computing

Create Train breakdown notifications

Imagine walking 10 mins to the train station, finds the train has broken down and the bus stop is 20 mins walk away in opposite direction from the station. This is extremely frustrating especially if you are living in a country with relatively frequent train delay and breakdown. The solution: create a simple alert system to your phone using Python, Pattern and Pushbullet.

The below script will scrape the MRT website for latest announcements using Python Pattern and send to the phone using Pushbullet. In this version of script, it will always return the latest post on the website. As such, the latest post might be a few days ago if there is no new breakdown.

We will assume that MRT is working well if it returns a non-current post. In such case, we will also pull the date and time of latest post for date comparison. The script is then scheduled to run every day at specific timing preferably before going out for work.

import os, sys, time, datetime
from pattern.web import URL, extension, download, DOM, plaintext
from pyPushBullet.pushbullet import PushBullet

#target website url
target_website = 'https://twitter.com/SMRT_Singapore'

# Require user-agent in the download field
html = download(target_website, unicode=True,
                user_agent='"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36"',
                cached=False)

#optional, for checking purpose
with open(r'c:\data\temp\ans.html','wb') as f:
    f.write(html.encode('utf8'))

dom = DOM(html)

#X-path for scraping target parameters
time_str =  dom('a[class="tweet-timestamp js-permalink js-nav js-tooltip"]')[0].attributes['title']
content_str =  plaintext(dom('p[class="TweetTextSize TweetTextSize--26px js-tweet-text tweet-text"]')[0].content)

full_str = time_str + '\n' + content_str

api_key_path = r'API key str filepath'
with open(api_key_path,'r') as f:
    apiKey = f.read()

p = PushBullet(apiKey)
p.pushNote('all', 'mrt alert', full_str ,recipient_type="random1")

Modification can be done such that it will only create alert if there is current news simply by comparing the date of the post to today’s date.

Related posts

  1. Configuring mobile alert with pushbullet: “Sending alerts to iphone or Android phone using python“.
  2. Example of web scrape using pattern “Simple Python Script to retrieve all stocks data from Google Finance Screener

Scraping housing prices using Python Scrapy Part 2

This is the continuation of the previous post on “Scraping housing prices using Python Scrapy“. In this session, we will use Xpath to retrieve the corresponding fields from the targeted website instead of just having the full html page. For a preview on how to extract the information from a particular web page, you can refer to the following post “Retrieving stock news and Ex-date from SGX using python“.

Parsing the web page using Scrapy will require the use of Scrapy spider “parse” function. To test out the function, it might be an hassle to run Scrapy crawl command each time you try out a field as this means making requests to the website every single time.

There are two ways to go about it. One way is to let Scrapy cache the data. The other is to make use of the html webpage downloaded in the previous session. I have not really try out caching the information using scrapy but it is possible to run using Scrapy Middleware. Some of the links below might help to provide some ideas.

  1. https://doc.scrapy.org/en/0.12/topics/downloader-middleware.html
  2. http://stackoverflow.com/questions/22963585/using-middleware-to-ignore-duplicates-in-scrapy
  3. http://stackoverflow.com/questions/40051215/scraping-cached-pages

For utilizing the downloaded copy of the html page which is what I have been using, the following script demonstrate how it is done. The downloaded page is taken from this property website link. Create an empty script and input the following snippets, run the script as normal python script.

