def __call__(self, frame_set, mdf=False):
#print frame_set
if mdf:
frame_set = frame_set[:, 0]
frame_set = np.asarray(frame_set) - self.init_frame
thetas = frame_set * self.ang_vel + self.ang_off
radii = frame_set * self.rad_vel + self.rad_off
#coords in obital axis frame
frame_xs = radii * np.cos(thetas)
frame_ys = radii * np.sin(thetas)
frame_zs = frame_set * self.hel_vel + self.hel_off
frame_coords = np.asarray([frame_set, frame_xs, frame_ys, frame_zs]).T
#print frame_coords
#transform in to world coords
world_coords = []
for bundle in np.atleast_2d(frame_coords):
centre = self.centre_func(bundle[0])
frame_coord = bundle[1:]
frame_coord.shape = (3, 1)
centre.shape = 3
world_coords.append(
coord_transform(self.basis[0],
self.basis[1],
self.basis[2],
centre,
frame_coord,
inv=False,
homog=False,
tran=True))
world_coords = np.asarray(world_coords)
world_coords.shape = (len(world_coords), 3)
#print "obital path call"
return world_coords
def get_odom_delta(self, pose):
"""Calculates the delta of the odometry position since the last time called.
Returns None the first time, and a 3x1 state matrix thereafter.
"""
# Convert the pose into matrix form.
y_odom = column_vector(pose.x, pose.y, pose.theta)
# None is the fallback in case we can't get an actual delta.
odom_delta = None
if self.odom_state is not None:
# Transform the sensor state into the map frame.
y = coord_transform(y_odom, self.odom_origin)
# Calculate the change odom state, in map coords (delta y).
odom_delta = y - coord_transform(self.odom_state, self.odom_origin)
# Update the stored odom state.
self.odom_state = y_odom
# Get the odom frame origin in the map frame and store it for next time.
self.odom_origin = get_offset(self.state, self.odom_state)
return odom_delta
def get_odom_delta(self, pose):
"""Calculates the delta of the odometry position since the last time called.
Returns None the first time, and a 3x1 state matrix thereafter.
"""
# Convert the pose into matrix form.
y_odom = column_vector(pose.x, pose.y, pose.theta)
# None is the fallback in case we can't get an actual delta.
odom_delta = None
if self.odom_state is not None:
# Transform the sensor state into the map frame.
y = coord_transform(y_odom, self.odom_origin)
# Calculate the change odom state, in map coords (delta y).
odom_delta = y - coord_transform(self.odom_state, self.odom_origin)
# Update the stored odom state.
self.odom_state = y_odom
# Get the odom frame origin in the map frame and store it for next time.
self.odom_origin = get_offset(self.state, self.odom_state)
return odom_delta
def get_odom_delta(self, pose):
# odom_state is the previous x, y, theta of odom_pose
# y_odom is the current x, y, theta of odom_pose
# odom_origin kind keeps track of all the odom changes till date
y_odom = column_vector(pose.x, pose.y, pose.theta)
odom_delta = None
if self.odom_state is not None:
# y = y_odom * rot(odom_origin.theta) + odom_origin
y = coord_transform(y_odom, self.odom_origin)
# current change - previous change
odom_delta = y - coord_transform(self.odom_state, self.odom_origin)
self.odom_state = y_odom
# these two should be exactly the same. Using y_odom to
# differentiate between previous and new odom values
#self.odom_origin = get_offset(self.state, self.odom_state)
self.odom_origin = get_offset(self.state, y_odom)
return odom_delta
def get_odom_delta(self, pose): # odom_state is the previous x, y, theta of odom_pose # y_odom is the current x, y, theta of odom_pose # odom_origin kind keeps track of all the odom changes till date y_odom = column_vector(pose.x, pose.y, pose.theta) odom_delta = None if self.odom_state is not None: # y = y_odom * rot(odom_origin.theta) + odom_origin y = coord_transform(y_odom, self.odom_origin) # current change - previous change odom_delta = y - coord_transform(self.odom_state, self.odom_origin) self.odom_state = y_odom # these two should be exactly the same. Using y_odom to # differentiate between previous and new odom values #self.odom_origin = get_offset(self.state, self.odom_state) self.odom_origin = get_offset(self.state, y_odom) return odom_delta
def __call__(self, frame_set, mdf=False):
'''generate the coordinates for given frame numbers
Args:
frame_set [array/real]: frame number/s to calculate the new coords for
mdf (optional) [bool]: if the frames given are multidimentional, take only the
first column. Blame numpy.piecewise for requiring the same array sizes for
input and output >:|
Returns:
coords [array]: numpy array of coords [x,y,z] for new frame numbers'''
if mdf:
