| |
| import streamlit as st |
| from shapely.geometry import Point |
| import pandas as pd |
| from tqdm import tqdm |
|
|
| import numpy as np |
| import json, requests |
| import pandas as pd |
| |
|
|
| import matplotlib.pyplot as plt |
| import seaborn as sns |
| from math import radians, cos, sin, asin, sqrt |
|
|
|
|
| from sentence_transformers import SentenceTransformer, util |
|
|
| path = "Climate_site/python_scripts/" |
|
|
| @st.cache_resource |
| def model_nlp(): |
| model = SentenceTransformer('all-MiniLM-L6-v2') |
| return model |
|
|
|
|
| @st.cache_data |
| def load_data(): |
| url = path + "institutions.tsv" |
| dic = pd.read_csv(url, delimiter = "\t" , index_col = 0).to_dict('index') |
| return dic |
|
|
| dic_institutions = load_data() |
|
|
|
|
| import unicodedata |
|
|
| from metaphone import doublemetaphone |
| from fuzzywuzzy import fuzz |
| from difflib import SequenceMatcher |
| import re |
|
|
| import geopandas as gpd |
| from geopandas import GeoDataFrame |
| |
|
|
| 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 |
|
|
| |
|
|
|
|
| def reconstruction_abstract(abstract_inverted_index): |
| |
| |
| if abstract_inverted_index != None: |
| |
| list_values = list(abstract_inverted_index.values()) |
| list_keys = list(abstract_inverted_index.keys()) |
| |
| |
| 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 |
| |
| |
| |
|
|
| 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) |
|
|
| |
|
|
| def print_extracted_text(name_file): |
|
|
| file = open('iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
| count = 0 |
| for index, line in enumerate(lines): |
| read_line = line.strip() |
| print(read_line) |
|
|
| file.close() |
| |
| |
| iea.txt |
|
|
|
|
| def details(name_file , display): |
| |
| file = open(path + "iea.txt", "r") |
| lines = file.readlines() |
|
|
| mark = 0 |
| dic_details = {} |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| count += 1 |
|
|
|
|
| if mark == 1 and line != "" and line[0] == "*": |
| |
| if display == True: |
| print(count) |
| print(text) |
| print(" ") |
| dic_details[count] = text |
| mark = 0 |
|
|
|
|
| if mark == 1: |
| text = text + line + " " |
|
|
| if line.split(" ")[-1] == "Details" or line.split(" ")[-1] == "Hide": |
| mark = 1 |
| text = "" |
| |
| return dic_details |
|
|
|
|
|
|
|
|
|
|
| def key_initiatives(name_file , display ): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
|
|
| |
| mark = 0 |
| dic_key_initiatives = {} |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
|
|
| count += 1 |
|
|
|
|
|
|
| if mark == 1 and line != "" and ( (line[0].isnumeric() and ">" in line and " " in line) or line == "*Deployment targets:*" or line == "*Announced development targets:*"): |
| if display == True: |
| print(count) |
| print(text) |
| print(" ") |
| |
| dic_key_initiatives[count] = text |
| mark = 0 |
|
|
|
|
| if mark == 1: |
| text = text + line + " " |
|
|
| if line == "*Key initiatives:*": |
|
|
|
|
| mark = 1 |
| text = "" |
| |
| return dic_key_initiatives |
|
|
|
|
|
|
|
|
| def deployment_target(name_file , display): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
|
|
|
|
| mark = 0 |
| dic_target = {} |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| count += 1 |
|
|
|
|
|
|
| if mark == 1 and line != "" and ((line[0].isnumeric() and ">" in line and " " in line) or line == "*Announced cost reduction targets:*" or line == "*Announced development targets:*"): |
| |
| if display == True: |
| print(count) |
| print(text) |
| print(" ") |
| |
| dic_target[count] = text |
| mark = 0 |
|
|
|
|
| if mark == 1: |
| text = text + line + " " |
|
|
| if line == "*Deployment targets:*" or line == "*Announced development targets:*": |
|
|
| mark = 1 |
| text = "" |
| |
| return dic_target |
|
|
|
|
| |
| |