    import os, sys, time, datetime, re
    from scrapy.http import HtmlResponse

    #Enter file path
    filename = r'targeted file location'

    with open(filename,'r') as f:
        html =  f.read()

    response = HtmlResponse(url="my HTML string", body=html) # Key line to allow Scrapy to parse the page

    item = dict()

    for sel in response.xpath("//tr")[10:]:
        item['id'] = sel.xpath('td/text()')[0].extract()
        item['block_add'] = sel.xpath('td/a/span/text()')[0].extract()
        individual_block_link = sel.xpath('td/a/@href')[0].extract()
        item['individual_block_link'] = response.urljoin(individual_block_link)
        item['date'] = sel.xpath('td/text()')[3].extract()

        price = sel.xpath('td/text()')[4].extract()
        price = int(price.replace(',',''))
        price_k = price/1000
        item['price'] = price
        item['price_k'] = price_k
        item['size'] = sel.xpath('td/text()')[5].extract()
        item['psf'] = sel.xpath('td/text()')[6].extract()
        #agent = sel.xpath('td/a/span/text()')[1].extract()
        item['org_url_str'] = response.url

        for k, v in item.iteritems():
            print k, v

Once verified there are no issue retrieving the various components, we can paste the portion to the actual Scrapy spider parse function. Remember to exclude the statement “response = HtmlResponse …”.

From the url, we noticed that the property search results are available in multiple pages. The idea is to traverse each page and obtain the desired information from each page. This would need Scrapy to know the next url to go to. To parse the information, the same method can be use to retrieve the url link to the next page.

Below show the parse function use in the Scrapy spider.py.

def parse(self, response):

    for sel in response.xpath("//tr")[10:]:
        item = ScrapePropertyguruItem()
        item['id'] = sel.xpath('td/text()')[0].extract()
        item['block_add'] = sel.xpath('td/a/span/text()')[0].extract()
        individual_block_link = sel.xpath('td/a/@href')[0].extract()
        item['individual_block_link'] = response.urljoin(individual_block_link)
        item['date'] = sel.xpath('td/text()')[3].extract()

        price = sel.xpath('td/text()')[4].extract()
        price = int(price.replace(',',''))
        price_k = price/1000
        item['price'] = price
        item['price_k'] = price_k
        item['size'] = sel.xpath('td/text()')[5].extract()
        item['psf'] = sel.xpath('td/text()')[6].extract()
        #agent = sel.xpath('td/a/span/text()')[1].extract()
        item['org_url_str'] = response.url

        yield item

    #get next page link
    next_page = response.xpath("//div/div[6]/div/a[10]/@href")
    if next_page:
        page_url = response.urljoin(next_page[0].extract())
        yield scrapy.Request(page_url, self.parse)

For the next post, I will share how to migrate the running of spider to Scrapy Cloud

Related Posts

  1. Scraping housing prices using Python Scrapy
  2. Retrieving stock news and Ex-date from SGX using python

Automating Google Sheets with Python

This post demonstrate basic use of python to read/edit Google sheets. For fast setup, you can visit this link. Below is the setup procedure copied from the link itself.

  1. Use this wizard to create or select a project in the Google Developers Console and automatically turn on the API. Click Continue, then Go to credentials.
  2. On the Add credentials to your project page, click the Cancel button.
  3. At the top of the page, select the OAuth consent screen tab. Select an Email address, enter a Product name if not already set, and click the Save button.
  4. Select the Credentials tab, click the Create credentials button and select OAuth client ID.
  5. Select the application type Other, enter the name “Google Sheets API Quickstart”, and click the Create button.
  6. Click OK to dismiss the resulting dialog.
  7. Click the file_download (Download JSON) button to the right of the client ID.
  8. Move this file to your working directory and rename it client_secret.json.

The next step  will be to install the google client using pip.

pip install --upgrade google-api-python-client

The final step is to copy the sample from the same link. For the first time running the script, you would need to sign in with Google. Use the below command to link the sheets credentials to the targeted gmail account. Follow the instruction as from the prompt.

$ python name_of_script.py --noauth_local_webserver

You can easily access/modify the contents of the sheets especially if it is in the table format by linking it with Python Pandas.

