wlmargin[0] = 16
noise_proto0 = toy.DataCycleNoise()
noise_proto0.load('noises/merged_000886-adc_W201_Ch00.npz')
noise_LNGS = toy.DataCycleNoise(allow_break=True)
noise_LNGS.load('noises/nuvhd_lf_3x_tile57_77K_64V_6VoV_1-noise.npz')
noise_white = toy.WhiteNoise()
noise_obj = [noise_proto0, noise_LNGS, noise_white]
noise_name = ['proto0', 'LNGS', 'white']
template = _template.Template.load(
'templates/nuvhd_lf_3x_tile57_77K_64V_6VoV_1-template.npz')
toys = []
for name, noise in zip(noise_name, noise_obj):
t = toy.Toy(template, tau, snr, noise, upsampling=False)
# upsampling=True reduces speed dramatically
print(f'running with {name} noise...')
t.run(1000, pbar=10, seed=202012071818)
# Make window center argument for Toy.run_window.
isnr = np.searchsorted(snr, 2.4)
res = t.templocres() # shape == (nfilter, ntau, nsnr)
ifilterisnritau = np.array([
(3, -1, -1), # matched, high snr, high tau
(3, isnr, 7), # matched, tau=64
(2, isnr, np.argmin(res[2, :, isnr])), # exp, best tau
(1, isnr, np.argmin(res[1, :, isnr])), # movavg, best tau
])
wcenter = t.window_center(*ifilterisnritau.T)
basenoise = noise
basetb = tb
noise_ratio[tb] = np.std(toy.downsample(
basenoise.generate(20, 5000), tb // basetb),
axis=None)
for tb in timebases:
filename = simfile(n, tb)
if os.path.exists(filename):
continue
nr = noise_ratio[tb] / noise_ratio[8]
snr = snr_125MSa / nr
sim = toy.Toy(template, [tau_125MSa * 8 // tb],
snr,
noises[tb],
timebase=tb)
sim.noise_ratio = nr
sim.run(1000, pbar=10, seed=202102191411)
print(f'save {filename}')
sim.save(filename)
sim = {}
for n, timebases in timebase.items():
sim[n] = {}
for tb in timebases:
filename = simfile(n, tb)
print(f'load {filename}')
sim[n][tb] = toy.Toy.load(filename)
import numpy as np from matplotlib import pyplot as plt import figlatex import toy import num2si import template as _template templfile = 'templates/nuvhd_lf_3x_tile57_77K_64V_6VoV_1-template.npz' noisefile = 'noises/merged_000886-adc_W201_Ch00.npz' tau = 256 snr = 5 ########################### noise = toy.DataCycleNoise(maxcycles=1, chunk_skip=1000) noise.load(noisefile) template = _template.Template.load(templfile) sim = toy.Toy(template, [tau], [snr], noise) sim.run(1000, pbar=10, seed=202102172153) fig, axs = plt.subplots(2, 2, num='figlochist', clear=True, figsize=[7, 4.3]) sim.plot_loc(0, 0, axs=axs, center=True) fig.tight_layout() fig.show() figlatex.save(fig)
import os
import logging
import fuse
import time
import toy
fuse.fuse_python_api = (0, 2)
# Logging
logger = logging.getLogger("toyfs")
handler = logging.FileHandler('toyfs.log')
logger.addHandler(handler)
logger.setLevel(logging.INFO)
fs = toy.Toy()
# ToyFS
class ToyFS(fuse.Fuse):
def getattr(self, path):
logger.info("[getattr] (path=%s)" % path)
st = fs.getattr(path)
return st
def readdir(self, path, offset):
logger.info("[readdir] (path=%s, offset=%s)" % (path, offset))
directories = fs.readdir(path)
for directory in directories:
yield fuse.Direntry(directory)
"""
A simple example:
- Solve a snake cube of length 3
- write all solutions to `example_solutions.py`
- visualize the first solution
"""
import toy
import visualize
if __name__ == "__main__":
strip_lengths = [2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2]
toy = toy.Toy(strip_lengths)
toy.run()
if toy.solutions():
print("I found " + str(len(toy.solutions())) + " solutions! =D")
file_name = "example_solutions.py"
with open(file_name, 'w') as fh:
fh.write("strip_lengths = " + str(strip_lengths) + "\n\n")
str_sols = "".join([" " + str(sol)+",\n" for sol in toy.solutions()])
fh.write("solutions = {\n" + str_sols + " }")
print("solutions written to `" + file_name + "`")
# Visualizing a solution
print("... visualizing ...")
