Tide Indicator Pi Project #9 - Calculation of Current Tide Completed (No bugs)

The last version I posted, v2.2, turned out not to work for all tide states, due to some maths errors. These have all been fixed and the code seems to work well.

I've think I've finally got the hang of updating to github, so here's the latest code:

tideproject/tidenow3.0.py

This is the output:

(datetime.datetime(2016, 1, 14, 21, 34, 13, 517280), u'10.5')
(datetime.datetime(2016, 1, 15, 4, 9, 13, 518325), u'1.9')
Tide is currently:  falling
Tidal Range =  -8.6
Current Tide :  9.55460304533


Next job is to have it running continuously and outputting to this webpage.

Tide Indicator Pi Project #8 - Calculation of Current Tide Completed

The program below seems to work!

Output:

('Next: ', (datetime.datetime(2016, 1, 3, 6, 18, 23, 116073), u'4.3'), ' is ', datetime.timedelta(0, 21180, 2472), ' away. /n Previous: ', (datetime.datetime(2016, 1, 2, 23, 49, 23, 115191), u'8.1'), ' was ', datetime.timedelta(0, 2159, 998410), ' ago.')
('Sum of both gaps is ', datetime.timedelta(0, 23340, 882))
('Tide is Currently: ', 'falling')
('tide difference = ', -3.8)
('lower tide value', 4.299999999999999)
('Normalised Time =', 2159, 23340, 0.29060405051843885)
0.958070971113
('Current tide : ', 7.940669690228617)

Code:

#version 1.0
#This program pulls tide data from the ports of Jersey Website
#Under a licence from the UKHO
#
#It then calculates the current tide using a simplified sinusoidal harmonic approximation
#By finding the two tide data points either side of now and working out the current tide height


import urllib2
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt
import math

#open site and grab html

rawhtml = urllib2.urlopen("http://www.ports.je/Pages/tides.aspx").read(40000)
soup = BeautifulSoup(rawhtml, "html.parser")


#get the tide data (it's all in tags)

rawtidedata = soup.findAll('td')


#parse all data points (date, times, heights) to one big list
#format of the list is [day,tm,ht,tm,ht,tm,lt,tm,lt]

n=0
parsedtidedata=[]
for i in rawtidedata: 
 parsedtidedata.append(rawtidedata[n].get_text())
 n += 1

#extract each class of data (day, time , height) to a separate list (there are 10 data items for each day)

tidetimes=[]
tideheights=[]
tideday=[]
lastdayofmonth=int(parsedtidedata[-10])

for n in range(0,lastdayofmonth*10,10):

 tideday.append(parsedtidedata[n])
 tidetimes.extend([parsedtidedata[n+1],parsedtidedata[n+3],parsedtidedata[n+5],parsedtidedata[n+7]])
 tideheights.extend([parsedtidedata[n+2],parsedtidedata[n+4],parsedtidedata[n+6],parsedtidedata[n+8]])

#get time now:

currentTime = dt.datetime.now()


#create a list of all the tide times as datetime objects:

dtTideTimes=[]
tideDataList=[]

for j in range (0,lastdayofmonth*4):
 #print tidetimes[j][0:2], tidetimes[j][3:6]
 if tidetimes[j]=='**':
  dtTideTimes.append('**')
 else:

  dtTideTimes.append(dt.datetime.now().replace(day=int(j/4+1), hour=int(tidetimes[j][0:2]), minute=int(tidetimes[j][3:5])))

 #make a tuple for each data point and add it to a list
 tupleHolder =(dtTideTimes[j], tideheights[j])
 tideDataList.append(tupleHolder)
 
 #print what we've got so far
# print tideDataList[j]

#find the two closest times in the list to now:

gap1 = abs(tideDataList[0][0] - currentTime)
gap2 = abs(tideDataList[0][0] - currentTime)
nearest1 = tideDataList[0]

#print gap1 

for j in range (0,lastdayofmonth*4):

 if (tideDataList[j][0] !="**"):                      
  gapx = abs(tideDataList[j][0] - currentTime) 

#check if the data point is the first or second nearest to now. 
#Generates the datapoints either side of now

  if (gapx <= gap1):                            
   nearest1 = tideDataList[j]            
   gap1 = gapx
  if (gap1 < gapx and gapx <= gap2): 
   nearest2 = tideDataList[j]                   
   gap2 = gapx             

#print (nearest1, gap1)
#print (nearest2, gap2)
#print (gap1+gap2)    

