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##packages code
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
#from pandas.io.json import json_normalize
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 # 👈 Add the caching decorator
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
#################### General Functions #############################
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 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]
#if " Production" in line:
#line = line.replace(" Production" , "")
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)):
# remove multiple spaces
line[i] = re.sub(' +', ' ', line[i])
# remove trailing spaces
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 ):
# Filepath too specific, need to change to relative path
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)):
# remove multiple spaces
line[i] = re.sub(' +', ' ', line[i])
# remove trailing spaces
line[i] = line[i].strip()
if display == True:
print(line)
print(" ")
list_categories.append([count , line])
return list_categories
#################### Patent Functions #############################
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
## Ranking 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]["assignee_city"] = dic_patents[ids]["assignee_city"]
#dic_scores[ids]["assignee_country"] = dic_patents[ids]["assignee_country"]
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)
#normalize a string dat that represents often a name.
def normalize(data):
normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')
val = normal.decode("utf-8")
# lower full name in upper
val = re.sub(r"[A-Z]{3,}", lambda x: x.group().lower(), val)
# add space in front of upper case
val = re.sub(r"(\w)([A-Z])", r"\1 \2", val)
# Lower case
val = val.lower()
# remove special characters
val = re.sub('[^A-Za-z0-9 ]+', ' ', val)
# remove multiple spaces
val = re.sub(' +', ' ', val)
# remove trailing spaces
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
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
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()]
return map_df
#geometry = [Point(xy) for xy in zip(map_df['Longitude'], map_df['Latitude'])]
#gdf = GeoDataFrame(map_df, geometry=geometry)
#this is a simple map that goes with geopandas
#world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
#gdf.plot(ax=world.plot( color='white', edgecolor='black' ), marker='o', color='red', markersize=15 , zorder = 1);
#plt.xlim([-180, 180])
#plt.ylim([-90, 90])
#plt.title("Main inventors: geographic location")
#plt.xlabel("Longitude")
#plt.ylabel("Latitude")
#plt.show()
## Extract quantitative data
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(" ")
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] = "~"
text = "".join(list_text)
text = text.split("~")
for sentence in text:
if any(char.isdigit() for char in sentence):
if "CO2" in sentence:
print("\x1b[31mCARBON RELATED:\x1b[0m", sentence)
print(" ")
if "GJ" in sentence or "MJ" in sentence:
print("\x1b[31mENERGY:\x1b[0m" , sentence)
print(" ")
##price
if "€" in sentence or "$" in sentence or "EUR" in sentence or "dollars" in sentence.lower():
print("\x1b[31mPRICE:\x1b[0m" , sentence)
print(" ")
##dates
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(" ")
##CO quantity
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]
#print("\033[96mFROM IEA website: ")
#print("\033[92mTechnology details: \x1b[0m" , reference_text)
#print(" ")
#encoded_text = model.encode(reference_text, convert_to_tensor=False).tolist()
if number_technology in dic_target:
cost_target_text = dic_target[number_technology]
#print("\033[96mFROM IEA website: ")
#sentences = extract_sentences_with_numbers(cost_target_text , "\033[92mDeployment target and Announced development target\x1b[0m")
if number_technology in dic_cost:
cost_text = dic_cost[number_technology]
#print("\033[96mFROM IEA website: ")
#sentences = extract_sentences_with_numbers(cost_text , "\033[92mAnnounced cost reduction targets\x1b[0m")
return reference_text, cost_target_text , cost_text
def extract_quantitative_data_patent(patent_id):
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"])) ] )
return url_google , title , abstract , data["patent_date"] , co_inventors , assignees
################################### Extracted texts ###############################################################
#@title Which patents are related to the technology?
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 ))
list_climate = [ ("Any related patents" , False ) , ("Climate related patents" , True)]
dic_categories = {}
for elem in list_technologies:
list_words = elem[0].split(",")[-3:]
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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