I'm trying to write an app that you can input tasks. When you input these tasks, you give a time too, for example 10:30 AM. When that time comes, you get alerted of the task. I figured out the alerting part, but I just don't know how to call a function at some point in the future without interrupting the rest of the code while its running. Kivy's built in clock function seems to small scale for something like this.
What I tried to do that interrupted the rest of the code:
def submit_button(self):
#account id and auth token omitted
account_id = ""
auth_token = ""
client = Client(account_id, auth_token)
self.name_in_q = self.ids.the_name.text
self.time = f'{self.ids.time_spinner_1.text}:{self.ids.time_spinner_2.text} {self.ids.time_spinner_3.text}'
waiting_for_task = True
while waiting_for_task:
tz_hous = pytz.timezone('America/Chicago')
datetime_houston = datetime.now(tz_hous)
ds = datetime_houston.strftime("%H:%M")
t = time.strptime(ds, "%H:%M")
ds = time.strftime("%I:%M %p", t)
if(ds == self.time):
client.messages.create(
body = f"Complete your task: {self.name_in_q} for {self.time}",
# phone numbers omitted
from_ = "+",
to = "+"
)
break
Related
So here is what I am trying to do I am trying to get my Twitter bot to give maths answers to users using WolframAlpha API
here is what problem I am facing
as people will mention my Twitter username to active the bot example: #twitterusername 2+2
the WolframAlpha will take it as the whole input #twitterusername 2+2 which will give me the error I want it to ignore the username
here is my code
def respondToTweet(file='tweet_ID.txt'):
last_id = get_last_tweet(file)
mentions = api.mentions_timeline(last_id, tweet_mode='extended')
if len(mentions) == 0:
return
new_id = 0
logger.info("someone mentioned me...")
for mention in reversed(mentions):
logger.info(str(mention.id) + '-' + mention.full_text)
new_id = mention.id
status = api.get_status(mention.id)
if '#Saketltd01' in mention.full_text.lower():
logger.info("Responding back with QOD to -{}".format(mention.id))
client = wolframalpha.Client(app_id)
query = mention.full_text.lower()
rest = client.query(query)
answer = next(rest.results).text
Wallpaper.get_wallpaper(answer)
media = api.media_upload("created_image.png")
logger.info("liking and replying to tweet")
api.create_favorite(mention.id)
api.update_status('#' + mention.user.screen_name, mention.id,
media_ids=[media.media_id])
put_last_tweet(file, new_id)
def main():
respondToTweet()
When you take the whole input remember to strip it down by simply removing your username from the actual input string and then perform the mathematical operation on it:
myUsername = "#my_username"
equation = userInput.lstrip(myUsername)
perform_desired_operation_on(equation) // User defined function
It seems every celery questions are like 5 years to 10 years old and
utilizing old celery versions and design patterns
Using celery version 5.0.5
I have a celery task that queries the database and then performs some computations/calculations on each row of the rows returned by query
Issue is this task is taking several minutes to complete because of the thousands of rows returned from query so i am trying to distribute to multiple celery workers in parallel
#celery.task()
def send_sms(to, body):
from twilio.rest import Client
account_sid = os.environ["ACCOUNT_SID"]
auth_token = os.environ["AUTH_TOKEN"]
from_ = os.environ["NUMBER"]
client = Client(
account_sid,
auth_token,
)
message = client.messages.create(
to=to,
from_=from_,
body=body,
)
#celery.task()
def notify_users():
session = create_session()
query = session.query(Rentals).filter(Rentals.enabled == True)
today = datetime.now()
for q in query:
if q.returned_date is not None:
if (today - q.returned_date).total_seconds() < q.rental_period:
continue
user = session.query(Users).filter(User.id == q.user_id).one()
to = send_notification_get_to.get(q.notification_method)(user)
body = f"sending email to {user.email}"
send_sms.delay(to, body)
What will be the best way to distribute these tasks to multiple workers as opposed to letting one worker run it for several minutes which gets slower exponentially as the number of rows returned increase from a few thousands to tens of thousands
I had the same use case earlier, what I did was
I paginated the query (broke the records into smaller chunks) and each page was processed by a celery worker
You can also try using different worker pools like gevent, eventlet pools for better performance.
the code would look like this.
