def test12():
tel = SKA1_low()
tel.dec_deg = tel.lat_deg
tel.obs_length_h = 0
tel.num_times = 1
tel.grid_cell_size_m = tel.station_diameter_m / 2
def taper_r_profile(r, amps):
n = amps.shape[0]
i = np.round(r * (n - 1)).astype(np.int)
return amps[i]
from scipy import interpolate
n = 100
x = np.linspace(0, 1, n)
y = taylor_win(n, -28)
f = interpolate.interp1d(x, y, kind='cubic')
# tel.plot_min_sep(500, f)
tel.num_trials = 3
tel.trail_timeout_s = 20
tel.add_tapered_core(150, 500, f)
try_id = tel.layouts['tapered_core']['info']['attempt_id']
info = tel.layouts['tapered_core']['info'][try_id]
print(try_id, info.keys(), info['max_tries'], info['total_tries'],
info['time_taken'], info['total_tries']/info['time_taken'])
def test1():
tel = SKA1_low()
tel.dec_deg = tel.lat_deg
tel.obs_length_h = 0
tel.num_times = 1
tel.grid_cell_size_m = tel.station_diameter_m / 2
def taper_r_profile(r, num_amps, amps):
"""0.0 <= r < 1.0"""
i = int(r * num_amps)
return amps[i]
# from scipy import interpolate
n = 5000
y = taylor_win(n, -28)
# x = np.linspace(0, 1, n)
# f = interpolate.interp1d(x, y, kind='cubic')
opts = dict(num_amps=n, amps=y)
tel.num_trials = 1
tel.trail_timeout_s = 2
tel.add_ska1_v5(r_max=5000)
# tel.uv_sensitivity(100, 100, 5000, log_bins=True, )
tel.plot_grid(show=True)
# tel.eval_psf_rms_r(100)
# tel.uv_hist(100, b_max=1000, plot=True, log_bins=True, bar=True)
# tel.uv_sensitivity(100)
# tel.eval_psf_rms_r(100)
tel.eval_psf(plot1d=True)
def plot_core_profile():
def get_taper_profile(taper_func, r, **kwargs):
if kwargs is not None:
t = taper_func(r, **kwargs)
else:
t = taper_func(r)
return t
sll = -28
n_taylor = 10000
r = np.linspace(0, 1, 100)
t1 = get_taper_profile(AnalyseUnwrapV5._taper_r_profile,
r,
amps=taylor_win(n_taylor, sll),
taper_r_min=0.0)
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(r, 35 / t1, 'k-', label='v7 core taper profile')
ax.set_xlabel('Fractional radius')
ax.set_ylabel('Minimum station separation (m)')
ax.grid()
ax.set_ylim(34)
# ax.legend(loc='best')
fig.savefig('station_min_dist.png')
plt.close(fig)
def _add_core_01(tel, num_stations=224, core_radius_m=510, sll=-28):
n_taylor = 10000
tel.num_trials = 4
tel.trial_timeout_s = 45.0
tel.seed = 40011245
args = dict(amps=taylor_win(n_taylor, sll), taper_r_min=0.0)
tel.add_tapered_core(num_stations, core_radius_m,
AnalyseUnwrapV5._taper_r_profile, **args)
# print('seed =', tel.seed)
return tel
def test9():
def taper_func_1(r):
return 1 - (r / 2)**1.5
def taper_func_2(r, hwhm=1.5):
c = hwhm / (2 * log(2))**0.5
return np.exp(-r**2 / (2 * c**2))
def taper_r_profile(r, amps):
n = amps.shape[0]
