tide-forecast.py

'''
Created on 16 Apr 2015

@author: robert
'''
import lxml.html as html
import requests
import logging
import time

logger = logging.getLogger(__name__)

class TideScraper():
    def scrapeTideForecast(self,location):
        page = requests.post("http://www.tide-forecast.com/locations/catch",data={'query':location})
        root = html.document_fromstring(page.text)
        scripts = root.xpath("//script[@src]")
        locname = None
        for script in scripts:
            if location.lower() in script.get('src').lower():
                for part in script.get('src').split("/"):
                    if location.lower() in part.lower():
                        locname = part.split(".")[0]
                
                
        if not locname:
            logger.error("Could not find location {}".format(location))
            return
        
        data = []
        page = requests.get("http://www.tide-forecast.com/tides/{}.js".format(locname))
        if page.status_code != 200:
            logger.error(page.text)
            logger.error('locname = {}'.format(locname))
        for line in page.text.split():
            cleanline = line.strip('[').strip(',').strip(']')
            elements = cleanline.split(",")
            if len(elements) == 4:
                ld = {'x':int(elements[0]),
                      'y':int(elements[1]),
                      'epoch':int(elements[2]),
                      'height':float(elements[3]),
                }
                data.append(ld)
        
        return data
        
    def getHeightsForDays(self,data):
    #Convert some times
        days = {}
        for entry in data:
            entry['timestr'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(entry['epoch']))
            ydelta = time.localtime(entry['epoch'] + entry['y'])
            entry['date'] = time.strftime("%d-%m-%Y", ydelta)
            entry['time'] = time.strftime("%H:%M:%S", ydelta)
            
            if entry['date'] not in days:
                days[entry['date']] = []
                
            days[entry['date']].append(entry)
            
            #if entry['date'] == '17-04-2015':
            #   logger.info("{} - {:.1f}m".format(entry['time'],entry['height']))            
        
        return days


    def getTides(self,dataset):
        tides = []     
        state = 'unknown'
        for i in range(1,len(dataset)):
            if state == 'unknown':
                if dataset[i]['height'] < dataset[i-1]['height']:
                    state = 'dropping'
                else:
                    state = 'rising'
                
            #Rather than looking for the lowest value we look for when the tide turns
            #Possibly need to introduce some countback in case data sets are the same ie slack water
            if state == 'dropping':
                if dataset[i]['height'] > dataset[i-1]['height']:
                    #logger.debug("{} {:.1f}m - Low tide".format(dataset[i-1]['time'],dataset[i-1]['height']))
                    tides.append({'time':dataset[i-1]['time'], 'height':dataset[i-1]['height'], 'type':'Low tide'})
                    state = 'rising'
                    
            if state == 'rising':
                if dataset[i]['height'] < dataset[i-1]['height']:
                    #logger.debug("{} {:.1f}m - High tide".format(dataset[i-1]['time'],dataset[i-1]['height']))
                    tides.append({'time':dataset[i-1]['time'], 'height':dataset[i-1]['height'], 'type':'High tide'})
                    state = 'dropping'
                
            if dataset[i]['height'] == dataset[i-1]['height']:
                logger.info("Two adjacent tides with height {}m".format(dataset[i]['height']))
                    
        return tides
if __name__ == '__main__':
    logging.basicConfig(level=logging.DEBUG)
    ts = TideScraper()
    data = ts.scrapeTideForecast("Angochi")
    days = ts.getHeightsForDays(data)
    print days.keys()
    friday = ts.getTides(days['17-04-2015'])
    for tide in friday:
        print tide

Some code to scrape the tidal data from tide-forecast.com