def main():
prompt()
command_str = raw_input(">>>>")
if command_str == "1":
time_duration, switch = plan_trip_now()
print "time_duration and ifswitch:"
print time_duration
print switch
push_content = "Whether to switch?" + str(switch) + "; ETA: " + str(
time_duration / 60) + "mins"
client_sns = aws.getClient('sns', 'us-east-1')
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=push_content,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
})
elif command_str == "2":
subscribe()
elif command_str == "3":
pass
return 0
def imageRec(image):
'''
imageRecognition takes in a image file
Return the labels and texts detected from the image
The labels and texts are ordered by confidence
:image: file path
:rtype: str()
'''
with open(image, 'rb') as f:
pic = f.read()
rek = aws.getClient('rekognition', 'us-east-1')
res1 = rek.detect_labels(Image={"Bytes":
pic}) # , MaxLabels=10, MinConfidence=.7)
res2 = rek.detect_text(Image={"Bytes":
pic}) # , MaxLabels=10, MinConfidence=.7)
label = res1["Labels"]
text = res2["TextDetections"]
# print label
label.sort(reverse=True, key=lambda x: x['Confidence'])
# print label
labellst = []
for item in label:
if item != '':
labellst.append(item["Name"])
# print nmlst
# print text
txt = []
text.sort(reverse=True, key=lambda x: x["Confidence"])
for item in text:
if item != '' and item["DetectedText"].lower() not in txt:
txt.append(item["DetectedText"].lower())
labellst = labellst[:10]
txt = txt[:10]
d = {"labels": labellst, "texts": txt}
return genTxt(d)
def main(trainingData):
# Create an instance of S3 class ( S3.py)
s3 = S3.S3(trainingData)
# Upload data to S3
s3.uploadData()
# AWS ML client
aml = aws.getClient('machinelearning','us-east-1')
# Check if aml has permission to the s3 bucket
#s3.checkBucketPolicy()
# Create training and evaluation data source
trainingId = createDataSourceFromS3(aml,"training",0,70,True)
evaluationId = createDataSourceFromS3(aml,"evaluation",70,100,False)
# create ml model
mlModelId = "ml-mtaPredictions-{0}".format(TIMESTAMP)
# NOTE : In ML model target field( label) should be 0 or 1 , any other label will give error
aml.create_ml_model(MLModelId=mlModelId, MLModelType="BINARY", TrainingDataSourceId=trainingId)
evalMlId = "ev-ml-mtaPredictions-{0}".format(TIMESTAMP)
# create an ml evaluation using the evaluation data set
aml.create_evaluation(EvaluationId=evalMlId, MLModelId=mlModelId, EvaluationDataSourceId=evaluationId)
# Poll to check if model is ready
pollUntilReady(aml,evalMlId)
print("done")
evaluation = aml.get_evaluation(EvaluationId=evalMlId)
print("Performance metric on the evaluation dataset: Binary AUC: " + str(evaluation['PerformanceMetrics']['Properties']))
def main():
prompt()
command_str = raw_input(">>>>")
if command_str == "1":
time_duration, switch = plan_trip_now()
print "time_duration and ifswitch:"
print time_duration
print switch
push_content = "Whether to switch?" + str(switch) +"; ETA: "+ str(time_duration/60)+ "mins"
client_sns = aws.getClient('sns','us-east-1')
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=push_content,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
}
)
elif command_str == "2":
subscribe()
elif command_str == "3":
pass
return 0
def subscribe():
print "Please enter your phone number:"
cell_num = raw_input(">>>>")
client_sns = aws.getClient('sns', 'us-east-1')
response = client_sns.subscribe(
TopicArn='arn:aws:sns:us-east-1:486153037878:mtaSub',
Protocol='sms',
Endpoint=cell_num)
def build_model(data_s3_url, schema_fn, recipe_fn, name, train_percent=70):
ml = aws.getClient('machinelearning', 'us-east-1')
(train_ds_id, test_ds_id) = create_data_sources(ml, data_s3_url, schema_fn,
train_percent, name)
ml_model_id = create_model(ml, train_ds_id, recipe_fn, name)
eval_id = create_evaluation(ml, ml_model_id, test_ds_id, name)
return ml_model_id
def subscribe():
print "Please enter your phone number:"
cell_num = raw_input(">>>>")
client_sns = aws.getClient('sns','us-east-1')
response = client_sns.subscribe(
TopicArn='arn:aws:sns:us-east-1:486153037878:mtaSub',
Protocol='sms',
Endpoint= cell_num)
def run(self):
global open_fan_edison
global ml_flag
app_serverPORT=5555
while True:
try:
app_soc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
app_soc.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
app_soc.bind(('0.0.0.0',app_serverPORT))
app_soc.listen(5)
app_conn,app_addr = app_soc.accept()
print 'New app connection'
while True:
try:
app_conn.send("Ready"+'\r\n')
app_conn.settimeout(2.0)
app_msg1 = app_conn.recv(1024)
app_conn.settimeout(None)
if(app_msg1.find('OK') == -1):
app_conn.close()
break
except socket.timeout:
app_conn.close()
break
if (int(ml_flag) ==1):
snsResource = aws.getResource('sns', 'us-east-1')
snsClient = aws.getClient('sns', 'us-east-1')
subject_content = 'Alert! '
message_content = subject_content
topic = snsResource.Topic('**********')
snsClient.publish(TopicArn='**********', Message=message_content, Subject=subject_content)
alert_msg="Dangerous! Temperature Now is:"+str(temp_now)
print ("Alert!!",alert_msg)
app_conn.send(alert_msg+'\r\n')
print "msg has been sent"
app_msg2 = app_conn.recv(1024)
appThread = threading.Lock()
if appThread.acquire():
ml_flag = 0 #tempory 0 for one minute
appThread.release()
#If it can run to this sentence, it means that app has sent a msg,
#Change here to open fan
if (app_msg2.find('OPEN') == -1):
app_conn.close()
break
open_fan_edison =1
print ("change in ML in FAN FLAG", open_fan_edison)
print ("Got connection from",app_addr)
print (app_msg2)
except:
app_soc.close()
app_conn.close()
continue
def build_model(data_s3_url, schema_fn, recipe_fn, name, train_percent=70):
"""Creates all the objects needed to build an ML Model & evaluate its quality.
