def get_rules(rebuild=False):
mtime = (publications_file.stat().st_mtime_ns,
projects_file.stat().st_mtime_ns)
rules = _cached_rules.get(mtime)
if rebuild or not rules:
_cached_rules.clear()
_cached_rules[mtime] = rules = build_rules(
json_load(publications_file), json_load(projects_file))
threading.Thread(target=validate_permissions, args=(rules, )).start()
return rules
def make_dataframe(path):
threads = json_load(path)
all_messages = []
for i, th in enumerate(threads):
h = th['head']
if len(th['tail']) == 0:
continue
all_messages.append(
(h['id'],
i, h['title'],
(h['body'] or '') + ' ' + make_label_string(h['labels']),
h['sender'], h['created_at'])
)
for t in th['tail']:
all_messages.append(
(t['id'], i,
'RE: ' + h['title'],
t['body'],
t['sender'], t['created_at'])
)
return pd.DataFrame(all_messages,
columns=['message_id',
KEY_THREAD_ID, 'subject', 'body',
KEY_SENDER_ID, KEY_TIMESTAMP])
def add_sc_locale_to_history(repo_dir):
db = get_db()
source_dir = pathlib.Path(repo_dir) / 'suttacentral/client/localization/elements/'
docs = {}
files = list(source_dir.glob('*.json'))
lang = 'en'
for file in files:
entries = json_load(file)
for k, v in entries.items():
context = f'{file.name.split("_")[0]}_{k}'
if context in docs:
doc = docs[context]
else:
doc = {
'_key': f'sc_latest_{context}',
'context': context,
'origin': 'sc_latest',
'strings': {}
}
docs[context] = doc
doc['strings'][lang] = v
errors = db['historic'].import_bulk(docs.values(), on_duplicate='replace', halt_on_error=False)
return files, errors
def open_cache(suite, prefix):
try:
with open(os.path.join(awfy.path, prefix + '.json')) as fp:
cache = util.json_load(fp)
return cache['graph']
except:
return {'timelist': [], 'lines': [], 'direction': suite.direction}
def update_site(reference=None):
db = get_db()
db.collection('historic').truncate()
temp_dir_old = clone_repo(branch_or_tag='prelocale', reference=reference)
add_old_sc_locale_to_history(temp_dir_old.name)
temp_dir_latest = clone_repo('master', reference=reference)
files, _ = add_sc_locale_to_history(temp_dir_latest.name)
add_site_to_history()
# A mapping of pathlib.Path to the data contained within the file
root_files_and_data = {}
for file in files:
stem = file.stem.split('_')[0]
new_name = f'{stem}_root-en-site.json'
new_file = pathlib.Path('root/en/site') / new_name
data = json_load(file)
root_files_and_data[new_file] = data
temp_dir_old.cleanup()
temp_dir_latest.cleanup()
print("Waiting for ArangoDB to complete view update")
db.aql.execute(STALL_QUERY)
print('Restoring site projects from history')
files_and_data = restore_site_from_history(root_files_and_data)
files_and_data.update(root_files_and_data)
rewrite_site_projects(files_and_data)
def main():
opt = json_load('./option.json')
logging.basicConfig(filename=opt.PREFIX + '.log',
level=logging.DEBUG,
format='%(asctime)s %(message)s')
logger = logging.getLogger('default_logger')
logger.addHandler(logging.StreamHandler())
with tf.Session() as sess:
env = Environment(opt.ACTION_REPEAT)
age = Agent(env, sess, logger)
sess.run(tf.global_variables_initializer())
start_episode = 12345 # if load past
if start_episode > 0:
age.load(meta_graph='./save/' + opt.PREFIX +
'-%s.meta' % start_episode,
step=start_episode) #age.save(True)#
else:
age.save(True)
age.train(start_episode=0)
for epi in range(opt.TEST_EPISODE_MAX):
reward = age.play()
debug(logger, 'Test episode %d got reward %d' % (epi, reward))
def open_cache(suite, prefix):
try:
with open(os.path.join(awfy.path, prefix + ".json")) as fp:
cache = util.json_load(fp)
return cache["graph"]
except:
