Can't seem to figure out why this two conditional while loop is not working. It should iterate over JSON until it finds one where both 'producer' = producer given & transactions list is not empty.
It seems to be treating it as an OR, because to stops as soon as transactions are not empty.
# Performs a get info and looks for producer name, if found returns head_block num.
def get_testnetproducer_cpustats(producer):
# Get current headblock from testnet
current_headblock = headblock("testnet")
# Set producer to random name
blockproducer = "nobody"
transactions = []
while producer != blockproducer and len(transactions) == 0:
currentblock = getblock(current_headblock)
print(currentblock)
transactions = currentblock['transactions']
# Set producer of current block
blockproducer = currentblock['producer']
print(blockproducer)
# Deduct 1 from current block to check next block
current_headblock = current_headblock - 1
else:
return print(current_headblock)
Here is an example of the JSON:
{'timestamp': '2020-08-16T20:33:11.000', 'producer': 'waxtribetest', 'confirmed': 0, 'previous': '029abe7da7da6691681eb9e9c6310532ed313a93dca25026f7f3059d2765177f', 'transaction_mroot': '0000000000000000000000000000000000000000000000000000000000000000', 'action_mroot': '08bc38e7a4f1a832c3f9986e9c6553add7bbef8f0cfbe274772684b4f9d9a1a1', 'schedule_version': 4418, 'new_producers': None, 'producer_signature': 'SIG_K1_JzwAkDxatuPKBYVfP718JmNMTt5YGAR1E9Z5bBB4T3HtJxvDZq6SZU7HdjNK5SLigSAByWesPmPRjznWPF634czzRtbVwJ', 'transactions': [], 'id': '029abe7ec71e2af243c499b22036c350143d46054dec1659b4c3847650d7a3d2', 'block_num': 43695742, 'ref_block_prefix': 2996421699}
As #barny noted, use or instead of and:
while producer != blockproducer or len(transactions) == 0:
Related
Is it possible to use multi processing in Django on a request.
#so if I send a request to http://127.0.0.1:8000/wallet_verify
def wallet_verify(request):
walelts = botactive.objects.all()
#here I check if the user want to be included in the process or not so if they set it to True then i'll include them else ignore.
for active in walelts:
check_active = active.active
if check_active == True:
user_is_active = active.user
#for the ones that want to be included I then go to get their key data.
I need to get both api and secret so then I loop through to get the data from active users.
database = Bybitapidatas.objects.filter(user=user_is_active)
for apikey in database:
apikey = apikey.apikey
for apisecret in database:
apisecret = apisecret.apisecret
#since I am making a request to an exchange endpoint I can only include one API and secret at a time . So for 1 person at a time this is why I want to run in parallel.
for a, b in zip(list(Bybitapidatas.objects.filter(user=user_is_active).values("apikey")), list(Bybitapidatas.objects.filter(user=user_is_active).values("apisecret"))):
session =spot.HTTP(endpoint='https://api-testnet.bybit.com/', api_key=a['apikey'], api_secret=b['apisecret'])
#here I check to see if they have balance to open trades if they have selected to be included.
GET_USDT_BALANCE = session.get_wallet_balance()['result']['balances']
for i in GET_USDT_BALANCE:
if 'USDT' in i.values():
GET_USDT_BALANCE = session.get_wallet_balance()['result']['balances']
idx_USDT = GET_USDT_BALANCE.index(i)
GET_USDTBALANCE = session.get_wallet_balance()['result']['balances'][idx_USDT]['free']
print(round(float(GET_USDTBALANCE),2))
#if they don't have enough balance I skip the user.
if round(float(GET_USDTBALANCE),2) < 11 :
pass
else:
session.place_active_order(
symbol="BTCUSDT",
side="Buy",
type="MARKET",
qty=10,
timeInForce="GTC"
)
How can I run this process in parallel while looping through the database to also get data for each individual user.
I am still new to coding so hope I explained that it makes sense.
I have tried multiprocessing and pools but then I get that the app has not started yet and I have to run it outside of wallet_verify is there a way to do it in wallet_verify
and when I send the Post Request.
Any help appreciated.