| def cost_reduction_target(name_file , display): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
| |
| mark = 0 |
| dic_cost = {} |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
|
|
|
|
| count += 1 |
|
|
|
|
|
|
| if mark == 1 and line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| |
| if display == True: |
| print(count) |
| print(text) |
| print(" ") |
| |
| dic_cost[count] = text |
| mark = 0 |
|
|
|
|
| if mark == 1: |
| text = text + line + " " |
|
|
| if line == "*Announced cost reduction targets:*": |
|
|
| mark = 1 |
| text = "" |
| |
| return dic_cost |
|
|
|
|
|
|
| def key_words(name_file, display ): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| |
| lines = file.readlines() |
| |
| list_categories = [] |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| count += 1 |
| |
| if display == True: |
| print("Technologies" , count+1 , ":") |
|
|
| if line != "": |
|
|
| if line[0].isnumeric() and ">" in line and " " in line: |
| i = 0 |
| try: |
| line = line.split(" ")[2] |
| except: |
| print(line) |
| break |
| |
| if "Details" not in lines[index] and "Moderate" not in lines[index]: |
| |
| while " " not in line: |
| i += 1 |
| if "Details"==lines[index + i][:7] or "End-use"==lines[index + i][:7]: |
| break |
| else: |
| line = line + " " + lines[index + i] |
|
|
| |
| |
|
|
| line = line.replace("\n" , " ") |
| line = line.replace("/" , " ") |
| line = line.replace("-" , " ") |
| line = line.split(" ")[0] |
|
|
| if " " in line: |
| line = line.replace(" ", " ") |
| line = line.split(">") |
|
|
|
|
| if "(" in line[-1]: |
| line[-1] = line[-1].split("(")[0] |
|
|
|
|
| for i in range(len(line)): |
|
|
| |
| line[i] = re.sub(' +', ' ', line[i]) |
| |
| line[i] = line[i].strip() |
|
|
| |
|
|
| if display == True: |
| print(line) |
| print(" ") |
| |
| if '' in line: |
| line.remove('') |
|
|
| list_categories.append([count , line]) |
| |
| return list_categories |
|
|
|
|
|
|
| def technology(name_file, display ): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
| |
| list_categories = [] |
| count = -1 |
| for index, line in enumerate(lines): |
|
|
| line = line.strip() |
| |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| count += 1 |
| |
|
|
| if display == True: |
| print("Technologies" , count+1 , ":") |
|
|
| if line != "": |
|
|
| if line[0].isnumeric() and ">" in line and " " in line: |
| i = 0 |
| try: |
| line = line.split(" ")[1] |
| except: |
| print(line) |
| break |
| |
|
|
| line = line.replace("\n" , " ") |
| line = line.replace("/" , " ") |
| line = line.replace("-" , " ") |
| line = line.strip() |
| line = re.sub(' +', ' ', line) |
| line = line.split(" ")[0] |
| line = line.split(">") |
|
|
|
|
| if "(" in line[-1]: |
| line[-1] = line[-1].split("(")[0] |
|
|
|
|
| for i in range(len(line)): |
|
|
| |
| line[i] = re.sub(' +', ' ', line[i]) |
| |
| line[i] = line[i].strip() |
|
|
| |
|
|
| if display == True: |
| print(line) |
| print(" ") |
| |
|
|
| list_categories.append([count , line]) |
| |
| return list_categories |
|
|
|
|
| |
|
|
| def related_patents(number_technology , research_words, carbon_related , display): |
| |
| name_file = "iea" |
| list_categories = key_words( name_file, False) |
|
|
| dic_patents = {} |
|
|
| max_count = 0 |
| base_URL_PV = "https://api.patentsview.org/" |
| filter_works = "patents/query?" |
| filter_PV = "q={%22_and%22:[{%22_text_all%22:{%22patent_abstract%22:%22" |
| filter_PV += research_words.replace("," , "") |
| |
| if carbon_related == True: |
| filter_PV += "%22}},{%22_eq%22:{%22cpc_group_id%22:%22Y02E%22}}]}&f=[%22patent_number%22,%22patent_title%22,%22assignee_country%22,%22patent_date%22,%22inventor_id%22,%22assignee_organization%22,%22inventor_longitude%22,%22inventor_latitude%22,%22inventor_last_name%22,%22inventor_first_name%22,%22cpc_subsection_title%22,%22assignee_city%22,%22patent_abstract%22,%22patent_kind%22,%22cpc_group_id%22,%22assignee_organization%22,%22citedby_patent_number%22]&s={%22patent_date%22:%22desc%22}&o={%22per_page%22:200}" |
