def updateRefRADec(self, skip_rot_update=False):
""" Update the reference RA and Dec (true in J2000) from Alt/Az (apparent in epoch of date). """
if not skip_rot_update:
# Save the current rotation w.r.t horizon value
self.rotation_from_horiz = rotationWrtHorizon(self)
# Convert the reference apparent Alt/Az in the epoch of date to true RA/Dec in J2000
ra, dec = apparentAltAz2TrueRADec(
self.az_centre, self.alt_centre, self.JD, self.lat, self.lon, self.refraction)
# Assign the computed RA/Dec to platepar
self.RA_d = ra
self.dec_d = dec
if not skip_rot_update:
# Update the position angle so that the rotation wrt horizon doesn't change
self.pos_angle_ref = rotationWrtHorizonToPosAngle(self, self.rotation_from_horiz)
def updateAzAltGrid(grid, platepar):
"""
Updates the values of grid to form an azimuth and altitude grid
Arguments:
grid: [pg.PlotCurveItem]
platepar: [Platepar object]
"""
# Compute FOV size
fov_radius = np.hypot(*computeFOVSize(platepar))
# Determine gridline frequency (double the gridlines if the number is < 4eN)
grid_freq = 10**np.floor(np.log10(fov_radius))
if 10**(np.log10(fov_radius) - np.floor(np.log10(fov_radius))) < 4:
grid_freq /= 2
# Set a maximum grid frequency of 15 deg
if grid_freq > 15:
grid_freq = 15
# Grid plot density
plot_dens = grid_freq / 100
az_grid_arr = np.arange(0, 90, grid_freq)
alt_grid_arr = np.arange(0, 360, grid_freq)
x = []
y = []
cuts = []
# circles
for az_grid in az_grid_arr:
alt_grid_plot = np.arange(0, 360,
plot_dens) # how many degrees of circle
az_grid_plot = np.zeros_like(alt_grid_plot) + az_grid
# filter_arr = (angularSeparation(alt_centre,
# azim_centre,
# alt_grid_plot,
# az_grid_plot) < fov_radius)
#
# alt_grid_plot = alt_grid_plot[filter_arr]
# az_grid_plot = az_grid_plot[filter_arr]
ra_grid_plot, dec_grid_plot = apparentAltAz2TrueRADec(
alt_grid_plot, az_grid_plot, platepar.JD, platepar.lat,
platepar.lon, platepar.refraction)
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# Outward lines
for alt_grid in alt_grid_arr:
az_grid_plot = np.arange(0, 90, plot_dens)
alt_grid_plot = np.zeros_like(az_grid_plot) + alt_grid
# filter_arr = (angularSeparation(alt_centre,
# azim_centre,
# alt_grid_plot,
# az_grid_plot) < fov_radius)
#
# alt_grid_plot = alt_grid_plot[filter_arr]
# az_grid_plot = az_grid_plot[filter_arr]
ra_grid_plot, dec_grid_plot = apparentAltAz2TrueRADec(
alt_grid_plot, az_grid_plot, platepar.JD, platepar.lat,
platepar.lon, platepar.refraction)
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
r = 50
for i in range(len(x) - 1):
if (x[i] - x[i + 1])**2 + (y[i] - y[i + 1])**2 > r**2:
cuts.append(i)
connect = np.full(len(x), 1)
for i in cuts[:-1]:
connect[i] = 0
grid.setData(x=x, y=y, connect=connect)
def updateAzAltGrid(grid, platepar):
"""
Updates the values of grid to form an azimuth and altitude grid on a pyqtgraph plot.
