def __init__(self, ticker_list, seed_money, start_date = 1592294400, build_database = False):
self.gd = getData()
self.portfolio = {ticker: {'S':[0,0],'C':[0,0],'P':[0,0]} for ticker in ticker_list}
self.balance = seed_money
self.cost_basis = pd.DataFrame(columns=['Ticker', 'Type', 'Quantity', 'Cost', 'Time', 'Cash'])
if build_database:
self.build_database(start_date)
self.data = pd.read_csv('data.csv').drop(columns=['Unnamed: 0'])
self.data['datetime'] = self.data['datetime'].apply(lambda x: datetime.datetime.strptime( x,"%Y-%m-%d %H:%M:%S"))
def full(username):
data = api.getData(username)
# follows = data["follows"]["count"]
# followed_by = data["followed_by"]["count"]
# bio = data["biography"]
# images = []
# media = data["media"]["nodes"]
# for image in media:
# images.append({"image": image["display_src"], "likes": image["likes"]["count"]})
# formatted = {"follows": follows, "followed_by": followed_by, "bio": bio, "images": images}
return str(data)
def ratings(self):
if cherrypy.request.method == 'POST':
input_json = cherrypy.request.json
api.add(input_json)
api.print(input_json)
return 'Succes'
elif cherrypy.request.method == 'GET':
ratings = api.getData()[:100]
return ratings
elif cherrypy.request.method == 'DELETE':
api.delete()
return 'Succes'
def getUsage(type,phase,room):
""" type - percent -> percentage of your bandwidth used
- left -> quantity in GB of your bandwidth left
- usage -> quantity in GB of your bandwidth usage
- all -> summary of your usage
phase must be 1, 2 or 3
room must be an existing room in the block
"""
return api.getData(phase,room,datetime.now().month)
if type == "percent":
return "{:0.2f}%".format(pct)
if type == "left":
return "{:0.2f}GB".format(left)
if type =="usage":
return "{:0.2f}GB".format(usage/1024)
if type =="all":
return "Used :\t\t{:0.2f}GB ({:0.2f}%)\nLeft :\t\t{:0.2f}GB ({:0.2f}%)\nTotal :\t\t{:0.2f}GB".format(usage/1024,pct,left,100-pct,max/1024)
raise Exception('Must choose between "percent" and "left" ')
def upload():
# Get the name of the uploaded file
file = request.files['file']
username = request.form['username']
data = api.getData(username)
follows = data["follows"]["count"]
followers = data["followed_by"]["count"]
# Check if the file is one of the allowed types/extensions
if file and allowed_file(file.filename):
# Make the filename safe, remove unsupported chars
filename = secure_filename(file.filename)
# Move the file form the temporal folder to
# the upload folder we setup
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
# Redirect the user to the uploaded_file route, which
# will basicaly show on the browser the uploaded file
image = ClImage(file_obj=open('uploads/' + filename, 'rb'))
results = []
classes = []
probs = []
imageinfo = model.predict([image])
for i in imageinfo['outputs'][0]['data']['concepts']:
classes.append(i['name'])
probs.append(i['value'])
# results.append({'result': {'tag': {'classes': classes, 'probs': probs}}})
tag_pool = []
# for result in results:
# result = result["result"]["tag"]
# tag_pool.extend(result["classes"])
# users_omega['naimmiah08'] = results #needs to be changed for username
getTags = db.tags_pool.find()
for tags in getTags:
# print tags['tags']
tag_pool.extend(tags['tags'])
tag_pool = set(tag_pool)
# db.tags_pool.update({'id': 1}, { '$set' : {'tags': list(tag_pool)}})
image = []
for tag in tag_pool:
if tag in classes:
image.append(1)
idx = classes.index(tag)
image.append(probs[idx])
else:
image.append(0)
image.append(0)
# print follows, followers
image.append(follows >= follows_median)
image.append(followers >= followers_median)
likes = linearClassifier.predict(image)
# print len(image)
# print likes
# for user in users_omega:
# data = api.getData(user)
# follows = data["follows"]["count"]
# followed_by = data["followed_by"]["count"]
# bio = data["biography"]
# media = api.getPictures(user)
# media = data["media"]["nodes"]
# results = users_omega[user]
# i = 0
# for result in results:
# features = []
# result = result["result"]["tag"]
# item = media[i]
# likes.append(item["likes"]["count"])
# caption = item["caption"]
# classes = result["classes"]
# probs = result["probs"]
# for tag in tag_pool:
# if tag in classes:
# features.append(1)
# idx = classes.index(tag)
# features.append(probs[idx])
# else:
# features.append(0)
# features.append(0)
# features.append(follows)
# features.append(followed_by)
# following.append(follows)
# followers.append(followed_by)
# i = i + 1
# images.append(features)
# images = dataset[0]
# likes = dataset[1]
# print images
# print likes
return render_template("result.html",
