / 2
y *= delY
gy *= delY
ymin *= delY
ymax *= delY
scale = 1
dx = diff_xy[:, 0]
dy = diff_xy[:, 1]
fig = pyplot.figure(figsize=(12, 6))
gs = gridspec.GridSpec(100, 100)
gs.update(hspace=0, wspace=0)
kwargs = {
'angles': 'xy',
'scale_units': 'xy',
'scale': scale,
'cmap': cmap,
'clim': [-180, 180]
}
angles = np.degrees(np.arctan2(dy, dx))
ax = fig.add_subplot(gs[0:100, 0:48])
cax = ax.quiver(x, y, dx, dy, angles, **kwargs)
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
ax.set_xlabel("Distance from pointing centre / degrees")
ax.set_ylabel("Distance from pointing centre / degrees")
ax.set_title("Source position offsets / arcsec")
# cbar = fig.colorbar(cax, orientation='horizontal')
ax = fig.add_subplot(gs[0:100, 49:97])
cax = ax.quiver(gx, gy, mdx, mdy, np.degrees(np.arctan2(mdy, mdx)),
**kwargs)
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
ax.set_xlabel("Distance from pointing centre / degrees")
ax.tick_params(axis='y', labelleft='off')
ax.set_title("Model position offsets / arcsec")
# cbar = fig.colorbar(cax, orientation='vertical')
# Color bar
ax2 = fig.add_subplot(gs[0:100, 98:100])
cbar3 = pyplot.colorbar(cax, cax=ax2, use_gridspec=True)
cbar3.set_label('Angle CCW from West / degrees') #,labelpad=-75)
cbar3.ax.yaxis.set_ticks_position('right')
outname = os.path.splitext(fname)[0] + '.png'
# pyplot.show()
pyplot.savefig(outname, dpi=200)
return dxmodel, dymodel
def event(self):
"""
Create a neutrino event and run it through the simulation chain.
Creates a particle using the ``generator``, produces a signal from that
event, propagates that signal through the ice according to the
``ice_model`` and the ``ray_tracer``, and passes it into the
``antennas`` for processing.
Returns
-------
event : Event
The neutrino event generated which is responsible for the waveforms
on the antennas.
triggered : bool, optional
If the ``triggers`` parameter was specified, contains whether the
global trigger condition of the detector was met.
See Also
--------
pyrex.Event : Class for storing a tree of `Particle` objects
representing an event.
pyrex.Particle : Class for storing particle attributes.
"""
event = self.gen.create_event()
ray_paths = []
polarizations = []
for i in range(len(self.antennas)):
ray_paths.append([])
polarizations.append([])
for particle in event:
logger.info("Processing event for %s", particle)
for i, ant in enumerate(self.antennas):
rt = self.ray_tracer(particle.vertex,
ant.position,
ice_model=self.ice)
# If no path(s) between the points, skip ahead
if not rt.exists:
logger.debug("Ray paths to %s do not exist", ant)
continue
ray_paths[i].extend(rt.solutions)
for path in rt.solutions:
# nu_pol is the signal polarization at the neutrino vertex
# It's calculated as the (negative) vector rejection of
# path.emitted_direction onto particle.direction, making
# epol orthogonal to path.emitted_direction in the same
# plane as particle.direction and path.emitted_direction
# This is equivalent to the vector triple product
# (particle.direction x path.emitted_direction) x
# path.emitted_direction
# In the case when path.emitted_direction and
# particle.direction are equal, just let nu_pol be zeros
nu_pol = normalize(
np.vdot(path.emitted_direction, particle.direction) *
path.emitted_direction - particle.direction)
polarizations[i].append(nu_pol)
psi = np.arccos(
np.vdot(particle.direction, path.emitted_direction))
logger.debug("Angle to %s is %f degrees", ant,
np.degrees(psi))
# TODO: Support angles larger than pi/2
# (low priority since these angles are far from the
# cherenkov cone)
if psi > np.pi / 2:
continue
pulse = self.signal_model(
times=self.signal_times,
particle=particle,
viewing_angle=psi,
