import pandas as pd
import numpy as np
from functools import reduce

debug = False

csv_file = "data/offene_auftraege_eds_c11.csv"
cols_pkey = ["Hauptbetrieb", "Standort", "Nr", "Auftragsdatum"]
cols_str = [
    "Serviceberater",
    "Order Number",
    "Fabrikat",
    "Model",
    "Fahrzeug",
    "Kostenstelle",
    "Marke",
    "Kunde",
    "Turnover_Type_Desc",
]
cols_float = [
    "Durchg\u00e4nge (Auftrag)",
    "Arbeitswerte",
    "Teile",
    "Fremdl.",
    "Anzahl Tage",
]


def update(d, other):
    d.update(dict(dict(other)))
    return d


def get_dict(cols, type):
    return dict(dict(zip(cols, [type] * len(cols))))


cols_dict = reduce(
    update,
    (
        get_dict(cols_pkey, np.str),
        get_dict(cols_str, np.str),
        get_dict(cols_float, np.float),
    ),
    {},
)

df = pd.read_csv(
    csv_file,
    decimal=",",
    sep=";",
    encoding="latin-1",
    usecols=cols_dict.keys(),
    dtype=cols_dict,
)
df["pkey"] = reduce(lambda x, y: x + "_" + df[y], cols_pkey, "")
df_sum = df.groupby("pkey").sum()
df_unique = df[cols_pkey + cols_str + ["pkey"]].drop_duplicates()

df_join = df_sum.join(df_unique.set_index("pkey"), rsuffix="_other")
df_join["Gesamt"] = df_join["Arbeitswerte"] + df_join["Teile"] + df_join["Fremdl."]
df_result = df_join[(df_join["Gesamt"] != 0) & (df_join["Serviceberater"] != "")]

with open("data/offene_auftraege.json", "w") as f:
    f.write(df_result.to_json(orient="split", indent=2))

print(df_result.shape)