return {"timelist": [], "lines": [], "direction": suite.direction}
def __init__(self):
self.userName = "******".format(
studentId=config.get("Joyrun", "StudentID"),
suffix=config.get("Joyrun", "suffix"))
self.password = config.get("Joyrun", "Password")
try:
cache = json_load(self.Cache_LoginInfo)
except (FileNotFoundError, JSONDecodeError):
cache = {}
if cache.get("userName") == self.userName:
self.uid = cache.get("uid", 0)
self.sid = cache.get("sid", '')
else: # userName 不匹配，则不使用缓存信息
self.uid = 0
self.sid = ''
self.session = requests.Session()
self.session.headers.update(self.base_headers)
self.session.headers.update(self.device_info_headers)
self.auth = JoyrunAuth(self.uid, self.sid)
if self.uid and self.sid: # 直接从缓存中读取登录状态
self.__update_loginInfo()
else:
self.login() # 否则重新登录
def __init__(self):
# filenames is the key which connecting image and text for pair
self.train_file_names = u.pickle_load(c.train_filename_path)
self.test_file_names = u.pickle_load(c.test_filename_path)
self.train_file_names = sorted(self.train_file_names)
self.test_file_names = sorted(self.test_file_names)
self.train_class_id = c.train_class_info
self.test_class_id = c.test_class_info
# self.train_class_id = u.pickle_load2(c.train_class_id_txt_path)
# self.test_class_id = u.pickle_load2(c.test_class_id_txt_path)
# text
self.tokenizer = RegexpTokenizer(r'\w+')
if not os.path.isfile(c.vocab_path):
train_text = self.__load_all_text(self.train_file_names)
test_text = self.__load_all_text(self.test_file_names)
all_text = train_text + test_text
self.__word_count_statistics(all_text)
vocab = v.vocab()
vocab.create(all_text)
self.word_2_index = u.json_load(c.vocab_path)
self.index_2_word = u.json_load(c.index_2_word_path)
# self.index_2_word = {v: k for k, v in self.word_2_index.items()}
self.vocab_size = len(self.word_2_index)
print("vocab_size : ", self.vocab_size)
# image
self.base_img_size = [64, 128, 256]
label_image_size = 256
rate = 76 / 64
self.image_transform = transforms.Compose([
transforms.Resize(int(label_image_size * rate)),
transforms.RandomCrop(label_image_size),
transforms.RandomHorizontalFlip()
])
self.norm = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.filenames_bbox = self.__load_bbox()
def load_metadata(prefix):
try:
with open(os.path.join(awfy.path, "metadata-" + prefix + ".json"), "r") as fp:
cache = util.json_load(fp)
except:
cache = {"earliest_run_id": 0}
return cache
def load_metadata(prefix):
try:
with open(os.path.join(awfy.path, 'metadata-' + prefix + '.json'), 'r') as fp:
cache = util.json_load(fp)
except:
cache = { 'last_stamp': 0 }
return cache
def __build(self):
""" 构造记录
"""
points_per_loop = json_load(self.A_Loop_GPS_JSON)
self.date = self.__get_date()
self.duration = int(self.distance * self.Distance_Per_Loop / 0.4 * (self.pace + self.__pace_delta()) * 60) # 跑步时间/s
self.step = int((self.stride_frequncy + self.__stride_frequncy_delta()) * self.duration / 60) # 总步数
self.detail = list(self.__point_generator(points_per_loop))
def get_rules():
mtime = publications_file.stat().st_mtime_ns
rules = _cached_rules.get(mtime)
if not rules:
_cached_rules.clear()
_cached_rules[mtime] = rules = build_rules(
json_load(publications_file))
threading.Thread(target=validate_permissions, args=(rules, )).start()
return rules
def open_cache(suite, prefix):
try:
with open(os.path.join(awfy.path, prefix + '.json')) as fp:
cache = util.json_load(fp)
return cache['graph']
except:
return { 'timelist': [],
'lines': [],
'direction': suite.direction
}
def count_strings(self, entry):
json_file = get_file(entry["path"])
data = json_load(json_file)
