| |
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| import streamlit as st |
| from shapely.geometry import Point |
| import pandas as pd |
| import re |
| import json |
|
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|
| path = 'Climate_site/python_scripts/' |
|
|
| @st.cache_data |
| def load_dic(): |
| f = open(path + "related_companies.json","r") |
| dic_companies = json.load(f) |
| return dic_companies |
| |
| @st.cache_data |
| def load_data(): |
| url = path + "preqin_companies_IEA.tsv" |
| table = pd.read_csv(url, delimiter = "\t" , index_col = 0) |
| table = table.astype({'portfolio_company_id': 'str'}) |
|
|
| return table |
|
|
| table_companies = load_data() |
| dic_companies = load_dic() |
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| |
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| def print_extracted_text(name_file): |
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| file = open(path + '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): |
|
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| line = line.strip() |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
| count += 1 |
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|
|
| if mark == 1 and line != "" and line[0] == "*": |
| |
| if display == True: |
| print(count) |
| print(text) |
| print(" ") |
| dic_details[count] = text |
| mark = 0 |
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|
|
| if mark == 1: |
| text = text + line + " " |
|
|
| if line.split(" ")[-1] == "Details" or line.split(" ")[-1] == "Hide": |
| mark = 1 |
| text = "" |
| |
| return dic_details |
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| def key_initiatives(name_file , display ): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
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| |
| mark = 0 |
| dic_key_initiatives = {} |
| count = -1 |
| for index, line in enumerate(lines): |
|
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| line = line.strip() |
| if line == "Close explanation": |
| break |
|
|
| if line != "" and (line[0].isnumeric() and ">" in line and " " in line) : |
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| count += 1 |
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| 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 |
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|
|
| if mark == 1: |
| text = text + line + " " |
|
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| if line == "*Key initiatives:*": |
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| mark = 1 |
| text = "" |
| |
| return dic_key_initiatives |
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|
| def deployment_target(name_file , display): |
| |
| file = open(path + 'iea.txt', "r", encoding='utf8') |
| lines = file.readlines() |
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|
| 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 |
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| 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 |
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|
|
| if mark == 1: |
| text = text + line + " " |
|
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| if line == "*Deployment targets:*" or line == "*Announced development targets:*": |
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| mark = 1 |
| text = "" |
| |
| return dic_target |
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| |
| |
| 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) : |
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| count += 1 |
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|
|
| 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 |
|
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|
|
| if mark == 1: |
| text = text + line + " " |
|
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| if line == "*Announced cost reduction targets:*": |
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| mark = 1 |
| text = "" |
| |
| return dic_cost |
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|
| 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 , ":") |
|
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| if line != "": |
|
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| 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] |
|
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| |
| |
|
|
| 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] |
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|
|
| for i in range(len(line)): |
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| |
| line[i] = re.sub(' +', ' ', line[i]) |
| |
| line[i] = line[i].strip() |
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| |
|
|
| if display == True: |
| print(line) |
| print(" ") |
| |
| if '' in line: |
| line.remove('') |
|
|
| list_categories.append([count , line]) |
| |
| return list_categories |
|
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|
| 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] |
|
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|
|
| for i in range(len(line)): |
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|
| |
| 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 |
|
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| |
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|
|
| def extract_quantitative_data_technology(technologies, number_technology): |
| |
| |
| 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] |
| |
|
|
| if number_technology in dic_target: |
| cost_target_text = dic_target[number_technology] |
|
|
| if number_technology in dic_cost: |
| cost_text = dic_cost[number_technology] |
|
|
| return reference_text, cost_target_text, cost_text |
|
|
|
|
| def related_VC_deals(category , number_technology , size): |
| |
| number_technology = str(number_technology) |
| |
| |
| list_results = list(dic_companies[number_technology].keys()) |
| res = table_companies[table_companies["portfolio_company_id"].isin(list_results)].set_index("portfolio_company_id") |
| |
| table = res.copy() |
| for elem in table.index: |
| table.loc[ elem , "score" ] = dic_companies[number_technology][str(elem)] |
| |
| table = table.sort_values("score" , ascending = False).head(size) |
| table = table[['portfolio_company_name', 'year_established','portfolio_company_website','firm_about', |
| 'portfolio_company_country', 'portfolio_company_state', |
| 'firm_othernames', 'industry_classification', |
| 'primary_industry', 'sub_industries', 'score']] |
|
|
|
|
| table["year_established"] = table["year_established"].replace(",", "", regex=True).astype(int, errors='ignore') |
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| return table |
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| |
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|
|
| def finder(): |
| name_file = 'iea' |
| res = technology("iea", False ) |
| list_categories_tech = [] |
| list_categories = key_words("iea" , 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 papers" , False ) , ("Climate related papers" , True)] |
|
|
| dic_categories = {} |
| for elem in list_technologies: |
| list_words = elem[0].split(",")[-3:] |
| for i in range(len(list_words)): |
| if "CCUS" in list_words[i]: |
| list_words[i] = list_words[i].replace("CCUS" , "carbon capture storage") |
| dic_categories[elem[1]] = [ ", ".join([ " ".join(words.split()[:3]) for words in list_words ] ) , ", ".join([ " ".join(words.split()[:3]) for words in list_words[:-1] ]) , ", ".join([ " ".join(words.split()[:3]) for words in list_words[1:] ] ) ] |
| |
| return dic_technologies, dic_categories, list_categories_tech, list_technologies |
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|