Arguments:
grid: [pg.PlotCurveItem]
platepar: [Platepar object]
"""
### COMPUTE FOV CENTRE ###
# Estimate RA,dec of the centre of the FOV
_, RA_c, dec_c, _ = xyToRaDecPP([jd2Date(platepar.JD)], [platepar.X_res/2], [platepar.Y_res/2], [1], \
platepar, extinction_correction=False)
# Compute alt/az of FOV centre
azim_centre, alt_centre = trueRaDec2ApparentAltAz(RA_c[0], dec_c[0], platepar.JD, platepar.lat, \
platepar.lon)
### ###
# Compute FOV size
fov_radius = getFOVSelectionRadius(platepar)
# Determine gridline frequency (double the gridlines if the number is < 4eN)
grid_freq = 10**np.floor(np.log10(2 * fov_radius))
if 10**(np.log10(2 * fov_radius) - np.floor(np.log10(2 * fov_radius))) < 4:
grid_freq /= 2
# Set a maximum grid frequency of 15 deg
if grid_freq > 15:
grid_freq = 15
# Grid plot density
plot_dens = grid_freq / 100
# Generate a grid of all azimuths and altitudes
alt_grid_arr = np.arange(0, 90, grid_freq)
az_grid_arr = np.arange(0, 360, grid_freq)
x = []
y = []
cuts = []
# Altitude lines
for alt_grid in alt_grid_arr:
# Keep the altitude fixed and plot all azimuth lines
az_grid_plot = np.arange(0, 360, plot_dens)
alt_grid_plot = np.zeros_like(az_grid_plot) + alt_grid
# Filter out all lines outside the FOV
filter_arr = np.degrees(angularSeparation(np.radians(azim_centre), np.radians(alt_centre), \
np.radians(az_grid_plot), np.radians(alt_grid_plot))) <= fov_radius
az_grid_plot = az_grid_plot[filter_arr]
alt_grid_plot = alt_grid_plot[filter_arr]
# Compute image coordinates
ra_grid_plot, dec_grid_plot = apparentAltAz2TrueRADec(az_grid_plot, alt_grid_plot, platepar.JD, \
platepar.lat, platepar.lon, platepar.refraction)
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
# Filter out all points outside the image
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# Azimuth lines
for az_grid in az_grid_arr:
# Keep the azimuth fixed and plot all altitude lines
alt_grid_plot = np.arange(0, 90 + plot_dens, plot_dens)
az_grid_plot = np.zeros_like(alt_grid_plot) + az_grid
# Filter out all lines outside the FOV
filter_arr = np.degrees(angularSeparation(np.radians(azim_centre), np.radians(alt_centre), \
np.radians(az_grid_plot), np.radians(alt_grid_plot))) <= fov_radius
az_grid_plot = az_grid_plot[filter_arr]
alt_grid_plot = alt_grid_plot[filter_arr]
# Compute image coordinates
ra_grid_plot, dec_grid_plot = apparentAltAz2TrueRADec(az_grid_plot, alt_grid_plot, platepar.JD, \
platepar.lat, platepar.lon, platepar.refraction)
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
# Filter out all points outside the image
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
r = 15 # adjust this parameter if you see extraneous lines
# disconnect lines that are distant (unfinished circles had straight lines completing them)
for i in range(len(x) - 1):
if (x[i] - x[i + 1])**2 + (y[i] - y[i + 1])**2 > r**2:
cuts.append(i)
connect = np.full(len(x), 1)
for i in cuts[:-1]:
connect[i] = 0
grid.setData(x=x, y=y, connect=connect)
def updateRaDecGrid(grid, platepar):
"""
Updates the values of grid to form a right ascension and declination grid
Arguments:
grid: [pg.PlotCurveItem]
platepar: [Platepar object]
"""
# Estimate RA,dec of the centre of the FOV
_, RA_c, dec_c, _ = xyToRaDecPP([jd2Date(platepar.JD)],
[platepar.X_res / 2], [platepar.Y_res / 2],
[1],
platepar,
extinction_correction=False)
# azim_centre, alt_centre = trueRaDec2ApparentAltAz(RA_c, dec_c, platepar.JD, platepar.lat, platepar.lon)
# Compute FOV size
fov_radius = np.hypot(*computeFOVSize(platepar))
# Determine gridline frequency (double the gridlines if the number is < 4eN)
grid_freq = 10**np.floor(np.log10(fov_radius))
