from rowers.models import (
    StandardCollection, CourseStandard,
    VirtualRaceResult, IndoorVirtualRaceResult,
)

import pandas as pd
import arrow
import datetime


def save_scoring(name, user, filename, id=0, notes=""):
    if id == 0:
        collection = StandardCollection(name=name, manager=user, notes=notes)
        collection.save()
        standards = CourseStandard.objects.filter(
            standardcollection=collection)
        for standard in standards:  # pragma: no cover
            standards.delete()

    else:  # pragma: no cover
        try:
            collection = StandardCollection.objects.get(id=id)
            collection.name = name
            collection.notes = notes
            collection.save()
            standards = CourseStandard.objects.filter(
                standardcollection=collection)
            for standard in standards:
                records1 = VirtualRaceResult.objects.filter(
                    entrycategory=standard)
                records2 = IndoorVirtualRaceResult.objects.filter(
                    entrycategory=standard)
                if records1.count()+records2.count() == 0:
                    standard.delete()

        except StandardCollection.DoesNotExist:
            return 0

    try:
        df = pd.read_csv(filename)
    except:  # pragma: no cover
        return 0

    df.rename(
        columns={
            'name': 'Name',
            'agemax': 'MaxAge',
            'agemin': 'MinAge',
            'adaptiveclass': 'AdaptiveClass',
            'coursedistance': 'CourseDistance',
            'coursetime': 'CourseStandard',
            'boatclass': 'BoatClass',
            'boattype': 'BoatType',
            'sex': 'Gender',
            'weightclass': 'WeightClass',
            'skillclass': 'SkillClass',
        },
        inplace=True)

    df = df.drop_duplicates(['Name'])

    for index, row in df.iterrows():
        try:
            name = row['Name']
        except KeyError:  # pragma: no cover
            continue

        try:
            coursedistance = row['CourseDistance']
            coursetime = row['CourseStandard']
            t = datetime.datetime.strptime(coursetime, '%M:%S.%f')
            delta = datetime.timedelta(
                hours=t.hour, minutes=t.minute, seconds=t.second, microseconds=t.microsecond)
            seconds = delta.total_seconds()

            referencespeed = coursedistance/seconds
        except KeyError:  # pragma: no cover
            continue

        try:
            agemin = row['MinAge']
            agemax = row['MaxAge']
            agemin = int(agemin)
            agemax = int(agemax)
        except KeyError:  # pragma: no cover
            agemin = 0
            agemax = 120

        try:
            boatclass = row['BoatClass']
            if boatclass.lower() in ['standard', 'olympic', 'normal', 'water']:
                boatclass = 'water'
            elif boatclass.lower() in ['erg', 'c2', 'concept', 'static', 'rower']:  # pragma: no cover
                boatclass = 'rower'
            elif boatclass.lower() in ['dynamic']:  # pragma: no cover
                boatclass = 'dynamic'
            elif boatclass.lower() in ['slides', 'slide', 'slider', 'sliders']:  # pragma: no cover
                boatclass = 'slides'
            elif boatclass.lower() in ['c', 'c-boat']:  # pragma: no cover
                boatclass = 'c-boat'
            elif boatclass.lower() in ['coastal', 'coast']:  # pragma: no cover
                boatclass = 'coastal'
            elif boatclass.lower() in ['church', 'churchboat', 'finnish', 'finland']:  # pragma: no cover
                boatclass = 'churchboat'
        except KeyError:  # pragma: no cover
            boatclass = 'water'

        try:
            boattype = row['BoatType']
        except KeyError:  # pragma: no cover
            boattype = '1x'

        try:
            sex = row['Gender']
            if sex.lower() in ['m', 'men', 'male', 'open']:
                sex = 'male'
            elif sex.lower() in ['mix', 'mixed']:
                sex = 'mixed'
            else:
                sex = 'female'
        except KeyError:  # pragma: no cover
            sex = 'female'

        try:
            weightclass = row['WeightClass']
            if weightclass.lower() in ['hwt', 'h', 'o', 'heavy', 'open']:
                weightclass = 'hwt'
            elif weightclass.lower() in ['lwt', 'l', 'light', 'lights', 'lighties']:
                weightclass = 'lwt'
        except KeyError:  # pragma: no cover
            weightclass = 'hwt'

        adaptiveclass = 'None'
        try:
            adaptiveclass = row['AdaptiveClass']
            if adaptiveclass.lower() in ['o', 'open', 'none', 'no']:
                adaptiveclass = 'None'
        except KeyError:  # pragma: no cover
            adaptiveclass = 'None'
        except AttributeError:
            adaptiveclass = 'None'

        try:
            skillclass = row['SkillClass']
        except KeyError:  # pragma: no cover
            skillclass = 'Open'

        # finding existing standard
        existingstandards = CourseStandard.objects.filter(
            name=name, standardcollection=collection)
        # print(existingstandards,collection)
        if existingstandards:  # pragma: no cover
            existingstandards.update(
                name=name,
                coursedistance=coursedistance,
                referencespeed=referencespeed,
                coursetime=coursetime,
                agemin=agemin,
                agemax=agemax,
                boatclass=boatclass,
                boattype=boattype,
                sex=sex,
                weightclass=weightclass,
                adaptiveclass=adaptiveclass,
                skillclass=skillclass,
                standardcollection=collection,
            )
        else:
            # print('not')
            standard = CourseStandard(
                name=name,
                coursedistance=coursedistance,
                referencespeed=referencespeed,
                coursetime=coursetime,
                agemin=agemin,
                agemax=agemax,
                boatclass=boatclass,
                boattype=boattype,
                sex=sex,
                weightclass=weightclass,
                adaptiveclass=adaptiveclass,
                skillclass=skillclass,
                standardcollection=collection,
            )

            standard.save()

    return collection.id