[[DA series - Learn Python with Steem]](https://steemit.com/da-learnpythonwithsteem/@deanliu/da-series-learn-python-with-steem-00-coding) 是DA(@deanliu & @antonsteemit)關於「從Python程式語言實做Steem區塊鏈的入門」的系列,歡迎趕緊入列學習! 前情提要:[[DA series - Learn Python with Steem #12] Steem 小工具DIY #4 - 投票幫手](https://steemit.com/da-learnpythonwithsteem/@deanliu/da-series-learn-python-with-steem-12-steem-diy-4) https://steemitimages.com/0x0/https://cdn.steemitimages.com/DQmbeN1PpqQC2JE5HbpXatb3apUfFhe68fPLctT95FiiRHq/cover.png 第#13堂課,嗨,繼續Python學習之旅,今天繼續「Steem 小工具」系列! ## 前言 最近剛好幫忙別人寫了Steem帳號的「收支報表」小程式,就順便上來分享了。其實這個功能個人覺得滿實用的,有時候很想知道自己究竟在Steem上面是賺錢還是花錢,就可以透過這樣一個小程式來看看究竟收支有沒有平衡。 今天的內容只會涵蓋到`transfer`,也就是轉帳的收支計算,並不包含文章收益、`Claim Reward`等等。我在文末也會提供一支順便產生**非轉帳**紀錄報表的腳本,有興趣的朋友可以自己玩玩看。 ## Memo Key 其實只是從區塊鏈上抓下`transfer` 進出紀錄有點小看各位了,畢竟待會看程式就會發現其實方法還是陽春的爆蒐法,直接跑過以前到現在自己所有operation,找出屬於轉帳的項目,並進行紀錄。所以我決定加入**Memo Key**,來把整個程式功能變得更完整,大家也可以進一步了解memo key的權限。 大家應該都知道,用Steem轉帳時有一個欄位叫做`memo`,當作傳遞訊息的欄位。例如在使用一些voting bot的時後,就需要利用該欄位傳送要upvote的文章網址。在一般情況下,我們可以直接公開memo讓所有人看,也就是正大光明的transfer,但實際上STEEM也提供了大家加密此欄訊息的選項。在上一篇文章中我們介紹到了三種key,分別是`active`,`posting`,`memo`,其中每一種key其實都是一組**key pair**,含有一個**公鑰**以及一個**私鑰**。memo key當運作方式可以算是全部的key裡面最好理解的:當我傳送一個訊息想要加密時,我就拿別人的memo 公鑰進行加密。如此一來,就只有持有memo私鑰的人,也就是帳號所有人,有辦法還原這一則訊息。 如果有使用Steem上面其他的一些APP,其中包含資產轉帳的時候,常常都會提供加密這個選項,或是預設的通知就是加密過的。我們可能不會發現是因為登入Steemit錢包觀看轉帳memo時,有加密的都會自動解密了。但如果我們去看別人的wallet交易紀錄,就不難找到一些`#`開頭看似亂碼的 memo 訊息,這些就是被加密過的訊息。今天我們要做的Script就是可以解密這些訊息的。 ### 來寫程式囉~ 今天的主要程式架構也是透過`get_account_history()`來取得所有的歷史資料,再從中篩選`transfer`的項目來記錄。所以迴圈的架構跟之前的程式碼大同小異(程式碼可看[get_steem_transfer_record.py](https://github.com/antoncoding/Python-x-Steem-tutorial/blob/master/steem-python%20examples/get_steem_transfer_record.py))。我們先來看看執行這個腳本時需要給入的參數: ``` python get_steem_transfer_record.py antonsteemit 2018-01-03 2018-07-29 P5K_MY_PRIVATE_MEMO_KEY ``` 一共傳入了四個參數,分別是自己的帳號名稱、起訖日、還有最後是private memo key(memo私鑰的取得方法類似posting 私鑰,就是進入`wallet` -> `permission` 中點選`show private memo key`)。 第一部分的腳本就是讀取input參數,其中`datetime.datetime.strptime()`是用來**把字串轉為時間物件**,都成為時間物件之後就可以輕鬆比大小(明天>今天)。而最後一行中,我們建立Steem類別時跟上次一樣,多帶入了`keys=[]`的參數,並在其中放入我們從終端機讀取來的memo私鑰。這麼一來,我們的這個`s`待會就可以直接調用我們的memo key了。 ``` from steem import Steem from steem.amount import Amount from steem.account import Account import sys import datetime account_name = sys.argv[1] start_date = datetime.datetime.strptime(sys.argv[2], '%Y-%m-%d') end_date = datetime.datetime.strptime(sys.argv[3], '%Y-%m-%d') memoPrivkey = sys.argv[4] print('Searching Transfer Balance: @{} from {} to {}'.format(account_name, start_date, end_date)) target_account = Account(account_name) s = Steem(keys=[memoPrivkey]) ``` <br> 第二部分是我們熟悉的「抓取所有operation總量」,在這裡多做了一個開檔,準備好`output/transfer_{USERNAME}_memo.csv`作為待會要表格形式輸出的檔名,並且先把我的欄位寫好: ``` total_operations = latest_operation[0][0] num_iteration = int(total_operations/1000) + 1 # Num of times we have to request get_account_history transfer_file = open('output/transfers_{}_memo.csv'.format(account_name), 'w') transfer_file.write('Timestamp,Transfer Type,Dealer,STEEM,SBD,TX ID,memo\n') ``` <br> 第三部份就是迴圈了,對於迴圈的架構我們是熟悉的,跟之前不同的只在於我們現在不再搜尋`operation['type']=='post'`,而是要找尋`operation['type']=='transfer'`的交易紀錄。