| |
| else: |
| filter_PV += "%22}}]}&f=[%22patent_number%22,%22patent_title%22,%22assignee_country%22,%22patent_date%22,%22assignee_organization%22,%22inventor_longitude%22,%22inventor_latitude%22,%22inventor_last_name%22,%22inventor_id%22,%22inventor_first_name%22,%22cpc_subsection_title%22,%22assignee_city%22,%22patent_abstract%22,%22patent_kind%22,%22cpc_group_id%22,%22assignee_organization%22,%22citedby_patent_number%22]&s={%22patent_date%22:%22desc%22}&o={%22per_page%22:200}" |
|
|
| |
| filter_PV = filter_PV.replace(" " , "%20") |
|
|
| |
|
|
| url = URL(base_URL_PV , filter_works, filter_PV) |
| data = get_data(url) |
|
|
|
|
| if display == True: |
| print( data["total_patent_count"] , elem[-1] ) |
| print(url) |
|
|
|
|
|
|
| |
| for i in range(data["count"]): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]] = {} |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["title"] = data["patents"][i]["patent_title"] |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["abstract"] = data["patents"][i]["patent_abstract"] |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee"] = str(data["patents"][i]["assignees"][0]["assignee_organization"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_city"] = str(data["patents"][i]["assignees"][0]["assignee_city"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_country"] = str(data["patents"][i]["assignees"][0]["assignee_country"]) |
| for j in range(1, len(data["patents"][i]["assignees"])): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_organization"]) |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee_city"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_city"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_country"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_country"]) |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["list_inventors"] = data["patents"][i]["inventors"] |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["inventors"] = str(data["patents"][i]["inventors"][0]["inventor_first_name"]) + " " + str(data["patents"][i]["inventors"][0]["inventor_last_name"]) |
| for j in range(1, len(data["patents"][i]["inventors"])): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["inventors"] += ", " + str(data["patents"][i]["inventors"][j]["inventor_first_name"]) + " " + str(data["patents"][i]["inventors"][j]["inventor_last_name"]) |
|
|
|
|
| dic_patents["US-" + data["patents"][i]["patent_number"]]["date"] = data["patents"][i]["patent_date"] |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["number_citations"] = len(data["patents"][i]["citedby_patents"]) |
|
|
|
|
| if display == True: |
| print(" ") |
| |
| |
| return dic_patents |
|
|
|
|
|
|
| |
|
|
| def ranking_patents(number_technology , research_words, carbon_related , display): |
| |
| model = model_nlp() |
| |
| |
| name_file = "iea" |
| list_categories = key_words( name_file, False) |
| dic_details = details(name_file , False) |
| dic_patents = related_patents(number_technology , research_words, carbon_related , display) |
| |
|
|
| dic_scores = {} |
|
|
| if display == True: |
| print("Key words: " , list_categories[number_technology][1]) |
| |
| if number_technology in dic_details: |
| reference_text = dic_details[number_technology] |
| if display == True: |
| print("Technology details: " , reference_text) |
| print(" ") |
| encoded_text = model.encode(reference_text, convert_to_tensor=False).tolist() |
| |
| |
| if len(dic_patents ) == 0: |
| return "Select other key words" |
| |
| |
| else: |
| for ids in list(dic_patents.keys()): |
|
|
| dic_scores[ids] = {} |
|
|
