def web010_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
uid = event['uid']
print('uid = {}'.format(uid))
tfs = dao.table('twitter_friends_sum').find_by_key(uid)
ranks = sorted(tfs['rank'].items(),
key=lambda r: len(r[1]['ds']),
reverse=True)
ranks = ranks[:min(20, len(ranks))]
ret = []
for r in ranks:
rank = {
'cd': r[0],
'nm': r[1]['nm'],
'ct': len(r[1]['ds']),
'd': r[1]['ds'][0]['d']
}
ret.append(rank)
tweet = {}
if 'tid' in tfs:
tweet = dao.table('tweet').find_by_key(tfs['tid'])
del tweet['id_str']
del tweet['user_id']
del tweet['user_name']
del tweet['user_screen_name']
cond = dao.table('condition').find_by_key('Task002_tweet_report_update')
ob = {'d': cond['update_time'], 't': tweet, 'r': ret}
return response({"v": "1", "tfs": ob})
def run(self, dao: Dao, h1):
h1('stock_brandsを全件delete')
self._delete_stock_brands()
h1('ブランド一覧取得')
ys = YahooStock()
ret = ys.get_brands()
for r in ret:
r['market'] = r['market'].strip()
r['info'] = r['info'].strip()
h1('stock_brandsコレクションに追加')
dao.table('stock_brands').insert(ret)
h1('Job007をキック')
boto3.client('batch').submit_job(
jobName='Job007',
jobQueue=
"arn:aws:batch:ap-northeast-1:007575903924:job-queue/Job007_stock_brands_patch_recreate",
jobDefinition="Job007_stock_brands_patch_recreate:1")
h1('Job011をキック')
boto3.client('batch').submit_job(
jobName='Job011',
jobQueue=
"arn:aws:batch:ap-northeast-1:007575903924:job-queue/Job011_populate_stock_detail",
jobDefinition="Job011_populate_stock_detail:1")
h1('終了')
def tbl_report():
# os.environ["PRODUCTION_DAO"] = "True"
mongo = OldDao(DB())
dynamo = Dao()
cur_friends = mongo.table("twitter_friends").find({},{'_id': 0, 'tweet_summary': 0})
friends = list(cur_friends)
dynamo.table("twitter_friends").delete_all()
dynamo.table("twitter_friends").insert(friends)
def tbl_price_up():
os.environ["PRODUCTION_DAO"] = "True"
mongo = OldDao(DB())
dynamo = Dao()
dirs = os.listdir('c:/temp/pac_price')
# dynamo.table('stock_price_history').delete_all()
rows = []
for ccode in dirs:
last_file = sorted(os.listdir(
'c:/temp/pac_price/{}'.format(ccode)))[-1]
if last_file != '201806.csv':
# print(ccode, last_file)
continue
df = pd.read_csv('c:/temp/pac_price/{}/{}'.format(ccode, last_file))
df['cd'] = ccode
# row = df.tail(1).to_dict('records')
row = df.to_dict('records')
if row[len(row) - 1]['d'].split('_')[0] != '2018/04/20':
print(ccode, row[0]['d'])
continue
row = row[:len(row) - 1]
dat = []
for r in row:
r = {
k: '-' if type(v) == float and math.isnan(v) else v
for k, v in r.items()
}
r = {k: str(v) if type(v) == int else v for k, v in r.items()}
# r = {k: str(int(v)) if type(v) == float else v for k, v in r.items()}
r['o'] = str(int(r['o'])) if r['o'] != '-' else '-'
r['l'] = str(int(r['l'])) if r['l'] != '-' else '-'
r['h'] = str(int(r['h'])) if r['h'] != '-' else '-'
r['c'] = str(int(r['c'])) if r['c'] != '-' else '-'
r['v'] = str(int(r['v'])) if r['v'] != '-' else '-'
r['j'] = str(int(r['j'])) if r['j'] != '-' else '-'
r['b'] = str(r['b'])
r['e'] = str(r['e'])
r['t'] = str(int(r['t'])) if r['t'] != '-' else '-'
r['y'] = str(int(r['y'])) if r['y'] != '-' else '-'
r['k'] = str(int(r['k'])) if r['k'] != '-' else '-'
r['d'] = r['d'].split('_')[0].replace('/', '')
dat.append(r)
rows.extend(dat)
for r in rows:
for k, v in r.items():
if type(v) == float:
print(k, v, r)
dynamo.table('stock_price_history').insert(rows)