#and now the maths begins
#print ('tide height 1 = ', nearest1[1])
#print ('tide height 2 = ', nearest2[1])

#need to get them in order of time: (this works)

if nearest1[0] > nearest2[0]:
 nextDataPoint = nearest1
 prevDataPoint = nearest2
 gapToNext = gap1
 gapToPrev = gap2

else:
 nextDataPoint = nearest2
 prevDataPoint = nearest1
 gapToNext = gap2
 gapToPrev = gap1

gapSum = gapToNext + gapToPrev

print('Next: ', nextDataPoint,' is ',gapToNext, ' away. /n Previous: ', prevDataPoint, ' was ', gapToPrev, ' ago.')
print('Sum of both gaps is ', gapSum) #this works

#is the tide rising or falling?
tideDifference = float(nextDataPoint[1])-float(prevDataPoint[1])

if (tideDifference<0 data-blogger-escaped-0="prev" data-blogger-escaped-:="" data-blogger-escaped-all="" data-blogger-escaped-code="" data-blogger-escaped-currently:="" data-blogger-escaped-currenttide="" data-blogger-escaped-data="" data-blogger-escaped-difference=", tideDifference) #this works


lowerTide = (float(nearest1[1]) + float(nearest2[1]) - abs(tideDifference))/2
print (" data-blogger-escaped-doesn="" data-blogger-escaped-else:="" data-blogger-escaped-falling="" data-blogger-escaped-for="" data-blogger-escaped-ide="" data-blogger-escaped-is="" data-blogger-escaped-lower="" data-blogger-escaped-lowertide="" data-blogger-escaped-math.cos="" data-blogger-escaped-math.pi="" data-blogger-escaped-normalisedtime="" data-blogger-escaped-ormalised="" data-blogger-escaped-pi="next" data-blogger-escaped-print="" data-blogger-escaped-scaled="" data-blogger-escaped-t="" data-blogger-escaped-this="" data-blogger-escaped-tide="" data-blogger-escaped-tidedifference="" data-blogger-escaped-tidestate="" data-blogger-escaped-time=", gapToPrev.seconds, gapSum.seconds, normalisedTime)

print (math.cos(normalisedTime))

if tideState == " data-blogger-escaped-to="" data-blogger-escaped-urrent="" data-blogger-escaped-value="" data-blogger-escaped-work="" data-blogger-escaped-works="">

Tide Indicator Pi Project #7 - Finding the two tide data points nearest to the current time.

This project is taking ages! I've done a lot since the last post however, but documented very little, so I'll do my best to recall how I got from there to here. You can see all the posts so far here.

The problem in a nutshell: The program needs to get the two tide data points either side of the current time, to work out what the tide is doing now.

Since the last post, the code has been modified to create a list of tuples, with each tuple having two data points (tide time, tide height)

It then works out the gap between each data point and the current time, and tries to store the two nearest times as 'nearest1' and 'nearest2'. Sometime it works:

Time Now:
2015-01-02 16:33

Output:
(datetime.datetime(2016, 1, 2, 17, 52, 40, 854958), u'4.0'),
(datetime.datetime(2016, 1, 2, 11, 9, 40, 854071), u'8.4')

Sometimes it doesn't and misses a point.

#
import urllib2
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt


#open site and grab html

rawhtml = urllib2.urlopen("http://www.ports.je/Pages/tides.aspx").read(40000)
soup = BeautifulSoup(rawhtml, "html.parser")


#get the tide data (it's all in tags)

rawtidedata = soup.findAll('td')


#parse all data points (date, times, heights) to one big list
#format of the list is [day,tm,ht,tm,ht,tm,lt,tm,lt]

n=0
parsedtidedata=[]
for i in rawtidedata: 
 parsedtidedata.append(rawtidedata[n].get_text())
 n += 1

#extract each class of data (day, time , height) to a separate list (there are 10 data items for each day):

tidetimes=[]
tideheights=[]
tideday=[]
lastdayofmonth=int(parsedtidedata[-10])

for n in range(0,lastdayofmonth*10,10):

 tideday.append(parsedtidedata[n])
 tidetimes.extend([parsedtidedata[n+1],parsedtidedata[n+3],parsedtidedata[n+5],parsedtidedata[n+7]])
 tideheights.extend([parsedtidedata[n+2],parsedtidedata[n+4],parsedtidedata[n+6],parsedtidedata[n+8]])

#get time now:

currentTime = dt.datetime.now()