找到transfer的紀錄之後,首先我們一樣要用`strptime()`換算成時間,拿來跟起訖時間比大小,看有沒有在欲查詢的範圍內。太早的就skip掉、如果已經過期就可以用break結束迴圈。 接著我們要用交易的`to`和`from`來判斷這筆轉帳是轉出還是轉入。如果`from`是自己,表示是轉出,反之則是轉入。這裡我們還會用到Steem提供的一個很好用的Class叫做`Amount`。如果你把`operation['amount']`印出來看會發現他是`'0.05 STEEM'`這樣的字串格式,我們可以直接把這個字串丟入Amount()中,並用`.amount()`來呼叫它的**值**,並且用`.asset() `來判斷這個轉帳是屬於`'Steem'`還是`'SBD'`轉帳。我設計的表格是把Steem跟SBD分成兩欄紀錄,所以最後一段寫檔時,交易的是哪種貨幣的差異在於要寫在哪個欄位。 ``` for i in range(1, num_iteration+1): _index_from = i*1000 history = target_account.get_account_history(index=_index_from,limit=1000, order=1) for operation in history: if operation['type'] =='transfer': # Check if Transaction Timestamp is in the given period of time timestamp = datetime.datetime.strptime(operation['timestamp'],"%Y-%m-%dT%H:%M:%S") if timestamp < start_date: continue elif timestamp > end_date: break # Classify transfers into IN transfers and OUT transfers if operation['from'] == account_name: dealer = operation['to'] transfer_type = 'transfer to' amount = -1* Amount(operation['amount']).amount else: dealer = operation['from'] transfer_type = 'transfer from' amount = Amount(operation['amount']).amount # Decrypt Memo if the message is decrypted try: if operation['memo'][0] == '#': memo = s.decode_memo(operation['memo']).replace(',',' ').replace('\n',' ') else: memo = operation['memo'].replace(',',' ').replace('\n',' ') except: memo = '' # Write File if Amount(operation['amount']).asset =='STEEM': transfer_file.write('{},{},{},{},{},{},{}\n'.format(operation['timestamp'],transfer_type, dealer, amount,0,operation['trx_id'], memo)) else: transfer_file.write('{},{},{},{},{},{},{}\n'.format(operation['timestamp'],transfer_type, dealer,0, amount,operation['trx_id'], memo)) ``` ### decode_memo() `s.decode_memo()`是我們用來解密memo用的method。 可以看到我在一段`try - except`中,判斷memo是否是`'#'`開頭來決定要不要進行`s.decode_memo()`。這裡用`try`的理由是有可能有一般訊息以`'#'`開頭,這時候解密就會失敗,但是這種情況我也不希望程式終止,所以就忽略這種小錯誤繼續執行。 由於我們一開始創建`s = Steem(keys=[PRIVATE_MEMO])`時已經導入私鑰,這時解密就不會有任何問題。但若是你想要用這個程式來搜尋別人的帳號交易紀錄,在遇到解密環節時就會失敗,因為我們沒有別人的private key啊~ ![](https://cdn.steemitimages.com/DQmYvM9AEUDKYKZKwfndDD5BGp3gmo9siqBebRwHnLrK7PR/image.png) 執行結果滿漂亮的,秀一下吧~怎麼都是來廣告的人帶來的收入啊zzz ## 後記 就這樣我們就又寫完一個腳本啦!有沒有越來越上手的感覺呢~ 這裡給大家參考另外一個版本,並沒有用到private memo key,但是多了紀錄`delegation`以及`claim_reward`的紀錄,也就是更精確地記錄了所有資產包括**SP**的成長啦!有興趣的人也可以拿來玩玩看呦~ 程式碼:[get_steem_balance_sheet.py](https://github.com/antoncoding/Python-x-Steem-tutorial/blob/master/steem-python%20examples/get_steem_balance_sheet.py) ![class-377117_1280.jpg](https://cdn.steemitimages.com/DQmbTcEoAB7HRTxy11a1eDN8vREiGqxEMd7P3jJ3TGvGSTv/class-377117_1280.jpg) <sub>*image - pixabay*</sub>