"""
ml = aws.getClient('machinelearning', 'us-east-1')
(train_ds_id, test_ds_id) = create_data_sources(ml, data_s3_url, schema_fn,
train_percent, name)
ml_model_id = create_model(ml, train_ds_id, recipe_fn, name)
eval_id = create_evaluation(ml, ml_model_id, test_ds_id, name)
return ml_model_id
def main(fileName):
# connect to kinesis
kinesis = aws.getClient('kinesis','us-east-1')
data = [] # list of dictionaries will be sent to kinesis
with open(fileName,'rb') as f:
dataReader = csv.DictReader(f)
for row in dataReader:
kinesis.put_record(StreamName=KINESIS_STREAM_NAME, Data=json.dumps(row), PartitionKey='0')
break
f.close()
def main(fileName):
# connect to kinesis
kinesis = aws.getClient('kinesis','us-east-1')
data = [] # list of dictionaries will be sent to kinesis
with open(fileName,'rb') as f:
dataReader = csv.DictReader(f)
for row in dataReader:
kinesis.put_record(StreamName=KINESIS_STREAM_NAME, Data=json.dumps(row), PartitionKey='0')
break
f.close()
def main():
dynamodb = aws.getResource('dynamodb','us-east-1')
snsClient = aws.getClient('sns','us-east-1')
snsResource = aws.getResource('sns','us-east-1')
dynamoTable = dynamodb.Table(DYNAMODB_TABLE_NAME)
# Get list of all stopIds
stations = buildStationssDB()
while True:
prompt()
sys.stdout.write(">select a command : ")
userIn = sys.stdin.readline().strip()
if len(userIn) < 1 :
print "Command not recognized"
else:
if userIn == '1':
sys.stdout.write(">Enter source : ")
sourceStop = sys.stdin.readline().strip()
if sourceStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
sys.stdout.write(">Enter destination : ")
destinationStop = sys.stdin.readline().strip()
if destinationStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
sys.stdout.write(">Type N for uptown, S for downtown: ")
direction = sys.stdin.readline().strip()
# Validate direction
if direction not in ['N','S']:
sys.stdout.write(">Invalid direction. Enter a valid direction")
sys.stdout.flush()
continue
###############################
# YOUR CODE HERE #
###############################
elif userIn == '2':
sys.stdout.write(">Enter phonenumber")
phoneNumber = sys.stdin.readline().strip()
###############################
# YOUR CODE HERE #
###############################
else:
sys.exit()
def main(fileName):
# connect to kinesis
with open('../utils/key.txt', 'rb') as keyfile:
APIKEY = keyfile.read().rstrip('\n')
keyfile.close()
feedurl = 'http://datamine.mta.info/mta_esi.php?feed_id=1&key='
feedurl = feedurl + APIKEY
feed = gtfs_realtime_pb2.FeedMessage()
try:
with contextlib.closing(urllib2.urlopen(feedurl)) as response:
d = feed.ParseFromString(response.read())
except (urllib2.URLError, google.protobuf.message.DecodeError) as e:
print "Error while connecting to mta server " + str(e)
row = []
nearest = 0
#nearest_tripId =''
for entity in feed.entity:
if entity.vehicle and entity.vehicle.trip.trip_id and entity.vehicle.stop_id:
vehicleData = entity.vehicle
tripId = vehicleData.trip.trip_id
stopId = vehicleData.stop_id
if (tripId[7] == '1') and (stopId[3] == 'S') and (int(
str(stopId)[0:3]) <= 117) and (int(str(stopId)[0:3]) >
nearest):
nearest = int(stopId[0:3])
nearest_tripId = tripId
push = int(str(nearest_tripId)[0:6]) / 100
row.append(push)
row.append(datetime.today().weekday())
with open(fileName, 'a') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(row)
kinesis = aws.getClient('kinesis', 'us-east-1')
data = [] # list of dictionaries will be sent to kinesis
with open(fileName, 'rb') as f:
dataReader = csv.DictReader(f)
for row in dataReader:
#print row
kinesis.put_record(StreamName=KINESIS_STREAM_NAME,
Data=json.dumps(row),
PartitionKey='0')
break
f.close()
def main(fileName):
print "1"
# connect to kinesis
kinesis = aws.getClient('kinesis','us-east-1')
data = [] # list of dictionaries will be sent to kinesis
print "2"
with open(fileName,'rb') as f:
print "hello"
dataReader = csv.DictReader(f)