viewing_distance=path.path_length,
ice_model=self.ice)
ant_pulses, ant_pols = path.propagate(signal=pulse,
polarization=nu_pol)
ant.receive(ant_pulses,
direction=path.received_direction,
polarization=ant_pols)
if self.triggers is None:
triggered = None
elif isinstance(self.triggers, dict):
triggered = {
key: trigger_func(self.antennas)
for key, trigger_func in self.triggers.items()
}
else:
triggered = self.triggers(self.antennas)
if self.writer is not None:
self.writer.add(event=event,
triggered=triggered,
ray_paths=ray_paths,
polarizations=polarizations,
events_thrown=self.gen.count - self._gen_count)
self._gen_count = self.gen.count
if triggered is None:
return event
elif isinstance(self.triggers, dict):
return event, triggered['global']
else:
return event, triggered
def calibeovsa(vis=None,
caltype=None,
interp=None,
docalib=True,
doflag=True,
flagant=None,
doimage=False,
imagedir=None,
antenna=None,
timerange=None,
spw=None,
stokes=None,
doconcat=False,
msoutdir=None,
keep_orig_ms=True):
'''
:param vis: EOVSA visibility dataset(s) to be calibrated
:param caltype:
:param interp:
:param docalib:
:param qlookimage:
:param flagant:
:param stokes:
:param doconcat:
:return:
'''
if type(vis) == str:
vis = [vis]
for idx, f in enumerate(vis):
if f[-1] == '/':
vis[idx] = f[:-1]
for msfile in vis:
casalog.origin('calibeovsa')
if not caltype:
casalog.post(
"Caltype not provided. Perform reference phase calibration and daily phase calibration."
)
caltype = ['refpha', 'phacal',
'fluxcal'] ## use this line after the phacal is applied
# caltype = ['refcal']
if not os.path.exists(msfile):
casalog.post("Input visibility does not exist. Aborting...")
continue
if msfile.endswith('/'):
msfile = msfile[:-1]
if not msfile[-3:] in ['.ms', '.MS']:
casalog.post(
"Invalid visibility. Please provide a proper visibility file ending with .ms"
)
# if not caltable:
# caltable=[os.path.basename(vis).replace('.ms','.'+c) for c in caltype]
# get band information
tb.open(msfile + '/SPECTRAL_WINDOW')
nspw = tb.nrows()
bdname = tb.getcol('NAME')
bd_nchan = tb.getcol('NUM_CHAN')
bd = [int(b[4:]) - 1 for b in bdname] # band index from 0 to 33
# nchans = tb.getcol('NUM_CHAN')
# reffreqs = tb.getcol('REF_FREQUENCY')
# cenfreqs = np.zeros((nspw))
tb.close()
tb.open(msfile + '/ANTENNA')
nant = tb.nrows()
antname = tb.getcol('NAME')
antlist = [str(ll) for ll in range(len(antname) - 1)]
antennas = ','.join(antlist)
tb.close()
# get time stamp, use the beginning of the file
tb.open(msfile + '/OBSERVATION')
trs = {'BegTime': [], 'EndTime': []}
for ll in range(tb.nrows()):
tim0, tim1 = Time(tb.getcell('TIME_RANGE', ll) / 24 / 3600,
format='mjd')
trs['BegTime'].append(tim0)
trs['EndTime'].append(tim1)
tb.close()
trs['BegTime'] = Time(trs['BegTime'])
trs['EndTime'] = Time(trs['EndTime'])
btime = np.min(trs['BegTime'])
etime = np.max(trs['EndTime'])
# ms.open(vis)
# summary = ms.summary()
# ms.close()
# btime = Time(summary['BeginTime'], format='mjd')
# etime = Time(summary['EndTime'], format='mjd')
## stop using ms.summary to avoid conflicts with importeovsa
t_mid = Time((btime.mjd + etime.mjd) / 2., format='mjd')
print "This scan observed from {} to {} UTC".format(
btime.iso, etime.iso)
gaintables = []
if ('refpha' in caltype) or ('refamp' in caltype) or ('refcal'
in caltype):
refcal = ra.sql2refcalX(btime)
pha = refcal['pha'] # shape is 15 (nant) x 2 (npol) x 34 (nband)
pha[np.where(refcal['flag'] == 1)] = 0.
amp = refcal['amp']
amp[np.where(refcal['flag'] == 1)] = 1.
t_ref = refcal['timestamp']
# find the start and end time of the local day when refcal is registered
try:
dhr = t_ref.LocalTime.utcoffset().total_seconds() / 60. / 60.
except:
dhr = -7.