def web002_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
cd = event['cd']
print('cd={}'.format(cd))
brand = dao.table("stock_brands").find_by_key(cd)
thema = dao.table("stock_thema_ccode").find_by_key(cd)
if thema:
brand['thema'] = thema['nms']
return response({"v": "1", "brand": brand})
def run(self, dao: Dao, h1):
ys = YahooStock()
h1('ストップ高取得')
high_list = ys.get_dayly_limit(mode='high')
h1('ストップ安取得')
low_list = ys.get_dayly_limit(mode='low')
h1('stock_brands_high_lowコレクションに追加')
high_list.extend(low_list)
if high_list:
self.date_to_string(high_list)
dao.table('stock_brands_high_low').insert(high_list)
def tbl_report():
# os.environ["PRODUCTION_DAO"] = "True"
mongo = OldDao(DB())
dynamo = Dao()
cur_repo = mongo.table("stock_report_pre").find({}, {
'_id': 0
}).sort([('created_at', -1)])
repos = []
for repo in cur_repo:
repo['tweets'] = [t['id_str'] for t in repo['tweets']]
repos.append(repo)
dynamo.table("stock_report").delete_all()
dynamo.table("stock_report").insert(repos)
def tbl_tweet():
mongo = OldDao(DB())
os.environ["PRODUCTION_DAO"] = "True"
dynamo = Dao()
cur_twt = mongo.table("tweet").find({}).sort([('created_at', -1)])
ids = [t['id_str'] for t in cur_twt]
# ids = []
# with open('non_exists_tweet_ids7.txt', 'r') as f:
# ids = f.readlines()
# ids = [s.rstrip('\n') for s in ids]
# old_tweet = []
# for id in ids[3000:]:
# old_t = mongo.table("tweet").find_one({"id_str": id}, {'_id': 0, 'retweet': 0})
# old_tweet.append(old_t)
#
# # for r in old_tweet:
# # dynamo.table("tweet").delete_item_silent({"S": r['id_str']})
# dynamo.table("tweet").insert(old_tweet)
# print(len(old_tweet))
ids_bit = ids[15000:30000]
rets = dynamo.table("tweet").find_batch(ids_bit)
non_exists_ids = []
for id_str in ids_bit:
if len([r for r in rets if r["id_str"] == id_str]) == 0:
non_exists_ids.append(id_str)
if non_exists_ids:
with open("non_exists_tweet_ids8.txt", "a") as f:
f.write("\n".join(non_exists_ids) + "\n")
def run(self, dao: Dao, h1):
h1('twitter_friends取得')
friends = dao.table('twitter_friends').full_scan()
s3 = boto3.resource('s3').Bucket("kabupac.com")
h1('画像取得してBase64化')
err_list = []
friend = None
all_data = {}
piece_up = False
try:
for fr in friends:
friend = fr
url = fr['profile_image_url']
data = request.urlopen(url).read()
str_img = base64.b64encode(data).decode('utf-8')
# 個別
json = {"i":"data:image/{};base64,{}".format(self.get_ext(url), str_img)}
if piece_up:
s3.put_object(Key="prof/{}".format(fr['id_str']), Body=JSON.dumps(json))
all_data[fr['id_str']] = "data:image/{};base64,{}".format(self.get_ext(url), str_img)
except:
Util.print_stack_trace()
err_list.append(friend['id_str'])
h1('s3にアップロード')
s3.put_object(Key="prof/profimg.json", Body=JSON.dumps(all_data))
print(err_list)
h1('終了')
def task_exp_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
print(DateTimeUtil.str_now())
dao = Dao()
dao_edi = dao.table('stock_edinet')
edis = dao_edi.full_scan()
print('len(edi) = {}'.format(len(edis)))
for edi in edis:
if 'holders' not in edi:
edi['holders'] = 'non'
if 'holder_rate' not in edi:
edi['holder_rate'] = 'non'
if 'outstanding_share' not in edi:
edi['outstanding_share'] = 'non'
cd = edi['ccode']
nm = edi['name']
hr = edi['holder_rate']
ot = edi['outstanding_share']
tot = get(hr, 'tot')
tabs = '{}\t' * 6
print(
tabs.format(cd, nm, get(hr, 'unit'), get(tot, 1),
get(ot, 'hutuu_hakkou'), get(ot, 'unit')))