for row in dataReader:
print row
#kinesis.put_record(StreamName=mtaStream, Data=json.dumps(row), PartitionKey='0')
break
f.close()
def main(fileName):
# Ideally you should write code to get data from mta feed,clean it up and send to kinesis
# Kinesis triggers a lambda function whcih will send the data to aws ML for classification
# But for demo I am going to use the csv file given and in aws lambda we remove the label before
# sending it to aws ml
# connect to kinesis
kinesis = aws.getClient('kinesis','us-east-1')
data = [] # list of dictionaries will be sent to kinesis
with open(fileName,'rb') as f:
dataReader = csv.DictReader(f)
for row in dataReader:
kinesis.put_record(StreamName=KINESIS_STREAM_NAME, Data=json.dumps(row), PartitionKey='0')
break
f.close()
def main(fileName):
# connect to kinesis
with open('../utils/key.txt', 'rb') as keyfile:
APIKEY = keyfile.read().rstrip('\n')
keyfile.close()
feedurl = 'http://datamine.mta.info/mta_esi.php?feed_id=1&key='
feedurl = feedurl + APIKEY
feed = gtfs_realtime_pb2.FeedMessage()
try:
with contextlib.closing(urllib2.urlopen(feedurl)) as response:
d = feed.ParseFromString(response.read())
except (urllib2.URLError, google.protobuf.message.DecodeError) as e:
print "Error while connecting to mta server " +str(e)
row=[]
nearest = 0
#nearest_tripId =''
for entity in feed.entity:
if entity.vehicle and entity.vehicle.trip.trip_id and entity.vehicle.stop_id:
vehicleData = entity.vehicle
tripId = vehicleData.trip.trip_id
stopId = vehicleData.stop_id
if (tripId[7] == '1') and (stopId[3] == 'S') and (int(str(stopId)[0:3]) <= 117) and (int(str(stopId)[0:3]) > nearest):
nearest = int(stopId[0:3])
nearest_tripId = tripId
push = int(str(nearest_tripId)[0:6])/100
row.append(push)
row.append(datetime.today().weekday())
with open(fileName, 'a') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(row)
kinesis = aws.getClient('kinesis','us-east-1')
data = [] # list of dictionaries will be sent to kinesis
with open(fileName,'rb') as f:
dataReader = csv.DictReader(f)
for row in dataReader:
#print row
kinesis.put_record(StreamName=KINESIS_STREAM_NAME, Data=json.dumps(row), PartitionKey='0')
break
f.close()
def main():
prompt()
command_str = raw_input(">>>>")
if command_str == "1":
start_stop_id = raw_input("Enter your source station: ")
destination_stop_id = raw_input("Enter your destination station")
if (int(start_stop_id) < int(destination_stop_id)):
direction = 'S'
start_stop_id = start_stop_id + 'S'
destination_stop_id = destination_stop_id + 'S'
else:
direction = 'N'
start_stop_id = start_stop_id + 'N'
destination_stop_id = destination_stop_id + 'N'
time_duration, switch = plan_trip_now(start_stop_id,
destination_stop_id, direction)
print "time_duration and whether to take Line 2,3:"
print time_duration
print switch
push_content = "Whether to take Train 2/3?" + str(
switch) + "; ETA: " + str(int(time_duration / 6) / 10) + "mins"
client_sns = aws.getClient('sns', 'us-east-1')
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=push_content,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
})
elif command_str == "2":
subscribe()
elif command_str == "3":
pass
return 0
def main():
with open('./key.txt', 'rb') as keyfile:
APIKEY = keyfile.read().rstrip('\n')
keyfile.close()
newRecord = mtaUpdates.mtaUpdates(APIKEY)
station = prompt()
data = getUpdates(newRecord,station)
if len(data) < 1:
print "Sorry, there is no available prediction at this time"
return
# connect to kinesis
kinesis = aws.getClient('kinesis','us-east-1')
# with open('trainRecordFinal.csv','rb') as f:
# dataReader = csv.DictReader(f)
# for row in dataReader:
# print row
response = kinesis.describe_stream(
StreamName=KINESIS_STREAM_NAME,
)
print data
kinesis.put_record(StreamName=KINESIS_STREAM_NAME, Data=json.dumps(data), PartitionKey='0')
def main():
prompt()
command_str = raw_input(">>>>")
if command_str == "1":
