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alert.py
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alert.py
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# Tsunami Warning System By Mohammad Moradi All Rights Reserved
# -*- coding: utf-8 -*-
from BeautifulSoup import BeautifulSoup as bsp
from email.MIMEMultipart import MIMEMultipart
from email.MIMEText import MIMEText
import matplotlib.dates as md
import matplotlib.pyplot as plt
from dateutil import parser
from math import sqrt
import pandas as pd
import numpy as np
import matplotlib
import datetime
import smtplib
import urllib
import time
import pytz
import os
# Buoy station number
station = 23227
# Normal Water columns height at buoy position
Hmean = 3792.662
# Hieght that will add to mean height to raise alert if heigher levels detected
Iheight = 10
dataframe, df1, d15, d15s, time_serie, data = [], [], [], [], [], []
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
data_dir = BASE_DIR + "app/data/"
def get():
print "Downloading Latest Data..."
url = "http://www.ndbc.noaa.gov/station_page.php?station=%s" % station
url = urllib.urlopen(url)
html = bsp(url)
raw_data = html.pre.contents
# Writing extracted contents to disk
f = open(os.path.join(data_dir, 'Data.dart'), 'wb', 2048)
for i in raw_data:
i = str(i)
f.write(i)
f.close()
#get()
def read():
global dataframe, heights, data
data = pd.read_table(os.path.join(data_dir, 'Data.dart'), delimiter=r'\s+', skiprows=(1), skipfooter=True, engine='python')
Data = pd.read_table(os.path.join(data_dir, 'Data.dart'), delimiter=r'\s+', skiprows=(1), skipfooter=True, engine='python')
heights = np.array(Data['m'])
year = np.array(Data['#yr'])
month = np.array(Data['mo'])
day = np.array(Data['dy'])
hour = np.array(Data['hr'])
minute = np.array(Data['mn'])
second = np.array(Data['s'])
time = np.array([year, month, day, hour, minute, second])
time = time.transpose()
timedata = np.array(xrange(len(time)))
timedata = timedata.tolist()
#print "Adjusting Date and Time..."
for i in xrange(len(time)):
s = '%d-%d-%d %d:%d:%d'%(time[i][0],time[i][1],time[i][2], time[i][3],time[i][4], time[i][5])
timedata[i] = s
dt = np.array(xrange(len(timedata)))
dt = dt.tolist()
tlen = len(timedata)
for i in xrange(tlen):
dt[i] = parser.parse(timedata[i])
#print "Putting data to dataframe structure..."
dataframe = pd.DataFrame({'Height':heights, 'time':dt})
def splitter(DF, YR, MO, DY, HR, MN, S, M, YRd, MOd, DYd, HRd, MNd, Sd, H, T):
global df15
for i in xrange(len(DF)):
j = i + 1
if j == len(DF):
break
if DF[i]-DF[j] == T:
YRd.append(YR[i])
MOd.append(MO[i])
DYd.append(DY[i])
HRd.append(HR[i])
MNd.append(MN[i])
Sd.append(S[i])
H.append(M[i])
if DF[j] == 0:
YRd.append(YR[i])
MOd.append(MO[i])
DYd.append(DY[i])
HRd.append(HR[i])
MNd.append(MN[i])
Sd.append(S[i])
H.append(M[i])
def add_nan(DF, T):
global df1
start = DF.time[len(DF)-1]
stop = DF.time[0]
rng = pd.date_range(start, stop, freq=T)
DF = DF.drop_duplicates('time').set_index('time').reindex(rng).ffill(limit=True)
return DF
def make_dataframes(DF, label):
heights = np.array(DF['m'])
year = np.array(DF['#yr'])
month = np.array(DF['mo'])
day = np.array(DF['dy'])
hour = np.array(DF['hr'])
minute = np.array(DF['mn'])
second = np.array(DF['s'])
time = np.array([year, month, day, hour, minute, second])
time = time.transpose()
timedata = np.array(xrange(len(time)))
timedata = timedata.tolist()
#print "Adjusting Date and Time..."
for i in xrange(len(time)):
s = '%d-%d-%d %d:%d:%d'%(time[i][0],time[i][1],time[i][2], time[i][3],time[i][4], time[i][5])
timedata[i] = s
dt = np.array(xrange(len(timedata)))
dt = dt.tolist()
tlen = len(timedata)
for i in xrange(tlen):
dt[i] = parser.parse(timedata[i])
#print "Putting data to dataframe structure..."
DF = pd.DataFrame({label:heights, 'time':dt})
return DF
def init_dataframes():
global df15, df1, df15s
YRd, MOd, DYd, HRd, MNd, Sd, H = [], [], [], [], [], [], []
splitter(data.mn, data['#yr'], data.mo, data.dy, data.hr, data.mn, data.s, data.m, YRd, MOd, DYd, HRd, MNd, Sd, H, 15)
df15 = pd.DataFrame({"#yr": YRd, "mo":MOd, "dy": DYd, "hr":HRd, "mn":MNd, "s":Sd, "m":H})
YRd, MOd, DYd, HRd, MNd, Sd, H = [], [], [], [], [], [], []
splitter(data.mn, data['#yr'], data.mo, data.dy, data.hr, data.mn, data.s, data.m, YRd, MOd, DYd, HRd, MNd, Sd, H, 1)
df1 = pd.DataFrame({"#yr": YRd, "mo":MOd, "dy": DYd, "hr":HRd, "mn":MNd, "s":Sd, "m":H})
YRd, MOd, DYd, HRd, MNd, Sd, H = [], [], [], [], [], [], []
splitter(data.s, data['#yr'], data.mo, data.dy, data.hr, data.mn, data.s, data.m, YRd, MOd, DYd, HRd, MNd, Sd, H, 15)
df15s = pd.DataFrame({"#yr": YRd, "mo":MOd, "dy": DYd, "hr":HRd, "mn":MNd, "s":Sd, "m":H})
def mode_detector():
print "Checking buoy state mode..."
