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show_helix.py
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show_helix.py
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import numpy as np
import matplotlib.pyplot as plt
import pickle
import skyflux as sf
import skyflux.deprecated.polSims as pol
def hauto_show(fname, pt_override=None):
"""
Load simulated visibilities from the file named
@fname
and visually interpret the results as a
delay-spectrum helix a la (Parsons, 2012).
@pt_override is a way to override the title with which
a simulation came. It is extremely bad form to use this,
but it can come in handy during lapses of diligence.
"""
fourierc, raw_vis, fs, ts, ptitle = \
build_fourier_candidates(fname)
if pt_override is not None:
ptitle = pt_override
print("Data from file re-organized.")
fouriered = transform_power(fourierc, fs, ts)
print("Fourier transforms computed.")
visual = collect_helix_points(fouriered, fs, ts)
print("Points collected.")
plt.title("88m, 200 MHz bandwidth")
plt.xlabel("Delays [ns]")
plt.ylabel("LST [hr]")
plot_3D(visual, ptitle)
return visual, fouriered, fs, ts, raw_vis
def build_fourier_candidates(fname):
sim_file = open(fname + ".pickle", "rb")
meta = pickle.load(sim_file)
ptitle = meta['title']
fs = meta['frequencies']
num_f = len(fs)
ts = meta['times']
num_t = len(ts)
sim = meta['picture']
# 0: I 1: Q 2: U 3: V
fourierc = [[], [], [], []]
raw_vis = []
for ti in range(num_t):
for parameter in fourierc:
parameter.append([])
raw_vis.append([])
for ni in range(num_f):
v = sim[ni][ti]
for p_idx in range(len(fourierc)):
fourierc[p_idx][ti].append(v[p_idx])
raw_vis[ti].append(v)
#norm = np.linalg.norm(sim[ni][ti]) same outcome
for parameter in fourierc:
parameter[ti] = np.array(parameter[ti])
raw_vis[ti] = np.array(raw_vis[ti])
for parameter in fourierc:
parameter = np.array(parameter)
fourierc = np.array(fourierc)
raw_vis = np.array(raw_vis)
return fourierc, raw_vis, fs, ts, ptitle
def transform_power(original, fs, ts):
num_f = len(fs)
num_t = len(ts)
import copy
fourier = copy.deepcopy(original)
window = pol.genWindow(num_f)
for ti in range(num_t):
"""
# option 6
for parameter in fourier:
parameter[ti] = \
np.fft.fftshift(np.fft.fft(parameter[ti])
"""
"""
# what I had been doing before 2/17/21
# aka option 5
for parameter in fourier:
parameter[ti] = np.fft.fft(parameter[ti])
"""
# fft with window: option 9
for parameter in fourier:
parameter[ti] = np.fft.fft(parameter[ti] * window)
"""
# ifft: option 7
for parameter in fourier:
parameter[ti] = np.fft.ifft(parameter[ti])
"""
"""
# ifft with window: option 8 [next 4 lines]
for parameter in fourier:
parameter[ti] = np.fft.ifft(parameter[ti] * window)
"""
return fourier
def collect_helix_points(fouriered, fs, ts):
num_t = len(ts)
num_f = len(fs)
visual = []
etas = pol.f2etas(fs)
for ti in range(num_t):
for ni in range(num_f):
dspecvec = np.array([
parameter[ti][ni] for parameter in fouriered
])
norm = np.linalg.norm(dspecvec)
visual.append(np.array((
etas[ni] * 1e9,
ts[ti] * 12 / np.pi,
np.log10(norm)
)))
return np.array(visual)
def plot_3D(visual, title, scaled=False):
"""
Primitive 3D plotter.
For use with the return value of either
static_wedge_vis
or
dynamic_wedge_vis
Disable @scaled if you are using values such as logarithms
"""
x = visual[:, 0]
y = visual[:, 1]
z = visual[:, 2]
colors = None
if (scaled):
scaled_z = (z - z.min()) / z.ptp()
colors = plt.cm.viridis(scaled_z)
else:
colors = z
plt.title(title)
print("Minimum:", z.min())
print("PTP:", z.ptp())
plt.scatter(x, y, marker='.', c=colors)
plt.colorbar()
plt.show()
### This is a really bad ad-hoc testing script.
### We want to scrap this ASAP
def micro_wedge(h1, f1, b1, h2, f2, b2, h3, f3, b3):
"""
The axes do not line up with Nunhokee et al.
Probably something wrong with your constants
or usage thereof.
"""
center_f1 = np.average(f1)
z1 = pol.fq2z(center_f1)
lambda1 = pol.C / center_f1
k_par1 = pol.k_parallel(h1[:, 0], z1)
k_orth1 = pol.k_perp(z1) / lambda1 * b1
center_f2 = np.average(f2)
z2 = pol.fq2z(center_f2)
lambda2 = pol.C / center_f2
k_par2 = pol.k_parallel(h2[:, 0], z2)
k_orth2 = pol.k_perp(z2) / lambda2 * b2
center_f3 = np.average(f3)
z3 = pol.fq2z(center_f3)
lambda3 = pol.C / center_f3
k_par3 = pol.k_parallel(h3[:, 0], z3)
k_orth3 = pol.k_perp(z3) / lambda3 * b3
y = np.concatenate((k_par1, k_par2, k_par3))
x = np.concatenate((
np.repeat(k_orth1, len(k_par1)),
np.repeat(k_orth2, len(k_par2)),
np.repeat(k_orth3, len(k_par3))
))
colors = np.concatenate((h1[:, 2], h2[:, 2], h3[:, 2]))
plt.title("Helix concatenation")
#print("Minimum:", z.min())
#print("PTP:", z.ptp())
plt.scatter(x, y, marker='.', c=colors)
plt.colorbar()
plt.show()