| import urllib.request | |
| import json | |
| link = "https://klimalog.die-gdi.de/ndc/open-data/dataset.json" | |
| def get_document(country_code: str): | |
| """ | |
| read the country NDC data from | |
| https://klimalog.die-gdi.de/ndc/open-data/dataset.json | |
| using the country code. | |
| Params | |
| ------- | |
| country_code:""" | |
| with urllib.request.urlopen(link) as urlfile: | |
| data = json.loads(urlfile.read()) | |
| categoriesData = {} | |
| categoriesData['categories']= data['categories'] | |
| categoriesData['subcategories']= data['subcategories'] | |
| keys_sub = categoriesData['subcategories'].keys() | |
| documentType= 'NDCs' | |
| if documentType in data.keys(): | |
| if country_code in data[documentType].keys(): | |
| get_dict = {} | |
| for key, value in data[documentType][country_code].items(): | |
| if key not in ['country_name','region_id', 'region_name']: | |
| get_dict[key] = value['classification'] | |
| else: | |
| get_dict[key] = value | |
| else: | |
| return None | |
| else: | |
| return None | |
| country = {} | |
| for key in categoriesData['categories']: | |
| country[key]= {} | |
| for key,value in categoriesData['subcategories'].items(): | |
| country[value['category']][key] = get_dict[key] | |
| return country | |
| def countrySpecificCCA(cca_sent:dict, threshold:int, countryCode:str): | |
| """ | |
| based on the countrycode, reads the country data from | |
| https://klimalog.die-gdi.de/ndc/open-data/dataset.json | |
| using get_documents from utils.ndc_explorer.py | |
| then based on thereshold value filters the Climate Change Adaptation | |
| targets assigned by NDC explorer team to that country. Using the sentences | |
| create by Data services team of GIZ for each target level, tries to find the | |
| relevant passages from the document by doing the semantic search. | |
| Params | |
| ------- | |
| cca_sent: dictionary with key as 'target labels' and manufactured sentences | |
| reflecting the target level. Please see the docStore/ndcs/cca.txt | |
| threshold: NDC target have many categoriees ranging from [0-5], with 0 | |
| refelcting most relaxed attitude and 5 being most aggrisive towards Climate | |
| change. We select the threshold value beyond which we need to focus on. | |
| countryCode: standard country code to allow us to fetch the country specific | |
| data. | |
| """ | |
| temp = {} | |
| doc = get_document(countryCode) | |
| for key,value in cca_sent.items(): | |
| id_ = doc['climate change adaptation'][key]['id'] | |
| if id_ >threshold: | |
| temp[key] = value['id'][id_] | |
| return temp | |
| def countrySpecificCCM(ccm_sent, threshold, countryCode): | |
| """ | |
| see the documentation of countrySpecificCCA. This is same instead of | |
| this gets the data pertaining to Adaptation | |
| """ | |
| temp = {} | |
| doc = get_document(countryCode) | |
| for key,value in ccm_sent.items(): | |
| id_ = doc['climate change mitigation'][key]['id'] | |
| if id_ >threshold: | |
| temp[key] = value['id'][id_] | |
| return temp | |