# authorization: reference from link
credentials = get_credentials()
http = credentials.authorize(httplib2.Http())
discoveryUrl = ('https://sheets.googleapis.com/$discovery/rest?'
'version=v4')
service = discovery.build('sheets', 'v4', http=http,
discoveryServiceUrl=discoveryUrl)

# Target spreadsheet
spreadsheetId = 'your_spreadsheet_name'
rangeName = 'Sheet1!A1:N'

# read from spreadsheet
result = service.spreadsheets().values().get(
spreadsheetId=spreadsheetId, range=rangeName).execute()
values = result.get('values', [])

import pandas
# Pandas Dataframe with values and header
data_df = pd.DataFrame(values[1:], columns = values[0])
print data_df

Related Posts:

  1. Automating Ms Powerpoint with Python: https://simply-python.com/2014/07/04/rapid-generation-of-powerpoint-report-with-template-scanning
  2. Using Excel with Python: https://simply-python.com/2014/08/20/manage-and-extract-data-using-python-and-excel-tables

 

Simple Python Script to retrieve all stocks data from Google Finance Screener (Part 2)

Upgraded version from previous “Simple Python Script to retrieve all stocks data from Google Finance Screener“. The new version allows options to select the various stocks exchange including all US exchange and expand on the financial metrics present.

To run the script, you can simply run the following commands.

from google_screener_data_extract import GoogleStockDataExtract

hh = GoogleStockDataExtract()
hh.target_exchange = 'NASDAQ' #SGX, NYSE, NYSEMKT
hh.retrieve_all_stock_data()
hh.result_google_ext_df.to_csv(r'c:\data\temp.csv', index =False) #save filename

The new script allows easy installation via pip. To install:
pip install google_screener_data_extract

The script is also available in GitHub.

Packaging with cookie cutter

The following link demonstrates a simple way to create and package a pip install ready module with the help of cookie cutter. The link provided very clear explanation of the steps.

To add on, if you experience difficulties or problems using command prompt to enter GitHub commands. The git portion can be skipped and use the GitHub GUI instead to upload the package to GitHub.

For uploading to pip, would need to have the latest python 2.7 (2.7.11 above) to upload the package successfully.

More links below on creating packages.

  1. Cookiecutter tutorial
  2. Python Packaging

 

Retrieving Singapore housing (HDB) resale prices with Python

This post is more suited for Singapore context with the aim of retrieving the Housing Development Board (HDB) resale prices for the year 2015 grouped by different parts of Singapore. All the prices information are retrieved from the HDB main website. The website retrieves the past 1 yr records for each block or by postcode. Hence, in order to retrieve all the records, one would need to retrieve all the postcode in Singapore first. Below outline the list of information required in order to form the full picture.

  1. Retrieve the full postcode from following sg postcode database.
  2. The above only have postcode, next will have to merge the postcode to the actual address. This website also provide the search of post code and retrieve the corresponding address. You can automate using the same process with python, python pattern and pandas.
  3. Retrieve the HDB resale prices by iterating all the postcode retrieved from above.
  4. The optional steps will also be retrieving the Geocodes correspond to the post code so all the data can be put into a map. This post “Retrieving Geocodes from ZipCodes using Python and Selenium” describes the retrieval method.

The 1st code snippet will be applied to item 1, i.e.,  retrieving the post code. For item 2, it is a two steps process, first have to search the postcode, get the link and from the link, retrieve the address.


import pandas as pd
from pattern.web import  URL, extension

def retrieve_postal_code_fr_web_1(target_url, savefilelocation):
    """ 
        target_url (str): url from function.
        savefilelocation (str): full file path.
    """
    savefile = target_url.split('=')[-1] + '.csv'
    fullsavefile = os.path.join(savefilelocation,savefile)
    
    contents = URL(target_url).download()

    w = pd.read_html(contents)
    w[0].to_csv(fullsavefile, index =False)

The next snippet will describe the method to retrieve the HDB resale prices. By exploring the HDB website, the dataset are in the xml format, The url are as followed: http://services2.hdb.gov.sg/webapp/BB33RTIS/BB33SResaleTransMap?postal=<postcode>. For easy retrieval of data in xml format,  one way is to convert the xml to dict form and then convert to pandas dataframe object from the dict. This python module xmltodict will serve the required function.