#create a list of all the tide times as datetime objects:

dtTideTimes=[]
tideDataList=[]

for j in range (0,lastdayofmonth*4):
 #print tidetimes[j][0:2], tidetimes[j][3:6]
 if tidetimes[j]=='**':
  dtTideTimes.append('**')
 else:
  dtTideTimes.append(dt.datetime.now().replace(day=int(j/4+1), hour=int(tidetimes[j][0:2]), minute=int(tidetimes[j][3:5])))


#create a tuple of time and height, and add each tuple to a list



 tupleHolder =(dtTideTimes[j], tideheights[j])
 tideDataList.append(tupleHolder)






#print what we've got so far



for j in range (0,lastdayofmonth*4):
 print tideDataList[j]

#find the two closest data points to now in the list:

gap1 = abs(tideDataList[0][0] - currentTime)
nearest1 = tideDataList[0]
print gap1 

for j in range (0,lastdayofmonth*4):


 if (tideDataList[j][0] !="**"):


  gap2 = abs(tideDataList[j][0] - currentTime)
  print tideDataList[j][0], gap2, nearest1


  if (gap2 < gap1):


   nearest2 = nearest1
   nearest1 = tideDataList[j]
   gap1 = gap2

print (nearest1, nearest2)
    
#this nearly works!!! Gave the two nearest high tides, not nearest high and low.

Tide Indicator Pi Project #6 - Converting tide times to datetime format

Crikey..that was tricky.

Ended up that the best way was to take year and month info from the datetime.now() and just replace the day, hour and time for each tide time data point. Output for

print dtTideTimes[j], tideheights[j]

looks like this:

2015-10-01 09:21:22.975449 11.7
2015-10-01 21:42:22.976826 11.5
2015-10-01 03:48:22.977813 0.6
2015-10-01 16:07:22.978737 0.9
2015-10-02 10:00:22.979654 11.1
2015-10-02 22:23:22.980587 10.6
2015-10-02 04:27:22.981501 1.1
2015-10-02 16:47:22.982419 1.5
2015-10-03 10:37:22.983506 10.2
2015-10-03 23:03:22.984480 9.6
2015-10-03 05:06:22.985411 1.9
2015-10-03 17:27:22.986337 2.4

etc

Problem now is that this is not sorted in strict time order. It is in the format HT, HT, LT, LT for each day. I created a dictionary thinking it would be easy to sort, but it's not.

I think I have a plan though, to find the two data points in the dictionary nearest to the current time.

---

import urllib2
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt


#open site and grab html

rawhtml = urllib2.urlopen("http://www.ports.je/Pages/tides.aspx").read(40000)
soup = BeautifulSoup(rawhtml, "html.parser")


#get the tide data (it's all in tags)

rawtidedata = soup.findAll('td')


#parse all data points (date, times, heights) to one big list
#format of the list is [day,tm,ht,tm,ht,tm,lt,tm,lt]

n=0
parsedtidedata=[]
for i in rawtidedata: 
 parsedtidedata.append(rawtidedata[n].get_text())
 n += 1

#extract each class of data (day, time , height) to a separate list (there are 10 data items for each day)

tidetimes=[]
tideheights=[]
tideday=[]
lastdayofmonth=int(parsedtidedata[-10])

for n in range(0,lastdayofmonth*10,10):

 tideday.append(parsedtidedata[n])
 tidetimes.extend([parsedtidedata[n+1],parsedtidedata[n+3],parsedtidedata[n+5],parsedtidedata[n+7]])
 tideheights.extend([parsedtidedata[n+2],parsedtidedata[n+4],parsedtidedata[n+6],parsedtidedata[n+8]])

#get time now:

currentTime = dt.datetime.now()


#create a list of all the tide times as datetime objects:

dtTideTimes=[]

for j in range (0,lastdayofmonth*4):
 #print tidetimes[j][0:2], tidetimes[j][3:6]
 if tidetimes[j]=='**':
  dtTideTimes.append('**')
 else:
  dtTideTimes.append(dt.datetime.now().replace(day=int(j/4+1), hour=int(tidetimes[j][0:2]), minute=int(tidetimes[j][3:5])))
 print dtTideTimes[j], tideheights[j]
 
#create a dictionary linking dtTideTimes:tideheights

tidedatadict={}

for k in range (0,lastdayofmonth*4):
 tidedatadict[dtTideTimes[k]]=tideheights[k]
 
 

---

Tide Indicator Pi Project #5 - Parsing a months worth of tide data into different lists

Today I learned to use 

list.extend([item[1],item[2]])

to split the parsed tide data to different lists. :-)

full code below.