#celery.task()
def send_sms(to, body):
from twilio.rest import Client
account_sid = os.environ["ACCOUNT_SID"]
auth_token = os.environ["AUTH_TOKEN"]
from_ = os.environ["NUMBER"]
client = Client(
account_sid,
auth_token,
)
message = client.messages.create(
to=to,
from_=from_,
body=body,
)
#celery.task()
def notify_range_of_users(num_chunks, skip):
session = create_session()
today = datetime.now()
query = session.query(Rentals).filter(Rentals.enabled == True)
paginated_query = query.limit(num_chunks).offset(skip * num_chunks)
for q in paginated_query:
if q.returned_date is not None:
if (today - q.returned_date).total_seconds() < q.rental_period:
continue
user = session.query(Users).filter(User.id == q.user_id).one()
to = send_notification_get_to.get(q.notification_method)(user)
body = f"sending email to {user.email}"
send_sms.delay(to, body)
#celery.task()
def notify_users():
session = create_session()
today = datetime.now()
query = session.query(Rentals).filter(Rentals.enabled == True)
total_rentals = query.count()
# each chunk will contain, 100 rows/objects
num_chunks = 100
# find total number of chunks
quo, remainder = divmod(total_rentals, num_chunks)
# each job will contain a certain number of chunks
jobs = quo
if remainder:
jobs = jobs + 1
skip = 0
for i in range(jobs):
notify_range_of_users.delay(num_chunks, skip)
# increment skip to go the next page
skip = skip + 1
I am pretty new to coding and aws chalice. I tried writing a code that gets messages from trading-view and executes orders depending on the signals.
I tested the code locally and everything worked fine, but when I test the Rest API I get the following error:
{"message":"Missing Authentication Token"}
I set up my credentials via "aws configure" as explained here: https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html
I also created a config.txt file in my aws folder and checked my settings via "aws configure get" and they were fine.
The index function in the beginning worked too, so there should be a problem within my code?
I changed some values and cut some functions and the strategy part out, but the code looks somewhat like this:
from chalice import Chalice
from datetime import datetime
from binance.client import Client
from binance.enums import *
import ccxt
exchange = ccxt.binance({
'apiKey': 'KEY',
'secret': 'SECRET',
'enableRateLimit': True,
'options': {
'defaultType': 'future',
},
})
def buy_order(quantity, symbol, order_type = ORDER_TYPE_MARKET,side=SIDE_BUY,recvWindow=5000):
try:
print("sending order")
order = client.futures_create_order(symbol = symbol, type = order_type, side = side, quantity = quantity,recvWindow=recvWindow)
print(order)
except Exception as e:
print("an exception occured - {}".format(e))
return False
return True
app = Chalice(app_name='tradingview-webhook-alert')
indicator1 = "x"
indicator2 = "y"
TRADE_SYMBOL = "Test123"
in_position = False
def diff_time(time1, time2):
fmt = '%Y-%m-%dT%H:%M:%SZ'
tstamp1 = datetime.strptime(time1, fmt)
tstamp2 = datetime.strptime(time2, fmt)
if tstamp1 > tstamp2:
td = tstamp1 - tstamp2
else:
td = tstamp2 - tstamp1
td_mins = int(round(td.total_seconds() / 60))
return td_mins
#app.route('/test123', methods=['POST'])
def test123():
global indicator1, indicator2
request = app.current_request
message = request.json_body
indicator = message["indicator"]
price = message["price"]
value = message["value"]
if indicator == "indicator1":
indicator1 = value
if indicator == "indicator2":
indicator2 = value
if in_position == False:
if (indicator1 >123) & (indicator2 < 321):
balance = exchange.fetch_free_balance()
usd = float(balance['USDT'])
TRADE_QUANTITY = (usd / price)*0.1
order_succeeded = buy_order(TRADE_QUANTITY, TRADE_SYMBOL)
if order_succeeded:
in_position = True
return {"test": "123"}
I tested it locally with Insomnia and tried the Rest API link there and in my browser, both with the same error message. Is my testing method wrong or is it the code? But even then, why isn't the Rest API link working, when I include the index function from the beginning again? If I try the index function from the beginning, I get the {"message": "Internal server error"} .
This is probably a very very basic question but I couldn't find an answer online.
Any help would be appreciated!
I am not pretty sure if that helps you because I don't really understand your question but:
You are using a POST-request which will not be executed by opening a URL.
Try something like #app.route('/test123', methods=['POST', 'GET']) so that if you just open the URL, it will execute a GET-request
Some more information:
https://www.w3schools.com/tags/ref_httpmethods.asp
I'm struggling to get a Lambda function working. I have a python script to access twitter API, pull information, and export that information into an excel sheet. I'm trying to transfer python script over to AWS/Lambda, and I'm having a lot of trouble.
What I've done so far: Created AWS account, setup S3 to have a bucket, and poked around trying to get things to work.