post_id | 62,081,831 |
---|---|
author | deanliu |
permlink | da-series-learn-python-with-steem-13-steem-diy-5 |
category | da-learnpythonwithsteem |
json_metadata | {"app":"steemit\/0.1","image":["https:\/\/steemitimages.com\/0x0\/https:\/\/cdn.steemitimages.com\/DQmbeN1PpqQC2JE5HbpXatb3apUfFhe68fPLctT95FiiRHq\/cover.png"],"users":["deanliu","antonsteemit"],"tags":["da-learnpythonwithsteem","python","steem","cn-programming","cn"],"links":["https:\/\/steemit.com\/da-learnpythonwithsteem\/@deanliu\/da-series-learn-python-with-steem-00-coding","https:\/\/steemit.com\/da-learnpythonwithsteem\/@deanliu\/da-series-learn-python-with-steem-12-steem-diy-4","https:\/\/github.com\/antoncoding\/Python-x-Steem-tutorial\/blob\/master\/steem-python%20examples\/get_steem_transfer_record.py","https:\/\/github.com\/antoncoding\/Python-x-Steem-tutorial\/blob\/master\/steem-python%20examples\/get_steem_balance_sheet.py"],"format":"markdown"} |
created | 2018-09-10 12:46:45 |
last_update | 2018-09-10 12:46:45 |
depth | 0 |
children | 8 |
net_rshares | 12,190,634,611,823 |
last_payout | 2018-09-17 12:46:57 |
cashout_time | 1969-12-31 23:59:59 |
total_payout_value | 10.976 SBD |
curator_payout_value | 2.016 SBD |
pending_payout_value | 0.000 SBD |
promoted | 0.000 SBD |
body_length | 6,275 |
author_reputation | 726,291,750,173,620 |
root_title | "[DA series - Learn Python with Steem #13] Steem 小工具DIY #5 交易紀錄查詢" |
beneficiaries | [] |
max_accepted_payout | 1,000,000.000 SBD |
percent_steem_dollars | 10,000 |
author_curate_reward | "" |
voter | weight | wgt% | rshares | pct | time |
---|---|---|---|---|---|
abit | 0 | 274,400,244,722 | 100% | ||
jademont | 0 | 30,255,752,904 | 10% | ||
coinbitgold | 0 | 50,102,111,264 | 80% | ||
wongshiying | 0 | 145,761,245 | 100% | ||
blockchainbilly | 0 | 4,657,666,111 | 50% | ||
deanliu | 0 | 1,337,794,134,948 | 100% | ||
daynewright | 0 | 19,668,864,698 | 65% | ||
team-leibniz | 0 | 49,929,178,284 | 40% | ||
ace108 | 0 | 335,009,182,910 | 25% | ||
laoyao | 0 | 35,790,086,378 | 100% | ||
somebody | 0 | 1,326,658,596,092 | 100% | ||
midnightoil | 0 | 128,358,184,716 | 100% | ||
xiaohui | 0 | 720,923,351,590 | 100% | ||
oflyhigh | 0 | 2,572,740,274,978 | 100% | ||
xiaokongcom | 0 | 246,049,256 | 100% | ||
yulan | 0 | 15,106,488,386 | 100% | ||
rivalhw | 0 | 1,483,561,940,743 | 100% | ||
chinadaily | 0 | 140,762,553,655 | 100% | ||
helene | 0 | 896,444,828,165 | 100% | ||
ethansteem | 0 | 222,625,191,340 | 100% | ||
damarth | 0 | 722,024,315 | 3% | ||
rynow | 0 | 11,319,758,775 | 2% | ||
luneknight | 0 | 380,486,769 | 100% | ||
redes | 0 | 1,136,842,721,303 | 22% | ||