import re, os, sys, datetime, time
import pandas as pd
import pattern
import xmltodict

from pattern.web import  URL, extension

class HDBResalesQuery(object):
    """ 
        For retrieving the resales prices from HDB webpage.
    """
    def __init__(self):
        """ List of url parameters -- for url formation """
        self.com_data_start_url = 'http://services2.hdb.gov.sg/webapp/BB33RTIS/BB33SResaleTransMap?postal='
        self.postal_portion_url = ''
        self.com_data_full_url = ''
        self.postal_list = [] #multiple postal code list

        ## storage
        self.single_postal_df = pd.DataFrame()
        self.multi_postal_df = pd.DataFrame()

        ## debugging
        self.en_print = 1
        
    def set_postal_code(self, postalcode):
        """ Set the postal code to url part.
            Set to self.postal_portion_url.
            Args:
                postalcode (str): can be str or int??
        """
        self.postal_portion_url = str(postalcode)

    def set_postal_code_list(self, postalcodelist):
        """ Set list of postal code. Set to self.postal_list
            Args:
                postalcodelist(list): list of postal code
        """
        self.postal_list = postalcodelist

    def form_url_str(self):
        """ Form the url str necessary to get the xml

        """           
        self.com_data_full_url = self.com_data_start_url + self.postal_portion_url
        
    def get_com_data(self):
        """ Combine the url str and get html contents
        """
        self.form_url_str()
        if self.en_print: print self.com_data_full_url
        contents = URL(self.com_data_full_url).download()
        return contents

    def process_single_postal_code(self):
        """ process single postal code and retrieve the relevant information from HDB.

        """
        contents = self.get_com_data()
        if self.en_print: print contents
        obj = xmltodict.parse(contents)

        data_dict_list = []
        if obj['Datasets'].has_key('Dataset'):
            data_set = obj['Datasets']['Dataset']
            if type(data_set) == list:
                for single_data in data_set:
                    data_dict_list.append(dict(single_data))
            else:
                data_dict_list.append(dict(data_set))
        
        #Can convert to pandas dataframe w = pd.DataFrame(data_dict_list)
        self.single_postal_df = pd.DataFrame(data_dict_list)
        if self.en_print: print self.single_postal_df

    def process_mutli_postal_code(self):
        """ for processing multiple postal code.
        """
        self.multi_postal_df = pd.DataFrame()
        
        for postalcode in self.postal_list:
            if self.en_print: print 'processing postalcode: ', postalcode
            self.set_postal_code(postalcode)
            self.process_single_postal_code()
            if len(self.single_postal_df) == 0: #no data
                continue
            if len(self.multi_postal_df) == 0:
                self.multi_postal_df = self.single_postal_df
            else:
                self.multi_postal_df = self.multi_postal_df.append(self.single_postal_df)

            

if __name__ == '__main__':
        """ Trying out the class"""
        postallist = ['640525','180262']
        w = HDBResalesQuery()
        w.set_postal_code_list(postallist)
        w.process_mutli_postal_code()
        print w.multi_postal_df

Note that all the processes require large number of queries (110k) to the website. It is best to schedule it to retrieve in batches or the website will shut you out (identify you as a bot).

The following is the Tableau representation of all the data. It is still a prelim version.

HDB Resale Prices

Retrieving Geocodes from ZipCodes using Python and Selenium

Alternative to using GoogleMapAPI to retrieve the geo codes (Latitude and Longitude) from zip codes. This website allows batch processing of the zip code which make it very convenient for automated batch processing.

Below illustrate the general steps in retrieving the data from the website which involve just enter the zipcode, press the “geocode” button and get the output from secondary text box.