count = 0
for k, v in data.items():
if k == "_meta":
continue
if v:
count += 1
return count
def load_json(entry):
_meta = entry.get("_meta", {})
_meta["path"] = entry.get("path")
json_file = get_file(entry["path"])
if json_file.exists():
segments = json_load(json_file)
else:
segments = {}
return {**deepcopy(_meta), "segments": segments}
def main():
import os
import cPickle as pkl
import pandas as pd
from util import json_load
from max_cover import k_best_trees
import argparse
parser = argparse.ArgumentParser('Evaluate the events')
parser.add_argument('-c', '--cand_trees_path', required=True, nargs='+')
parser.add_argument('--interactions_path', required=True)
parser.add_argument('--events_path', required=True)
args = parser.parse_args()
interactions = json_load(args.interactions_path)
true_events = json_load(args.events_path)
methods = [metrics.adjusted_rand_score,
metrics.adjusted_mutual_info_score,
metrics.homogeneity_score,
metrics.completeness_score,
metrics.v_measure_score]
K = 10
indexes = []
scores = []
for p in args.cand_trees_path:
cand_trees = pkl.load(open(p))
pred_trees = k_best_trees(cand_trees, K)
indexes.append(os.path.basename(p))
scores.append(evaluate_meta_tree_result(
true_events,
pred_trees,
[i['message_id'] for i in interactions],
methods
))
df = pd.DataFrame(scores, index=indexes,
columns=[m.__name__ for m in methods] +
[m.__name__ + "(all)" for m in methods] +
['precision', 'recall', 'f1'])
df.to_csv('tmp/evaluation.csv')
def get_topic_meta_graph_from_synthetic(cls,
path,
preprune_secs,
**kwargs):
return cls.get_topic_meta_graph(json_load(path),
cosine,
preprune_secs=preprune_secs,
decompose_interactions=False,
given_topics=True,
convert_time=False,
**kwargs
)
class Memories(PowerDict, Capability):
personal = json_load(os.path.join(REFERENCE_DIR, 'personal.json'))
def __init__(self, info={}, person=None):
PowerDict.__init__(self, info)
self.update(Memories.calculated(self))
Capability.__init__(self, person)
@classmethod
def calculated(cls, self):
return {
'full_name': self['first_name'] + " " + self['last_name'],
'unique_id': self['first_name'] + "_" + self['last_name']
}
def run_phrase(self):
return "" # TODO: Alex
def __json__(self):
return self
@classmethod
def from_json(cls, obj):
return cls(obj)
@classmethod
def random_gender(cls):
return first_sample(cls.personal['gender']).lower()
@classmethod
def names(cls):
return cls.personal['names']
@classmethod
def random_first_name(cls, gender):
names = cls.names()
if gender in names:
pool = names[gender]
else:
pool = names['male'] + names['female']
return first_sample(pool)
@classmethod
def random_last_name(cls):
return first_sample(cls.personal['surnames'])
@classmethod
def random(cls):
mem = cls()
mem.gender = cls.random_gender()
mem.first_name = cls.random_first_name(mem.gender)
mem.last_name = cls.random_last_name()
return mem
def main():
import os
import cPickle as pkl
import pandas as pd
from util import json_load
from max_cover import k_best_trees
import argparse
parser = argparse.ArgumentParser('Evaluate the events')
parser.add_argument('-c', '--cand_trees_path', required=True, nargs='+')
parser.add_argument('--interactions_path', required=True)
parser.add_argument('--events_path', required=True)
args = parser.parse_args()
interactions = json_load(args.interactions_path)
true_events = json_load(args.events_path)
methods = [
metrics.adjusted_rand_score, metrics.adjusted_mutual_info_score,
metrics.homogeneity_score, metrics.completeness_score,
metrics.v_measure_score
]
K = 10
indexes = []
scores = []
for p in args.cand_trees_path:
cand_trees = pkl.load(open(p))
pred_trees = k_best_trees(cand_trees, K)
indexes.append(os.path.basename(p))
scores.append(
evaluate_meta_tree_result(true_events, pred_trees,
[i['message_id'] for i in interactions],
methods))
df = pd.DataFrame(scores,
index=indexes,
columns=[m.__name__ for m in methods] +
[m.__name__ + "(all)"
for m in methods] + ['precision', 'recall', 'f1'])
df.to_csv('tmp/evaluation.csv')
def load(cls, unique_id):
folder = os.path.join(PEOPLE_DIR, unique_id)
# check if they exist
if not os.path.exists(folder):
return None
caps = {}
for k, v in Person.cap_classes.iteritems():
filepath = os.path.join(folder, k + '.json')
if os.path.exists(filepath):
caps[k] = json_load(filepath, cls=v)
else:
caps[k] = v()
return cls(caps)
def __point_generator(self):
""" GPS 坐标 生成器
无限循环跑圈，并对每个点都引入一个偏差量
"""
points_per_loop = json_load(self.A_Loop_GPS_JSON)
points_num_per_loop = int(self.pace * 0.4 * 60 / self.sampleinterval)
while True:
for i in range(points_num_per_loop):
idx = math.floor(i / points_num_per_loop *
len(points_per_loop))
point = points_per_loop[idx] # .copy()
point[0] += self.__point_delta()
point[1] += self.__point_delta()
yield point.copy() # 一定要 copy 否则会同步修改
def main():
opt = json_load('./option.json')
logging.basicConfig(filename=opt.PREFIX+'.log', level=logging.DEBUG, format='%(asctime)s %(message)s')
with tf.Session() as sess:
env = Environment()
age = Agent(env, sess)
sess.run(tf.global_variables_initializer())
start_episode = 0 # if load past
if start_episode > 0:
age.load(meta_graph='./save/BREAKOUT_DQN-%s.meta' % start_episode, step=start_episode)#age.save(True)#
age.train(start_episode=start_episode)#age.train()#
else:
age.save(True)
age.train(start_episode=0)
for epi in range(opt.TEST_EPISODE_MAX):
reward = age.play()
print('Test episode %d got reward %d' % (epi, reward))
debug('Test episode %d got reward %d' % (epi, reward))
def find_all_months(cx, prefix, name):
pattern = prefix + 'raw-' + name + '-(\d\d\d\d)-(\d+)\.json'
files = []
for file in os.listdir(awfy.path):
m = re.match(pattern, file)
if not m:
continue
year = int(m.group(1))
month = int(m.group(2))
files.append(((year, month), file))
files = sorted(files, key=lambda key: key[0][0] * 12 + key[0][1])
graphs = []
for when, file in files:
with open(os.path.join(awfy.path, file)) as fp:
cache = util.json_load(fp)
graphs.append((when, cache['graph']))
return graphs
def get_token(self, refresh=False):
""" 如果 token 没有过期，则返回缓存 token ，否则重新登录
Args:
refresh bool 是否立即重新登录并刷新缓存（默认 false）
Returns:
token str token 字符串
"""
try:
if refresh:
token = self.__login()
else:
tokenCache = json_load(self.Cache_AccessToken)
if tokenCache["expire_in"] < time.time():
token = self.__login()
else:
token = tokenCache["token"]
except (FileNotFoundError, JSONDecodeError): # 无缓存文件或者文件为空
token = self.__login()
finally:
return token
def find_all_months(cx, prefix, name):
pattern = prefix + 'raw-' + name + '-(\d\d\d\d)-(\d+)\.json'
files = []
for file in os.listdir(awfy.path):
m = re.match(pattern, file)
if not m:
continue
year = int(m.group(1))
month = int(m.group(2))
files.append(((year, month), file))
files = sorted(files, key=lambda key: key[0][0] * 12 + key[0][1])
graphs = []
for when, file in files:
with open(os.path.join(awfy.path, file)) as fp:
cache = util.json_load(fp)
graphs.append((when, cache['graph']))
return graphs
def add_old_sc_locale_to_history(repo_dir):
"""
Historically SuttaCentral contained both roots and translations for the site localization
"""
db = get_db()
source_dir = pathlib.Path(repo_dir) / 'suttacentral/client/localization/elements/'