| encoded_title = model.encode(dic_patents[ids]["title"], convert_to_tensor=False).tolist() |
| score_title = cosine_similarity2(encoded_title, encoded_text) |
|
|
|
|
| if dic_patents[ids]["abstract"] != None: |
| encoded_abstract = model.encode(dic_patents[ids]["abstract"], convert_to_tensor=False).tolist() |
| score_abstract = cosine_similarity2(encoded_abstract, encoded_text) |
| else: |
| score_abstract = None |
|
|
| dic_scores[ids]["title comparision"] = score_title |
| dic_scores[ids]["abstract comparison"] = score_abstract |
| dic_scores[ids]["title"] = dic_patents[ids]["title"] |
| dic_scores[ids]["citations"] = dic_patents[ids]["number_citations"] |
| dic_scores[ids]["date"] = dic_patents[ids]["date"][:4] |
| dic_scores[ids]["assignee"] = dic_patents[ids]["assignee"] |
| |
| |
| dic_scores[ids]["inventors"] = dic_patents[ids]["inventors"] |
| dic_scores[ids]["number of co-inventors"] = len(dic_patents[ids]["inventors"].split(",")) |
| return dic_patents , dic_scores |
|
|
|
|
| def get_ranking_patents(technologies, number_technology ,category , carbon_related , size): |
| dic , dic_patents = ranking_patents(number_technology , category, carbon_related , False) |
| if dic_patents == {}: |
| return "No patent found, select other key words" |
|
|
| elif dic_patents == "Select other key words": |
| return dic_patents |
| |
| else: |
| return pd.DataFrame(dic_patents).T.sort_values(by="abstract comparison" , ascending = False).head(size) |
| |
| |
|
|
|
|
|
|
| |
|
|
| def normalize(data): |
| normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore') |
| val = normal.decode("utf-8") |
|
|
| |
| val = re.sub(r"[A-Z]{3,}", lambda x: x.group().lower(), val) |
| |
| val = re.sub(r"(\w)([A-Z])", r"\1 \2", val) |
| |
| val = val.lower() |
| |
| val = re.sub('[^A-Za-z0-9 ]+', ' ', val) |
| |
| val = re.sub(' +', ' ', val) |
| |
| val = val.strip() |
|
|
|
|
| return val |
|
|
|
|
| def main_inventors(technologies, number_technology , carbon_related , category , size ): |
| display = False |
| dic_patents , dic_ranked = ranking_patents(number_technology , category, carbon_related , display) |
| dic_patents_co_inventors = {} |
| |
|
|
| for patent in list(dic_ranked.keys())[:size]: |
| for k in range(len(dic_patents[patent]["list_inventors"])): |
| |
| inventor_id = dic_patents[patent]["list_inventors"][k]["inventor_id"] |
| inventor_name = dic_patents[patent]["list_inventors"][k]["inventor_first_name"] + " " + dic_patents[patent]["list_inventors"][k]["inventor_last_name"] |
| inventor_name_norm = normalize(inventor_name).split() |
| inventor_name_norm = inventor_name_norm[0] + " " + inventor_name_norm[-1] |
|
|
| if inventor_name_norm not in dic_patents_co_inventors: |
| dic_patents_co_inventors[inventor_name_norm] = {} |
| dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] = inventor_name |
| dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] = inventor_id |
| dic_patents_co_inventors[inventor_name_norm]["Number of occurence"] = 1 |
| dic_patents_co_inventors[inventor_name_norm]["Number of related citations"] = dic_patents[patent]["number_citations"] |
|
|
| else: |
| if inventor_id not in dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] : |
| dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] += ", " + inventor_id |
| if inventor_name not in dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] : |
| dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] += ", " + inventor_name |
| dic_patents_co_inventors[inventor_name_norm]["Number of occurence"] += 1 |
| dic_patents_co_inventors[inventor_name_norm]["Number of related citations"] += dic_patents[patent]["number_citations"] |
|
|
|
|
| dic_patents_co_inventors = {k: v for k, v in sorted(dic_patents_co_inventors.items(), key=lambda item: item[1]["Number of occurence"] , reverse = True)} |