lalala | 0 | 146,361,097,762 | 100% | ||
devilwsy | 0 | 2,272,842,554 | 100% | ||
janiceting | 0 | 2,270,847,765 | 100% | ||
lydiachan | 0 | 28,333,019,799 | 100% | ||
jackhircus | 0 | 147,905,406,157 | 75% | ||
blackbunny | 0 | 121,999,312,676 | 100% | ||
elizacheng | 0 | 4,192,513,027 | 20% | ||
lingfei | 0 | 64,272,558,669 | 100% | ||
yyyy | 0 | 463,266,610 | 100% | ||
wylo | 0 | 609,881,658 | 100% | ||
jkkim | 0 | 76,723,970 | 10% | ||
nanosesame | 0 | 154,448,612,280 | 100% | ||
victorier | 0 | 91,364,277,476 | 100% | ||
tradealert | 0 | 26,445,435,880 | 100% | ||
steemitlotteries | 0 | 245,716,308 | 79% | ||
exec | 0 | 86,638,029,740 | 100% | ||
eval | 0 | 811,586,327 | 100% | ||
nationalpark | 0 | 2,298,553,819 | 100% | ||
katesmith | 0 | 151,224,637 | 65% | ||
that1consultant | 0 | 302,353,392 | 100% | ||
sanzo | 0 | 370,234,736 | 100% | ||
sweetieprincess | 0 | 30,692,681,089 | 50% | ||
travelgirl | 0 | 35,279,089,247 | 41% | ||
zeekcryptominer | 0 | 310,473,235 | 100% | ||
syh7758520 | 0 | 3,502,122,759 | 100% | ||
kona | 0 | 7,369,820,267 | 100% | ||
liangfengyouren | 0 | 1,500,623,044 | 50% | ||
idx | 0 | 15,041,077,814 | 100% | ||
unsw | 0 | 335,759,298 | 55% | ||
jiangchen | 0 | 6,072,386,489 | 100% | ||
ingdirect | 0 | 463,896,702 | 76% | ||
marcoharley1995 | 0 | 609,061,897 | 100% | ||
fr3eze | 0 | 82,171,545,459 | 50% | ||
abcbullion | 0 | 476,104,510 | 78% | ||
tvb | 0 | 31,487,704,114 | 50% | ||
steemabuse | 0 | 414,849,984 | 68% | ||
liumei | 0 | 254,479,395 | 35% | ||
dgorbunov | 0 | 547,171,039 | 100% | ||
ms8988 | 0 | 602,893,997 | 100% | ||
kettleandseagull | 0 | 10,769,890,256 | 100% | ||
xiaoshancun | 0 | 244,144,717 | 100% | ||
rollsman | 0 | 1,344,265,477 | 100% | ||
bobdos | 0 | 36,266,043,887 | 50% | ||
winniex | 0 | 3,524,738,311 | 10% | ||
kimxinfo | 0 | 39,063,582,138 | 100% | ||
windowglass | 0 | 14,187,063,007 | 50% | ||
cnbuddy | 0 | 5,977,367,023 | 0.1% | ||
chann | 0 | 6,356,360,055 | 38% | ||
santiagocazorla | 0 | 607,880,917 | 100% | ||
coindzs | 0 | 193,103,241 | 100% | ||
cathvanlael | 0 | 118,052,621 | 100% | ||
comingback | 0 | 256,356,244 | 12% | ||
youandme | 0 | 166,995,610 | 100% | ||
yjcps | 0 | 1,979,968,587 | 100% | ||
dariusbelpendua | 0 | 609,771,151 | 100% | ||
btccurrency1 | 0 | 51,697,135 | 100% | ||
ethanlee | 0 | 6,242,346,573 | 100% | ||
norinke | 0 | 372,338,143 | 100% | ||
eondas | 0 | 221,605,734 | 50% | ||
liuzg | 0 | 288,749,686 | 25% | ||
honoru | 0 | 751,161,525 | 25% | ||
lzg | 0 | 98,483,012 | 25% | ||
pgr | 0 | 290,874,827 | 25% | ||
china-mobile | 0 | 200,948,667 | 50% | ||
xiaoyuanwmm | 0 | 128,279,591 | 50% | ||
ybeyond | 0 | 103,538,418 | 50% | ||
team-cn | 0 | 139,028,387,958 | 50% | ||
weitz | 0 | 128,567,476 | 25% | ||
abcallen | 0 | 7,183,978,051 | 10% | ||
chick-fil-a | 0 | 291,927,798 | 50% | ||