docs = {}
for folder in source_dir.glob('*'):
if not folder.is_dir():
continue
for file in folder.glob('*.json'):
lang = file.stem
data = json_load(file)
entries = data[lang]
for k, v in entries.items():
context = f'{folder.name}_{k}'
if context in docs:
doc = docs[context]
else:
doc = {
'_key': f'sc_old_{context}',
'context': context,
'origin': 'sc_old',
'strings': {}
}
docs[context] = doc
doc['strings'][lang] = v
errors = db['historic'].import_bulk(docs.values(), on_duplicate='replace', halt_on_error=False)
return errors
def __init__(self):
self.userName = "******" % config.get("Joyrun", "StudentID")
self.password = config.get("Joyrun", "Password")
try:
cache = json_load(self.Cache_LoginInfo)
except (FileNotFoundError, JSONDecodeError):
cache = {}
self.uid = cache.get("uid", 0)
self.sid = cache.get("sid", '')
self.session = requests.Session()
self.session.headers.update(self.base_headers)
self.session.headers.update(self.device_info_headers)
self.auth = JoyrunAuth(self.uid, self.sid)
if self.uid and self.sid: # 直接从缓存中读取登录状态
self.__update_loginInfo()
else:
self.login() # 否则重新登录
def retrieve_graph(cx, file):
with open(os.path.join(awfy.path, file)) as fp:
cache = util.json_load(fp)
return cache['graph']
def retrieve_graph(cx, file):
with open(os.path.join(awfy.path, file)) as fp:
cache = util.json_load(fp)
return cache['graph']
def recurse(folder, meta_definitions=None):
subtree = {}
meta_definitions = meta_definitions.copy()
metafiles = set(folder.glob("_*.json"))
if metafiles:
for metafile in sorted(metafiles, key=humansortkey):
file_data = json_load(metafile)
meta_definitions.update(file_data)
for k, v in file_data.items():
if k not in _meta_definitions:
_meta_definitions[k] = v
for file in sorted(folder.glob("*"), key=humansortkey):
if file.name.startswith("."):
continue
if file in metafiles:
continue
long_id = file.stem
meta = {}
for part in file.parts:
if part.endswith(".json"):
part = part[:-5]
if part in meta_definitions:
meta[part] = meta_definitions[part]
if file.is_dir():
subtree[file.name] = recurse(file,
meta_definitions=meta_definitions)
subtree[file.name]["_meta"] = meta
elif file.suffix == ".json":
mtime = file.stat().st_mtime_ns
path = str(file.relative_to(WORKING_DIR))
obj = subtree[long_id] = {
"path": path,
"mtime": mtime,
"_meta": meta
}
if "_" in long_id:
uid, muids = get_uid_and_muids(file)
else:
uid = file.name if file.is_dir() else file.stem
muids = None
obj["uid"] = uid
if uid not in uid_index:
uid_index[uid] = set()
uid_index[uid].add(long_id)
if long_id in file_index:
logging.error(f"{str(file)} not unique")
file_index[long_id] = obj
if muids:
for muid in muids:
if muid not in muid_index:
muid_index[muid] = set()
muid_index[muid].add(long_id)
# Create Virtual Files
if 'translation' in muids:
uid, muids = long_id.split('_')
muids = muids.replace('translation', 'comment')
comment_stem = f"{uid}_{muids}"
if comment_stem in uid_index:
continue
parent = pathlib.Path('comment') / file.relative_to(
WORKING_DIR / 'translation').parent
virtual_file = parent / (comment_stem + '.json')
meta = {
part: meta_definitions[part]
for part in muids.split('-')
if part in meta_definitions
}
obj = {
"uid": uid,
"path": str(virtual_file),
"mtime": None,
"_meta": meta
}
uid_index[uid].add(comment_stem)
file_index[comment_stem] = obj
for muid in muids.split('-'):
muid_index[muid].add(comment_stem)
return subtree
def recurse(folder, meta_definitions=None, depth=0):
subtree = {}
meta_definitions = meta_definitions.copy()
metafiles = set(folder.glob("_*.json"))
if metafiles:
for metafile in sorted(metafiles, key=humansortkey):