| |
| if dic_patents_co_inventors == {}: |
| |
| |
| return "No patent, select other key words" |
| else: |
| |
| for inventor_name_norm in list(dic_patents_co_inventors.keys()): |
| list_inventors = dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"].split(", ") |
| work_count = 0 |
| cited_by_count = 0 |
| |
| for elem in list_inventors: |
| url = "https://api.patentsview.org/inventors/query?q={%22inventor_id%22:[%22" + elem + "%22]}&f=[%22inventor_total_num_patents%22,%22patent_num_cited_by_us_patents%22]" |
| data = get_data(url)["inventors"][0] |
| work_count += int(data["inventor_total_num_patents"]) |
| for k in range(len(data["patents"])): |
| cited_by_count += int(data["patents"][k]["patent_num_cited_by_us_patents"]) |
| |
| dic_patents_co_inventors[inventor_name_norm]["Number of patents"] = work_count |
| dic_patents_co_inventors[inventor_name_norm]["Number of US patents citations"] = cited_by_count |
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| return pd.DataFrame(dic_patents_co_inventors , index = ["Inventor's name", "PatentsView inventor's id", "Number of occurence" , "Number of patents" ,"Number of US patents citations" , "Number of related citations"]).T |
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| def map_inventors(technologies, number_technology , carbon_related , category , size): |
| display = False |
| dic_patents , dic_ranked = ranking_patents(number_technology , category, carbon_related , display) |
| dic_patents_co_inventors = {} |
| count = 0 |
| |
| for patent in list(dic_ranked.keys())[:size]: |
| for k in range(len(dic_patents[patent]["list_inventors"])): |
| |
| dic_patents_co_inventors[count] = {} |
| |
| dic_patents_co_inventors[count]["latitude"] = dic_patents[patent]["list_inventors"][k]["inventor_latitude"] |
| dic_patents_co_inventors[count]["longitude"] = dic_patents[patent]["list_inventors"][k]["inventor_longitude"] |
| dic_patents_co_inventors[count]["longitude"] = dic_patents[patent]["list_inventors"][k]["inventor_longitude"] |
| dic_patents_co_inventors[count]["inventor_name"] = str(dic_patents[patent]["list_inventors"][k]["inventor_first_name"]) + " " + str(dic_patents[patent]["list_inventors"][k]["inventor_last_name"]) |
| dic_patents_co_inventors[count]["patent_date"] = dic_patents[patent]["date"] |
| count += 1 |
| |
| if dic_patents_co_inventors == {}: |
| return "No patent, select other key words" |
| |
| |
| map_df = pd.DataFrame(dic_patents_co_inventors).T |
| map_df["longitude"]=map_df['longitude'].astype(float) |
| map_df['latitude']=map_df['latitude'].astype(float) |
| map_df = map_df[map_df["latitude"].notnull()] |
|
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| return map_df |
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| def extract_sentences_with_numbers(text , text_name): |
| |
| if text != None: |
| text = text.replace("CO 2" , "CO2") |
| text = text.replace("CO 3" , "CO3") |
| text = text.replace("CO(2)" , "CO2") |
| text = text.replace("CO(3)" , "CO3") |
| |
| print(text_name + ": " , text) |
| print(" ") |
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| list_text = list(text) |
| for i in range(1,len(list_text)-1): |
| if list_text[i] == " " and list_text[i-1] == "." and list_text[i+1].isupper(): |
| list_text[i] = "~" |
|
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| text = "".join(list_text) |
| text = text.split("~") |
| for sentence in text: |
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| if any(char.isdigit() for char in sentence): |
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| |
| |
| if "CO2" in sentence: |
| print("\x1b[31mCARBON RELATED:\x1b[0m", sentence) |
| print(" ") |
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| if "GJ" in sentence or "MJ" in sentence: |
| print("\x1b[31mENERGY:\x1b[0m" , sentence) |
