import z_personal_functions as my_functions
import requests, json, random, pandas as pd


print("\n\nRunning concatenate-enrich-dois.py")

# ______0______ load DOIs and remove duplicate

## specifier la liste des entrepôts à importer
files_to_load = [ "zenodo", "datacite", "rdg", "bso-via-hal", "nakala" ] 

dois_raw = my_functions.from_files_load_dois(files_to_load)
print("\n\tDOIs loaded\t\t\t", len(dois_raw))

dois = list(set(dois_raw)) ## remove duplicate
print("\tDOIs to treat\t\t", len(dois))


# ______1_____ load DOIs already treater & get md from DataCite for new one

## pour essayer avec un seul DOI
# temp_doi = dois[random.randint(0, len(dois))]
# #temp_doi = "10.57745/QYIAWX" - 10.25656/01:8509
# print(temp_doi)
# raw_metadatas = my_functions.get_md_from_datacite(temp_doi)

doi_error = [] # retrieve doi error
temp_rows = [] # put data in dict before df

df_old = pd.read_csv("../dois-uga.csv")
print(f"\n\tnb of DOIs already treated\t{len(df_old)}")

# req dataCite and paste data following instructions
for doi in dois : #[:300]

	## if doi already treated
	if doi in df_old["doi"].values : 
		#print(f"\talready treated\t\t{doi}")
		continue

	## ___n___ get md from datacite
	raw_md = my_functions.get_md_from_datacite(doi)
	## to debug print(f"\t{doi}")

	### if doi not in datacite
	if raw_md == "error" : 
		doi_error.append(doi)
		continue

	## ___n___ from manual instructions retrieve appropriate data
	selected_md = my_functions.parse_datacite_md(raw_md) ## placer les resultats dans un dictionnaire
	temp_rows.append(selected_md) ## ajouter ce dictionnaire à une liste
	print(f"\tadded\t\t{doi}")

## if new datasets has been founded
if temp_rows :
	df_fresh = pd.DataFrame(temp_rows)
	df_concat = pd.concat([df_old, df_fresh], ignore_index=True)

	## remove not wanted datacite type
	type_to_explude = ["Book", "ConferencePaper", "ConferenceProceeding", "JournalArticle", "BookChapter", "Service", "Preprint"]
	df_out = df_concat[ ~df_concat["resourceTypeGeneral"].isin(type_to_explude) ].copy()
	
	## output main CSV
	df_out.to_csv("../dois-uga.csv", index = False)
	print(f"\n\nnb of doi exported \t{len(df_out)}")

	# write the number of dois found in a file to display on the website
	with open("nb-dois.txt", 'w') as outf :
		outf.write(str(len(df_out)))

	## output another csv with datacite client and number of datasets
	df_client_raw = df_out["client"].value_counts().to_frame()

	## get informations about each client
	client_names = []
	client_years = []
	client_urls = []
	for i in range(0, len(df_client_raw)):
		client = df_client_raw.iloc[i].name
		req = requests.get('https://api.datacite.org/clients?query=id:%22'+str(client)+'%22')
		client_names.append(req.json()["data"][0]["attributes"]["name"])
		client_years.append(req.json()["data"][0]["attributes"]["year"])
		client_urls.append(req.json()["data"][0]["attributes"]["url"])
	
	## add informations to the output csv
	df_client_raw["name"] = client_names
	df_client_raw["year"] = client_years
	df_client_raw["url"] = client_urls
	df_client_raw.to_csv("all_datacite_clients_for_uga.csv")