bt = Time(np.fix(t_ref.mjd + dhr / 24.) - dhr / 24., format='mjd')
et = Time(bt.mjd + 1., format='mjd')
(yr, mon, day) = (bt.datetime.year, bt.datetime.month,
bt.datetime.day)
dirname = caltbdir + str(yr) + str(mon).zfill(2) + '/'
if not os.path.exists(dirname):
os.mkdir(dirname)
# check if there is any ROACH reboot between the reference calibration found and the current data
t_rbts = db.get_reboot(Time([t_ref, btime]))
if not t_rbts:
casalog.post(
"Reference calibration is derived from observation at " +
t_ref.iso)
print "Reference calibration is derived from observation at " + t_ref.iso
else:
casalog.post(
"Oh crap! Roach reboot detected between the reference calibration time "
+ t_ref.iso + ' and the current observation at ' +
btime.iso)
casalog.post("Aborting...")
print "Oh crap! Roach reboot detected between the reference calibration time " + t_ref.iso + ' and the current observation at ' + btime.iso
print "Aborting..."
para_pha = []
para_amp = []
calpha = np.zeros((nspw, 15, 2))
calamp = np.zeros((nspw, 15, 2))
for s in range(nspw):
for n in range(15):
for p in range(2):
calpha[s, n, p] = pha[n, p, bd[s]]
calamp[s, n, p] = amp[n, p, bd[s]]
para_pha.append(np.degrees(pha[n, p, bd[s]]))
para_amp.append(amp[n, p, bd[s]])
if 'fluxcal' in caltype:
calfac = pc.get_calfac(Time(t_mid.iso.split(' ')[0] + 'T23:59:59'))
t_bp = Time(calfac['timestamp'], format='lv')
if int(t_mid.mjd) == int(t_bp.mjd):
accalfac = calfac['accalfac'] # (ant x pol x freq)
caltb_autoamp = dirname + t_bp.isot[:-4].replace(
':', '').replace('-', '') + '.bandpass'
if not os.path.exists(caltb_autoamp):
bandpass(vis=msfile,
caltable=caltb_autoamp,
solint='inf',
refant='eo01',
minblperant=1,
minsnr=0,
bandtype='B',
docallib=False)
tb.open(caltb_autoamp, nomodify=False) # (ant x spw)
bd_chanidx = np.hstack([[0], bd_nchan.cumsum()])
for ll in range(nspw):
cparam = np.zeros((2, bd_nchan[ll], nant))
cparam[:, :, :-3] = 1.0 / np.moveaxis(
accalfac[:, :, bd_chanidx[ll]:bd_chanidx[ll + 1]],
0, 2)
tb.putcol('CPARAM', cparam + 0j, ll * nant, nant)
paramerr = tb.getcol('PARAMERR', ll * nant, nant)
paramerr = paramerr * 0
tb.putcol('PARAMERR', paramerr, ll * nant, nant)
bpflag = tb.getcol('FLAG', ll * nant, nant)
bpant1 = tb.getcol('ANTENNA1', ll * nant, nant)
bpflagidx, = np.where(bpant1 >= 13)
bpflag[:] = False
bpflag[:, :, bpflagidx] = True
tb.putcol('FLAG', bpflag, ll * nant, nant)
bpsnr = tb.getcol('SNR', ll * nant, nant)
bpsnr[:] = 100.0
bpsnr[:, :, bpflagidx] = 0.0
tb.putcol('SNR', bpsnr, ll * nant, nant)
tb.close()
msg_prompt = "Scaling calibration is derived for {}.".format(
msfile)
casalog.post(msg_prompt)
print msg_prompt
gaintables.append(caltb_autoamp)
else:
msg_prompt = "Caution: No TPCAL is available on {}. No scaling calibration is derived for {}.".format(
t_mid.datetime.strftime('%b %d, %Y'), msfile)
casalog.post(msg_prompt)
print msg_prompt
if ('refpha' in caltype) or ('refcal' in caltype):
# caltb_pha = os.path.basename(vis).replace('.ms', '.refpha')
# check if the calibration table already exists
caltb_pha = dirname + t_ref.isot[:-4].replace(':', '').replace(
'-', '') + '.refpha'
if not os.path.exists(caltb_pha):
gencal(vis=msfile,
caltable=caltb_pha,
caltype='ph',
antenna=antennas,
pol='X,Y',
spw='0~' + str(nspw - 1),
parameter=para_pha)
gaintables.append(caltb_pha)
if ('refamp' in caltype) or ('refcal' in caltype):
# caltb_amp = os.path.basename(vis).replace('.ms', '.refamp')
caltb_amp = dirname + t_ref.isot[:-4].replace(':', '').replace(
'-', '') + '.refamp'
if not os.path.exists(caltb_amp):
gencal(vis=msfile,
caltable=caltb_amp,
caltype='amp',
antenna=antennas,
pol='X,Y',
spw='0~' + str(nspw - 1),
parameter=para_amp)
gaintables.append(caltb_amp)
