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| import pandas as pd |
| import numpy as np |
| import json, requests |
| import pandas as pd |
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| def URL(base_URL , entity_type , filters): |
| url = base_URL + entity_type + filters |
| return url |
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| def get_data(url): |
| url = requests.get(url) |
| text = url.text |
| import json |
| data = json.loads(text) |
| return data |
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| def reconstruction_abstract(abstract_inverted_index): |
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| if abstract_inverted_index != None: |
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| list_values = list(abstract_inverted_index.values()) |
| list_keys = list(abstract_inverted_index.keys()) |
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| size_abstract = max([ max(elem) for elem in abstract_inverted_index.values() ] ) |
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| abstract = [""]*(size_abstract +1) |
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| for i in range(len(list_values)): |
| for pos in list_values[i]: |
| abstract[pos] = list_keys[i] |
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| return " ".join(list(abstract)) |
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| else: |
| return None |
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| def norm(vector): |
| return np.sqrt(sum(x * x for x in vector)) |
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| 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) |
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| def extract_quantitative_data_paper( work_id): |
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| 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(" ") |
| |
| return [url, date, title, abstract, concepts, authors, institutions] |
| except: |
| print("Enter a valid work id from OpenAlex") |
| |
| |
| def extract_quantitative_data_patent( patent_id): |
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| 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") |
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| return data, url, assignees, inventors |
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