("step", "step", 8631697, 0),
("box", "box2", 5521451, 0),
("transit", "transit", 8505215, 0),
]
fig, axes = pl.subplots(2, 2, figsize=(8, 8))
os.makedirs("cache", exist_ok=True)
for a, ax, (disp, name, kicid, peak_id) in zip("abcd", axes.flatten(), models):
fn = os.path.join("cache", "{0}.pkl".format(kicid))
if os.path.exists(fn):
print("using cached file: {0}".format(fn))
with open(fn, "rb") as f:
results = pickle.load(f)
else:
results = search((kicid, None), detect_thresh=15, verbose=True, all_models=True)
with open(fn, "wb") as f:
pickle.dump(results, f, -1)
peak = results.peaks[peak_id]
t0 = peak["transit_time"]
rng = t0 + np.array([-2, 2])
gp, y0 = peak["gps"][name]
x, y = peak["data"][:2]
m = (rng[0] < x) & (x < rng[1])
x0 = np.linspace(rng[0], rng[1], 500)
ax.plot(24 * (x[m] - t0), y[m], ".", color=COLORS["DATA"])
mu = gp.predict(y0, x0, return_cov=False)
ax.plot(24 * (x0 - t0), mu, color=COLORS["MODEL_2"], lw=2.5, alpha=0.8)
import numpy as np
import matplotlib.pyplot as pl
from peerless.search import search
kicid = 8505215
# kicid = 10842718
# kicid = 10287723
# kicid = 6551440
fn = os.path.join("cache", "init-{0}.pkl".format(kicid))
if os.path.exists(fn):
print("using cached file: {0}".format(fn))
with open(fn, "rb") as f:
results = pickle.load(f)
else:
results = search((kicid, None), verbose=True, delete=False)
with open(fn, "wb") as f:
pickle.dump(results, f, -1)
fig, ax = pl.subplots(1, 1, figsize=(8, 4))
ax.plot(results.search_time, results.search_scalar, color=COLORS["DATA"])
ax.plot(results.search_time,
results.search_background,
color=COLORS["MODEL_2"])
ax.plot(results.search_time,
results.detect_thresh * results.search_background,
color=COLORS["MODEL_2"],
ls="dashed")
ax.set_xlim(results.search_time.min(), results.search_time.max())
fig.savefig("initial_candidates.pdf", bbox_inches="tight")
import os
import pickle
import numpy as np
import matplotlib.pyplot as pl
from peerless.search import search
kicid = 8505215
# kicid = 10842718
# kicid = 10287723
# kicid = 6551440
fn = os.path.join("cache", "init-{0}.pkl".format(kicid))
if os.path.exists(fn):
print("using cached file: {0}".format(fn))
with open(fn, "rb") as f:
results = pickle.load(f)
else:
results = search((kicid, None), verbose=True, delete=False)
with open(fn, "wb") as f:
pickle.dump(results, f, -1)
fig, ax = pl.subplots(1, 1, figsize=(8, 4))
ax.plot(results.search_time, results.search_scalar, color=COLORS["DATA"])
ax.plot(results.search_time, results.search_background,
color=COLORS["MODEL_2"])
ax.plot(results.search_time, results.detect_thresh*results.search_background,
color=COLORS["MODEL_2"], ls="dashed")
ax.set_xlim(results.search_time.min(), results.search_time.max())
fig.savefig("initial_candidates.pdf", bbox_inches="tight")
("box", "box1", 9411471, 0),
# ("box", "box2", 5521451, 0),
("transit", "transit", 8505215, 0),
]
fig, axes = pl.subplots(2, 2, figsize=(8.8, 8))
os.makedirs("cache", exist_ok=True)
for a, ax, (disp, name, kicid, peak_id) in zip("abcd", axes.flatten(), models):
fn = os.path.join("cache", "{0}.pkl".format(kicid))
if os.path.exists(fn):
print("using cached file: {0}".format(fn))
with open(fn, "rb") as f:
results = pickle.load(f)
else:
results = search((kicid, None), detect_thresh=15, verbose=True,
all_models=True)
with open(fn, "wb") as f:
pickle.dump(results, f, -1)
peak = results.peaks[peak_id]
t0 = peak["transit_time"]
gp, y0 = peak["gps"][name]
if disp == "step":
t0 = gp.mean["t0"]
elif disp == "box":
t0 = 0.5 * (gp.mean.mn + gp.mean.mx)
rng = t0 + np.array([-2, 2])
x, y = peak["data"][:2]
m = (rng[0] < x) & (x < rng[1])
x0 = np.linspace(rng[0], rng[1], 500)