# calibration for the change of delay center between refcal time and beginning of scan -- hopefully none!
xml, buf = ch.read_calX(4, t=[t_ref, btime], verbose=False)
if buf:
dly_t2 = Time(stf.extract(buf[0], xml['Timestamp']), format='lv')
dlycen_ns2 = stf.extract(buf[0], xml['Delaycen_ns'])[:15]
xml, buf = ch.read_calX(4, t=t_ref)
dly_t1 = Time(stf.extract(buf, xml['Timestamp']), format='lv')
dlycen_ns1 = stf.extract(buf, xml['Delaycen_ns'])[:15]
dlycen_ns_diff = dlycen_ns2 - dlycen_ns1
for n in range(2):
dlycen_ns_diff[:, n] -= dlycen_ns_diff[0, n]
print 'Multi-band delay is derived from delay center difference at {} & {}'.format(
dly_t1.iso, dly_t2.iso)
# print '=====Delays relative to Ant 14====='
# for i, dl in enumerate(dlacen_ns_diff[:, 0] - dlacen_ns_diff[13, 0]):
# ant = antlist[i]
# print 'Ant eo{0:02d}: x {1:.2f} ns & y {2:.2f} ns'.format(int(ant) + 1, dl
# dlacen_ns_diff[i, 1] - dlacen_ns_diff[13, 1])
# caltb_mbd0 = os.path.basename(vis).replace('.ms', '.mbd0')
caltb_dlycen = dirname + dly_t2.isot[:-4].replace(':', '').replace(
'-', '') + '.dlycen'
if not os.path.exists(caltb_dlycen):
gencal(vis=msfile,
caltable=caltb_dlycen,
caltype='mbd',
pol='X,Y',
antenna=antennas,
parameter=dlycen_ns_diff.flatten().tolist())
gaintables.append(caltb_dlycen)
if 'phacal' in caltype:
phacals = np.array(
ra.sql2phacalX([bt, et], neat=True, verbose=False))
if not phacals.any() or len(phacals) == 0:
print "Found no phacal records in SQL database, will skip phase calibration"
else:
# first generate all phacal calibration tables if not already exist
t_phas = Time([phacal['t_pha'] for phacal in phacals])
# sort the array in ascending order by t_pha
sinds = t_phas.mjd.argsort()
t_phas = t_phas[sinds]
phacals = phacals[sinds]
caltbs_phambd = []
for i, phacal in enumerate(phacals):
# filter out phase cals with reference time stamp >30 min away from the provided refcal time
if (phacal['t_ref'].jd -
refcal['timestamp'].jd) > 30. / 1440.:
del phacals[i]
del t_phas[i]
continue
else:
t_pha = phacal['t_pha']
phambd_ns = phacal['pslope']
for n in range(2):
phambd_ns[:, n] -= phambd_ns[0, n]
# set all flagged values to be zero
phambd_ns[np.where(phacal['flag'] == 1)] = 0.
caltb_phambd = dirname + t_pha.isot[:-4].replace(
':', '').replace('-', '') + '.phambd'
caltbs_phambd.append(caltb_phambd)
if not os.path.exists(caltb_phambd):
gencal(vis=msfile,
caltable=caltb_phambd,
caltype='mbd',
pol='X,Y',
antenna=antennas,
parameter=phambd_ns.flatten().tolist())
# now decides which table to apply depending on the interpolation method ("neatest" or "linear")
if interp == 'nearest':
tbind = np.argmin(np.abs(t_phas.mjd - t_mid.mjd))
dt = np.min(np.abs(t_phas.mjd - t_mid.mjd)) * 24.