file_data = json_load(metafile)
if isinstance(file_data, dict):
meta_definitions.update(file_data)
for k, v in file_data.items():
if k not in _meta_definitions:
_meta_definitions[k] = v
for file in sorted(folder.glob("*"), key=humansortkey):
if file.name.startswith("."):
continue
if file in metafiles:
continue
long_id = file.stem
meta = {}
for part in file.parts:
if part.endswith(".json"):
part = part[:-5]
if part in meta_definitions:
meta[part] = meta_definitions[part]
if file.is_dir():
subtree[file.name] = recurse(
file,
meta_definitions=meta_definitions,
depth=depth + 1)
subtree[file.name]["_meta"] = meta
elif file.suffix == ".json":
mtime = file.stat().st_mtime_ns
path = str(file.relative_to(WORKING_DIR))
obj = subtree[long_id] = {
"path": path,
"mtime": mtime,
"_meta": meta
}
if "_" in long_id:
uid, muids = get_uid_and_muids(file)
else:
uid = file.name if file.is_dir() else file.stem
muids = None
obj["uid"] = uid
if uid not in uid_index:
uid_index[uid] = set()
uid_index[uid].add(long_id)
if long_id in file_index:
logging.error(f"{str(file)} not unique")
file_index[long_id] = obj
if muids:
for muid in muids:
if muid not in muid_index:
muid_index[muid] = set()
muid_index[muid].add(long_id)
# Create Virtual Files
if 'translation' in muids:
uid, muids = long_id.split('_')
_add_virtual_comment_file(uid, muids, file,
uid_index, muid_index,
file_index,
meta_definitions)
if depth == 0:
_add_virtual_project_files(uid_index, muid_index, file_index,
subtree, _meta_definitions)
return subtree
def get_interaction_ids(path):
return [i["message_id"] for i in json_load(path)]
def from_str(cls, s): data = json_load(s) t = Transaction(**data['transaction']) return cls(t, data['data'])
import gensim
import networkx as nx
from scipy.spatial.distance import cosine
from datetime import timedelta
from interactions import InteractionsUtil as IU
from util import json_load
interactions = json_load('data/enron/interactions.json')
lda_model = gensim.models.ldamodel.LdaModel.load(
'data/enron/model-50-50.lda'
)
dictionary = gensim.corpora.dictionary.Dictionary.load(
'data/enron/dict.pkl'
)
different_weights = [
{'topics': 0.2,
'bow': 0.8},
{'topics': 1.0},
{'bow': 1.0},
]
for weights in different_weights:
meta_graph_kws = {
'distance_weights': weights,
}
g = IU.get_topic_meta_graph(
def get_interaction_ids(path):
return [i['message_id'] for i in json_load(path)]
def load_dataset(conf, lang, bert=None):
if conf.best_vocab_size:
conf.vocab_size = json_load(conf.best_vocab_size_file)[conf.lang]
data = datasets[conf.dataset].load(conf, lang, bert=bert)
data.describe()
return data
def test_convert_json():
for filename in glob.glob(os.path.join(SCRIPT_DIR, '*.json')):
example = json_load(filename)
normalize_dict(example)
yield assert_converts, example
def running_record(self):
""" 构造一次跑步记录
"""
points_per_loop = json_load(self.A_Loop_GPS_JSON) # 一圈的坐标
distance = config.getfloat("PB", "distance") # 总距离 km
pace = config.getfloat("PB", "pace") # 速度 min/km
stride_frequncy = config.getint("PB", "stride_frequncy") # 步频 步/min
duration = distance * pace * 60 # 用时 s
cal_per_loop = lambda: 20 + random.random() * (23 - 20) # 20-23 每圈
point_delta = lambda: (random.random() - 0.5) * 0.00005 # 随机坐标偏差
distance_delta_rate = lambda: 1 + (random.random() - 0.5
) * 0.1 # 0.95 - 1.05 的距离随机倍率
stride_frequncy_delta = lambda: int(
(random.random() - 0.5) * 2 * 10) # 10步/min 的随机步频偏差
random_alt = lambda: round(42 + random.random() *
(48 - 42), 1) # 42-48 海拔
random_speed = lambda: round(3.1 + random.random() *
(4.4 - 3.1), 2) # 没搞懂 speed 怎么定义的 ...