visualize.visualize(toy.solutions()[0])
else:
print("I found no solutions :'(")
import template as _template
prefix = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1'
snr = 5
mftau = 256
locfilter = 2
loctau = 16
wlen = 64
wlmargin = 16
###########################
noise = toy.DataCycleNoise(allow_break=True)
noise.load(f'noises/{prefix}-noise.npz')
template = _template.Template.load(f'templates/{prefix}-template.npz')
sim = toy.Toy(template, [loctau, mftau], [snr], noise)
sim.run(100, seed=202102190959)
wcenter = sim.window_center([locfilter], [0], [0])
sim.run_window([wlen], [wlmargin], wcenter)
fig, axs = plt.subplots(1,
2,
num='figwindowevent',
clear=True,
sharex=True,
sharey=True,
figsize=[8.94, 4.41])
sim.plot_event(ievent=0, ifilter=locfilter, isnr=0, itau=0, ax=axs[0])
sim.plot_event_window(ievent=0, isnr=0, itau=1, iwlen=0, icenter=1, ax=axs[1])
"""
import numpy as np
import toy
import template as _template
tau = np.array([4, 8, 16, 24, 32, 40, 48, 64, 96, 128, 192, 256, 384]) * 8
snr = np.linspace(1.8, 6, 15)
noise_LNGS = toy.DataCycleNoise(allow_break=True, timebase=1)
noise_LNGS.load('noises/nuvhd_lf_3x_tile57_77K_64V_6VoV_1-noise.npz')
noise_white = toy.WhiteNoise(timebase=1)
noise_obj = [noise_LNGS, noise_white]
noise_name = ['LNGS', 'white']
template = _template.Template.load(
'templates/nuvhd_lf_3x_tile57_77K_64V_6VoV_1-template.npz')
toys = []
for name, noise in zip(noise_name, noise_obj):
t = toy.Toy(template, tau, snr, noise, timebase=1)
print(f'running with {name} noise...')
t.run(1000, pbar=10, seed=202012081136)
toys.append(t)
for inoise in range(len(toys)):
filename = f'toy1gsa-{noise_name[inoise]}.npz'
print(f'saving to {filename}...')
toys[inoise].save(filename)
simfile = lambda n: f'figthesis/figwindowtempres-{n}.npz'
if not all(os.path.exists(simfile(n)) for n in names):
noise_proto0 = toy.DataCycleNoise(maxcycles=2, chunk_skip=1000)
noise_proto0.load(noisefile)
noise_lngs = toy.DataCycleNoise(maxcycles=2)
noise_lngs.load(f'noises/{prefix}-noise.npz')
noise = dict(proto0=noise_proto0, lngs=noise_lngs)
template = _template.Template.load(f'templates/{prefix}-template.npz')
for n in names:
if os.path.exists(simfile(n)):
continue
sim = toy.Toy(template, tau, snr, noise[n])
sim.run(1000, pbar=10, seed=202102191139)
res = sim.templocres() # shape == (nfilter, ntau, nsnr)
i = isnr(2.4)
ifilterisnritau = np.array([
(3, i, itau(64)), # matched N=64
(2, i, np.argmin(res[2, :, i])), # exp, best tau
])
wcenter = sim.window_center(*ifilterisnritau.T)
sim.run_window(wlen, wlmargin, wcenter, pbar=10)
print(f'save {simfile(n)}')
sim.save(simfile(n))
os.makedirs(savedir, exist_ok=True)
for whitenoise in [True, False]:
if whitenoise:
noise = [toy.WhiteNoise(timebase=t) for t in timebase]
noise_name = 'white'
else:
noise_file = f'noises/{prefix}-noise.npz'
print(f'read {noise_file}...')
noise = []
for i in range(len(timebase)):
n = toy.DataCycleNoise(maxcycles=2, timebase=timebase[i])
n.load(noise_file)
noise.append(n)
noise_name = 'lngs'
for i in range(len(timebase)):
tb = timebase[i]
noise_ratio = np.std(toy.downsample(noise[0].generate(100, 100), tb),
axis=None)
snr = snr_1GSa / noise_ratio
t = toy.Toy(template, tau // tb, snr, noise[i], timebase=tb)
print(
f'running with timebase={tb} (SNR *= {1/noise_ratio:.2g}), {noise_name} noise...'
)
t.run(1000, pbar=10, seed=202012111417)
filename = f'{savedir}/toy1gvs125m-{prefix}-{timebase[i]}-{noise_name}.npz'
print(f'saving to {filename}...')
t.save(filename)
if not all(os.path.exists(simfile(n)) for n in names):
noise_proto0 = toy.DataCycleNoise(maxcycles=2, chunk_skip=1000)
noise_proto0.load(noisefile)
noise_lngs = toy.DataCycleNoise(maxcycles=2)
noise_lngs.load(f'noises/{prefix}-noise.npz')
noise_white = toy.WhiteNoise()
noise = dict(proto0=noise_proto0, white=noise_white, lngs=noise_lngs)
template = _template.Template.load(f'templates/{prefix}-template.npz')
for n in names:
if os.path.exists(simfile(n)):
continue
sim = toy.Toy(template, tau, snr, noise[n], **options.get(n, {}))
sim.run(1000, pbar=10, seed=202102181611)
print(f'save {simfile(n)}')
sim.save(simfile(n))
sim = {}
for n in names:
print(f'load {simfile(n)}')
sim[n] = toy.Toy.load(simfile(n))
assert np.array_equal(sim[n].tau, tau)
assert np.array_equal(sim[n].snr, snr)
fig, ax = plt.subplots(num='figrescomp', clear=True, figsize=[7, 10])
def isomething(a, x, strict=True):