redlobster | 0 | 291,240,880 | 50% | ||
fiveguys | 0 | 291,977,126 | 50% | ||
marcoy2j | 0 | 112,240,307 | 25% | ||
dealmoon | 0 | 292,462,137 | 50% | ||
moonbbs | 0 | 292,428,762 | 50% | ||
bonefish | 0 | 292,369,193 | 50% | ||
chilis | 0 | 292,387,755 | 50% | ||
olive-garden | 0 | 292,369,193 | 50% | ||
shine.wong | 0 | 1,163,795,540 | 100% | ||
shuxuan | 0 | 219,355,811 | 50% | ||
xiaowucw | 0 | 221,862,504 | 50% | ||
menshealthblog | 0 | 419,677,998 | 100% | ||
new-voices | 0 | 291,764,389 | 50% | ||
sunai | 0 | 476,543,585 | 100% | ||
nozuonodie | 0 | 291,237,858 | 50% | ||
yamibuy | 0 | 291,432,691 | 50% | ||
jidgabol | 0 | 154,883,427 | 100% | ||
julian2013 | 0 | 2,932,219,154 | 25% | ||
bunnymandy | 0 | 66,737,136 | 25% | ||
teamcn-weekly | 0 | 293,052,546 | 50% | ||
teamcn-news | 0 | 293,052,546 | 50% | ||
roy-rogers | 0 | 293,052,546 | 50% | ||
wenxuecity | 0 | 293,052,546 | 50% | ||
huaren | 0 | 293,054,144 | 50% | ||
mitbbs | 0 | 293,054,144 | 50% | ||
cn-doctors | 0 | 292,695,347 | 50% | ||
wongmanman | 0 | 139,668,041 | 25% | ||
sanketbhatkar | 0 | 335,039,339 | 100% | ||
kissfirer | 0 | 276,103,212 | 50% | ||
lkvictor2005 | 0 | 8,492,455,669 | 100% | ||
technos | 0 | 194,209,333 | 100% |
Ouch! 😭(this is my face right now). I wish you could translate this to English. I just loving coding tutorials
post_id | 62,100,696 |
---|---|
author | lexymaine |
permlink | re-deanliu-da-series-learn-python-with-steem-13-steem-diy-5-20180910t165005616z |
category | da-learnpythonwithsteem |
json_metadata | {"app":"steemit\/0.1","tags":["da-learnpythonwithsteem"]} |
created | 2018-09-10 16:50:12 |
last_update | 2018-09-10 16:50:12 |
depth | 1 |
children | 1 |
net_rshares | 0 |
last_payout | 2018-09-17 19:56:51 |
cashout_time | 1969-12-31 23:59:59 |
total_payout_value | 0.000 SBD |
curator_payout_value | 0.000 SBD |
pending_payout_value | 0.000 SBD |
promoted | 0.000 SBD |
body_length | 111 |
author_reputation | 2,210,265,497,970 |
root_title | "[DA series - Learn Python with Steem #13] Steem 小工具DIY #5 交易紀錄查詢" |
beneficiaries | [] |
max_accepted_payout | 1,000,000.000 SBD |
percent_steem_dollars | 10,000 |
Maybe you can watch those links. https://developers.steem.io/tutorials/#tutorials-python https://www.python.org/
post_id | 62,229,125 |
---|---|
author | shine.wong |
permlink | re-lexymaine-re-deanliu-da-series-learn-python-with-steem-13-steem-diy-5-20180912t035233223z |
category | da-learnpythonwithsteem |
json_metadata | {"tags":["da-learnpythonwithsteem"],"community":"steempeak","app":"steempeak"} |
created | 2018-09-12 03:52:42 |
last_update | 2018-09-12 03:52:42 |
depth | 2 |
children | 0 |
net_rshares | 0 |
last_payout | 2018-09-19 03:52:42 |
cashout_time | 1969-12-31 23:59:59 |
total_payout_value | 0.000 SBD |
curator_payout_value | 0.000 SBD |