def locus_generator():
end_time = int(time.time() * 1000)
start_time = now_time = end_time - int(duration * 1000) # 从开始时间起
now_stepcount = 0
now_distance = 0.00
while now_time <= end_time:
for point in points_per_loop:
per_distance = 0.4 / len(
points_per_loop) * distance_delta_rate() # 两点间距离 km
now_stepcount += int(
(stride_frequncy + stride_frequncy_delta()) *
per_distance * pace)
now_distance += per_distance
yield {
# "id": ??? # 拿不到主键，但是不加主键也可以提交成功，数据库应该设置了主键自增长
"alt": random_alt(),
"speed": random_speed(),
"heartrate": 0,
"distance": now_distance,
"lat": round(point['lat'] + point_delta(), 8),
"lng": round(point['lng'] + point_delta(), 8),
"stepcount": now_stepcount,
"traintime": now_time,
}
now_time += int(per_distance * pace * 60 * 1000) # 时间间隔 ms
locuslist = list(locus_generator())
distance = locuslist[-1]['distance'] # 实际的距离 km
duration = (locuslist[-1]['traintime'] -
locuslist[0]['traintime']) / 1000 # 实际的用时 s
return json.dumps({
"biggerId":
self.biggerId,
"token":
self.token,
"locusrlist": [{
"cal": int(cal_per_loop() * distance * 1000 / 400),
"distance": round(distance, 2),
"duration": int(duration),
"heartrate": 0,
"team": 1,
# "pace": ??? # 此处的 pace 与跑步记录中的 pace 含义不统一
# "intermittent": ???
"locuslist": [locuslist],
}],
}).encode('utf-8')
def update_segment(segment, user):
"""
segment looks like:
{
"segmentId": "dn1:1.1",
"field": "translation-en-sujato",
"value": "..", "oldValue": "..."
}
"""
segment_id = segment["segmentId"]
if not is_id_legal(segment_id):
logging.error(f"Invalid Segment ID: {segment_id}")
return {"error": "Invalid Segment ID"}
uid, _ = segment_id.split(":")
parent_uid = get_parent_uid(uid)
long_id = f'{parent_uid}_{segment["field"]}'
try:
filepath = get_file_path(long_id)
except KeyError as e:
logging.exception(e)
logging.error('f"{long_id}" not found, {segment}')
return {"error": "file not found"}
file = get_file(filepath)
permission = get_permissions(filepath, user['login'])
if permission != Permission.EDIT:
logging.error("User not allowed to edit")
return {"error": "Inadequate Permission"}
with git_fs._lock:
try:
file_data = json_load(file)
except FileNotFoundError:
file.parent.mkdir(parents=True, exist_ok=True)
file_data = {}
current_value = file_data.get(segment_id)
result = {}
if current_value and current_value != segment.get("oldValue"):
result["clobbered"] = current_value
if current_value != segment["value"]:
result["changed"] = True
file_data[segment_id] = segment["value"]
sorted_data = dict(sorted(file_data.items(), key=bilarasortkey))
try:
json_save(sorted_data, file)
result["success"] = True
except Exception:
logging.exception(f"could not write segment: {segment}")
return {"error": "could not write file"}
executor.submit(background_update, filepath, user, segment)
return result
# filter out articles with single company tag
if has_multiple_companies(a):
valid_articles.append(
transform_article(a)
)
return valid_articles
def dump2interactions(db, collection_name, output_path):
articles = articles_articles(db, collection_name)
print('# valid articles: ', len(articles))
json_dump(articles, output_path)
return articles
def collect_people_info(articles):
participant_ids = set(
itertools.chain(*[a['participant_ids'] for a in articles])
)
print('# unique participants: ', len(participant_ids))
return [{'id': p} for p in participant_ids]
if __name__ == '__main__':
# articles = dump2interactions(MongoClient()['bloomberg'],
# 'articles',
# 'data/bloomberg/interactions.json')
articles = json_load('data/bloomberg/interactions.json')
json_dump(collect_people_info(articles),
'data/bloomberg/people.json')