Batch Geocode processing website

The above tasks can be automated using Selenium and python which can emulate the users action by using just a few lines of codes. A preview of the code are as shown below. You will notice that the it calls each element [textbox, button etc] by id. This is also an advantage of this website which provide the id tag for each required element. The data retrieved are converted to Pandas object for easy processing.

Currently, the waiting time is set manually by the users.  The script can be further modified to retrieve the number of data being processed before retrieving the final output. Another issue is that this website also make use of GoogleMapAPI engine which restrict the number of query (~2500 per day).  If require massive query of data, one way is to schedule the script to run at fix interval each day or perhaps query from multiple websites that have this conversion features.

For my project, I may need to pull more than 100,000 data set. Pulling only 2500 query is relatively limited even though I can run it on multiple computers. Would welcome suggestions.


import re, os, sys, datetime, time
import pandas as pd
from selenium import webdriver
from selenium.webdriver import Firefox

from time import gmtime, strftime

def retrieve_geocode_fr_site(postcode_list):
    """ Retrieve batch of geocode based on postcode list.
        Based on site: http://www.findlatitudeandlongitude.com/batch-geocode/#.VqxHUvl96Ul
        Args:
            postcode_list (list): list of postcode.
        Returns:
            (Dataframe): dataframe containing postcode, lat, long

        NOte: need to calcute the time --. 100 entry take 94s

    """
    ## need to convert input to str
    postcode_str = '\n'.join([str(n) for n in postcode_list])

    #target website
    target_url = 'http://www.findlatitudeandlongitude.com/batch-geocode/#.VqxHUvl96Ul' 

    driver = webdriver.Firefox()
    driver.get(target_url)

    #input the query to the text box
    inputElement = driver.find_element_by_id("batch_in") 
    inputElement.send_keys(postcode_str)

    #press button
    driver.find_element_by_id("geocode_btn").click()

    #allocate enough time for data to complete
    # 100 input ard 2-3 min, adjust according
    time.sleep(60*10)

    #retrieve ooutput
    output_data = driver.find_element_by_id("batch_out").get_attribute("value")
    output_data_list = [n.split(',') for n in output_data.splitlines()]

    #processing the output
    #last part create it to a pandas dataframe object for easy processng.
    headers = output_data_list.pop(0)
    geocode_df = pd.DataFrame(output_data_list, columns = headers)
    geocode_df['Postcode'] = geocode_df['"original address"'].str.strip('"')
    geocode_df = geocode_df.drop('"original address"',1)

    ## printing a subset
    print geocode_df.head()

    driver.close()

    return geocode_df

 

 

Simple Python Script to retrieve all stocks data from Google Finance Screener

A simple python script to retrieve key financial metrics for all stocks from Google Finance Screener. Google screener have more metrics avaliable compared to SGX screener and also contains comprehensive stocks data for various stock exchanges.

In addition, retrieving data from Google Screener is much faster compared to data retrieved from Yahoo Finance or Yahoo Finance API (See the respective blog post from links).

The reason for the fast retrieval is that the information are stored in the form of single json format for all stocks such that it will reduce the number of request calls and downloading. Being in json format also allows easy conversion to a Pandas Dataframe object.

To retrieve the json url of the stock data, go to the Google Screener and select the criteria (like what is normally done when setting up a filter).  Open up the criteria to full range of the particular metrics. In this way, all the stocks will be selected instead of being filtered off. Using the developer tab of any browser, retrieve the full url. For further description of how to retrieve the url, you can refer to the following post: “Getting historic financial statistics of stocks using Python

Two points to take note:  Firstly the URL only include stock list from 1 -20 due to page setting. Set the end stock to a large number eg 3000 (in blue) to include the full stock list. Below is a sample of the corresponding url.

https://www.google.com/finance?output=json&start=0&amp;num=3000&noIL=1&q=[%28exchange%20%3D%3D%20%22SGX%22%29%20%26%20%28dividend_next_year%20%3E%3D%200%29%20%26%20%28dividend_next_year%20%3C%3D%201.46%29%20%26%20%28price_to_sales_trailing_12months%20%3C%3D%20850%29]&restype=company&ei=BjE7VZmkG8XwuASFn4CoDg

Secondly, as Google only allows 12 criteria to be set at any one go, you would need to repeat process multiple times to obtain all the parameters. Repeat the above process by selecting different criteria and join all the parameters together.

