def test_n_closest():
"""
Get ts 100; run search by id in on, confirm we get back 3 close ts
and that distances in returned dict match actual distances
"""
# Attempt to get non-existent time series
with raises(ValueError):
n_closest = simsearch_by_id(500, 3)
with raises(ValueError):
_ = get_by_id(500)
# Get ts 100
ats_100 = get_by_id(100)
n_closest = simsearch_by_id(100, 3)
assert (len(n_closest) <= 3)
# Confirm that distance measures are accurate
for dist in n_closest:
tsid = n_closest[dist]
other_ts = get_by_id(tsid)
assert (abs(
dist -
kernel_dist(standardize(ats_100), standardize(other_ts)) < .0001))
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 find_closest_vp(vps_dict, ts):
"""
Calculates distances from time series 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 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 test_add_ts():
""" Create a ts, add to db, retrieve it, assert that it's the same ts"""
new_ts = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
new_tsid = add_ts(new_ts)
ts_as_saved = get_by_id(new_tsid)
assert (kernel_dist(standardize(ts_as_saved), standardize(new_ts)) <
.00001)
# Confirm that we get the same id back when we attempt to add it a second time
assert (add_ts(new_ts) == new_tsid)
def test_save_ts_to_db_two():
new_ts = ArrayTimeSeries(values=[0, 1, 2, 3, 10],
times=[0., .2, .3, .5, 1])
#new_ts = ArrayTimeSeries(values=[ 1.90015224,4.11290636,2.45059022,2.45251473,-4.1988066], times=[ 0.,0.2,0.4,0.6,0.8])
#new_ts = (tsmaker(0.5, 0.1, random.uniform(0,10),5))
new_tsid = s_client.save_ts_to_db(new_ts)
echo_ts = s_client.get_ts_with_id(new_tsid)
interpolated_ats = new_ts.interpolate(
np.arange(0.0, 1.0, (1.0 / TS_LENGTH)))
assert (kernel_dist(standardize(echo_ts), standardize(interpolated_ats)) <
.00001)
def test_crosscorr():
t1 = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
# First confirm that the kernel correlation and distance methods
# return 1 and 0 when comparing a ts with itself
assert (kernel_corr(t1, t1) == 1)
assert (kernel_dist(t1, t1) == 0)
t2 = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
t3 = standardize(random_ts(0.5))
# Now let's do the opposite -- ensure that we see some distance for different curves
assert (kernel_dist(t1, t2) > 0)
assert (kernel_dist(t1, t3) > 0)
assert (kernel_corr(t1, t2) < 1)
assert (kernel_corr(t1, t3) < 1)
def test_simsearch_by_ts():
ats_75 = get_by_id(75)
n_closest_dict, tsid, is_new = simsearch_by_ts(ats_75, 5)
assert (tsid == 75)
assert (is_new == False)
assert (n_closest_dict == simsearch_by_id(75, 5))
new_ts = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
n_closest_dict, tsid, is_new = simsearch_by_ts(new_ts, 5)
assert (is_new == True)
assert (tsid > 250)
assert (len(n_closest_dict) == 5)
def add_ts_to_vpdb(data_tuple):
"""
Worker function called by add_ts_to_vpdbs above.
This process is repeated on each vantage point.
"""
file, fsm, s_ts, ts_fn, db_dir = data_tuple
vp_ts = load_ts(file[:-5], fsm)
dist_to_vp = kernel_dist(standardize(vp_ts), s_ts)
# print("Adding " + ts_fn + " to " + (db_dir + file))
db = connect(db_dir + file)
db.set(dist_to_vp, ts_fn)
db.commit()
db.close()
def test_crosscorr_errors():
"""Test that we have checks for varies error conditions"""
t1 = standardize(tsmaker(0.5, 0.1, random.uniform(0, 10)))
t4 = standardize(random_ts(0.5, 200))
t5 = tsmaker(0.5, 0.1, random.uniform(0, 10))
#Confirm that we raise value error if we attempt to compare time series
# that are not the same length
with raises(ValueError):
ccor(t1, t4)
with raises(ValueError):
kernel_dist(t1, t4)
with raises(ValueError):
kernel_corr(t1, t4)
#Confirm that we raise value error if we attempt to compare time series
# that have not been standardized first
t5 = tsmaker(0.5, 0.1, random.uniform(0, 10))
with raises(ValueError):
kernel_dist(t4, t5)
def search_vpdb_for_n(vp_t, ts, db_dir, lc_dir, n):
"""
Searches for n 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:
Dict: A dict of n closet time series ids, with distances as the keys and ts ids as the values
Note:
Uses processes pool to calculate distances in parallel, and heap queue data to minimize time
for sorting final distance list to n smallest distances.
"""
# 1. Setup data to be processed in parallel
vp_fn, dist_to_vp = vp_t
lc_candidates, fsm = find_lc_candidates(vp_t, db_dir, lc_dir)
lc_candidates.append((dist_to_vp, vp_fn))
existing_ts_id = -1
s_ts = standardize(ts)
lc_candidate_data = [(ts_fn, fsm, s_ts)
for d_to_vp, ts_fn in lc_candidates]
# 2. Calculate distances in parallel
with ProcessPoolExecutor() as pool:
dist_list = pool.map(calc_distance, lc_candidate_data)
# 3. Sort distances for n+1 smallest
n_smallest = heapq.nsmallest(n + 1, dist_list)
# 4. Look through sublist of closest time series to see if any of have a distance of zero.
# If so, mark it as an existing time series.
# Otherwise, trim the list by 1.
for dist_to_ts, tsid in n_smallest:
if dist_to_ts < .00001:
existing_ts_id = tsid
if (existing_ts_id == -1):
n_smallest = n_smallest[:-1]
else:
n_smallest = [(d, id) for d, id in n_smallest
if (id != existing_ts_id)]
# 5. Return n_smallest dict, and exiting id (or -1 if not in db)
return (dict(n_smallest), existing_ts_id)
def add_ts_to_vpdbs(ts, ts_fn, db_dir, lc_dir):
"""
Based on names of vantage point db files, adds single new time series to vp indexes
(Does not re-pick vantage points)
Uses ProcessPoolExecutor to run processes in parallel.
"""
fsm = FileStorageManager(lc_dir)
s_ts = standardize(ts)
# Setup data for process poll execution
vp_fns = [
file for file in os.listdir(db_dir)
if file.startswith("ts_datafile_") and file.endswith(".dbdb")
]
vp_tuples = [(vp_fn, fsm, s_ts, ts_fn, db_dir) for vp_fn in vp_fns]
# Create processes
with ProcessPoolExecutor() as pool:
_ = pool.map(add_ts_to_vpdb, vp_tuples)
def test_save_ts_to_db():
# Save a ts, request it by id, compare to original
new_ts = (tsmaker(0.5, 0.1, random.uniform(0, 10)))
new_tsid = s_client.save_ts_to_db(new_ts)
echo_ts = s_client.get_ts_with_id(new_tsid)
assert (kernel_dist(standardize(echo_ts), standardize(new_ts)) < .00001)
def calc_distance(lc_candidate_data):
"""Working function called by search_vpdb_for_n above"""
ts_fn, fsm, s_ts = lc_candidate_data
candidate_ts = load_ts(ts_fn, fsm)
dist_to_ts = kernel_dist(standardize(candidate_ts), s_ts)
return (dist_to_ts, tsfn_to_id(ts_fn))