image=filename,
username=username,
likes=ceil(likes[0]))
return "jam ciemny jest wschrod"
def lambda_handler(event,context):
s3_client = boto3.client('s3',
aws_access_key_id=config.AWS_ACCESS_KEY,
aws_secret_access_key=config.AWS_SECRET_KEY)
csv_obj = s3_client.get_object(Bucket = config.AWS_S3_BUCKET,Key = config.AWS_S3_FILE_KEY)
body = csv_obj['Body']
json_string = body.read().decode('utf-8')
staffRecord = json.loads(json_string)
body.close()
frame = {
"frameId" : event['frameId'],
"timestamp" : event['eventTimestamp'],
"imageUrl" : event['imageUrl'],
"site" : event['site']
}
recordList = []
eventList = api.getData(event)
for member in config.memberList:
flag = 0
for i in eventList:
if member in i:
flag = 1
if flag ==1:
continue
print(member)
if member not in staffRecord:
continue
judge = staffRecord[member]
# member last Record In not trigger this function
eventProfile = {
"frameId" : judge['frameId'],
"eventTimestamp" : judge['eventTimestamp'],
"name" : member,
"frameUrl":judge['frameUrl'],
"site" :"OUT"
}
staffRecord[eventProfile['name']] = eventProfile
api.writeData(staffRecord)
frame2 = {
"frameId" : judge['frameId'],
"timestamp" :judge['eventTimestamp'],
"imageUrl" : judge['frameUrl'],
"site" : "OUT"
}
behaviorDetection = {
"personId" : member,
"inTime" : 0,
"outTime" : judge['eventTimestamp'],
"isMember" :1,
"stayTime" : judge['eventTimestamp']- event['eventTimestamp'],
"coordinate_x" : 0.0,
"coordinate_y" : 0.0
}
fraudModel = {
"frame" :[frame2],
"behaviorDetection" : behaviorDetection
}
recordList.append(fraudModel)
if len(eventList) !=0:
print(len(eventList))
for count in range(len(eventList)):
eventProfile = {
"frameId" : eventList[count][0],
"eventTimestamp" : eventList[count][1],
"name" : eventList[count][2],
"frameUrl":eventList[count][3],
"site" :eventList[count][4]
}
print(eventProfile)
if eventProfile['name'] not in staffRecord:
staffRecord[eventProfile['name']] = eventProfile
api.writeData(staffRecord)
continue
recordProfile = staffRecord[eventProfile['name']]
print(recordProfile)
frame2 = {
"frameId" : recordProfile['frameId'],
"timestamp" :recordProfile['eventTimestamp'],
"imageUrl" : recordProfile['frameUrl'],
"site" : recordProfile['site']
}
if eventProfile['site'] == 'IN':
# last Time is "OUT"
# stay Time = in -out
#[frame,frame2] = [IN,OUT]
behaviorDetection = {
"personId" : eventProfile['name'],
"inTime" : eventProfile['eventTimestamp'],
"outTime" : recordProfile['eventTimestamp'],
"isMember" :1,
"stayTime" : recordProfile['eventTimestamp'] - eventProfile['eventTimestamp'],
"coordinate_x" : 0.0,
"coordinate_y" : 0.0
}
fraudModel = {
"frame" :[frame,frame2],
"behaviorDetection" : behaviorDetection
}
staffRecord[eventProfile['name']] = eventProfile
api.writeData(staffRecord)
recordList.append(fraudModel)
return recordList
tag_pool = []
users_omega = {}
images = []
likes = []
following = []
followers = []
max_clarifai_limit = 128
# predict with the model
for user in users:
print "Doing user " + user
data = api.getData(user)
media = api.getPictures(user)
# media = data["media"]["nodes"]
image_links = []
results = []
for mediaItem in media:
image_links.append(mediaItem["display_src"])
imageinfo = model.predict_by_url(mediaItem["display_src"])
classes = []
probs = []
for i in imageinfo['outputs'][0]['data']['concepts']:
classes.append(i['name'])
probs.append(i['value'])
results.append({'result': {'tag': {'classes': classes, 'probs': probs}}})
classes = []
probs = []
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 30 01:48:15 2020
@author: apolat
"""
from api import getData
import pandas as pd
from datetime import datetime
gd = getData()
ticker = 'AAPL'
def option_chain_summary(ticker, strikeCount = 5):
option_data = gd.OptionChain(ticker, strikeCount = strikeCount)
df = []
if option_data['callExpDateMap'].keys() == option_data['putExpDateMap'].keys():
for key in option_data['callExpDateMap']:
for strike in option_data['callExpDateMap'][key]:
l=[]
l.append(key)
l.append(option_data['callExpDateMap'][key][strike][0]['last'])
l.append(option_data['callExpDateMap'][key][strike][0]['totalVolume'])
l.append(float(strike))
l.append(option_data['putExpDateMap'][key][strike][0]['last'])
l.append(option_data['putExpDateMap'][key][strike][0]['totalVolume'])
def list():
ratings = api.getData()[:100]
return json.dumps(ratings)