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##packages code
import pandas as pd
import numpy as np
import json, requests
import pandas as pd
#import spacy
#!python -m spacy download en_core_web_lg
#spacy_nlp = spacy.load("en_core_web_lg")
#import en_core_web_lg
#spacy_nlp = en_core_web_lg.load()
#################### Test Scripts #############################
def URL(base_URL , entity_type , filters):
url = base_URL + entity_type + filters
return url
def get_data(url):
url = requests.get(url)
text = url.text
import json
data = json.loads(text)
return data
## encoding the abstract
def reconstruction_abstract(abstract_inverted_index):
# return the abstract is the abstract exists in the database, else, return None
if abstract_inverted_index != None:
list_values = list(abstract_inverted_index.values())
list_keys = list(abstract_inverted_index.keys())
#from the words in the abstract (keys of abstract_inverted_index) and their position in the text (values of abstract_inverted_index), reconstruct the abstract
size_abstract = max([ max(elem) for elem in abstract_inverted_index.values() ] )
abstract = [""]*(size_abstract +1)
for i in range(len(list_values)):
for pos in list_values[i]:
abstract[pos] = list_keys[i]
return " ".join(list(abstract))
else:
return None
## calculate efficiently the dot product between two vectors
def norm(vector):
return np.sqrt(sum(x * x for x in vector))
def cosine_similarity2(vec_a, vec_b):
norm_a = norm(vec_a)
norm_b = norm(vec_b)
dot = sum(a * b for a, b in zip(vec_a, vec_b))
return dot / (norm_a * norm_b)
################################### Extracted texts ###############################################################
def extract_quantitative_data_paper( work_id):
try:
url = "https://api.openalex.org/works/" + str(work_id)
data = get_data(url)
date = data["publication_date"]
title = data["title"]
abstract = reconstruction_abstract(data["abstract_inverted_index"])
concepts = ", ".join( [elem["display_name"] for elem in data["concepts"]] )
authors = ", ".join( [elem["author"]["display_name"] for elem in data["authorships"]] )
institutions = ", ".join( set([elem["institutions"][0]["display_name"] for elem in data["authorships"]]) )
print("\033[96mFROM OpenAlex: ")
print("\033[92mPaper link: " + url)
print(" ")
print("\033[92mTitle: \x1b[0m" + title)
print(" ")
print("\033[92mConcepts: \x1b[0m" + concepts)
print(" ")
print("\033[92mDate: \x1b[0m" + date)
print(" ")
print("\033[92mAuthors: \x1b[0m" + authors)
print(" ")
print("\033[92mInstitutions: \x1b[0m" + institutions)
print(" ")
# sentences = extract_sentences_with_numbers(abstract , "\033[92mAbstract\x1b[0m")
return [url, date, title, abstract, concepts, authors, institutions]
except:
print("Enter a valid work id from OpenAlex")
def extract_quantitative_data_patent( patent_id):
try:
url = "https://api.patentsview.org/patents/query?q={%22patent_id%22:%22" + str(patent_id) + "%22}&f=[%22patent_number%22,%22patent_title%22,%22patent_abstract%22,%22patent_date%22,%22inventor_last_name%22,%22inventor_first_name%22,%22assignee_organization%22]"
data = get_data(url)["patents"][0]
title = data["patent_title"]
abstract = data["patent_abstract"]
print("\033[96mFROM PATENTSVIEW:")
print("\033[92mPatent link: \x1b[0m" + url)
print(" ")
print("\033[92mTitle: \x1b[0m" + title)
print(" ")
print("\033[92mAbstract: \x1b[0m" + abstract)
print(" ")
print("\033[92mDate: \x1b[0m" + data["patent_date"])
print(" ")
print("\033[92mInventors: \x1b[0m" , ", ".join([ data["inventors"][i]["inventor_first_name"] + " " + data["inventors"][i]["inventor_last_name"] for i in range(len(data["inventors"])) ]) )
print(" ")
print("\033[92mAssignee: \x1b[0m" , ", ".join( [ str(data["assignees"][i]["assignee_organization"]) for i in range(len(data["assignees"])) ] ) )
assignees = ', '.join( [ str(data["assignees"][i]["assignee_organization"]) for i in range(len(data["assignees"])) ] )
inventors = ", ".join([ data["inventors"][i]["inventor_first_name"] + " " + data["inventors"][i]["inventor_last_name"] for i in range(len(data["inventors"])) ])
except:
print("Enter a valid patent_id from PatentsView")
return data, url, assignees, inventors
#print("Do you want the quantitative information corresponding to a paper?")
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