I think the main area I'm struggling is how to go from a python script that I'm executing via local CLI and transforming that code into lambda-capable code. I'm not sure I understand how the lambda_handler function works, what the event or context arguments actually mean (despite watching a half dozen different tutorial videos), or how to integrate my existing functions into Lambda in the context of the lambda_handler, and I'm just very confused and hoping someone might be able to help me get some clarity!
Code that I'm using to pull twitter data (just a sample):
import time
import datetime
import keys
import pandas as pd
from twython import Twython, TwythonError
import pymysql
def lambda_handler(event, context):
def oauth_authenticate():
twitter_oauth = Twython(keys.APP_KEY, keys.APP_SECRET, oauth_version=2)
ACCESS_TOKEN = twitter_oauth.obtain_access_token()
twitter = Twython(keys.APP_KEY, access_token = ACCESS_TOKEN)
return twitter
def get_username():
"""
Prompts for the screen name of targetted account
"""
username = input("Enter the Twitter screenname you'd like information on. Do not include '#':")
return username
def get_user_followers(username):
"""
Returns data on all accounts following the targetted user.
WARNING: The number of followers can be huge, and the data isn't very valuable
"""
#username = get_username()
#import pdb; pdb.set_trace()
twitter = oauth_authenticate()
datestamp = str(datetime.datetime.now().strftime("%Y-%m-%d"))
target = twitter.lookup_user(screen_name = username)
for y in target:
target_id = y['id_str']
next_cursor = -1
index = 0
followersdata = {}
while next_cursor:
try:
get_followers = twitter.get_followers_list(screen_name = username,
count = 200,
cursor = next_cursor)
for x in get_followers['users']:
followersdata[index] = {}
followersdata[index]['screen_name'] = x['screen_name']
followersdata[index]['id_str'] = x['id_str']
followersdata[index]['name'] = x['name']
followersdata[index]['description'] = x['description']
followersdata[index]['date_checked'] = datestamp
followersdata[index]['targeted_account_id'] = target_id
index = index + 1
next_cursor = get_followers["next_cursor"]
except TwythonError as e:
print(e)
remainder = (float(twitter.get_lastfunction_header(header = 'x-rate-limit-reset')) \
- time.time())+1
print("Rate limit exceeded. Waiting for:", remainder/60, "minutes")
print("Current Time is:", time.strftime("%I:%M:%S"))
del twitter
time.sleep(remainder)
twitter = oauth_authenticate()
continue
followersDF = pd.DataFrame.from_dict(followersdata, orient = "index")
followersDF.to_excel("%s-%s-follower list.xlsx" % (username, datestamp),
index = False, encoding = 'utf-8')
I modified the code published on smbrown.wordpress.com which can extract the top tracks using the Last.fm API as below:
#!/usr/bin/python
import time
import pylast
import re
from md5 import md5
user_name = '*******'
user_password = '*******'
password_hash = pylast.md5("*******")
api_key = '***********************************'
api_secret = '****************************'
top_tracks_file = open('top_tracks_wordle.txt', 'w')
network = pylast.LastFMNetwork(api_key = api_key, api_secret = api_secret, username = user_name, password_hash = password_hash)
# to make the output more interesting for wordle viz.
# run against all periods. if you just want one period,
# delete the others from this list
time_periods = ['PERIOD_12MONTHS', 'PERIOD_6MONTHS', 'PERIOD_3MONTHS', 'PERIOD_OVERALL']
# time_periods = ['PERIOD_OVERALL']
#####
## shouldn't have to edit anything below here
#####
md5_user_password = md5(user_password).hexdigest()
sg = pylast.SessionKeyGenerator(network) #api_key, api_secret
session_key = sg.get_session_key(user_name, md5_user_password)
user = pylast.User(user_name, network) #api_key, api_secret, session_key
top_tracks = []
for time_period in time_periods:
# by default pylast returns a seq in the format:
# "Item: Andrew Bird - Fake Palindromes, Weight: 33"
tracks = user.get_top_tracks(period=time_period)
# regex that tries to pull out only the track name (
# for the ex. above "Fake Palindromes"
p = re.compile('.*[\s]-[\s](.*), Weight: [\d]+')
for track in tracks:
m = p.match(str(track))
**track = m.groups()[0]** <-----------Here---------------
top_tracks.append(track)
# be nice to last.fm's servers
time.sleep(5)
top_tracks = "\n".join(top_tracks)
top_tracks_file.write(top_tracks)
top_tracks_file.close()
When the script is run to the position where marked by " <-----------Here--------------", I got a error message :".... line 46, in
track = m.groups()[0]
AttributeError: 'NoneType' object has no attribute 'groups'"
I just stuck here for over a day, and do not know what to do next. Can anyone give me some clue about this problem?
Apparently some track names do not match your regex, so match() returns None. Catch the exception and examine track.