start_stop_id = raw_input("Enter your source station: ")
destination_stop_id = raw_input("Enter your destination station")
if (int(start_stop_id) < int(destination_stop_id)):
direction = 'S'
start_stop_id = start_stop_id + 'S'
destination_stop_id = destination_stop_id + 'S'
else:
direction = 'N'
start_stop_id = start_stop_id + 'N'
destination_stop_id = destination_stop_id + 'N'
time_duration, switch = plan_trip_now(start_stop_id, destination_stop_id, direction)
print "time_duration and whether to take Line 2,3:"
print time_duration
print switch
push_content = "Whether to take Train 2/3?" + str(switch) +"; ETA: "+ str(int(time_duration/6)/10)+ "mins"
client_sns = aws.getClient('sns','us-east-1')
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=push_content,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
}
)
elif command_str == "2":
subscribe()
elif command_str == "3":
pass
return 0
# Hayato Nakamura
# hn2357
# Copyright 2020 Hayato Nakamura
import sys, time
import numpy as np
from collections import OrderedDict
from decimal import *
from datetime import datetime
from PIL import Image
sys.path.append('amazon/')
import aws
dynamodb = aws.getResource('dynamodb', 'us-east-1')
s3 = aws.getClient('s3', 'us-east-1')
rekog = aws.getClient('rekognition', 'us-east-1')
def check_picture(attributes = ['ALL']):
name = 'temp_5_20' + '.jpg'
response = rekog.detect_faces(
Image={
"S3Object": {
"Bucket": 'hayatopia',
"Name": name,
}
},
Attributes=attributes,
)
for face in response['FaceDetails']:
print(face)
boundingBox = face['BoundingBox']
# Creating aws machine learning model
# This program uploads the finalData.csv file to S3, and used it as a data source to train a binary
# classification model
import time,sys,random
import boto3
import S3
sys.path.append('utils')
import aws
client = aws.getClient('machinelearning','us-east-1')
TIMESTAMP = time.strftime('%Y-%m-%d-%H-%M-%S')
S3_BUCKET_NAME = "finalBaochan"
S3_FILE_NAME = 'training_instrumental.csv'
S3_URI = "s3://{0}/{1}".format(S3_BUCKET_NAME, S3_FILE_NAME)
DATA_SCHEMA =open("Instrumentalness.csv.schema").read()
DATA_SOURCE_ID = 'Instrumentalness0'
ML_MODEL_ID = 'Instrumentalness0'
#EVALUATION_ID = 'lab5Evaluation_13'
testvariable = S3.S3(S3_FILE_NAME)
#testvariable.uploadData()
response = client.create_data_source_from_s3(
DataSourceId=DATA_SOURCE_ID,
DataSourceName='finaldataInstrumentalness',
DataSpec={
# boto3 s3
import boto3
import aws
from boto3.dynamodb.conditions import Key,Attr
# ml
import predict
from sqlalchemy import *
# initial s3
s3 = aws.getResource('s3', '')
dynamodb = aws.getResource('dynamodb', "")
curtime = calendar.timegm(time.gmtime())
"""sns"""
client = aws.getClient("","")
topic_arn = ""
TIMEPOINTER = None
expire_table = dynamodb.Table('fruit-expire')
def download_img(filename):
s3.meta.client.download_file('iotfruit',filename, filename)
def delete_img(filename):
os.remove(filename)
s3.Object('iotfruit',filename).delete()
def __init__(self):
self.ml = aws.getClient('machinelearning', 'us-east-1')
import boto3
import mraa
import aws
import math
temp_pin_number=1
temp = mraa.Aio(temp_pin_number)
temperature = float(temp.read())
R = 1023.0/(temperature)-1.0
R = 100000.0*R
temperature=1.0/(math.log(R/100000.0)/4275+1/298.15)-273.15
temperature = str(temperature)
client_sns = aws.getClient('sns','us-east-1');
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=temperature,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
}
)
import aws
from datetime import datetime
import calendar
from PIL import Image
# postgresql
# dynamoDB
dynamodb = aws.getResource('dynamodb', "us-east-1")
# S3
s3 = aws.getResource('s3', 'us-east-1')
# s3 = aws.getClient('s3', 'us-east-1')
# sns
snsClient = aws.getClient("sns","us-east-1")
# topic_arn = "arn:aws:sns:us-east-1:810588570945:fruit-alert"
import sys
MACHINE = sys.argv[1]
print('machine{}'.format(MACHINE))