init_dataframes()
if len(df1) > 0:
return True
def data_table():
#Latest Height
lastHeight = dataframe.Height[0]
lasttime = pd.to_datetime(str(dataframe.time[np.where(dataframe.Height == lastHeight)[0][0]]))
#Maximum Height
maxheight = np.nanmax(dataframe.Height)
maxtime = pd.to_datetime(str(dataframe.time[np.where(dataframe.Height == maxheight)[0][0]]))
#Minimum Height
minheight = np.nanmin(dataframe.Height)
mintime = pd.to_datetime(str(dataframe.time[np.where(dataframe.Height == minheight)[0][0]]))
#Mean Height
meanheight = '%.3f' % np.nanmean(dataframe.Height)
#Today
os.environ['TZ'] = 'GMT'
time.tzset()
today = time.strftime('%Y-%m-%d %H:%M:%S')
#date_time = datetime.datetime.today()
#tl = list(date_time.timetuple())
#today = "%d-%d-%d %d:%d:%d" % (tl[0],tl[1],tl[2],tl[3],tl[4],tl[5])
dic ={"lastHeight":lastHeight , "lasttime":lasttime, "maxheight":maxheight, "maxtime":maxtime, "minheight":minheight,
"mintime":mintime, "meanheight":meanheight, "today":today}
D = pd.DataFrame(dic, index=[0])
D.to_csv(os.path.join(BASE_DIR + "app/static/report/", "data_table.csv"))
def plot():
print "Plotting Graph..."
if len(df1) > 0:
pass
df1m = make_dataframes(df1, '1-min')
df1m = add_nan(df1m, '1min')
if len(df15):
df15m = make_dataframes(df15, '15-min')
df15m = add_nan(df15m, '15min')
if len(df15s) > 0:
df15S = make_dataframes(df15s, '15-sec')
df15S = add_nan(df15S, '15S')
try:
ax = df1m.plot(color='g', figsize=(9,4), x_compat=True)
df15m.plot(ax=ax, color='b', x_compat=True)
df15S.plot(ax=ax, style='.-r', x_compat=True)
except NameError:
ax = df15m.plot(color='b', figsize=(9,4), x_compat=True)
ax.set_yticklabels(ax.get_yticks())
ax.set_title('Water Column Height at Station %s' % station)
ax.set_ylabel('meters')
ax.set_xlabel('Event Mode')
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.055, box.width, box.height * 0.9])
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=5, borderaxespad=1.4,
fontsize='large')
ax.xaxis.set_major_formatter(md.DateFormatter('%m/%d'))
plt.tight_layout()
plt.savefig(os.path.join(BASE_DIR +'app/data', 'WCH.png'))
plt.show()
def send_alert():
print "Sending Alert..."
receiver = 'prvmrd@gmail.com'
msg = MIMEMultipart()
msg['From'] = 'tswsystem@gmail.com'
msg['To'] = 'prvmrd@gmail.com'
msg['Subject'] = 'Tsunami Warning '
message = """
هشدار سونامی در ایستگاه %s
ارتفاع ستون آب: %.2f m
سرعت سونامی: %.2f km/h
سونامی %d ساعت و %d دقیقه دیگر به چابهار خواهد رسید.
""" % (station, latest_mean, speed, hour, minute)
msg.attach(MIMEText(message))
mailserver = smtplib.SMTP('smtp.gmail.com',587)
# identify ourselves to smtp gmail client
mailserver.ehlo()
# secure email with tls encryption
mailserver.starttls()
# re-identify as an encrypted connection
mailserver.ehlo()
mailserver.login('tswsystem@gmail.com', 'tsunami@me')
mailserver.sendmail('',receiver ,msg.as_string())
mailserver.quit()
print "Alert sent"
def alert():
print "Checking Tsunami Event..."
global latest_mean, speed, hour, minute
#mean = np.nanmean(dataframe.Height)
alert_mean = Hmean + Iheight
latest_mean = np.nanmean(dataframe.Height[:10])
speed = sqrt(latest_mean * 9.80665) * 3600/1000
travel_time = 1500.564 / speed * 60
hour, minute = divmod(travel_time, 60)
hour, minute = int(hour), int(minute)
if latest_mean >= alert_mean:
send_alert()
f = open(os.path.join(BASE_DIR + 'app/static/report/', 'report.cfg'), 'w')
f.write('True')
f.close()
print 'True'
return True
else:
f = open(os.path.join(BASE_DIR + 'app/static/report/', 'report.cfg'), 'w')
f.write('False')
f.close()
print 'False'
def run():
read()
data_table()
if mode_detector() == True:
init_dataframes()
alert()
plot()
else:
plot()
#run()