from pathlib import Path
import re
import pandas


# converts a cnrs geslab type t001 report to a single table
def geslabt001_to_sheet(in_tsv_file_path: Path, out_tsv_file_path: Path):
    with open(in_tsv_file_path) as inf, open(out_tsv_file_path, 'wt') as outf:
        table_header_has_been_written = False
        for line in inf.readlines():
            ignore_line = False
            # Entité dépensière : 				AESJULLIEN		AES RENNES METROPOLE MC JULLIEN									Crédits reçus : 								40,000.00	
            # 															Disponible : 								24,743.14	
            # 																								
            # 																								
            # N° commande	Souche		Libellé commande				Date commande	Raison sociale fournisseur	Montant consommé sur exercice antérieur	Montant consommé sur l'exercice			Montant réservé					Montant facturé		Code origine	Nature dépense	Statut		Cde groupée            
            if re.match(r'^Entité dépensière', line):
                ignore_line = True
            is_table_header = re.match(r'^N° commande', line) is not None
            # 19,572.00	19AESMCJ		CAMERA ZYLA 5.5 sCMOS				04/11/19	ANDOR TECHNOLOGY LIMITED	0.00	13,681.56			0.00					0.00		635991	IM			
            if is_table_header and not table_header_has_been_written:
                outf.write('# %s' % line)
                table_header_has_been_written = True
            if re.match(r'^[0-9,./]+\t', line):
                outf.write(line)


# converts a cnrs geslab type t001 report to a single table
def geslabt002_to_sheet(in_tsv_file_path: Path, out_tsv_file_path: Path):
    with open(in_tsv_file_path) as inf, open(out_tsv_file_path, 'wt') as outf:
        table_header_has_been_written = False
        for line in inf.readlines():
            # Entité dépensière : 				AESJULLIEN		AES RENNES METROPOLE MC JULLIEN									Crédits reçus : 								40,000.00	
            # 															Disponible : 								24,743.14	
            # 																								
            # 																								
            # N° commande	Souche		Libellé commande				Date commande	Raison sociale fournisseur	Montant consommé sur exercice antérieur	Montant consommé sur l'exercice			Montant réservé					Montant facturé		Code origine	Nature dépense	Statut		Cde groupée            
            is_table_header = re.match(r'^N° com. GESLAB', line) is not None
            # for some strange reason, the column 'N° com. GESLAB''s contents are alternatively something like '1952-12-17 12:00:00 AM' and something like '19,855.00'
            if is_table_header and not table_header_has_been_written:
                outf.write('# %s' % line)
                table_header_has_been_written = True
            if re.match(r'^[0-9,.]+\t', line):
                outf.write(line)
            elif re.match(r'^[0-9][0-9][0-9][0-9]-[0-9]+-[0-9]+ [0-9][0-9]:[0-9][0-9]:[0-9][0-9] [AP]M\t', line):
                outf.write(line)
            else:
                print('ignoring line : %s' % line)


def geslabt001_to_itorders(geslabt001_file_path: Path, itorders_file_path: Path):
    sheet_file_path = Path('./tmp/commandes-2019-cnrs.tsv')
    geslabt001_to_sheet(geslabt001_file_path, sheet_file_path)

    df = pandas.read_csv(sheet_file_path, sep='\t')

    # delete the colums for which the label is of the form 'Unnamed: <n>'. They come from the csv export of libre office
    unnamed_columns = [column_label for column_label in df.keys() if re.match(r'^Unnamed', column_label) is not None]
    print(unnamed_columns)
    df = df.drop(columns=unnamed_columns)

    print(df.columns)
    print(df.keys())
    print(df)
    it_df = df[(df['Raison sociale fournisseur'] == 'DELL SAS') | (df['Raison sociale fournisseur'] == 'ECONOCOM PRODUCTS & SOLUTIONS')]
    # 'AMAZON EU SARL SUCCURSALE FRANCAISE'
    # 'INMAC'
    # 'RETIS'
    # 'APIXIT'
    print(it_df)

    print(it_df[['Montant facturé', 'Raison sociale fournisseur', 'Libellé commande']])
    it_df.to_csv(itorders_file_path, sep='\t')


def geslabt002_to_itorders(geslabt001_file_path: Path, itorders_file_path: Path):
    sheet_file_path = Path('./tmp/commandes-2019-cnrs.tsv')
    geslabt002_to_sheet(geslabt001_file_path, sheet_file_path)

    df = pandas.read_csv(sheet_file_path, sep='\t')

    # delete the colums for which the labve is of the form 'Unnamed: <n>'. They come from the csv export of libre office
    unnamed_columns = [column_label for column_label in df.keys() if re.match(r'^Unnamed', column_label) is not None]
    print(unnamed_columns)
    df = df.drop(columns=unnamed_columns)

    print(df.columns)
    print(df.keys())
    print(df)
    PETIT_MATERIEL_INFORMATIQUE = '1100'
    EQUIPEMENT_INFORMATIQUE = '2100'
    INFORMATIQUE_ACHAT = 'D3--'
    it_df = df[(df['Matière'] == PETIT_MATERIEL_INFORMATIQUE) | (df['Matière'] == EQUIPEMENT_INFORMATIQUE) | (df['Matière'] == INFORMATIQUE_ACHAT)]
    print(it_df)

    # to remove clutter, drop the columns that we don't need
    print(it_df.keys())
    it_df = it_df.drop(columns=['# N° com. GESLAB'])  # this column seems to contain anything but ordering number
    it_df = it_df.drop(columns=['N° ligne'])  # I don't know the meaning of this column
    it_df = it_df.drop(columns=['Code origine'])  # I don't know the meaning of this column
    it_df = it_df.drop(columns=['Elément analytique'])  # I don't know the meaning of this column
    it_df = it_df.drop(columns=['S'])  # I don't know the meaning of this column

    print(it_df[['Facturé ligne', 'Raison sociale fournisseur', 'Libellé ligne']])
    it_df.to_csv(itorders_file_path, sep='\t')


def main():
    geslabt001_to_itorders(Path('./achats-ipr/2019/commandes-2019-cnrs-t001.tsv'), Path('./tmp/commandes-it-2019-cnrs-001.tsv'))
    geslabt002_to_itorders(Path('./achats-ipr/2019/commandes-2019-cnrs-t002.tsv'), Path('./tmp/commandes-it-2019-cnrs-002.tsv'))


main()