Once the url is formed, the same process is used when scraping web data using python as described in most posts in this blog. The main tools are Python Pandas and Python Pattern. Python Pattern is to help with the json file download and Pandas to convert the json file to Data frame which can then be used to join with other parameters.

The difficult part of the script is to obtain the url. Once the url is known, other methods can be employed to download and read the data from the json file.

The script (for all stocks in Singapore) is available in Github. Due to the long url format, the script will form the full url by concatenating the start and end url with the middle portion (which are all the criteria) stored in a file. File is also found in Github.

 

 

Retrieving historical financial data from MorningStar Using Python

Retrieving historical financial data from MorningStar Using PythonMorning star website contains all the historical financial data such as Net income, EPS (earning per share) per year over 10 years for each stocks. It also provides the historical valuation data such as historical P/E and P/B which are quite difficult to source for. The purpose of the following script is to retrieve the historical data of all desired stocks in a format that is easily represented in Tableau for interactive representation. Below stock information are only catered for Singapore stocks but can be easily changed to other regions as will be shown below.

The first part is to retrieve the company historical financial stats. MorningStar website provides an option to download the data in excel or CSV format. Retrieving in csv format allows easy cleaning and subsequent formatting of the data. To obtain the url for the excel downloading, use any browser and open the developer tab. The network tab will display the url for the excel after pressing on the excel/csv download button. The url will be as below format. Note the region (in blue) can be changed for stocks in another region.

http://financials.morningstar.com/ajax/exportKR2CSV.html?&callback=?&t=XSES:STOCKSYMBOL&region=sgp&culture=en-US&cur=&order=asc

To download and process the information, two major modules are required: python pattern and Python Pandas. Python pattern to handle most of the HTML calls and requests while Pandas to handle the data cleaning and formatting.

For the first part of data extraction, the downloading will be in csv format and using pandas to read the csv. A couple of things to take notes for pulling the data for the first set.

  1. Due to the different line formats, some of the lines are skipped when using pandas to read from csv.
  2. Revenue, income and dividend may be in native currencies for different stocks hence giving rise to different column names (Column names will have the currency displayed). For each of the different currencies, remove the currencies label and consolidate all under same column and extra column for the currency values.
  3. The excel default to two decimal places. Extra calculation are needed to get the actual values without rounding off.

For the second part of retrieving the historical valuation, the method of getting the table will be different as there is no default csv file to be download. In this case, will have to make use of the pandas io html table read function. This pandas method will convert any table like object (html tag td, tr) in website to DataFrame. Some processing is required when pulling this table as it is not a conventional table format. It requires ignoring some lines, renaming the columns and transposing the table.

pandas.io.html.read_html(url_html, tupleize_cols = True,header=0 )

The process is looped over the various stocks hence the full range of stocks can be retrieved. In addition, the information can be combined with the SG company stock information such as industries etc. Information on how to retrieve the SG company stock information such as current price, current valuation etc are available in the following post “Retrieving stock news and Ex-date from SGX using python”.

The full data can be displayed in Tableau as shown bleow. You can also view the interactive mode (WordPress does not allow interactive mode) in my other blog. The script are available  in GitHub.

blogview

 

Saving images from google search using Selenium and Python

Below is a short python script that allows users to save searched images to local drive using Image search on Google. It requires Selenium as Google requires users to press the “show more results” button and the scroll bar to move all the way to the bottom of page for more images to be displayed. Using Selenium will be an easier choice for this function.