print "Selected nearest phase calibration table at " + t_phas[
tbind].iso
gaintables.append(caltbs_phambd[tbind])
if interp == 'linear':
# bphacal = ra.sql2phacalX(btime)
# ephacal = ra.sql2phacalX(etime,reverse=True)
bt_ind, = np.where(t_phas.mjd < btime.mjd)
et_ind, = np.where(t_phas.mjd > etime.mjd)
if len(bt_ind) == 0 and len(et_ind) == 0:
print "No phacal found before or after the ms data within the day of observation"
print "Skipping daily phase calibration"
elif len(bt_ind) > 0 and len(et_ind) == 0:
gaintables.append(caltbs_phambd[bt_ind[-1]])
elif len(bt_ind) == 0 and len(et_ind) > 0:
gaintables.append(caltbs_phambd[et_ind[0]])
elif len(bt_ind) > 0 and len(et_ind) > 0:
bphacal = phacals[bt_ind[-1]]
ephacal = phacals[et_ind[0]]
# generate a new table interpolating between two daily phase calibrations
t_pha_mean = Time(np.mean(
[bphacal['t_pha'].mjd, ephacal['t_pha'].mjd]),
format='mjd')
phambd_ns = (bphacal['pslope'] +
ephacal['pslope']) / 2.
for n in range(2):
phambd_ns[:, n] -= phambd_ns[0, n]
# set all flagged values to be zero
phambd_ns[np.where(bphacal['flag'] == 1)] = 0.
phambd_ns[np.where(ephacal['flag'] == 1)] = 0.
caltb_phambd_interp = dirname + t_pha_mean.isot[:-4].replace(
':', '').replace('-', '') + '.phambd'
if not os.path.exists(caltb_phambd_interp):
gencal(vis=msfile,
caltable=caltb_phambd_interp,
caltype='mbd',
pol='X,Y',
antenna=antennas,
parameter=phambd_ns.flatten().tolist())
print "Using phase calibration table interpolated between records at " + bphacal[
't_pha'].iso + ' and ' + ephacal['t_pha'].iso
gaintables.append(caltb_phambd_interp)
if docalib:
clearcal(msfile)
applycal(vis=msfile,
gaintable=gaintables,
applymode='calflag',
calwt=False)
# delete the interpolated phase calibration table
try:
caltb_phambd_interp
except:
pass
else:
if os.path.exists(caltb_phambd_interp):
shutil.rmtree(caltb_phambd_interp)
if doflag:
if flagant:
try:
flagdata(vis=msfile, antenna=flagant)
except:
print "Something wrong with flagant. Abort..."
if doimage:
from matplotlib import pyplot as plt
from suncasa.utils import helioimage2fits as hf
from sunpy import map as smap
if not antenna:
antenna = '0~12'
if not stokes:
stokes = 'XX'
if not timerange:
timerange = ''
if not spw:
spw = '1~3'
if not imagedir:
imagedir = '.'
#(yr, mon, day) = (bt.datetime.year, bt.datetime.month, bt.datetime.day)
#dirname = imagedir + str(yr) + '/' + str(mon).zfill(2) + '/' + str(day).zfill(2) + '/'
#if not os.path.exists(dirname):
# os.makedirs(dirname)
bds = [spw]
nbd = len(bds)
imgs = []
for bd in bds:
if '~' in bd:
bdstr = bd.replace('~', '-')
else:
bdstr = str(bd).zfill(2)
imname = imagedir + '/' + os.path.basename(msfile).replace(
'.ms', '.bd' + bdstr)
print 'Cleaning image: ' + imname
try:
clean(vis=msfile,
imagename=imname,
antenna=antenna,
spw=bd,
timerange=timerange,
imsize=[512],
cell=['5.0arcsec'],
stokes=stokes,
niter=500)
except:
print 'clean not successfull for band ' + str(bd)
else:
imgs.append(imname + '.image')
junks = ['.flux', '.mask', '.model', '.psf', '.residual']
for junk in junks:
if os.path.exists(imname + junk):
shutil.rmtree(imname + junk)
tranges = [btime.iso + '~' + etime.iso] * nbd
fitsfiles = [img.replace('.image', '.fits') for img in imgs]
hf.imreg(vis=msfile,
timerange=tranges,
imagefile=imgs,
fitsfile=fitsfiles,
usephacenter=False)
plt.figure(figsize=(6, 6))
for i, fitsfile in enumerate(fitsfiles):