Filtering the Database to get Users who have set it to True
Listi - [1,3](these are user ID's Returned
processess = botactive.objects.filter(active=True).values_list('user')
listi = [row[0] for row in processess]
Get the Users from the listi and perform the action.
def wallet_verify(listi):
# print(listi)
database = Bybitapidatas.objects.filter(user = listi)
print("---------------------------------------------------- START")
for apikey in database:
apikey = apikey.apikey
print(apikey)
for apisecret in database:
apisecret = apisecret.apisecret
print(apisecret)
start_time = time.time()
session =spot.HTTP(endpoint='https://api-testnet.bybit.com/', api_key=apikey, api_secret=apisecret)
GET_USDT_BALANCE = session.get_wallet_balance()['result']['balances']
for i in GET_USDT_BALANCE:
if 'USDT' in i.values():
GET_USDT_BALANCE = session.get_wallet_balance()['result']['balances']
idx_USDT = GET_USDT_BALANCE.index(i)
GET_USDTBALANCE = session.get_wallet_balance()['result']['balances'][idx_USDT]['free']
print(round(float(GET_USDTBALANCE),2))
if round(float(GET_USDTBALANCE),2) < 11 :
pass
else:
session.place_active_order(
symbol="BTCUSDT",
side="Buy",
type="MARKET",
qty=10,
timeInForce="GTC"
)
print ("My program took", time.time() - start_time, "to run")
print("---------------------------------------------------- END")
return HttpResponse("Wallets verified")
Verifyt is what I use for the multiprocessing since I don't want it to run without being requested to run. also initialiser starts apps for each loop
def verifyt(request):
with ProcessPoolExecutor(max_workers=4, initializer=django.setup) as executor:
results = executor.map(wallet_verify, listi)
return HttpResponse("done")
```
So I'm looking for a way to speed up the output of the following code, calling google's natural language API:
tweets = json.load(input)
client = language.LanguageServiceClient()
sentiment_tweets = []
iterations = 1000
start = timeit.default_timer()
for i, text in enumerate(d['text'] for d in tweets):
document = types.Document(
content=text,
type=enums.Document.Type.PLAIN_TEXT)
sentiment = client.analyze_sentiment(document=document).document_sentiment
results = {'text': text, 'sentiment':sentiment.score, 'magnitude':sentiment.magnitude}
sentiment_tweets.append(results)
if (i % iterations) == 0:
print(i, " tweets processed")
sentiment_tweets_json = [json.dumps(sentiments) for sentiments in sentiment_tweets]
stop = timeit.default_timer()
The issue is the tweets list is around 100k entries, iterating and making calls one by one does not produce an output on a feasible timescale. I'm exploring potentially using asyncio for parallel calls, although as I'm still a beginner with Python and unfamiliar with the package, I'm not sure if you can make a function a coroutine with itself such that each instance of the function iterates through the list as expected, progressing sequentially. There is also the question of managing the total number of calls made by the app to be within the defined quota limits of the API. Just wanted to know if I was going in the right direction.
I use this method for concurrent calls:
from concurrent import futures as cf
def execute_all(mfs: list, max_workers: int = None):
"""Excecute concurrently and mfs list.
Parameters
----------
mfs : list
[mfs1, mfs2,...]
mfsN = {
tag: str,
fn: function,
kwargs: dict
}
.
max_workers : int
Description of parameter `max_workers`.
Returns
-------
dict
{status, result, error}
status = {tag1, tag2,..}
result = {tag1, tag2,..}
error = {tag1, tag2,..}
"""
result = {
'status': {},
'result': {},
'error': {}
}
max_workers = len(mfs)
with cf.ThreadPoolExecutor(max_workers=max_workers) as exec:
my_futures = {
exec.submit(x['fn'], **x['kwargs']): x['tag'] for x in mfs
}
for future in cf.as_completed(my_futures):
tag = my_futures[future]
try:
result['result'][tag] = future.result()
result['status'][tag] = 0
except Exception as err:
result['error'][tag] = err
result['result'][tag] = None
result['status'][tag] = 1
return result
Where each result returns indexed by a given tag (if matters to you identify the which call return which result) when:
mfs = [
{
'tag': 'tweet1',
'fn': process_tweet,
'kwargs': {
'tweet': tweet1
}
},
{
'tag': 'tweet2',
'fn': process_tweet,
'kwargs': {
'tweet': tweet2
}
},
]
results = execute_all(mfs, 2)
While async is one way you could go, another that might be easier is using the multiprocessing python functionalities.
from multiprocessing import Pool
def process_tweet(tweet):
pass # Fill in the blanks here
# Use five processes at once
with Pool(5) as p:
processes_tweets = p.map(process_tweet, tweets, 1)
In this case "tweets" is an iterator of some sort, and each element of that iterator will get passed to your function. The map function will make sure the results come back in the same order the arguments were supplied.
