Electron microscopy
 
Output the Links Obtained by Google Search
- Python for Integrated Circuits -
- An Online Book -
Python for Integrated Circuits                                                                                   http://www.globalsino.com/ICs/        


Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

Installation for such function presented here:
          i) pip install beautifulsoup4. Google package has one dependency on beautifulsoup.
          ii) pip install google.
Required Function and its parameters:
          i) query: query string that we want to search for.
          ii) TLD: the top-level domain. Default: tld = 'com'.
          iii) lang: lang stands for language. By default, we have English, similar to lang="en". For instance, to get results in French, we can
               change the code to search(query, lang="fr").
          iv) num: Number of results. or "num_results=10". By default, googlesearch returns 10 results, but this can be changed.
          v) start: The first result to retrieve. Default: start = 0.
          vi) stop: The last result to retrieve. Use None to keep searching forever. Default: stop = None.
          vii) pause: Lapse to wait between HTTP requests. Default: pause = 2.0. Lapse too short may cause Google to block your IP.
               Keeping significant lapses will make your program slow but it’s a safe and better option.
          viii) Return: Generator (iterator) that yields found URLs. If the stop parameter is None the iterator will loop forever.

============================================

Output the links obtained by Google Search. code:          
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
         Find the best similarity with Word2Vec Models/word embeddings

============================================

Output the links as a list. code:          
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
         Find the best similarity with Word2Vec Models/word embeddings

============================================

Output the links as a list. code:          
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
         Find the best similarity with Word2Vec Models/word embeddings

         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         

 

 

 

 

 



















































 

 

 

 

 

=================================================================================