import pickle


cache_files = ['/mnt/hdd_24tb_1/videos/ftp/leopards1/2025/09/12/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/mnt/hdd_24tb_1/videos/ftp/leopards1/2025/09/13/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/14/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/15/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/16/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/17/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/18/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl', '/srv/ftp_tcc/leopards1/2025/09/19/embedding_scores@0.0@926895f71538e3683e9af0956af94cf4.pkl']
import time
from datetime import timedelta, datetime
start_time = time.time()
all_c = list()

start_time = 1757892175.042
end_time =   1757894197.548

def check_if_overlap(start_1, end_1, start_2, end_2):
    ff = sorted([[start_1, end_1],[start_2, end_2]],key=lambda x: x[0])
    return ff[0][1] > ff[1][0]

def get_cache_data(start_time, end_time, cache_files):
    targvals = [start_time, end_time]
    for f in cache_files:
        fold_start_time = datetime(*[int(x) for x in f.split('/')[-4:-1]]).timestamp()
        fold_end_time = fold_start_time + 86400.0

        has_overlap = check_if_overlap( start_time, end_time, fold_start_time, fold_end_time)

        if not has_overlap:
            continue

        print(f'Loading {f}')

        with open(f,'rb') as ff:
            all_c.append(pickle.load(ff))
    return all_c

st = time.time()
all_cach = get_cache_data(start_time, end_time, cache_files)

vids = list()
for c_c in all_cach:
    vids.extend(c_c['videos'])

data_filt = list()
for v in vids:
    if check_if_overlap( v['start_time'], v['end_time'], start_time, end_time):
        data_filt.append(v)


        
time_vec = np.hstack([ np.asarray(f['embed_scores']['time'])+f['start_time'] for f in data_filt])
score_vec = np.hstack([f['embed_scores']['score'] for f in data_filt])


s_time, s_ind = np.unique(time_vec, return_index=True)
s_score = score_vec[s_ind]


out_array = np.asarray([s_time, s_score]).T.tolist()