def create_topic():
table = dynamodb.Table('fruit-topic')
response = table.get_item(Key={'machine' : MACHINE})
try:
response['Item']
except:
topic = snsClient.create_topic(
Name = str(MACHINE),
import time, sys, random
import boto3
import S3
sys.path.append('../utils')
import aws
TIMESTAMP = time.strftime('%Y-%m-%d-%H-%M-%S')
S3_BUCKET_NAME = "mtaedisondatax007"
S3_FILE_NAME = 'trainingData.csv'
S3_URI = "s3://{0}/{1}".format(S3_BUCKET_NAME, S3_FILE_NAME)
DATA_SCHEMA = "aml.csv.schema"
machineLearningClient = aws.getClient('machinelearning', 'us-east-1')
# Create a Data Source
dataSourceResponse = machineLearningClient.create_data_source_from_s3(
DataSourceId='ds_id' + TIMESTAMP,
DataSourceName='ml_data1',
DataSpec={
'DataLocationS3': S3_URI,
'DataRearrangement':
'{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}',
'DataSchemaLocationS3': "s3://{0}/{1}".format(S3_BUCKET_NAME,
DATA_SCHEMA)
},
ComputeStatistics=True)
# Create an ML model with a data source
python realtime.py ml_model_id --deleteEndpoint
Multi-word text attributes can be specified like:
python realtime.py ml-12345678901 "textVar=Multiple words grouped
together" numericVar=123
"""
import boto
import boto3
import json
import sys
import time
import aws
danceclient = aws.getClient('machinelearning','us-east-1')
instrumentclient = aws.getClient('machinelearning','us-east-1')
acousticclient = aws.getClient('machinelearning','us-east-1')
energyclient = aws.getClient('machinelearning','us-east-1')
genreclient = aws.getClient('machinelearning','us-east-1')
#ml_model_id = "MLModelId='finalmodel2'"
"""
def parse_args_to_dict(argv):
##Returns a record as a python dict by parsing key=value pairs
##from the command line
record = {}
for kv in argv:
try:
(k, v) = kv.split('=')
def __init__(self):
self.snsClient = aws.getClient('sns', 'us-east-1')
self.setTopic(self.topicName)
def main():
dynamodb = aws.getResource('dynamodb','us-east-1')
snsClient = aws.getClient('sns','us-east-1')
snsResource = aws.getResource('sns','us-east-1')
dynamoTable = dynamodb.Table(DYNAMODB_TABLE_NAME)
# Get list of all stopIds
stations = buildStationssDB()
while True:
prompt()
sys.stdout.write(">select a command : ")
userIn = sys.stdin.readline().strip()
if len(userIn) < 1 :
print "Command not recognized"
else:
if userIn == '1':
sys.stdout.write(">Enter source : ")
sourceStop = sys.stdin.readline().strip()
if sourceStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
sys.stdout.write(">Enter destination : ")
destinationStop = sys.stdin.readline().strip()
if destinationStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
sys.stdout.write(">Type N for uptown, S for downtown: ")
direction = sys.stdin.readline().strip()
# Validate direction
if direction not in ['N','S']:
sys.stdout.write(">Invalid direction. Enter a valid direction")
sys.stdout.flush()
continue
response = getLocalTrains(dynamoTable,direction,'1',int(sourceStop[:-1]))
earliestTrainData = getEarliestTrain(response,sourceStop)
print "local train is ",earliestTrainData['tripId']
timeToReachExpressStation = getTimeToReachDestination(earliestTrainData,'120S')
# get list of 2 and 3 trains
# List of trains with route id 2 and are either at 96th or before it
# Note that 2 train's stop starts from 201S and goes till 227S and merges with 96th street(120S)
# range2 is a list of all stations on 2 train route before and including 96th
range2 = range(201,228)
range2.append(120)
expressTrains2 = getExpress(dynamoTable,direction,'2',range2)
# List of trains with route id 3 and are either at 96th or before it
# Note that 3 train's stop starts from 301S and goes till 302S and merges with 2 train lane at 135th street lenox 224S
# range3 is a list of all stations on 3 train route before and including 96th
range3 = range(224,228)
range3.extend([301,302,120])
expressTrains3 = getExpress(dynamoTable,direction,'3',range3)