# Find the index nearest to r
# FIXME(BM) check this is right...
i = np.round(r * (n - 1)).astype(np.int)
return amps[i]
def get_taper_profile(taper_func, r, **kwargs):
if kwargs is not None:
t = taper_func(r, **kwargs)
else:
t = taper_func(r)
return t
r = np.linspace(0, 1, 100)
# r = np.random.rand(100)
t1 = get_taper_profile(taper_func_1, r)
t2 = get_taper_profile(taper_func_2, r, hwhm=0.7)
t3 = get_taper_profile(taper_r_profile, r, amps=taylor_win(1000, -22))
fig, ax = plt.subplots()
ax.plot(r, t1, 'b.', label='t1')
ax.plot(r, t2, 'r.', label='gauss')
ax.plot(r, t3, 'g.', label='r profile (taylor)')
ax.plot(ax.get_xlim(), [0.5, 0.5], 'r--')
ax.plot([0.7, 0.7], ax.get_ylim(), 'r--')
ax.grid()
ax.legend()
plt.show()
return
# r_max = 500
# n = 11
# sll = -28
# nbar = int(np.ceil(2.0 * (np.arccosh(10**(-sll / 20.0)) / np.pi)**2 + 0.5))
# # r = np.arange(w.shape[0]) * ((r_max * 2) / (n + 1))
# r = np.linspace(0, 1, n)
# print(r.shape, w.shape)
fig, ax = plt.subplots()
ax.plot(r, w, '.-')
ax.grid()
plt.show()
def test10():
n = 5
x = np.linspace(0, 1, n)
y = taylor_win(n, -28)
from scipy import interpolate
f = interpolate.interp1d(x, y, kind='cubic')
x_new = np.random.rand(10000)
x_new.sort()
fig, ax = plt.subplots()
ax.plot(x, y, '+-')
ax.plot(x_new, f(x_new), '-')
plt.show()
def model08(self, name='model08', add_core=True):
"""Proposed SKA v6 configuration option 1
4 rings of 21 stations (84 total)
r0 = 500m, r5 = 1700m with no ring at r0
72 station arms from r0 = 1700m with no point at 1700 to r21 at 6.4 km
(ie 6 stations symmmetric about 6.4km)
Note(BM) might have to shrink this a bit to match 1km LV reticulation
core of 224 + 6 stations (uniform or tapered up to 500m)
"""
metrics = Metrics(self.out_dir)
# TODO(BM) hybrid of model1 with a custom generated core
# and avoiding cable lengths > 1km in arm clusters.
# For the log spiral arms also generate from a single arm and rotate
# with stride 3 to generate arms. (put 162 - 18? in arms) and
# (224 + 18 in the core) to recover some short baselines.
from math import log, exp
import matplotlib.pyplot as plt
tel = SKA1_low_analysis(name + '_r08')
tel.station_diameter_m = 35
tel.obs_length_h = self.obs_length_h
tel.num_times = self.num_times
tel.dec_deg = tel.lat_deg
# Core
core_radius_m = 480
sll = -24
n_taylor = 10000
tel.num_trials = 2
tel.trial_timeout_s = 30.0
# Inner rings
num_rings = 5
num_per_ring = 17
ring_r0 = 580
ring_r5 = 1700
ring_radii = np.logspace(np.log10(ring_r0), np.log10(ring_r5),
num_rings)
print('ring radii =', ring_radii)
print('ring spacing = ', np.diff(ring_radii))
# Arms
# arm_r0 = 1805
arm_r0 = ring_r5 + core_radius_m / 2
# arm_r1 = 7135
arm_r1 = 6400
# ============== Core
tel.seed = 74383209
args = dict(amps=taylor_win(n_taylor, sll), taper_r_min=0.30)
# 510m worked ok
tel.add_tapered_core(224 + 2, 480, AnalyseUnwrapV5.__taper_r_profile,
**args)
print('final seed =',
tel.layouts['tapered_core']['info']['final_seed'])
# tel.add_ska1_v5(r_max=500)
# ============== Rings
# TODO(BM) rotate every other ring so radii don't align
np.random.seed(1)
for i, r in enumerate(ring_radii[0:2]):
print('xxx', i, r)
# tel.add_ring(num_per_ring, r,
# delta_theta=(360/(num_per_ring * 2)) * (i%2))
tel.add_ring(num_per_ring,
r,
delta_theta=np.random.randint(low=0, high=360))
# tel.add_ring(num_per_ring, r, delta_theta=0)
# tel.layouts['test_st'] = dict(x=np.array([2e3]), y=np.array([0e3]))
tel.grid_cell_size_m = tel.station_diameter_m
metrics.analyse_telescope(tel, 0, self.eval_metrics)
return tel
# ============= Spiral arms
# tel.add_log_spiral_2(25, arm_r0, arm_r1, 0.515, 3, 'inner_arms', 0)
tel.add_log_spiral_3(25, self.start_inner, arm_r0, arm_r1, 0.515,
self.num_arms, self.theta0_deg)
# tel.add_symmetric_log_spiral(25, arm_r0, arm_r1, 0.515, 3,
# 'inner_arms', self.delta_theta_deg_inner)
x, _, _ = tel.get_coords_enu()
print('total stations =', x.size)
# Plotting layout
metrics.analyse_telescope(tel, 0, self.eval_metrics)
metrics.save_results(name)
# metrics.plot_cable_length_compare()
metrics.plot_comparisons()
return tel
def model06(self, name='model06', add_core=True, r_values=None):
"""Model06 == no cables > 1km, an improved core"""
# Initialise metrics class
metrics = Metrics(self.out_dir)