pending_payout_value | 0.000 SBD |
promoted | 0.000 SBD |
body_length | 112 |
author_reputation | 1,373,339,077,399 |
root_title | "[DA series - Learn Python with Steem #13] Steem 小工具DIY #5 交易紀錄查詢" |
beneficiaries | [] |
max_accepted_payout | 1,000,000.000 SBD |
percent_steem_dollars | 10,000 |
感謝,太有用了~
post_id | 62,149,973 |
---|---|
author | kona |
permlink | re-deanliu-da-series-learn-python-with-steem-13-steem-diy-5-20180911t062541620z |
category | da-learnpythonwithsteem |
json_metadata | {"app":"steemit\/0.1","tags":["da-learnpythonwithsteem"]} |
created | 2018-09-11 06:25:42 |
last_update | 2018-09-11 06:25:42 |
depth | 1 |
children | 0 |
net_rshares | 0 |
last_payout | 2018-09-18 06:25:42 |
cashout_time | 1969-12-31 23:59:59 |
total_payout_value | 0.000 SBD |
curator_payout_value | 0.000 SBD |
pending_payout_value | 0.000 SBD |
promoted | 0.000 SBD |
body_length | 8 |
author_reputation | 39,204,266,552,701 |
root_title | "[DA series - Learn Python with Steem #13] Steem 小工具DIY #5 交易紀錄查詢" |
beneficiaries | [] |
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恭喜你!您的这篇文章入选 @justyy 今日 (2018-09-11) 榜单 [【优秀的文章】](https://steemit.com/cn/@justyy/--daily-cn-updates-cncnpower-downyy2018-09-11), 回复本条评论24小时内领赏,点赞本评论将支持 @dailychina 并增加将来您的奖赏。 Congratulations! This post has been selected by @justyy as today's (2018-09-11) [【Good Posts】](https://steemit.com/cn/@justyy/--daily-cn-updates-cncnpower-downyy2018-09-11), Steem On! Reply to this message in 24 hours to get rewards. Upvote this comment to support the @dailychina and increase your future rewards! ^_^
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root_title | "[DA series - Learn Python with Steem #13] Steem 小工具DIY #5 交易紀錄查詢" |
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✌✌ Posted using [Partiko Android](https://steemit.com/@partiko-android)
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permlink | deanliu-re-dailychina-re-da-series-learn-python-with-steem-13-steem-diy-5-20180911t080242-20180911t082257890z |
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看完了,明天试试!
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permlink | re-deanliu-da-series-learn-python-with-steem-13-steem-diy-5-20180911t083723124z |
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应该是上次作业的问题,但是我今天才想起来问~ 为什么voting_power不是实时的vp百分比,为什么要现在时间-最后点赞时间,红色划线部分的计算时什么意思?为什么要除86400又除5? ![image.png](https://files.steempeak.com/file/steempeak/shine.wong/Y9AH5Qjn-image.png)
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permlink | re-deanliu-da-series-learn-python-with-steem-13-steem-diy-5-20180913t082101570z |
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呼叫 @antonsteemit 😋 Posted using [Partiko Android](https://steemit.com/@partiko-android)
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