expressTrains = expressTrains2 + expressTrains3
# filter the trains that reach 96th before the earliest one reaches.
for e in expressTrains:
stops = json.loads(e['futureStopData'])
if stops['120S'][0]['arrivalTime'] - timeToReachExpressStation < 10:
expressTrains.remove(e)
timeTakenByLocal = getTimeToReachDestination(earliestTrainData,destinationStop)
print "time taken by local to 96",timeToReachExpressStation
# If there is an express train find the time at which it reaches time square
if expressTrains:
expressTrainData = getEarliestTrain(expressTrains,'120S')
print expressTrainData['tripId']
print "time taken by express to 96",getTimeToReachDestination(expressTrainData,'120S')
timeTakenByExpress = getTimeToReachDestination(expressTrainData,destinationStop)
timeTakenByLocal = getTimeToReachDestination(earliestTrainData,destinationStop)
print "time taken by local to 42nd",timeTakenByLocal
print "Time taken by expres to 42nd",timeTakenByExpress
if timeTakenByLocal > timeTakenByExpress:
print ">Switch to express"
else:
print ">Do not switch to express"
else:
print "No express at this time"
print ">Do not switch to express"
elif userIn == '2':
sys.stdout.write(">Enter phonenumber")
phoneNumber = sys.stdin.readline().strip()
phoneNumber = '1'+phoneNumber
topic = snsResource.Topic('arn:aws:sns:us-east-1:013162866752:mtaSub')
topic.subscribe(Protocol='sms',
Endpoint=phoneNumber)
snsClient.publish(TopicArn='arn:aws:sns:us-east-1:013162866752:mtaSub',
Message='You have subscribed to mta data',
Subject='MTAFEED')
else:
sys.exit()
def __init__(self):
self.rek = aws.getClient('rekognition', 'us-east-1')
# Hayato Nakamura
# hn2357
# Copyright 2020 Hayato Nakamura
from __future__ import print_function #compatible with python 2.7
import sys, time
import numpy as np
from picamera import PiCamera
import aws
from collections import OrderedDict
from decimal import *
from datetime import datetime
#import threading
dynamodb = aws.getResource('dynamodb', 'us-east-1')
s3 = aws.getClient('s3', 'us-east-1')
def search(mat):
# Scan the matrix
for y in range(24 - 3):
for x in range(32 - 3):
window = mat[y:y + 3, x:x + 3]
print(window)
print(np.mean(window))
if (np.mean(window) > 36 and np.mean(window) < 45):
print("\n\nHIT\n\n")
return True
return False
def process_pic(name):
camera = PiCamera()
return month_string
#Command Type- getPurchaseHistory- Purpose: To get a summary of Purchase History in a specific month
if (request.json["type"] == "getPurchaseHistory"):
uniquequantity = {}
uniqueprices = {}
uniquenames = []
purchaseHistory = mongo.db.purchaseHistory
purchaseHistoryList = purchaseHistory.find(
{'month': request.json["msg"]})
month_string = ""
for item in purchaseHistoryList:
if item['itemname'] not in uniquequantity:
uniquenames.append(item['itemname'])
uniquequantity[item['itemname']] = int(item['quantity'])
uniqueprices[item['itemname']] = item['price']
else:
uniquequantity[item['itemname']] += int(item['quantity'])
for i in uniquenames:
month_string += str(
uniquequantity[i]) + 'X $' + uniqueprices[i] + ',' + i + ';'
print month_string
return month_string
return "done"
if __name__ == '__main__':
snsClient = aws.getClient('sns', 'us-east-1')
app.run(debug=True, host="0.0.0.0", port=80)
import boto3
import mraa
import aws
import math
temp_pin_number = 1
temp = mraa.Aio(temp_pin_number)
temperature = float(temp.read())
R = 1023.0 / (temperature) - 1.0
R = 100000.0 * R
temperature = 1.0 / (math.log(R / 100000.0) / 4275 + 1 / 298.15) - 273.15
temperature = str(temperature)
client_sns = aws.getClient('sns', 'us-east-1')
response = client_sns.publish(
TopicArn='arn:aws:sns:us-east-1:486153037878:lab4ass1',
Message='message from edison',
Subject=temperature,
MessageStructure='string',
MessageAttributes={
'string': {
'DataType': 'String',
'StringValue': 'String'
#'BinaryValue': 'Binary'
}
})
def main(fileName):
# connect to kinesis
kinesis = aws.getClient('kinesis', 'us-east-1')
data = [] # list of dictionaries will be sent to kinesis
listcsv = list()
l = []
try:
while (True):
tu, timest = importmta.getTripUpdates()
print 'Entering feed\n'
listlcl = []
listexp = []
for t in tu:
if t.routeId == '1' and t.direction == 'S':
if t.vehicleData[0] == '120S' and t.vehicleData[
1] == 'STOPPED_AT':
time96 = t.vehicleData[2]
time96_dt = datetime.fromtimestamp(time96, TIMEZONE)
elif '120S' in t.futureStops.keys():
time96 = t.futureStops['120S'][0]
time96_dt = datetime.fromtimestamp(time96, TIMEZONE)
else:
time96 = 'None'
time96_dt = 'None'
if t.vehicleData[0] == '127S' and t.vehicleData[