# TODO(BM) hybrid of model1 with a custom generated core
# and avoiding cable lengths > 1km in arm clusters.
# For the log spiral arms also generate from a single arm and rotate
# with stride 3 to generate arms. (put 162 - 18? in arms) and
# (224 + 18 in the core) to recover some short baselines.
from math import log, exp
import matplotlib.pyplot as plt
def taper_func_1(r):
return 1 - (r / 2)**1.5
def taper_func_2(r, hwhm=1.5, taper_r_min=0):
r = np.asarray(r)
c = hwhm / (2 * log(2))**0.5
values = np.exp(-((r - taper_r_min) / (1 - taper_r_min))**2 / 2 *
c**2)
values[r < taper_r_min] = 1.0
return values
def taper_r_profile(r, amps, taper_r_min=0):
"""Nearest neighbour matching. FIXME(BM) double check this"""
r = np.asarray(r)
idx = np.round(
((r - taper_r_min) / (1 - taper_r_min)) * (amps.size - 1))
values = np.asarray(amps[idx.astype(np.int)])
values[r < taper_r_min] = 1.0
return values
def get_taper_profile(taper_func, r, **kwargs):
if kwargs is not None:
t = taper_func(r, **kwargs)
else:
t = taper_func(r)
return t
tel = SKA1_low_analysis(name)
sll = -28
n_taylor = 10000
r = np.linspace(0, 1, 100)
t1 = get_taper_profile(taper_func_1, r)
t2 = get_taper_profile(taper_func_2, r, hwhm=1.0)
t2a = get_taper_profile(taper_func_2, r, hwhm=1.0, taper_r_min=0.25)
t3 = get_taper_profile(taper_r_profile,
r,
amps=taylor_win(n_taylor, sll))
t4 = get_taper_profile(taper_r_profile,
r,
amps=taylor_win(n_taylor, sll),
taper_r_min=0.25)
fig, ax = plt.subplots()
ax.plot(r, tel.station_diameter_m / t1, 'k-', label='Power law')
ax.plot(r, tel.station_diameter_m / t2, 'b-', label='Gaussian')
ax.plot(r, tel.station_diameter_m / t2a, 'b--', label='Gaussian rmin')
ax.plot(r, tel.station_diameter_m / t3, 'r-', label='Taylor %i' % sll)
ax.plot(r,
tel.station_diameter_m / t4,
'r--',
label='Taylor rmin %i' % sll)
ax.set_xlabel('Fractional radius')
ax.set_ylabel('Minimum station separation (m)')
ax.grid()
ax.set_ylim(34)
ax.legend(loc='best')
fig.savefig('station_min_dist.png')
# plt.show()
plt.close(fig)
core_radius_m = 480
tel.num_trials = 10
tel.trial_timeout_s = 30.0
tel.seed = 24183655
args = dict(amps=taylor_win(n_taylor, sll), taper_r_min=0.25)
tel.add_tapered_core(224 + 18, core_radius_m, taper_r_profile, **args)
tel.plot_layout(filename=name + '.png',
show_decorations=False,
plot_radii=[500])
print('final seed =',
tel.layouts['tapered_core']['info']['final_seed'])
print(tel.seed)
tel.add_log_spiral_2(20, 480, 6000, 0.515, 3, 'inner_arms', 0)
x1, y1, _ = tel.get_coords_enu()
tel_v5 = SKA1_low_analysis('ska1_v5')
tel_v5.add_ska1_v5(r_max=6400)
x2, y2, _ = tel_v5.get_coords_enu()
# Side by side comparison to ska v5 core
fig, axes = plt.subplots(figsize=(16, 8), ncols=2)
for xy in zip(x1, y1):
axes[0].add_artist(
plt.Circle(xy,
tel.station_diameter_m / 2,
fill=True,
color='k',
alpha=0.5))
for xy in zip(x2, y2):
axes[1].add_artist(
plt.Circle(xy,
tel.station_diameter_m / 2,
fill=True,
color='k',
alpha=0.5))
for ax in axes:
ax.add_artist(plt.Circle((0, 0), 500, fill=False, color='r'))
ax.set_aspect('equal')
ax.set_xlim(-600, 600)
ax.set_ylim(-600, 600)
plt.show()
plt.close(fig)
# Side by side comparison to ska v5 core
fig, axes = plt.subplots(figsize=(16, 8), ncols=2)
for xy in zip(x1, y1):
axes[0].add_artist(
plt.Circle(xy,
tel.station_diameter_m / 2,
fill=True,
color='k',
alpha=0.5))
for xy in zip(x2, y2):
axes[1].add_artist(
plt.Circle(xy,
tel.station_diameter_m / 2,
fill=True,
color='k',
alpha=0.5))
for ax in axes:
ax.add_artist(plt.Circle((0, 0), 500, fill=False, color='r'))
ax.set_aspect('equal')
ax.set_xlim(-7e3, 7e3)
ax.set_ylim(-7e3, 7e3)
plt.show()
plt.close(fig)
# TODO(BM) add cluster centres into the spiral arms...
tel.plot_layout(show_decorations=True,
xy_lim=2.5e3,
plot_radii=[500, 6400])
tel.plot_layout(show_decorations=True,
xy_lim=7e3,
plot_radii=[500, 6400])
