def test_ccorr():
from makelcs import tsmaker
from crosscorr import kernel_corr, kernel_dist, standardize
t1 = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
t2 = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
assert (kernel_corr(t1, t1) == 1)
assert (kernel_dist(t1, t1) == 0)
def calc_distances(vp_k, timeseries_dict):
"""Calculates kernel distance between vantage point and all loaded light curves"""
distances = []
vp = standardize(timeseries_dict[vp_k])
for k in timeseries_dict:
if k != vp_k:
k_dist = kernel_dist(vp, standardize(timeseries_dict[k]))
distances.append((k_dist, k))
return distances
def plot_two_ts(ts1, ts1_name, ts2, ts2_name, stand=True):
"""Plots two time series with matplotlib"""
import matplotlib.pyplot as plt
if (stand):
ts1 = standardize(ts1)
ts2 = standardize(ts2)
plt.plot(ts1, label=ts1_name)
plt.plot(ts2, label=ts2_name)
plt.legend()
plt.show()
def find_closest_vp(vps_dict, ts):
"""
Calculates distances from ts to all vantage points.
Returns tuple with filename of closest vantage point and distance to that vantage point.
"""
s_ts = standardize(ts)
vp_distances = sorted([(kernel_dist(s_ts, standardize(vps_dict[vp])), vp)
for vp in vps_dict])
dist_to_vp, vp_fn = vp_distances[0]
return (vp_fn, dist_to_vp)
def search_vpdb(vp_t, ts):
"""
Searches for most similar light curve based on pre-computed distances in vpdb
Args:
vp_t: tuple containing vantage point filename and distance of time series to vantage point
ts: time series to search on.
Returns:
Tuple: Distance to closest light curve, filename of closest light curve, ats object for closest light curve
"""
vp_fn, dist_to_vp = vp_t
db_path = DB_DIR + vp_fn[:-4] + ".dbdb"
db = connect(DB_DIR + vp_fn[:-4] + ".dbdb")
s_ts = standardize(ts)
# Identify light curves in selected vantage db that are up to 2x the distance
# that the time series is from the vantage point
lc_candidates = db.chop(2 * dist_to_vp)
db.close()
# Vantage point is ts to beat as we search through candidate light curves
min_dist = dist_to_vp
closest_ts_fn = vp_fn
closest_ts = load_ts(vp_fn)
for d_to_vp, ts_fn in lc_candidates:
candidate_ts = load_ts(ts_fn)
dist_to_ts = kernel_dist(standardize(candidate_ts), s_ts)
if (dist_to_ts < min_dist):
min_dist = dist_to_ts
closest_ts_fn = ts_fn
closest_ts = candidate_ts
return (min_dist, closest_ts_fn, closest_ts)