return ''
def run(self, dao: Dao, h1):
h1('twitter_friends_sum取得')
tbl_tfs = dao.table('twitter_friends_sum')
tfs_list = tbl_tfs.full_scan()
d = DateTimeUtil.now() - timedelta(days=15)
two_week_ago = d.strftime('%Y/%m/%d_00:00:00')
h1('集計開始')
for tfs in tfs_list:
for cd in tfs['rank']:
ds = tfs['rank'][cd]['ds']
n_ds = []
for r in ds:
if r['d'] >= two_week_ago:
n_ds.append(r)
if n_ds:
tfs['rank'][cd]['ds'] = n_ds
else:
print("tfs['rank'][cd] = 空. cd={}".format(cd))
del tfs['rank'][cd]
if tfs['rank']:
tbl_tfs.put_item(Item=tfs)
else:
print('空。 uid={}'.format(tfs['uid']))
tbl_tfs.delete_item_silent({"S": tfs['uid']})
h1('終了')
def task004_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
print(DateTimeUtil.str_now())
lambda_cnt = 6
dao = Dao()
brands = dao.table('stock_brands').full_scan()
ccode_list = [r['ccode'] for r in brands]
ccode_list.append('998407') # 日経平均
ccode_list.append('USDJPY') # ドル円
q = len(ccode_list) // lambda_cnt
chank_ccode = [ccode_list[i:i + q] for i in range(0, len(ccode_list), q)]
if len(chank_ccode) > lambda_cnt:
mod_chank = chank_ccode.pop(len(chank_ccode) - 1)
chank_ccode[-1].extend(mod_chank)
for i, ccodes in enumerate(chank_ccode, start=1):
func_name = 'arn:aws:lambda:ap-northeast-1:007575903924:function:Price{0:03d}'.format(
i)
cds = '_'.join(ccodes)
print(func_name)
boto3.client("lambda").invoke(FunctionName=func_name,
InvocationType="Event",
Payload=json.dumps({"cds": cds}))
return 0
def task006_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
print(DateTimeUtil.str_now())
dao = Dao()
ident_list = dao.table('stock_identify').full_scan()
data = {}
for ident in ident_list:
cd = ident['ccode']
dat = {} if cd not in data else data[cd]
if 'ns' in dat:
dat['ns'] = dat['ns'].split('_')
dat['ns'].append(ident['nm'])
else:
dat['ns'] = [ident['nm']]
if ident['main'] == 'y':
dat['n'] = ident['nm']
dat['ns'] = '_'.join(dat['ns'])
data[cd] = dat
ret = []
for d in data.items():
d[1]['c'] = d[0]
ret.append(d[1])
s3 = boto3.resource('s3').Bucket("kabupac.com")
s3.put_object(Key="suggest/dat.json", Body=JSON.dumps(ret))
return ''
def run(self, dao: Dao, h1):
ys = YahooStock()
h1('値上がり率取得')
rise_list = ys.get_rise_fall_price_rate(mode='rise',
period='w',
today=False)
h1('値下がり率取得')
fall_list = ys.get_rise_fall_price_rate(mode='fall',
period='w',
today=False)
h1('stock_brands_high_lowコレクションに追加')
rise_list.extend(fall_list)
if rise_list:
for i in range(len(rise_list)):
dat = rise_list[i]
dat['date'] = DateTimeUtil.strf_ymdhms(dat['date'])
dao.table('stock_brands_rise_fall').insert(rise_list)
def run(self, dao: Dao, h1):
h1('フォロー一覧削除')
self._delete_twitter_friends()
h1('フォロー一覧取得')
tw = TwitterInspector(Irebaburn)
friends = tw.get_friends()
h1('twitter_friendsコレクションに追加')
dao.table('twitter_friends').insert(friends)
h1('Job009をキック')
boto3.client('batch').submit_job(
jobName='Job009',
jobQueue=
"arn:aws:batch:ap-northeast-1:007575903924:job-queue/Job009_twitter_friends_profimg_create",
jobDefinition="Job009_twitter_friends_profimg_create:1")
def web001_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
page = event['p'] if 'p' in event else "1"
print('page = {}'.format(page))
tbl, uptime = get_available_table(dao)
if tbl == 'err':
return response({"v": "1", "pages": [], "repos": [], "upt": "err"})
repo_list_page = dao.table(tbl).find_by_key(page)
ccodes = repo_list_page["ccodes"].split(',')
