def task005_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
print(DateTimeUtil.str_now())
dao = Dao()
mecab_parser = MeCabParser()
mecab_logic = MeCabLogic()
dao_ir = dao.table('stock_ir')
# 前回取得した最後のtweetidを取得
dat = dao.table('condition').get_item(Key={'key': 'Task005_last_tweet_id'})
last_tweet_id = dat['val']
print('last_tweet_id: ', last_tweet_id)
tw = TwitterInspector(Kabpackab)
timeline = tw.get_list_timeline_rotate(list_name='IR',
count=1000,
last_data_id=last_tweet_id)
print(timeline)
if timeline:
data_list = []
for t in timeline:
tw_txt = t["text"]
# ツイートのクリーニング
clean_twtxt = Util.mask_twitter_name(Util.all_normalize(tw_txt))
# つぶやきを単語分割
mecab_dic = mecab_parser.parse(clean_twtxt)
# つぶやき内から銘柄取得
stocks_in_tweet, _ = mecab_logic.find_stock_code_in(mecab_dic)
for b in stocks_in_tweet.values():
tweet_in_ccode = b["ccode"]
Log.info('IR みっけ!! : {} {} user={}', tweet_in_ccode, b["nm"],
t["user_name"])
data = {
'cd': b["ccode"],
'nm': b["nm"],
'u': t["user_id"],
'd': t["created_at"],
't': tw_txt,
'tid': t['id_str']
}
data_list.append(data)
dao_ir.insert(data_list)
last_tweet_id = timeline[0]['id_str'] # 降順の先頭(取得した最新)
print('last_tweet_id for update: ', last_tweet_id)
# last_tweet_id 更新
dao.table("condition").update_item(
Key={"key": dat['key']},
ExpressionAttributeValues={
':val': last_tweet_id,
':dt': DateTimeUtil.str_now()
},
UpdateExpression="set val = :val, update_time = :dt")
return ''
def task002_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
print(DateTimeUtil.str_now(), 'Task002', event['ids'])
dao = Dao()
mecab_parser = MeCabParser()
tweet_ids = event['ids']
tbl_repo = dao.table("stock_report")
tbl_tweet = dao.table('tweet')
tbl_brands = dao.table('stock_brands')
tbl_price_now = dao.table('stock_price_now')
tbl_f_sum = dao.table('twitter_friends_sum')
is_stock_find = False
for id_str in tweet_ids:
t = tbl_tweet.find_by_key(id_str)
if t is None:
continue
t_sum = {}
tw_txt = t["text"]
if "retweet_text" in t:
tw_txt = t["retweet_text"]
# ツイートのクリーニング
clean_twtxt = Util.mask_twitter_name(Util.all_normalize(tw_txt))
# つぶやきを単語分割
mecab_dic = mecab_parser.parse(clean_twtxt)
# つぶやき内から銘柄取得
stocks_in_tweet, words = find_stock_code_in(mecab_dic, dao, tbl_brands)
if not is_stock_find:
is_stock_find = len(stocks_in_tweet) > 0
Log.info('len(stocks_in_tweet) = {}, is_stock_find = {}'.format(
len(stocks_in_tweet), is_stock_find))
for b in stocks_in_tweet.values():
tweet_in_ccode = b["ccode"]
Log.info('株 みっけ!! : {} {} user={}', tweet_in_ccode, b["nm"],
t["user_name"])
Log.info('tweet={}', clean_twtxt)
stock_repo = tbl_repo.find_by_key(tweet_in_ccode)
price = get_price(tweet_in_ccode, tbl_price_now)
if "prices" not in t:
t["prices"] = {}
if tweet_in_ccode in t["prices"]:
t["prices"][tweet_in_ccode].append(price)
else:
t["prices"][tweet_in_ccode] = [price]
if not t_sum:
t_sum = get_tweet_summary(t, tweet_in_ccode, tbl_f_sum)
if tweet_in_ccode not in t_sum['rank']:
t_sum['rank'][tweet_in_ccode] = {'ds': []}
t_sum['rank'][tweet_in_ccode]['nm'] = b['nm']
t_sum['rank'][tweet_in_ccode]['ds'].insert(0, {
'd': t["created_at"],
't': t['id_str']
})
if stock_repo:
tweets = stock_repo["tweets"]
# 価格をpriceに付けてしまったがTweetに付けるように変更したため、価格についたpriceがあれば削除する
if "prices" in stock_repo:
del stock_repo["prices"]
if len([tw for tw in tweets if tw == t['id_str']]) == 0:
Log.debug('カウントアップ!! : {} {}, user={}', b["ccode"],
b["nm"], t["user_name"])
tweets.append(t['id_str'])
stock_repo["tweets"] = tweets
stock_repo["ago_uptime"] = stock_repo["last_updated_at"]
stock_repo["last_updated_at"] = t["created_at"]
stock_repo["last_update_user"] = t["user_id"]
stock_repo["last_up_uname"] = t["user_name"]
retouch_stock_name(stock_repo, tbl_brands)
tbl_repo.put_item(Item=stock_repo)
else:
Log.debug('初登場!! : {} {}, user={}', b["ccode"], b["nm"],
t["user_name"])
data = {
'ccode': b["ccode"],
'name': b["nm"],
'create_user': t["user_id"],
'created_at': t["created_at"],
'last_updated_at': t["created_at"],
'last_update_user': t["user_id"],
'last_up_uname': t["user_name"],
'tweets': [t["id_str"]],
"prices": [price]
}
retouch_stock_name(data, tbl_brands)
tbl_repo.put_item(Item=data)
if words:
t['ws'] = words
tbl_tweet.put_item(Item=t)
if t_sum:
tbl_f_sum.put_item(Item=t_sum)
if is_stock_find:
# Task003呼び出し
Log.info('Task003 呼び出し')
boto3.client("lambda").invoke(
FunctionName=
"arn:aws:lambda:ap-northeast-1:007575903924:function:Task003",
InvocationType="Event",
Payload=json.dumps({}))
dao.table("condition").update_item(
Key={"key": "Task002_tweet_report_update"},
ExpressionAttributeValues={
':val': '-',
':dt': DateTimeUtil.str_now()
},
UpdateExpression="set val = :val, update_time = :dt")
return 0
def tweet_text_cleaning(self, t):
t.text = Util.mask_twitter_name(Util.all_normalize(t.text))