frame_set = frame_set[:, 0]
frame_set = np.asarray(frame_set) - self.init_frame
thetas = frame_set * self.ang_vel + self.ang_off
radii = frame_set * self.rad_vel + self.rad_off
#coords in obital axis frame
frame_xs = radii * np.cos(thetas)
frame_ys = radii * np.sin(thetas)
frame_zs = frame_set * self.hel_vel + self.hel_off
frame_coords = np.asarray([frame_set, frame_xs, frame_ys, frame_zs]).T
#print frame_coords
#transform in to world coords
world_coords = []
for bundle in np.atleast_2d(frame_coords):
centre = self.centre_func(bundle[0])
frame_coord = bundle[1:]
frame_coord.shape = (3, 1)
centre.shape = 3
world_coords.append(
coord_transform(self.basis[0],
self.basis[1],
self.basis[2],
centre,
frame_coord,
inv=False,
homog=False,
tran=True))
world_coords = np.asarray(world_coords)
world_coords.shape = (len(world_coords), 3)
#print "obital path call"
return world_coords
def story_board(txt_name, path_file, sim):
''' This function produce a soryboard of all the frames specified on a flight path,
saves as PNG files in the directory where the program is run
Args:
path file:A txt file of the flight path in the form frame, expansion factor, coordinates, x_basis, y_basis, z_basis
txt_name:The name or relative file path of the txt file
'''
#SQL to grab from the database
SQL = """
SELECT
PROG.GalaxyID as ID,
PROG.DescendantID as DesID,
DES.GalaxyID,
PROG.SnapNum,
PROG.MassType_DM,
(PROG.CentreOfPotential_x * %0.5f) as x,
(PROG.CentreOfPotential_y * %0.5f) as y,
(PROG.CentreOfPotential_z * %0.5f) as z,
PROG.Redshift
FROM
%s_Subhalo as PROG with(forceseek),
%s_Subhalo as DES
WHERE
DES.SnapNum = 28 and
DES.MassType_DM > 1.0e10 and
PROG.GalaxyID between DES.GalaxyID and DES.LastProgID
ORDER BY
PROG.GalaxyID,
PROG.SnapNum
""" % (h,h,h, sim, sim)
# PROG.MassType_DM > 1.0e11 and
filename = "%s_DBS.p" % sim
boxsize = 25 * h
dbs_data = dbsPull(SQL, filename)
frame, ts, xs, ys, zs, b1,b2,b3,b4,b5,b6,b7,b8,b9 = np.loadtxt(path_file, unpack=True)
z_basis = np.transpose(np.asarray([b7, b8, b9]))
y_basis = np.transpose(np.asarray([b4, b5, b6]))
x_basis = np.transpose(np.asarray([b1, b2, b3]))
line_coords = np.transpose(np.asarray([xs, ys, zs]))
fig = plt.figure()
#to loop over every frame which will be plot on the story board
for i in range(len(frame)):
print "creating image: " + str(i)
#to find the new camera position, cam_pos, on the path
cam_position = [xs[i], ys[i], zs[i]]
x_bas = x_basis[i]
y_bas = y_basis[i]
z_bas = z_basis[i]
scale_factor = ts[i]
#wrap coordinates image data get center image manipulations preiodic wrap
centre = utils.get_centre([x_bas,y_bas,z_bas], cam_position, region)
#all the galaxies posistions at the scale factor of interest
All_galaxies = utils.gal_interpolation(scale_factor, dbs_data)
All_galaxies[:,[3,4,5]] = utils.periodic_wrap(All_galaxies[:,[3,4,5]], boxsize, centre)
#transforms into the camera view
galaxies_trans = coord_transform(x_bas, y_bas, z_bas, cam_position, All_galaxies[:,[3,4,5]])
galaxies_afterT = np.transpose(galaxies_trans)
galaxies_to_plot = perspective_transfomation(galaxies_trans, region)
#this is to catch any frames that are of zero lenght i.e. no galaxies in view
if len(galaxies_to_plot) >= 1:
#to find the mass and distances of the galaxies in order to scale size
indexList = np.asarray(galaxies_to_plot[:,4], dtype=int)
galaxZs = galaxies_afterT[indexList][:,2]
galaxYs = galaxies_afterT[indexList][:,1]
galaxXs = galaxies_afterT[indexList][:,0]
galZsMass = All_galaxies[indexList][:,2]
dist = (galaxZs**2 + galaxYs**2 + galaxXs**2)**0.5
#the relative sizes of the galaxies, change if you want to adjust
perspec = []
perspec = 1./dist**3
perspec *= (galZsMass)**0.43
perspec.shape = (1, len(perspec))
galaxies_to_plot = np.asarray(sorted(np.concatenate((galaxies_to_plot, perspec.T), axis=1), key=lambda coords: -coords[2]))
#if you want to plot the images in behind the plot uncomment this line below
#img = imread("gas_%06i.png"%(i))
plt.scatter(galaxies_to_plot[:,0],galaxies_to_plot[:,1],marker='o', s=galaxies_to_plot[:,5], c='#7E317B',edgecolors='k')
plt.ylim( 0, region[1])
plt.xlim( - region[0]/2., region[0]/2.)
plt.ylim( -1., 1.)
plt.xlim( - 1., 1.)
#if you want to plot the images in behind the plot uncomment this line below
#plt.imshow(img,extent=[-1.,1.,-1.,1.], aspect='auto')
plt.savefig(txt_name + str(i))
plt.clf()
else:
#img = imread("gas_%06i.png"%(i))
plt.scatter(0.0,0.0, s=0.0)
plt.ylim( -1., 1.)
plt.xlim( - 1., 1.)
#plt.imshow(img,extent=[-1.,1.,-1.,1.], aspect='auto')
plt.savefig(txt_name + str(i))
plt.clf()