repo_list_page["ccodes"] = ccodes
repos = dao.table("stock_report").find_batch(ccodes)
for repo in repos:
repo['tweets'] = str(len(repo['tweets']))
return response({"v": "1", "pages": repo_list_page, "repos": repos, "upt": uptime })
def web003_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
cd = event['cd']
last_tweet_id = event['lt'] if 'lt' in event else None
direction = event['dr'] if 'dr' in event else None
print('cd = {}, last_tweet_id = {}, direction = {}'.format(cd, last_tweet_id, direction))
repo = dao.table("stock_report").find_by_key(cd)
if not repo:
return response({"v": "1", "tweets": []})
tweet_ids = repo['tweets']
end_id = tweet_ids[0]
tweets = dao.table("tweet").find_batch(tweet_ids)
if tweets:
tweets = sorted(tweets, key=lambda r: r['created_at'], reverse=True)
tweets = tweets[:30]
target_tweets = []
more_flg = False
for tw in tweets:
if direction == 'new':
if last_tweet_id == tw['id_str']:
break
target_tweets.append(tw)
else:
if last_tweet_id == tw['id_str']:
more_flg = True
continue
elif more_flg:
target_tweets.append(tw)
if target_tweets:
if target_tweets[-1]["id_str"] == end_id:
target_tweets[-1]["e"] = 1
return response({"v": "1", "tweets": target_tweets})
def run(self, dao: Dao, h1):
h1('銘柄一覧取得')
stock_brands = dao.table('stock_brands').full_scan()
h1('銘柄ごとに時系列データを取得してDB登録(並列処理)')
ys = YahooStock()
def j010_historical(param):
last_plus_one = param[1] + timedelta(days=1)
str_last_plus_one_date = last_plus_one.strftime('%Y-%m-%d')
prices = ys.get_historical_prices(ccode=param[0], str_start_date=str_last_plus_one_date)
if not prices:
return AsyncWorkerException(param[0])
return prices
# @tracelog
def c010_historical(results, param):
if results:
price_list = PriceLogic().convert_history_yahoo_to_pacpac(results, param[0])
dao.table('stock_price_history').insert(price_list)
ccode_list = [r['ccode'] for r in stock_brands]
ccode_list.append('998407') # 日経平均
ccode_list.append('USDJPY') # ドル円
param_list = []
db_history = dao.table('stock_price_history')
for ccode in ccode_list:
history = db_history.get_query_sorted_max({"cd": ccode}, 1)
if history and history[0]:
str_last_day = history[0]['d']
last_day = DateTimeUtil.str_to_date(str_last_day)
param_list.append((ccode, last_day))
result = AsyncWorker(j010_historical, c010_historical, 5).go(param_list)
h1('終了')
err_list = result[1]
if err_list:
print('[失敗銘柄] len(err) = {}, err_targets = {}'.format(len(err_list), err_list))
def web004_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
ids = event['ids']
print('ids = {}'.format(ids))
id_list = ids.split('_')
friends = dao.table("twitter_friends").find_batch(id_list)
return response({"v": "1", "friends": friends})
def web011_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
key = event['k']
p = int(event['p']) - 1
print('key = {}'.format(key))
thema = dao.table('stock_thema_nm').find_by_key(key)
ccodes, repos = [], []
if thema:
ccodes = thema['ccodes'].split(',')
repos = dao.table('stock_report').find_batch(ccodes)
repos = repos[p * 10:min((p + 1) * 10, len(repos))]
for repo in repos:
repo['tweets'] = str(len(repo['tweets']))
return response({"v": "1", "cds": ccodes, "repos": repos})
def run(self, dao: Dao, h1):
h1('stock_report取得')
repos = dao.table('stock_report').full_scan()
d = DateTimeUtil.now() - timedelta(days=15)
two_week_ago = d.strftime('%Y/%m/%d_00:00:00')
friend_tweet = {}