The below python script will have the following:

  1. Enable users to input multiple search keywords either by entry or get from file. Users can leave the program to download on its own after creating a series of search keywords.
  2. Based on each keyword, form the google search url. Most of the parameters inside the google search url can be fixed. The only part that required changing is the search keyword as highlighted below in red.
  3. Run google search and obtain page source for the images. This is run using Selenium. To obtain the full set of images, Selenium will help to press the button and scroll the scrollbar to bottom of pages so that Google can load the remaining images. There seems to be a hard quota of 1000 pics for image search on Google.
  4. Use python pattern and xpath to retrieve the corresponding url for each image. The xpath will use the following tag:
    • tag_list = dom(‘a.rg_l’) #a tag with class = rg_l
  5. Based on each url, it will check the following before downloading the image file:
    • whether there is any redirect of site. This is done using Python Pattern redirect function.
    • check the extension whether it is a valid image file type.
  6. The image files are downloaded to a local folder (generated by date). Each image will be label according to the search key and a counter. There will be a corresponding text file mapping the image label to the image url for reference.
import re, os, sys, datetime, time
import pandas
from selenium import webdriver
from contextlib import closing
from selenium.webdriver import Firefox
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC

from pattern.web import URL, extension, cache, plaintext, Newsfeed, DOM

class GoogleImageExtractor(object):

    def __init__(self, search_key = '' ):
        """ Google image search class
            Args:
                search_key to be entered.

        """
        if type(search_key) == str:
            ## convert to list even for one search keyword to standalize the pulling.
            self.g_search_key_list = [search_key]
        elif type(search_key) == list:
            self.g_search_key_list = search_key
        else:
            print 'google_search_keyword not of type str or list'
            raise

        self.g_search_key = ''

        ## user options
        self.image_dl_per_search = 200

        ## url construct string text
        self.prefix_of_search_url = "https://www.google.com.sg/search?q="
        self.postfix_of_search_url = '&source=lnms&tbm=isch&sa=X&ei=0eZEVbj3IJG5uATalICQAQ&ved=0CAcQ_AUoAQ&biw=939&bih=591'# non changable text
        self.target_url_str = ''

        ## storage
        self.pic_url_list = []
        self.pic_info_list = []

        ## file and folder path
        self.folder_main_dir_prefix = r'C:\data\temp\gimage_pic'

    def reformat_search_for_spaces(self):
        """
            Method call immediately at the initialization stages
            get rid of the spaces and replace by the "+"
            Use in search term. Eg: "Cookie fast" to "Cookie+fast"

            steps:
            strip any lagging spaces if present
            replace the self.g_search_key
        """
        self.g_search_key = self.g_search_key.rstrip().replace(' ', '+')

    def set_num_image_to_dl(self, num_image):
        """ Set the number of image to download. Set to self.image_dl_per_search.
            Args:
                num_image (int): num of image to download.
        """
        self.image_dl_per_search = num_image

    def get_searchlist_fr_file(self, filename):
        """Get search list from filename. Ability to add in a lot of phrases.
            Will replace the self.g_search_key_list
            Args:
                filename (str): full file path
        """
        with open(filename,'r') as f:
            self.g_search_key_list = f.readlines()

    def formed_search_url(self):
        ''' Form the url either one selected key phrases or multiple search items.
            Get the url from the self.g_search_key_list
            Set to self.sp_search_url_list
        '''
        self.reformat_search_for_spaces()
        self.target_url_str = self.prefix_of_search_url + self.g_search_key +\
                                self.postfix_of_search_url

    def retrieve_source_fr_html(self):
        """ Make use of selenium. Retrieve from html table using pandas table.