if 10**(np.log10(fov_radius) - np.floor(np.log10(fov_radius))) < 4:
grid_freq /= 2
# Set a maximum grid frequency of 15 deg
if grid_freq > 15:
grid_freq = 15
# Grid plot density
plot_dens = grid_freq / 100
ra_grid_arr = np.arange(0, 360, grid_freq)
dec_grid_arr = np.arange(-90, 90, grid_freq)
x = []
y = []
cuts = []
# Plot the celestial parallel grid (circles)
for dec_grid in dec_grid_arr:
ra_grid_plot = np.arange(0, 360, plot_dens)
dec_grid_plot = np.zeros_like(ra_grid_plot) + dec_grid
# Compute alt/az
az_grid_plot, alt_grid_plot = trueRaDec2ApparentAltAz(
ra_grid_plot, dec_grid_plot, platepar.JD, platepar.lat,
platepar.lon, platepar.refraction)
# Filter out points below the horizon and outside the FOV
filter_arr = (alt_grid_plot > 0) # & (angularSeparation(alt_centre,
# azim_centre,
# alt_grid_plot,
# az_grid_plot) < fov_radius)
ra_grid_plot = ra_grid_plot[filter_arr]
dec_grid_plot = dec_grid_plot[filter_arr]
# Compute image coordinates for every grid celestial parallel
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# Plot the celestial meridian grid (outward lines)
for ra_grid in ra_grid_arr:
dec_grid_plot = np.arange(-90, 90, plot_dens) # how close to horizon
ra_grid_plot = np.zeros_like(dec_grid_plot) + ra_grid
# Compute alt/az
az_grid_plot, alt_grid_plot = trueRaDec2ApparentAltAz(
ra_grid_plot, dec_grid_plot, platepar.JD, platepar.lat,
platepar.lon, platepar.refraction)
# Filter out points below the horizon
filter_arr = (alt_grid_plot > 0) #& (angularSeparation(alt_centre,
# azim_centre,
# alt_grid_plot,
# az_grid_plot) < fov_radius)
ra_grid_plot = ra_grid_plot[filter_arr]
dec_grid_plot = dec_grid_plot[filter_arr]
# Compute image coordinates for every grid celestial parallel
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# horizon
az_horiz_arr = np.arange(0, 360, plot_dens)
alt_horiz_arr = np.zeros_like(az_horiz_arr)
ra_horiz_plot, dec_horiz_plot = apparentAltAz2TrueRADec(
az_horiz_arr, alt_horiz_arr, platepar.JD, platepar.lat, platepar.lon,
platepar.refraction)
x_horiz, y_horiz = raDecToXYPP(ra_horiz_plot, dec_horiz_plot, platepar.JD,
platepar)
filter_arr = (x_horiz >= 0) & (x_horiz <= platepar.X_res) & (
y_horiz >= 0) & (y_horiz <= platepar.Y_res)
x_horiz = x_horiz[filter_arr]
y_horiz = y_horiz[filter_arr]
x.extend(x_horiz)
y.extend(y_horiz)
cuts.append(len(x) - 1)
r = 15 # adjust this parameter if you see extraneous lines
# disconnect lines that are distant (unfinished circles had straight lines completing them)
for i in range(len(x) - 1):
if (x[i] - x[i + 1])**2 + (y[i] - y[i + 1])**2 > r**2:
cuts.append(i)
# convert cuts into connect
connect = np.full(len(x), 1)
if len(connect) > 0:
for i in cuts:
connect[i] = 0
grid.setData(x=x, y=y, connect=connect)
def updateRaDecGrid(grid, platepar):
"""
Updates the values of grid to form a right ascension and declination grid on a pyqtgraph plot.
Arguments:
grid: [pg.PlotCurveItem]
platepar: [Platepar object]
"""
### COMPUTE FOV CENTRE ###
# Estimate RA,dec of the centre of the FOV
_, RA_c, dec_c, _ = xyToRaDecPP([jd2Date(platepar.JD)], [platepar.X_res/2], [platepar.Y_res/2], [1], \
platepar, extinction_correction=False)
# Compute alt/az of FOV centre
azim_centre, alt_centre = trueRaDec2ApparentAltAz(RA_c[0], dec_c[0], platepar.JD, platepar.lat, \
platepar.lon)
### ###
# Compute FOV size
fov_radius = getFOVSelectionRadius(platepar)
# Determine gridline frequency (double the gridlines if the number is < 4eN)
grid_freq = 10**np.floor(np.log10(2 * fov_radius))
if 10**(np.log10(2 * fov_radius) - np.floor(np.log10(2 * fov_radius))) < 4:
grid_freq /= 2
# Set a maximum grid frequency of 15 deg
if grid_freq > 15:
grid_freq = 15
# Grid plot density
plot_dens = grid_freq / 100