ret_list = []
h1('ツイート集計開始')
for repo in repos:
cd = repo['ccode']
tw_ids = repo['tweets']
tweets = dao.table("tweet").find_batch(tw_ids)
tweets = [t for t in tweets if t['created_at'] > two_week_ago]
tweets = sorted(tweets,
key=lambda r: r['created_at'],
reverse=True)
for t in tweets:
u_id = t['user_id']
d = t['created_at']
ob = {'nm': repo['name'], 'ds': [{'d': d, 't': t['id_str']}]}
if u_id in friend_tweet:
if cd in friend_tweet[t['user_id']]:
friend_tweet[u_id][cd]['ds'].append({
'd': d,
't': t['id_str']
})
else:
friend_tweet[u_id][cd] = ob
else:
friend_tweet[t['user_id']] = {}
friend_tweet[t['user_id']][cd] = ob
for k in friend_tweet:
r = {'uid': k, 'rank': friend_tweet[k]}
ret_list.append(r)
dao.table('twitter_friends_sum').insert(ret_list)
print('hoge')
def task003_handler(event, context):
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
repos = dao.table('stock_report').full_scan()
print('len(repos)={}'.format(len(repos)))
repos = list({v['ccode']: v for v in repos}.values())
print('unique. len(repos)={}'.format(len(repos)))
repos = sorted(repos, key=itemgetter('last_updated_at'), reverse=True)[:min(len(repos),1000)]
list_pages = [{"p": "1", "ccodes": []}]
page_cnt = 1
for i, repo in enumerate(repos, start=1):
list_pages[page_cnt - 1]["ccodes"].append(repo["ccode"])
if i < len(repos) and i % 10 == 0:
page_cnt += 1
list_pages.append({"p": str(page_cnt), "ccodes": []})
for row in list_pages:
row['ccodes'] = ','.join(row["ccodes"])
notify_list_pages_update_to_condition(dao, '0,1')
pages1 = dao.table('stock_report_list_pages_1')
pages1.delete_all()
pages1.insert(list_pages)
notify_list_pages_update_to_condition(dao, '1,0')
pages2 = dao.table('stock_report_list_pages_2')
pages2.delete_all()
pages2.insert(list_pages)
notify_list_pages_update_to_condition(dao, '1,1')
return 0
def web005_handler(event, context):
""" 現在価格API """
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
logic = PriceLogic()
uptime = logic.get_now_price_update_time()
cds = event['cds']
print('cds = {}'.format(cds))
cd_list = cds.split('_')
prices = dao.table("stock_price_now").find_batch(cd_list)
return response({ "v": "1", "prices": prices, "now_price_uptime": uptime })
def run(self, dao: Dao, h1):
dao_brand = dao.table('stock_brands')
dao_repo = dao.table('stock_report')
dao_hl = dao.table('stock_brands_high_low')
h1('stock_brands取得')
brands = dao_brand.full_scan()
for brand in brands:
# stock_brandsにhigh_lowデータ追加
cd = brand['ccode']
high_list, low_list = self.collect_high_low_list(cd, dao_hl)
brand['hs'] = high_list
brand['ls'] = low_list
dao_brand.put_item(Item=brand)
# stock_reportに3日以内S高S安をマーク
repo = dao_repo.find_by_key(cd)
if repo:
mark = repo['mk'] if 'mk' in repo else {}
mark = self.mark_high_low('hs', high_list, mark)
mark = self.mark_high_low('ls', low_list, mark)
repo['mk'] = mark
dao_repo.put_item(Item=repo)
def web009_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
cd = event['cd']
print('cd = {}'.format(cd))
high_low = dao.table("stock_brands_high_low").find_query({'ccode': cd},
asc=False)
r = {}
if high_low:
high = [r for r in high_low if r['mode'] == 'high']
low = [r for r in high_low if r['mode'] == 'low']
r = {'h': high, 'l': low}
rise_fall = dao.table("stock_brands_rise_fall").find_query({'ccode': cd},
asc=False)
if rise_fall:
rise = [r for r in rise_fall if r['mode'] == 'rise']