plt.subplot(1, nbd, i + 1)
eomap = smap.Map(fitsfile)
sz = eomap.data.shape
if len(sz) == 4:
eomap.data = eomap.data.reshape((sz[2], sz[3]))
eomap.plot_settings['cmap'] = plt.get_cmap('jet')
eomap.plot()
eomap.draw_limb()
eomap.draw_grid()
plt.show()
if doconcat:
if len(vis) > 1:
from suncasa.eovsa import concateovsa as ce
msname = os.path.basename(vis[0])
msname = msname.split('.')[0] + '_concat.ms'
visprefix = msoutdir + '/'
ce.concateovsa(msname,
vis,
visprefix,
doclearcal=False,
keep_orig_ms=keep_orig_ms,
cols2rm=["MODEL_DATA", "CORRECTED_DATA"])
return [visprefix + msname]
else:
return vis
def vector_angle(v1, v2):
v1_u = unit_vector(v1)
v2_u = unit_vector(v2)
tmp1 = np.degrees(np.arctan2(v1_u[1],v1_u[0]))#np.degrees(np.arctan((v1_u[1])/(v1_u[0])))
tmp2 = np.degrees(np.arctan2(v2_u[1],v2_u[0]))#np.degrees(np.arctan(v2_u[1]/v2_u[0]))
return tmp2-tmp1 #, tmp1-tmp2
file.write(str(r) + ': E=' + str(E(cor_arr)) + ' E_2=' + str(E_2(cor_arr)) + ' D=' + str(D(cor_arr)) + '\n')
vares = [mix_norm_dist(size) for i in range(0, 1000)]
vars_x = [[r[0] for r in var] for var in vares]
vars_y = [[r[1] for r in var] for var in vares]
cor_arr = [cor_coef[coef](vars_x[i], vars_y[i]) for i in range(0, len(vars_x))]
file.write('for mixin: E=' + str(E(cor_arr)) + ' E_2=' + str(E_2(cor_arr)) + ' D=' + str(D(cor_arr)) + '\n')
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 12))
ax = axes.flatten()
for i in range(0, 4):
if i != 3:
points = normal_dist(ro[i], size)
ax[i].set_title('n = ' + str(size) + ', r=' + str(ro[i]))
else:
points = mix_norm_dist(size)
ax[i].set_title('n = ' + str(size) + ', mix')
nstd = 2
r_x = [point[0] for point in points]
r_y = [point[1] for point in points]
ax[i].plot(r_x, r_y, 'bo', ms=4)
cov = np.cov(r_x, r_y)
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:, 0][::-1]))
w, h = 2 * nstd * np.sqrt(vals)
ell = Ellipse(xy=(np.mean(r_x), np.mean(r_y)),
width=w, height=h,
angle=theta, color='black')
ell.set_facecolor('none')
ax[i].add_artist(ell)
plt.tight_layout()
fig.savefig('ellipse n=' + str(size))
def update_particles(particles, cam, velocity, angular_velocity, world,
WIN_RF1, WIN_World):
raw_input()
print 'update: ' + str(angular_velocity)
cv2.waitKey(4)
num_particles = len(particles)
for p in particles:
# calculates new orientation
curr_angle = add_to_angular_v2(np.degrees(p.getTheta()), angular_velocity)
print 'theta_rad: ' + str(p.getTheta())
print 'theta_deg: ' + str(np.degrees(p.getTheta()))
print 'cur_ang_deg: ' + str(np.degrees(curr_angle))
if velocity > 0.0:
[x, y] = move_vector(p, velocity)
particle.move_particle(p, x, y, curr_angle)
else:
particle.move_particle(p, 0.0, 0.0, curr_angle)
print 'cur_ang_rad: ' + str(curr_angle)
if velocity != 0.0:
particle.add_uncertainty(particles, 12, 15)
if velocity == 0.0 and angular_velocity != 0.0:
particle.add_uncertainty(particles, 0, 15)
# Fetch next frame
colour, distorted = cam.get_colour()
# Detect objects
objectType, measured_distance, measured_angle, colourProb = cam.get_object(
colour)
if objectType != 'none':
obs_landmark = ret_landmark(colourProb, objectType)
observed_obj = [objectType, measured_distance, measured_angle,
obs_landmark]
list_of_particles = weight_particles(particles,
np.degrees(measured_angle),
measured_distance, obs_landmark)
particles = []
for count in range(0, num_particles):
rando = np.random.uniform(0.0,1.0) # np.random.normal(0.0, 1.0, 1)