| print(" ") |
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| |
| if "€" in sentence or "$" in sentence or "EUR" in sentence or "dollars" in sentence.lower(): |
| print("\x1b[31mPRICE:\x1b[0m" , sentence) |
| print(" ") |
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| digits = [] |
| for word in sentence.replace("," , "").replace("%" , "").replace("." , " ").split(): |
| if word.isdigit() and 1850 < int(word) < 2200 : |
| digits.append(word) |
| if digits != []: |
| print("\x1b[31mDATE:\x1b[0m" , sentence) |
| print(" ") |
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| if "Mt" in sentence or "tC" in sentence or "t-C" in sentence: |
| print("\x1b[31mCARBON QUANTITY:\x1b[0m" , sentence) |
| print(" ") |
| |
| print(" ") |
| |
| |
| def extract_quantitative_data_technology(technologies, number_technology): |
| count = 0 |
| |
| name_file = "iea" |
| dic_target = deployment_target(name_file , False) |
| dic_cost = cost_reduction_target(name_file , False) |
| dic_details = details(name_file , False) |
| cost_target_text = 'No information' |
| cost_text = 'No information' |
| |
| if number_technology in dic_details: |
| reference_text = dic_details[number_technology] |
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| if number_technology in dic_target: |
| cost_target_text = dic_target[number_technology] |
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| if number_technology in dic_cost: |
| cost_text = dic_cost[number_technology] |
| |
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| |
| return reference_text, cost_target_text , cost_text |
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| |
| def extract_quantitative_data_patent(patent_id): |
| |
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| patent_id = patent_id[3:] |
| |
| 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]" |
| url_google = "https://patents.google.com/patent/US" + str(patent_id) |
| |
| data = get_data(url)["patents"][0] |
| title = data["patent_title"] |
| abstract = data["patent_abstract"] |
| co_inventors = ", ".join([ data["inventors"][i]["inventor_first_name"] + " " + data["inventors"][i]["inventor_last_name"] for i in range(len(data["inventors"])) ]) |
| assignees = ", ".join([ str(data["assignees"][i]["assignee_organization"]) for i in range(len(data["assignees"])) ] ) |
|
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| return url_google , title , abstract , data["patent_date"] , co_inventors , assignees |
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| def finder(): |
| name_file = 'iea' |
|
|
| res = technology("iea", False ) |
| list_categories_tech = [] |
| list_categories = key_words(name_file , False) |
| list_technologies = [ ( ", ".join(list_categories[i][1]) , i ) for i in range(len(list_categories)) ] |
|
|
| dic_technologies = {} |
| for i in range(len(res)): |
| names = res[i][1] |
| if ", ".join(names) not in list_categories_tech: |
| list_categories_tech.append(", ".join(names)) |
| dic_technologies[", ".join(names)] = [] |
| dic_technologies[", ".join(names)].append( (", ".join(list_categories[i][1]) , i )) |
| |
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|
| list_climate = [ ("Any related patents" , False ) , ("Climate related patents" , True)] |
|
|
| dic_categories = {} |
| for elem in list_technologies: |
| list_words = elem[0].split(",")[-3:] |
|
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| dic_categories[elem[1]] = [ " ".join(list_words[-1].split()[:3]) , " ".join(list_words[-2].split()[:3]) if len(list_words) > 1 else "" , " ".join(list_words[-3].split()[:3]) if len(list_words) > 2 else "" ] |
| |
| return dic_technologies, dic_categories, list_categories_tech, list_technologies |
| |
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|