Looking for a second set of eyes here. I cannot figure out why the following loop will not continue past the first iteration.
The 'servicestocheck' sqlalchemy query returns 45 rows in my test, but I cannot iterate through the results like I'm expecting... and no errors are returned. All of the functionality works on the first iteration.
Anyone have any ideas?
def serviceAssociation(current_contact_id,perm_contact_id):
servicestocheck = oracleDB.query(PORTAL_CONTACT).filter(
PORTAL_CONTACT.contact_id == current_contact_id
).order_by(PORTAL_CONTACT.serviceID).count()
print(servicestocheck) # returns 45 items
servicestocheck = oracleDB.query(PORTAL_CONTACT).filter(
PORTAL_CONTACT.contact_id = current_contact_id
).order_by(PORTAL_CONTACT.serviceID).all()
for svc in servicestocheck:
#
# Check to see if already exists
#
check_existing_association = mysqlDB.query(
CONTACTTOSERVICE).filter(CONTACTTOSERVICE.contact_id ==
perm_contact_id,CONTACTTOSERVICE.serviceID ==
svc.serviceID).first()
#
# If no existing association
#
if check_existing_association is None:
print ("Prepare Association")
assoc_contact_id = perm_contact_id
assoc_serviceID = svc.serviceID
assoc_role_billing = False
assoc_role_technical = False
assoc_role_commercial = False
if svc.contact_type == 'Billing':
assoc_role_billing = True
if svc.contact_type == 'Technical':
assoc_role_technical = True
if svc.contact_type == 'Commercial':
assoc_role_commercial = True
try:
newAssociation = CONTACTTOSERVICE(
assoc_contact_id, assoc_serviceID,
assoc_role_billing,assoc_role_technical,
assoc_role_commercial)
mysqlDB.add(newAssociation)
mysqlDB.commit()
mysqlDB.flush()
except Exception as e:
print(e)
This function is called from a script, and it is called from within another loop. I can't find any issues with nested loops.
Ended up being an issue with SQLAlchemy ORM (see SqlAlchemy not returning all rows when querying table object, but returns all rows when I query table object column)
I think the issue is due to one of my tables above does not have a primary key in real life, and adding a fake one did not help. (I don't have access to the DB to add a key)
Rather than fight it further... I went ahead and wrote raw SQL to move my project along.
This did the trick:
query = 'SELECT * FROM PORTAL_CONTACT WHERE contact_id = ' + str(current_contact_id) + 'ORDER BY contact_id ASC'
servicestocheck = oracleDB.execute(query)
Been trying to extract websocket information from Bitfinex websocket client service. Below is the code. The script works fine when I search for under 30 crypto pairs (ie. "p" or "PAIRS" has 30 elements) but if I try to go higher the script never gets to the "save_data" co-routine. Any ideas why this could be happening.
I modified the script from: "https://mmquant.net/replicating-orderbooks-from-websocket-stream-with-python-and-asyncio/", kudos to Mmquant for making the code available and giving an awesome script description.
import aiohttp
import asyncio
import ujson
from tabulate import tabulate
from copy import deepcopy
import pandas as pd
from openpyxl import load_workbook
import datetime
from datetime import datetime
import numpy as np
from collections import OrderedDict
from time import sleep
"""
Load the workbook to dump the API data as well as instruct it to not generate a new sheet.
The excel work book must:
1. Be of the type ".xlsx", only this because the load_workbook function was set to call a specific sheet with .xlsx format. This can be changed.
2. Must have the worksheets, "apidata" and "Test". This can also be adjusted below.
3. The excel workbooks name is "bitfinexws.xlsx". This can be changed below.
4. The excel spreadsheet is in the same folder as this script.
"""
book = load_workbook('bitfinexwsasync.xlsx') #.xlsx Excel spreadsheet that will be used for the placement and extracting of data.
apdat = book['Sheet1'] #Assign a variable to the sheet where the trade ratios will be put. This is case sensitive.