#A python program to import tide data from a portsofjersey website
#tidenow.py
#It pulls the data in from the tide site, a month at a time
#It looks for the class headers associated with date,time and height information
#and then creates lists of these data


import urllib2
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt


#open site and grab html

rawhtml = urllib2.urlopen("http://www.ports.je/Pages/tides.aspx").read(40000)
soup = BeautifulSoup(rawhtml, "html.parser")


#get the tide data (it's all in 'td' tags)

rawtidedata = soup.findAll('td')


#get just the month and year (it's in the 1st 'h2' tag on the page)

rawmonthyear = soup.findAll('h2')[0].get_text()
print ('Month and Year: ', rawmonthyear)

#strip the html and parse it all to one big list

n=0
parsedtidedata=[]
for i in rawtidedata: 
   parsedtidedata.append(rawtidedata[n].get_text())
 # print (parsedtidedata[n]) #leave in for debugging for now
   n += 1


#create lists for each class of data

tidetimes=[]
tideheights=[]
tideday=[]


#extract data to each list (there are 10 data items for each day)

lastdayofmonth=int(parsedtidedata[-10])

for n in range(0,lastdayofmonth*10,10):

   tideday.append(parsedtidedata[n])
   tidetimes.extend([parsedtidedata[n+1],parsedtidedata[n+3],parsedtidedata[n+5],parsedtidedata[n+7]])
   tideheights.extend([parsedtidedata[n+2],parsedtidedata[n+4],parsedtidedata[n+6],parsedtidedata[n+8]])

print('data for the 1st of the month')
n=0
print tideday[n]
print tidetimes[n:n+4]
print tideheights[n:n+4] 

Tide Indicator Pi Project #4 - Scraping a months worth of tide data in one hit

Having realised in my previous post I needed to move away from daily tide processing to collecting as data for a longer period, I chose this site to gather from, as the html code looked easy to scrape.

The code below is a starting point. I collects a month's worth of tide data, and parses it to one long list. I like this data better than the last site because 'empty' data slots are filled with '***' as a useful place-holder. The output is shown below.

#A python program to import tide data from a Ports of Jersey website

#tidenow.py
#It pulls the data in from the tide site, a month at a time

#import tweepy
#import smtplib
import urllib2
#import re
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt


#open site and grab html

rawhtml = urllib2.urlopen("http://www.ports.je/Pages/tides.aspx").read(40000)
soup = BeautifulSoup(rawhtml, "html.parser")


#get the tide data (it's all in 'td'tags)

rawtidedata = soup.findAll('td')


#get just the month and year (it's in the 1st h2 tag on the page)

rawmonthyear = soup.findAll('h2')[0].get_text()
print ('Month and Year: ', rawmonthyear)

#parse it all to a list
n=0
parsedtidedata=[]
for i in rawtidedata: 
 parsedtidedata.append(rawtidedata[n].get_text())
 print (parsedtidedata[n])
 n += 1

Output:

Tide Indicator Pi Project #3 - Success! and Complete Rethink Required

The next step in my plan to collect tide data from the web and use it to make some kind of live tide gauge.

Darn it, I thought I had it:

#tides for #jerseyci today, Thursday 1 October:
03:48 0.6m
09:21 11.7m
16:07 0.9m
21:42 11.5m
data from http://mbcurl.me/13KDW

I've been successfully scraping daily tide data, and posting it on my Pi-hosted site here...

jcwyatt.ddns.net

and tweeting it here...

www.twitter.com/#jerseyci

which was a major goal (code is below). Chron runs this Python program every morning at 5:30am.

However now it has come to thinking about calculating live tide heights I've hit a wall when trying to use the data I'm currently scraping.

I've been working on daily data, when what is needed is continuous data over a longer time. Once I have that I think I can calculate live tide height with a rolling algorithm. Tides don't fit in neat daily chunks.

I'm going back to here: http://www.ports.je/Pages/tides.aspx to scrape a month's worth of data at a time and see how it goes.

The code below took a while and is pretty untidy, but it does what it needs to do, with some nifty string and list handling that I'm quite proud of. 