        """
        driver = webdriver.Firefox()
        driver.get(self.target_url_str)

        ## wait for log in then get the page source.
        try:
            driver.execute_script("window.scrollTo(0, 30000)")
            time.sleep(2)
            self.temp_page_source = driver.page_source
            #driver.find_element_by_css_selector('ksb _kvc').click()#cant find the class
            driver.find_element_by_id('smb').click() #ok
            time.sleep(2)
            driver.execute_script("window.scrollTo(0, 60000)")
            time.sleep(2)
            driver.execute_script("window.scrollTo(0, 60000)")

        except:
            print 'not able to find'
            driver.quit()

        self.page_source = driver.page_source

        driver.close()

    def extract_pic_url(self):
        """ extract all the raw pic url in list

        """
        dom = DOM(self.page_source)
        tag_list = dom('a.rg_l')

        for tag in tag_list[:self.image_dl_per_search]:
            tar_str = re.search('imgurl=(.*)&imgrefurl', tag.attributes['href'])
            try:
                self.pic_url_list.append(tar_str.group(1))
            except:
                print 'error parsing', tag

    def multi_search_download(self):
        """ Mutli search download"""
        for indiv_search in self.g_search_key_list:
            self.pic_url_list = []
            self.pic_info_list = []

            self.g_search_key = indiv_search

            self.formed_search_url()
            self.retrieve_source_fr_html()
            self.extract_pic_url()
            self.downloading_all_photos() #some download might not be jpg?? use selnium to download??
            self.save_infolist_to_file()

    def downloading_all_photos(self):
        """ download all photos to particular folder

        """
        self.create_folder()
        pic_counter = 1
        for url_link in self.pic_url_list:
            print pic_counter
            pic_prefix_str = self.g_search_key  + str(pic_counter)
            self.download_single_image(url_link.encode(), pic_prefix_str)
            pic_counter = pic_counter +1

    def download_single_image(self, url_link, pic_prefix_str):
        """ Download data according to the url link given.
            Args:
                url_link (str): url str.
                pic_prefix_str (str): pic_prefix_str for unique label the pic
        """
        self.download_fault = 0
        file_ext = os.path.splitext(url_link)[1] #use for checking valid pic ext
        temp_filename = pic_prefix_str + file_ext
        temp_filename_full_path = os.path.join(self.gs_raw_dirpath, temp_filename )

        valid_image_ext_list = ['.png','.jpg','.jpeg', '.gif', '.bmp', '.tiff'] #not comprehensive

        url = URL(url_link)
        if url.redirect:
            return # if there is re-direct, return

        if file_ext not in valid_image_ext_list:
            return #return if not valid image extension

        f = open(temp_filename_full_path, 'wb') # save as test.gif
        print url_link
        self.pic_info_list.append(pic_prefix_str + ': ' + url_link )
        try:
            f.write(url.download())#if have problem skip
        except:
            #if self.__print_download_fault:
            print 'Problem with processing this data: ', url_link
            self.download_fault =1
        f.close()

    def create_folder(self):
        """
            Create a folder to put the log data segregate by date

        """
        self.gs_raw_dirpath = os.path.join(self.folder_main_dir_prefix, time.strftime("_%d_%b%y", time.localtime()))
        if not os.path.exists(self.gs_raw_dirpath):
            os.makedirs(self.gs_raw_dirpath)

    def save_infolist_to_file(self):
        """ Save the info list to file.

        """
        temp_filename_full_path = os.path.join(self.gs_raw_dirpath, self.g_search_key + '_info.txt' )

        with  open(temp_filename_full_path, 'w') as f:
            for n in self.pic_info_list:
                f.write(n)
                f.write('\n')

if __name__ == '__main__':

    choice =4

    if choice ==4:
        """test the downloading of files"""
        w = GoogleImageExtractor('')#leave blanks if get the search list from file
        searchlist_filename = r'C:\data\temp\gimage_pic\imgsearch_list.txt'
        w.set_num_image_to_dl(200)
        w.get_searchlist_fr_file(searchlist_filename)#replace the searclist
        w.multi_search_download()