#The next 3 lines are critical to allow overwriting of data and not creating a new worksheet when using panda dataframes.
writer = pd.ExcelWriter('bitfinexwsasync.xlsx', engine='openpyxl')
writer.book = book
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
#Get a list of all the ratios and add the standard trade url: "https://api.bitfinex.com/v1/book/" before the ratios.
burl = 'https://api.bitfinex.com/v1/book/' #This is the standard url for retrieving trade ratios, the pair symbol must be added after this.
sym = pd.read_json('https://api.bitfinex.com/v1/symbols',orient='values') #This is a list of all the symbols on the Bitfinex website.
p=[]
p=[0]*len(sym)
for i in range(0,len(sym)):
p[i]=sym.loc[i,0]
p=tuple(p)
m=len(p) #Max number of trade ratios to extract for this script. Script cannot run the full set of 105 trade ratios, it will time-out.
p=p[0:m]
d=[]
e=[]
j=[]
"""
NOTE:
The script cannot run for the full 105 pairs, it timesout and becomes unresponsive.
By testig the stability it was found that calling 21 pairs per script at a refresh rate of 5seconds did not allow for any time-out problems.
"""
print('________________________________________________________________________________________________________')
print('')
print('Bitfinex Websocket Trading Orderbook Extraction - Asynchronous.')
print('There are a total of ', len(sym), ' trade ratios in this exchange.')
print('Only ',m,' trading pairs will be extracted by this script, namely:',p)
print('Process initiated at',datetime.now().strftime('%Y-%m-%d %H:%M:%S'),'.') #Tells me the date and time that the data extraction was intiated.
print('________________________________________________________________________________________________________')
print('')
# Pairs which generate orderbook for.
PAIRS = p
# If there is n pairs we need to subscribe to n websocket channels.
# This the subscription message template.
# For details about settings refer to https://bitfinex.readme.io/v2/reference#ws-public-order-books.
SUB_MESG = {
'event': 'subscribe',
'channel': 'book',
'freq': 'F0', #Adjust for real time
'len': '25',
'prec': 'P0'
# 'pair': <pair>
}
def build_book(res, pair):
""" Updates orderbook.
:param res: Orderbook update message.
:param pair: Updated pair.
"""
global orderbooks
# Filter out subscription status messages.
if res.data[0] == '[':
# String to json
data = ujson.loads(res.data)[1]
# Build orderbook
# Observe the structure of orderbook. The prices are keys for corresponding count and amount.
# Structuring data in this way significantly simplifies orderbook updates.
if len(data) > 10:
bids = {
str(level[0]): [str(level[1]), str(level[2])]
for level in data if level[2] > 0
}
asks = {
str(level[0]): [str(level[1]), str(level[2])[1:]]
for level in data if level[2] < 0
}
orderbooks[pair]['bids'] = bids
orderbooks[pair]['asks'] = asks
# Update orderbook and filter out heartbeat messages.
elif data[0] != 'h':
# Example update message structure [1765.2, 0, 1] where we have [price, count, amount].
# Update algorithm pseudocode from Bitfinex documentation:
# 1. - When count > 0 then you have to add or update the price level.
# 1.1- If amount > 0 then add/update bids.
# 1.2- If amount < 0 then add/update asks.
# 2. - When count = 0 then you have to delete the price level.
# 2.1- If amount = 1 then remove from bids
# 2.2- If amount = -1 then remove from asks
data = [str(data[0]), str(data[1]), str(data[2])]
if int(data[1]) > 0: # 1.
if float(data[2]) > 0: # 1.1
orderbooks[pair]['bids'].update({data[0]: [data[1], data[2]]})
elif float(data[2]) < 0: # 1.2
orderbooks[pair]['asks'].update({data[0]: [data[1], str(data[2])[1:]]})
elif data[1] == '0': # 2.