# dicto = {'i': 500,
# 'n': 2}
p = when_in_range(list_of_particles,
0,
num_particles,
rando)
particles.append(
particle.Particle(p.getX(), p.getY(), p.getTheta(),
1.0 / num_particles))
print 'list_of_particles: ' + str(list_of_particles)
print 'particles: ' + str(particles)
particle.add_uncertainty(particles, 5, 2)
# new random particles added
#for c in range(0, int(math.ceil(num_particles * 0.05))):
# p = particle.Particle(500.0 * np.random.ranf() - 100,
# 500.0 * np.random.ranf() - 100,
# 2.0 * np.pi * np.random.ranf() - np.pi, 0.0)
# particles.append(p)
# Draw detected pattern
cam.draw_object(colour)
else:
observed_obj = [None, None, None, None]
# No observation - reset weights to uniform distribution
for p in particles:
p.setWeight(1.0 / num_particles)
particle.add_uncertainty(particles, 5, 2)
# est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
# return [est_pose, observed_obj]
est_pose = particle.estimate_pose(
particles) # The estimate of the robots current pose
print 'Updated pose: ' + str([est_pose.getX(), est_pose.getY()])
draw_world(est_pose, particles, world)
# Show frame
cv2.imshow(WIN_RF1, colour)
# Show world
cv2.imshow(WIN_World, world)
return {'est_pos': est_pose,
'obs_obj': observed_obj,
'particles': particles}
def gt_callback(data):
global_ground_truth = data.pose.pose
gt_pose = global_ground_truth
# Transform ground truth in body frame wrt. world frame to body frame wrt. landing platform
##########
# 0 -> 2 #
##########
# Position
p_x = gt_pose.position.x
p_y = gt_pose.position.y
p_z = gt_pose.position.z
# Translation of the world frame to body frame wrt. the world frame
d_0_2 = np.array([p_x, p_y, p_z])
# Orientation
q_x = gt_pose.orientation.x
q_y = gt_pose.orientation.y
q_z = gt_pose.orientation.z
q_w = gt_pose.orientation.w
# Rotation of the body frame wrt. the world frame
r_0_2 = R.from_quat([q_x, q_y, q_z, q_w])
r_2_0 = r_0_2.inv()
##########
# 0 -> 1 #
##########
# Translation of the world frame to landing frame wrt. the world frame
offset_x = 1.0
offset_y = 0.0
offset_z = 0.495
d_0_1 = np.array([offset_x, offset_y, offset_z])
# Rotation of the world frame to landing frame wrt. the world frame
# r_0_1 = np.identity(3) # No rotation, only translation
r_0_1 = np.identity(3) # np.linalg.inv(r_0_1)
##########
# 2 -> 1 #
##########
# Transformation of the body frame to landing frame wrt. the body frame
# Translation of the landing frame to bdy frame wrt. the landing frame
d_1_2 = d_0_2 - d_0_1
# Rotation of the body frame to landing frame wrt. the body frame
r_2_1 = r_2_0
yaw = r_2_1.as_euler('xyz')[2]
r_2_1_yaw = R.from_euler('z', yaw)
# Translation of the body frame to landing frame wrt. the body frame
d_2_1 = -r_2_1_yaw.apply(d_1_2)
# Translation of the landing frame to body frame wrt. the body frame
# This is more intuitive for the controller
d_2_1_inv = -d_2_1
local_ground_truth = np.concatenate((d_2_1_inv, r_2_1.as_euler('xyz')))
# Transform to get the correct yaw
yaw = -np.degrees(local_ground_truth[5]) - 90
if yaw < -180:
gt_yaw = 360 + yaw
else:
gt_yaw = yaw
local_ground_truth[5] = gt_yaw
# local_ground_truth
ground_truth_msg = Twist()
ground_truth_msg.linear.x = local_ground_truth[0]
ground_truth_msg.linear.y = local_ground_truth[1]
ground_truth_msg.linear.z = local_ground_truth[2]
ground_truth_msg.angular.z = local_ground_truth[5]
pub_ground_truth.publish(ground_truth_msg)