# Make an array of RA and Dec
ra_grid_arr = np.arange(0, 360, grid_freq)
dec_grid_arr = np.arange(-90, 90, grid_freq)
x = []
y = []
cuts = []
# Generate points for the celestial parallels grid
for dec_grid in dec_grid_arr:
# Keep the declination fixed and evaluate all right ascensions
ra_grid_plot = np.arange(0, 360, plot_dens)
dec_grid_plot = np.zeros_like(ra_grid_plot) + dec_grid
# Compute alt/az
az_grid_plot, alt_grid_plot = trueRaDec2ApparentAltAz(ra_grid_plot, dec_grid_plot, platepar.JD, \
platepar.lat, platepar.lon, platepar.refraction)
# Filter out points below the horizon and outside the FOV
filter_arr = (alt_grid_plot >= 0) & (np.degrees(angularSeparation(np.radians(azim_centre), \
np.radians(alt_centre), np.radians(az_grid_plot), np.radians(alt_grid_plot))) <= fov_radius)
ra_grid_plot = ra_grid_plot[filter_arr]
dec_grid_plot = dec_grid_plot[filter_arr]
# Compute image coordinates for every grid celestial parallel
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
# Filter out all points outside the image
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
# Add points to the list
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# Generate points for the celestial meridian grid
for ra_grid in ra_grid_arr:
# Keep the RA fixed and evaluate all declinations
dec_grid_plot = np.arange(-90, 90 + plot_dens, plot_dens)
ra_grid_plot = np.zeros_like(dec_grid_plot) + ra_grid
# Compute alt/az
az_grid_plot, alt_grid_plot = trueRaDec2ApparentAltAz(ra_grid_plot, dec_grid_plot, platepar.JD, \
platepar.lat, platepar.lon, platepar.refraction)
# Filter out points below the horizon
filter_arr = (alt_grid_plot >= 0) & (np.degrees(angularSeparation(np.radians(azim_centre), \
np.radians(alt_centre), np.radians(az_grid_plot), np.radians(alt_grid_plot))) <= fov_radius)
ra_grid_plot = ra_grid_plot[filter_arr]
dec_grid_plot = dec_grid_plot[filter_arr]
# Compute image coordinates for every grid celestial parallel
x_grid, y_grid = raDecToXYPP(ra_grid_plot, dec_grid_plot, platepar.JD,
platepar)
# Filter out points outside the image
filter_arr = (x_grid >= 0) & (x_grid <= platepar.X_res) & (
y_grid >= 0) & (y_grid <= platepar.Y_res)
x_grid = x_grid[filter_arr]
y_grid = y_grid[filter_arr]
x.extend(x_grid)
y.extend(y_grid)
cuts.append(len(x) - 1)
# Generate points for the horizon
az_horiz_arr = np.arange(0, 360, plot_dens)
alt_horiz_arr = np.zeros_like(az_horiz_arr)
ra_horiz_plot, dec_horiz_plot = apparentAltAz2TrueRADec(az_horiz_arr, alt_horiz_arr, platepar.JD, \
platepar.lat, platepar.lon, platepar.refraction)
# Filter out all horizon points outside the FOV
filter_arr = np.degrees(angularSeparation(np.radians(alt_centre), np.radians(azim_centre), \
np.radians(alt_horiz_arr), np.radians(az_horiz_arr))) <= fov_radius
ra_horiz_plot = ra_horiz_plot[filter_arr]
dec_horiz_plot = dec_horiz_plot[filter_arr]
# Compute image coordinates of the horizon
x_horiz, y_horiz = raDecToXYPP(ra_horiz_plot, dec_horiz_plot, platepar.JD,
platepar)
# Filter out all horizon points outside the image
filter_arr = (x_horiz >= 0) & (x_horiz <= platepar.X_res) & (
y_horiz >= 0) & (y_horiz <= platepar.Y_res)
x_horiz = x_horiz[filter_arr]
y_horiz = y_horiz[filter_arr]
x.extend(x_horiz)
y.extend(y_horiz)
cuts.append(len(x) - 1)
r = 15 # adjust this parameter if you see extraneous lines
# disconnect lines that are distant (unfinished circles had straight lines completing them)
for i in range(len(x) - 1):
if (x[i] - x[i + 1])**2 + (y[i] - y[i + 1])**2 > r**2:
cuts.append(i)
# convert cuts into connect
connect = np.full(len(x), 1)
if len(connect) > 0:
for i in cuts:
connect[i] = 0
grid.setData(x=x, y=y, connect=connect)