if data[2] == '1': # 2.1
if orderbooks[pair]['bids'].get(data[0]):
del orderbooks[pair]['bids'][data[0]]
elif data[2] == '-1': # 2.2
if orderbooks[pair]['asks'].get(data[0]):
del orderbooks[pair]['asks'][data[0]]
async def save_data():
""" Save the data to the excel spreadsheet specified """
#NOTE, Adjusted this for every 5 seconds, ie "await asyncio.sleep(10)" to "await asyncio.sleep(5)"
global orderbooks
while 1:
d=[]
e=[]
j=[]
await asyncio.sleep(5)
for pair in PAIRS:
bids2 = [[v[1], v[0], k] for k, v in orderbooks[pair]['bids'].items()]
asks2 = [[k, v[0], v[1]] for k, v in orderbooks[pair]['asks'].items()]
bids2.sort(key=lambda x: float(x[2]), reverse=True)
asks2.sort(key=lambda x: float(x[0]))
table2 = [[*bid, *ask] for (bid, ask) in zip(bids2, asks2)]
d.extend(table2)
e.extend([0]*len(table2))
e[len(e)-len(table2)]=pair
j.extend([0]*len(d))
j[0]=datetime.now().strftime('%Y-%m-%d %H:%M:%S')
s = pd.DataFrame(d, columns=['bid:amount', 'bid:count', 'bid:price', 'ask:price', 'ask:count', 'ask:amount'])
r = pd.DataFrame(e, columns=['Trade pair'])
u = pd.DataFrame(j, columns=['Last updated'])
z = pd.concat([s, r, u], axis=1, join_axes=[s.index])
z.to_excel(writer, 'Sheet1', startrow=0, startcol=0, index=False)
writer.save()
print('Update completed at',datetime.now().strftime('%Y-%m-%d %H:%M:%S'),'.')
async def get_book(pair, session):
""" Subscribes for orderbook updates and fetches updates. """
#print('enter get_book, pair: {}'.format(pair))
pair_dict = deepcopy(SUB_MESG) #Allows for changes to a made within a variable.
pair_dict.update({'pair': pair}) #Updates the dictionary SUB_MESG with the new pair to be evaluated. Will be added to the end of the dictionary.
async with session.ws_connect('wss://api.bitfinex.com/ws/2') as ws:
asyncio.ensure_future(ws.send_json(pair_dict)) #This was added and replaced "ws.send_json(pair_dict)" as Ubuntu python required a link to asyncio for this function.
while 1: #Loops infinitely.
res = await ws.receive()
print(pair_dict['pair'], res.data) # debug
build_book(res, pair)
async def main():
""" Driver coroutine. """
async with aiohttp.ClientSession() as session:
coros = [get_book(pair, session) for pair in PAIRS]
# Append coroutine for printing orderbook snapshots every 10s.
coros.append(save_data())
await asyncio.wait(coros)
orderbooks = {
pair: {}
for pair in PAIRS
}
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
My code is as follows:
import json
def reformat(importscompanies):
#print importscompanies
container={}
child=[]
item_dict={}
for name, imports in importscompanies.iteritems():
item_dict['name'] = imports
item_dict['size'] = '500'
child.append(dict(item_dict))
container['name'] = name
container['children'] = child
if __name__ == '__main__':
raw_data = json.load(open('data/bricsinvestorsfirst.json'))
run(raw_data)
def run(raw_data):
raw_data2 = raw_data[0]
the_output = reformat(raw_data2)
My issue is, the code isn't going through the whole file. It's only outputting one entry. Why is this? Am I rewriting something and do I need another dict that appends with every loop?
Also, it seems as though the for loop is going through the iteritems for each dict key. Is there a way to make it pass only once?
The issue is indeed
raw_data2 = raw_data[0]
I ended up creating an iterator to access the dict values.
Thanks.
Lastly, I'm hoping my final Json file looks this way, using the data I provided above:
{'name': u'name', 'children': [{'name': u'500 Startups', 'size': '500'}, {'name': u'AffinityChina', 'size': '500'}]}
Try this. Though your sample input and output data don't really give many clues as to where the "name" fields should come from. I've assumed you wanted the name of the original item in your list.
original_json = json.load(open('data/bricsinvestorsfirst.json'),'r')
response_json = {}
response_json["name"] = "analytics"
# where your children list will go
children = []
size = 500 # or whatever else you want
# For each item in your original list
for item in original_json:
children.append({"name" : item["name"],
"size" : size})
response_json["children"] = children
print json.dumps(response_json,indent=2)
"It's only outputting one entry" because you only select the first dictionary in the JSON file when you say raw_data2 = raw_data[0]
Try something like this as a starting point (I haven't tested/ran it):
import json
def run():
with open('data/bricsinvestorsfirst.json') as input_file:
raw_data = json.load(input_file)
children = []
for item in raw_data:
children.append({
'name': item['name'],
'size': '500'
})
container = {}
container['name'] = 'name'
container['children'] = children
return json.dumps(container)
if __name__ == '__main__':
print run()