1] == 'STOPPED_AT':
time42 = t.vehicleData[2]
time42_dt = datetime.fromtimestamp(time42, TIMEZONE)
elif '127S' in t.futureStops.keys():
time42 = t.futureStops['127S'][0]
time42_dt = datetime.fromtimestamp(time42, TIMEZONE)
else:
time42 = 'None'
time42_dt = 'None'
if timest.strftime("%a") == 'Sun' or timest.strftime(
"%a") == 'Sat':
day = 'Weekend'
else:
day = 'Weekday'
route = 'local'
#mtaData.put_item(data={'tripId':t.tripId,'timeStamp':str(int(str(timest)[14:16])+int(str(timest)[11:13])*60),'Day of the week':day,'route':route,'Time at 96th':time96,'Time at 42nd':time42},overwrite=True)
if time96_dt != 'None' and time42_dt != 'None':
l = [
str(t.tripId),
str(
int(str(timest)[14:16]) +
int(str(timest)[11:13]) * 60),
str(day),
str(route),
str(
int(str(time96_dt)[14:16]) +
int(str(time96_dt)[11:13]) * 60)
if time96_dt != 'None' else 'None',
str(
int(str(time42_dt)[14:16]) +
int(str(time42_dt)[11:13]) * 60)
if time42_dt != 'None' else 'None'
]
listlcl.append(l)
elif (t.routeId == '2'
or t.routeId == '3') and t.direction == 'S':
if t.vehicleData[0] == '120S' and t.vehicleData[
1] == 'STOPPED_AT':
time96 = t.vehicleData[2]
time96_dt = datetime.fromtimestamp(time96, TIMEZONE)
elif '120S' in t.futureStops.keys():
time96 = t.futureStops['120S'][0]
time96_dt = datetime.fromtimestamp(time96, TIMEZONE)
else:
time96 = 'None'
time96_dt = 'None'
if t.vehicleData[0] == '127S' and t.vehicleData[
1] == 'STOPPED_AT':
time42 = t.vehicleData[2]
time42_dt = datetime.fromtimestamp(time42, TIMEZONE)
elif '127S' in t.futureStops.keys():
time42 = t.futureStops['127S'][0]
time42_dt = datetime.fromtimestamp(time42, TIMEZONE)
else:
time42 = 'None'
time42_dt = 'None'
if timest.strftime("%a") == 'Sun' or timest.strftime(
"%a") == 'Sat':
day = 'Weekend'
else:
day = 'Weekday'
route = 'exp'
#mtaData.put_item(data={'tripId':t.tripId,'timeStamp':str(int(str(timest)[14:16])+int(str(timest)[11:13])*60),'Day of the week':day,'route':route,'Time at 96th':time96,'Time at 42nd':time42},overwrite=True)
if time96_dt != 'None' and time42_dt != 'None':
l = [
str(t.tripId),
str(
int(str(timest)[14:16]) +
int(str(timest)[11:13]) * 60),
str(day),
str(route),
str(
int(str(time96_dt)[14:16]) +
int(str(time96_dt)[11:13]) * 60)
if time96_dt != 'None' else 'None',
str(
int(str(time42_dt)[14:16]) +
int(str(time42_dt)[11:13]) * 60)
if time42_dt != 'None' else 'None'
]
listexp.append(l)
else:
pass
for i in listlcl:
for j in listexp:
if i[4] != 'None' and j[4] != 'None':
if int(i[4]) <= int(j[4]):
row = {
'timest': i[1],
'day': i[2],
'time96lcl': i[4],
'time96exp': j[4]
}
kinesis.put_record(
StreamName=KINESIS_STREAM_NAME,
Data=json.dumps(row),
PartitionKey='0')
print 'Done Entering\n'
except KeyboardInterrupt:
exit
def main():
dynamodb = aws.getResource('dynamodb', 'us-east-1')
snsClient = aws.getClient('sns', 'us-east-1')
snsResource = aws.getResource('sns', 'us-east-1')
# Create topic if it doesn't already exist
createResponse = snsClient.create_topic(Name='mtaSub')
topicArn = createResponse['TopicArn']
dynamoTable = dynamodb.Table(DYNAMODB_TABLE_NAME)
# Get list of all stopIds
stations = buildStationssDB()
while True:
prompt()
sys.stdout.write(">select a command : ")
userIn = sys.stdin.readline().strip()
if len(userIn) < 1:
print "Command not recognized"
else:
if userIn == '1':
sys.stdout.write(">Enter source : ")
sourceStop = sys.stdin.readline().strip()
if sourceStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
if not isValidSourceStation(sourceStop):
sys.stdout.write(
'>You must start north of 96th street or at Times Sq.')
sys.stdout.flush()
continue
sys.stdout.write(">Enter destination : ")
destinationStop = sys.stdin.readline().strip()
if destinationStop not in stations:
sys.stdout.write(">Invalid stop id. Enter a valid stop id")
sys.stdout.flush()
continue
if not isValidDestinationStation(sourceStop):
sys.stdout.write(
'>You must end north of 96th street or at Times Sq.')
sys.stdout.flush()
continue
if sourceStop == destinationStop:
sys.stdout.write('>You are already at the station!')
sys.stdout.flush()
continue
sys.stdout.write(">Type N for uptown, S for downtown: ")
direction = sys.stdin.readline().strip()
# Validate direction
if direction not in ['N', 'S']:
sys.stdout.write(
">Invalid direction. Enter a valid direction")
sys.stdout.flush()
continue
# You can figure out the direction from the source and destinations
actualDirection = getDirectionOfStops(stations, sourceStop,
destinationStop)
if actualDirection != direction:
sys.stdout.write(">Wrong direction!")