#A python program to import tide data from a gov.je website
#tidescrape6.0.py - working fine
#It pulls the data in from the gov.je tide site, which is updated daily
#It looks for the class headers associated with date,time and height information
#and then creates a list of these bits of html

#this version(6.0) is called by a chrontab function and tweets at 5:30am everyday.

import tweepy
import smtplib
import urllib2
import re
from bs4 import BeautifulSoup
from time import sleep
import datetime as dt



#function to scrape tide data from website
def tidedatascrape():

 #open site
 rawhtml = urllib2.urlopen("http://www.gov.je/Weather/Pages/Tides.aspx").read(20000)

 soup = BeautifulSoup(rawhtml)

 #from http://stackoverflow.com/questions/14257717/python-beautifulsoup-wildcard-attribute-id-search

 #get the dates:
 tidedates = soup.findAll('td', {'class': re.compile('TidesDate.*')} )
 #get the times:
 tidetimes = soup.findAll('td', {'class': re.compile('TidesTime.*')} )
 #get the heights:
 tideheights = soup.findAll('td', {'class': re.compile('TidesHeight.*')} )

 #collect together the data for today

 todaysdate = tidedates[0].get_text()
 print (todaysdate)
 todaystimes = tidetimes[0].get_text()
 print (todaystimes)
 todaysheights = tideheights[0].get_text()
 print (todaysheights)


 #parse the times (always a 5 character string)
 ttime = [0,0,0,0]
 for i in range (0,4):
  ttime[i]=todaystimes[5*i:(5*i+5)]
  print ttime[i]


 #parse the heights (3 or 4 ch string delimited by 'm' e.g 2.5m3.4m etc)
 theight = ['','','','']
 list_index = 0
 for i in todaysheights:
  if i == 'm':
   list_index += 1
  else:
   theight[list_index] = theight[list_index] + i
 print theight[0]



 #create a tweetable string of all the data
 tweetstring = ('#tides for #jerseyci today, ' + todaysdate + ':\n')
 for i in range (0,4):
  tweetstring = tweetstring + (ttime[i] + ' ' + theight[i] + 'm\n')
 tweetstring = tweetstring + 'data from http://mbcurl.me/13KDW'
 print tweetstring
 return tweetstring
 

 #print len(tweetstring) #just to check it is within 140 characters

#function to write to a text file
def writetidestofile(tweetstring):
        with open('/var/www/dailytideoutput.txt','w') as f:
                f.write(str(tweetstring))
                f.close()


#function to tweet it
def tweettidedata(tweetstring):
 CONSUMER_KEY = '0000000000000000000'#keep the quotes, replace this with your consumer key
 CONSUMER_SECRET = '00000000000000000000000000000000000000'#keep the quotes, replace this with your consumer secret key

 ACCESS_KEY = '00000000000000000000000000000000000000'#keep the quotes, replace this with your access token

 ACCESS_SECRET = '00000000000000000000000000000000000000'#keep the quotes, replace this with your access token secret

 auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
 auth.set_access_token(ACCESS_KEY, ACCESS_SECRET)
 api = tweepy.API(auth)

 api.update_status(status=tweetstring) #THIS LINE TWEETS! - LEAVE DEACTIVATED UNTIL READY


#email it(commented out for now)
'''

fromaddr = 'jbloggs@gmail.com'
toaddr  = 'j.bloggette@free.sch.uk'

# Credentials (if needed)
username = raw_input('gmail un: ')
password = raw_input('gmail pw: ')

# The actual mail send
server = smtplib.SMTP('smtp.gmail.com:587')
server.ehlo()
server.starttls()
server.login(username,password)
headers = "\r\n".join(["from: " + fromaddr,
                       "subject: " + 'Tides Today',
                       "to: " + toaddr,
                       "mime-version: 1.0",
                       "cont#ent-type: text/html"])

# body_of_email can be plaintext or html!                    
content = headers + "\r\n\r\n" + tweetstring
server.sendmail(fromaddr, toaddr, content)
server.quit
'''

#main prog
#collect data
tweetstring = tidedatascrape()
#output to file
writetidestofile(tweetstring)
#tweet data
tweettidedata(tweetstring) 

  
 

Tide Indicator Pi Project #2 - Scraping the tidal data for St Helier

Tide Indicator Pi Project #2 - Scraping the tidal data for St Helier

Have working code that extracts the code and makes lists from it (with the html tags still attached for now):

#A python program to import tide data from a gov.je website
#tidescrape1.0.py - working fine
#It pulls the data in from the gov.je tide site, wkhich is updated daily
#It looks for the class headers associated with date,time and height information
#and then creates a list of these bits of html

#next step - try to extract just the data from current day and tweet it.