fall = [r for r in rise_fall if r['mode'] == 'fall']
r['r'] = rise
r['f'] = fall
return response({"v": "1", "hl": r})
def web008_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
prices = dao.table("stock_price_history").find_query({'cd': event['cd']})
if prices:
if 'lt' in event and event['lt']:
prices = [r for r in prices if r['d'] > event['lt']]
else:
s = 0 if len(prices) <= 30 else abs(len(prices) - 30)
prices = prices[s:]
else:
prices = []
return response({"v": "1", "ps": prices})
def get_stock_brands_from_search_row(self, search_row, dao: Dao):
"""検索一覧行オブジェクトを基にstock_brandsを検索する"""
name = self.company_name_normarize(search_row['company_name'])
name_s = self.norm(name, blank_cut=False)
brand = dao.table('stock_brands').find_one({
'$or': [{
'name': re.compile(name)
}, {
'name': re.compile(name_s)
}, {
'identify': {
"$in": [name, name_s]
}
}]
})
return brand
def task001_handler(event, context):
print(DateTimeUtil.str_now())
dao = Dao()
# 前回取得した最後のtweetidを取得
dat = dao.table('condition').get_item(Key={'key': 'Task001_last_tweet_id'})
last_tweet_id = dat['val']
print('last_tweet_id: ', last_tweet_id)
tw = TwitterInspector(Irebaburn)
timeline = tw.get_list_timeline_rotate(list_name='株',
count=1000,
last_data_id=last_tweet_id)
print(timeline)
if timeline:
ids = [t['id_str'] for t in timeline]
# 重複取得されたレコードがある場合、削除して入れなおすため、一旦消す。
dao.table('tweet').delete_batch_silent(ids)
# ids = []
# for tweet in timeline:
# # 重複取得されたレコードがある場合、削除して入れなおすため、一旦消す。
# dao.table('tweet').delete_item_silent({'id_str': tweet['id_str']})
#
# ids.append(tweet['id_str'])
# 追加
dao.table('tweet').insert(timeline)
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")
# Task002呼び出し
boto3.client("lambda").invoke(
FunctionName=
"arn:aws:lambda:ap-northeast-1:007575903924:function:Task002",
InvocationType="Event",
Payload=json.dumps({"ids": ids}))
return ''
def web007_handler(event, context):
print(DateTimeUtil.str_now())
os.environ["PRODUCTION_DAO"] = "True" # TODO 本番向けテスト用
dao = Dao()
cd = event['cd']
print('cd = {}'.format(cd))
edi_dat = dao.table("stock_edinet").find_by_key(cd)
r = {}
if edi_dat:
r = {
'ho': edi_dat['holders'] if 'holders' in edi_dat else {},
'ra': edi_dat['holder_rate'] if 'holder_rate' in edi_dat else {},
'os': edi_dat['outstanding_share']
if 'outstanding_share' in edi_dat else {},
}
return response({"v": "1", "edi": r})
def run(self, dao: Dao, h1):
h1('s3からmecab_dic.xlsxをダウンロード')
df_dic: DataFrame = self.load_mecab_dic_dataframe()
h1('stock_identifyを取得')
identifies = dao.table('stock_identify').full_scan()
h1('データフレームにstock_identifyをマージする')
idx = len(df_dic) + 1
stock_row = {}
for ident in identifies:
nm = ident['nm']
stock_row[idx] = [
nm, None, None, 10, '名詞', '一般', '*', '*', '*', '*', '*', '*',
'*', '株'
]
idx += 1
df_stock = DataFrame().from_dict(stock_row, orient='index')
df_stock.columns = df_dic.columns.values
df_dic = df_dic.append(df_stock)
h1('PacPac.csvを出力する')
df_dic.to_csv('./PacPac.csv',
encoding='utf-8',
line_terminator='\n',
index=False,
header=False,
mode='w')
h1('s3にPacPac.csvをアップロード')
s3_bucket = boto3.resource('s3').Bucket('kabupac.system')
s3_bucket.upload_file('./PacPac.csv', 'PacPac.csv')
h1('Job005をキック')
boto3.client('batch').submit_job(
jobName='Job005',
jobQueue=
"arn:aws:batch:ap-northeast-1:007575903924:job-queue/Job005_mecab_lambda_build",
jobDefinition="Job005_mecab_lambda_build:1")
h1('終了')