sys.stdout.flush()
continue
if sourceStop == timesSquareStopId:
sourceStop = getDirectedStopId(sourceStop, direction)
destinationStop = getDirectedStopId(
destinationStop, direction)
# Find incoming local trains
incomingLocals = getIncomingTrainsToStation(
dynamoTable, direction, '1', sourceStop)
print "Local trains incoming to ", sourceStop
for l in incomingLocals:
print l['tripId']
earliestLocal = getEarliestTrain(incomingLocals,
sourceStop, direction)
print "The next local train coming is ", earliestLocal[
'tripId']
print "It arrives at ", earliestLocal['futureStopData'][
sourceStop][0]['arrivalTime']
incomingExpresses = getIncomingTrainsToStation(
dynamoTable, direction, ['2', '3'], sourceStop)
print "Express trains incoming to ", sourceStop
for e in incomingExpresses:
print e["tripId"]
earliestExpress = getEarliestTrain(incomingExpresses,
sourceStop, direction)
print "The next express train coming is ", earliestExpress[
'tripId']
print "It arrives at ", earliestExpress['futureStopData'][
sourceStop][0]['arrivalTime']
# Go directly on local
localArrivalTime = getTimeToReachFutureStop(
earliestLocal, destinationStop, direction)
print "The local train will arrive at destination ", destinationStop, " at ", localArrivalTime
# Stop at 96th and get off the express
expressTo96thTime = getTimeToReachFutureStop(
earliestExpress, ninetySixthStopId, direction)
# Next local arriving at 96th after that
incomingLocals = getIncomingTrainsToStation(
dynamoTable, direction, '1', ninetySixthStopId)
earliestLocal = getEarliestTrain(incomingLocals,
ninetySixthStopId,
direction,
expressTo96thTime)
expressAndLocalArrivalTime = getTimeToReachFutureStop(
earliestLocal, destinationStop, direction)
print "If you take an express, you must switch at ", ninetySixthStopId
print "Express will be at your destination at ", expressAndLocalArrivalTime
if localArrivalTime < expressAndLocalArrivalTime:
print "Take the next local"
sendNotification(snsClient, topicArn, "Take Local")
else:
print "Take the express to 96th"
sendNotification(snsClient, topicArn, "Take Express")
continue
sourceStop = getDirectedStopId(sourceStop, direction)
destinationStop = getDirectedStopId(destinationStop, direction)
ninetySixthStop = getDirectedStopId(ninetySixthStopId,
direction)
# The next train coming at your source station
potentialTrains = getIncomingTrainsToStation(
table=dynamoTable,
direction=direction,
routeId='1',
stopId=sourceStop)
currentLocalTrain = getEarliestTrain(trains=potentialTrains,
stopId=sourceStop,
direction=direction)
print "The next local train to ", sourceStop, " will arrive at ", \
currentLocalTrain['futureStopData'][sourceStop][0]['arrivalTime']
# Time it gets to 96th street
arrival96th = currentLocalTrain['futureStopData'][
ninetySixthStop][0]['arrivalTime']
print "It will arrive at 96th Street at ", arrival96th
# Next local train when you arrive at 96th
print "When you get to 96th street . . . "
localsTo96th = getIncomingTrainsToStation(
dynamoTable, direction, '1', ninetySixthStop)
# Task 1: Print trip IDs
for l in localsTo96th:
print l['tripId']
local96th = getEarliestTrain(localsTo96th, ninetySixthStop,
direction, arrival96th)
# Task 3
print "Earliest Local Train: ", local96th['tripId']
print "It will arrive at ", local96th['futureStopData'][
ninetySixthStop][0]['arrivalTime']
# Next express train when you arrive at 96th
expressesTo96th = getIncomingTrainsToStation(
dynamoTable, direction, ['2', '3'], ninetySixthStop)
# Task 2: Print express trip IDs
for e in expressesTo96th:
print e['tripId']
express96th = getEarliestTrain(expressesTo96th,
ninetySixthStop, direction,
arrival96th)
print "Earliest Express Train: ", express96th['tripId']
print "It will arrive at ", express96th['futureStopData'][
ninetySixthStop][0]['arrivalTime']
# local time to get to 42nd
timeToDestinationLocal = currentLocalTrain['futureStopData'][
destinationStop][0]['arrivalTime']
timeToDestinationExpress = express96th['futureStopData'][
destinationStop][0]['arrivalTime']
# Task 5
print "Local will arrive at destination ", destinationStop, " at ", timeToDestinationLocal
print "Express will arrive at destination ", destinationStop, " at ", timeToDestinationExpress
# Task 6a
if timeToDestinationLocal > timeToDestinationExpress:
print "Switch over to express"
sendNotification(snsClient, topicArn,
"Switch to express @ 96th")
else:
print "Stay with Local"
sendNotification(snsClient, topicArn,
"Stay on local @ 96th")
elif userIn == '2':
sys.stdout.write(">Enter Phone No. ")
phone_number = sys.stdin.readline().strip()
# Task 9
addSubscriber(snsClient, topicArn, phone_number)
elif userIn == '3':
print "Bye, Felicia . . ."
exit(0)
else:
print "Unknown command. Terminating."
sys.exit(6)