import urllib2
import re
from bs4 import BeautifulSoup


#open site
rawhtml = urllib2.urlopen("http://www.gov.je/Weather/Pages/Tides.aspx").read(20000)

soup = BeautifulSoup(rawhtml)

#from http://stackoverflow.com/questions/14257717/python-beautifulsoup-wildcard-attribute-id-search
#get the dates:
tidedates = soup.findAll('td', {'class': re.compile('TidesDate.*')} )

print (tidedates[0])

#get the times:
tidetimes = soup.findAll('td', {'class': re.compile('TidesTime.*')} )

print (tidetimes[0])

#get the heights:
tideheights = soup.findAll('td', {'class': re.compile('TidesHeight.*')} )

print (tideheights[0])

Output looks like this:

<td class="TidesDate Weekend">Sunday 9 August</td>
<td class="TidesTime Weekend"><span style="color:#cc0000;">01:57</span><br/>08:42<br/>14:33<br/>21:27<br/></td>
<td class="TidesHeight Weekend"><span style="color:#cc0000;">8.5m</span><br/>3.6m<br/>8.4m<br/>3.7m<br/></td>


Next step is to somehow strip the text out.

Tide Indicator Pi Project #1 - Scraping the tidal data for St Helier - Update

A quick update to post 1 on this project.

This is the data from the Jerseymet site and it looks like it is indexed well and the required data sits in nice blocks.

Tide Indicator Pi Project #1 - Scraping the tidal data for St Helier

I have a plan to build a live tide indicator. This will lift tide data from the web, interpret and extrapolate it to work out the current tide height, and then output this data live to the web and a physical indicator. 

This is the data on the UK Hydrographic Office site:

(http://www.ukho.gov.uk/easytide/EasyTide/ShowPrediction.aspx?PortID=1605&PredictionLength=7)





and here's the html to scrape:


<table border="0" class="HWLWTable first"> <tbody> <tr> <th class="HWLWTableHeaderCell" colspan="4">Sun 26 Jul</th> </tr> <tr> <th class="HWLWTableHWLWCell">HW</th><th class="HWLWTableHWLWCell">LW</th><th class="HWLWTableHWLWCell">HW</th><th class="HWLWTableHWLWCell">LW</th> </tr> <tr> <td class="HWLWTableCell">00:41</td><td class="HWLWTableCell">07:26</td><td class="HWLWTableCell">13:32</td><td class="HWLWTableCell">20:10</td> </tr> <tr> <td class="HWLWTableCell">7.9 m</td><td class="HWLWTableCell">4.2 m</td><td class="HWLWTableCell">8.0 m</td><td class="HWLWTableCell">4.1 m</td> </tr> </tbody></table> <table border="0" class="HWLWTable"> <tbody> <tr> <th class="HWLWTableHeaderCell" colspan="4">Mon 27 Jul</th> </tr> <tr> <th class="HWLWTableHWLWCell">HW</th><th class="HWLWTableHWLWCell">LW</th><th class="HWLWTableHWLWCell">HW</th><th class="HWLWTableHWLWCell">LW</th> </tr> <tr> <td class="HWLWTableCell">02:04</td><td class="HWLWTableCell">08:47</td><td class="HWLWTableCell">14:47</td><td class="HWLWTableCell">21:25</td> </tr> <tr> <td class="HWLWTableCell">8.1 m</td><td class="HWLWTableCell">4.0 m</td><td class="HWLWTableCell">8.4 m</td><td pre=""><span style="font-family: Trebuchet MS, sans-serif;"> </span></td></tr> </tbody></table>  

Tidal info is also avalable here: http://www.portofjersey.je/Pages/tides.aspx

and here*: http://www.gov.je/Weather/Pages/Tides.aspx

*Of these it actually looks like this last one has the neatest, most 'scrapable' formatting and the current days tides will always be at the same position on the page.


To scrape the data I initially tried this: 

#python program to import tide data from a website
import urllib2

#open site
rawhtml = urllib2.urlopen("http://www.ukho.gov.uk/easytide/EasyTide/ShowPrediction.aspx?PortID=1605&PredictionLength=7").read(20000)

print (rawhtml)

Which collected the text from the site, but it appeared tricky to extract the meaningful data.

I googled and found this: http://docs.python-guide.org/en/latest/scenarios/scrape/

But it wasn't to easy to install the lxml module using pip but this worked:

sudo apt-get install python-lxml

Next job is to learn how to extract particular bits of data.