gestioCOF/kfet/statistic.py

# -*- coding: utf-8 -*-

import ast

from dateutil.relativedelta import relativedelta

from django.utils import timezone
from django.db.models import Sum

KFET_WAKES_UP_AT = 7


def kfet_day(year, month, day, start_at=KFET_WAKES_UP_AT):
    return timezone.datetime(year, month, day, hour=start_at)


def to_kfet_day(dt, start_at=KFET_WAKES_UP_AT):
    kfet_dt = kfet_day(year=dt.year, month=dt.month, day=dt.day)
    if dt.hour < start_at:
        kfet_dt -= timezone.timedelta(days=1)
    return kfet_dt


class StatScale(object):
    name = None
    step = None

    def __init__(self, n_steps=0, begin=None, end=None,
                 last=False, std_chunk=True):
        self.std_chunk = std_chunk
        if last:
            end = timezone.now()

        if begin is not None and n_steps != 0:
            self.begin = self.get_from(begin)
            self.end = self.do_step(self.begin, n_steps=n_steps)
        elif end is not None and n_steps != 0:
            self.end = self.get_from(end)
            self.begin = self.do_step(self.end, n_steps=-n_steps)
        elif begin is not None and end is not None:
            self.begin = self.get_from(begin)
            self.end = self.get_from(end)
        else:
            raise Exception('Two of these args must be specified: '
                            'n_steps, begin, end; '
                            'or use last and n_steps')

        self.datetimes = self.get_datetimes()

    @staticmethod
    def by_name(name):
        for cls in StatScale.__subclasses__():
            if cls.name == name:
                return cls
        return None

    def get_from(self, dt):
        return self.std_chunk and self.get_chunk_start(dt) or dt

    def __getitem__(self, i):
        return self.datetimes[i], self.datetimes[i+1]

    def __len__(self):
        return len(self.datetimes) - 1

    def do_step(self, dt, n_steps=1):
        return dt + self.step * n_steps

    def get_datetimes(self):
        datetimes = [self.begin]
        tmp = self.begin
        while tmp <= self.end:
            tmp = self.do_step(tmp)
            datetimes.append(tmp)
        return datetimes

    def get_labels(self, label_fmt=None):
        if label_fmt is None:
            label_fmt = self.label_fmt
        return [begin.strftime(label_fmt) for begin, end in self]


class DayStatScale(StatScale):
    name = 'day'
    step = timezone.timedelta(days=1)
    label_fmt = '%A'

    @classmethod
    def get_chunk_start(cls, dt):
        return to_kfet_day(dt)


class WeekStatScale(StatScale):
    name = 'week'
    step = timezone.timedelta(days=7)
    label_fmt = 'Semaine %W'

    @classmethod
    def get_chunk_start(cls, dt):
        dt_kfet = to_kfet_day(dt)
        offset = timezone.timedelta(days=dt_kfet.weekday())
        return dt_kfet - offset


class MonthStatScale(StatScale):
    name = 'month'
    step = relativedelta(months=1)
    label_fmt = '%B'

    @classmethod
    def get_chunk_start(cls, dt):
        return to_kfet_day(dt).replace(day=1)


def stat_manifest(scales_def=None, scale_args=None, **url_params):
    if scales_def is None:
        scales_def = []
    if scale_args is None:
        scale_args = {}
    return [
        dict(
            label=label,
            url_params=dict(
                scale=cls.name,
                scale_args=scale_args,
                **url_params,
            ),
        )
        for label, cls in scales_def
    ]


def last_stats_manifest(scales_def=None, scale_args=None, **url_params):
    scales_def = [
        ('Derniers mois', MonthStatScale, ),
        ('Dernières semaines', WeekStatScale, ),
        ('Derniers jours', DayStatScale, ),
    ]
    if scale_args is None:
        scale_args = {}
    scale_args.update(dict(
        last=True,
        n_steps=7,
    ))
    return stat_manifest(scales_def=scales_def, scale_args=scale_args,
                         **url_params)


# Étant donné un queryset d'operations
# rend la somme des article_nb
def tot_ventes(queryset):
    res = queryset.aggregate(Sum('article_nb'))['article_nb__sum']
    return res and res or 0


class ScaleMixin(object):

    def get_context_data(self, *args, **kwargs):
        context = super().get_context_data(*args, **kwargs)

        scale_name = self.request.GET.get('scale', None)

        cls = StatScale.by_name(scale_name)
        if cls is None:
            scale = self.get_default_scale()
        else:
            scale_args = self.request.GET.get('scale_args', {})
            if isinstance(scale_args, str):
                scale_args = ast.literal_eval(scale_args)
            scale = cls(**scale_args)

        self.scale = scale
        context['labels'] = scale.get_labels()
        return context

    def get_default_scale(self):
        return DayStatScale(n_steps=7, last=True)

    def chunkify_qs(self, qs, scale, field=None):
        if field is None:
            field = 'at'
        begin_f = '{}__gte'.format(field)
        end_f = '{}__lte'.format(field)
        return [
            qs.filter(**{begin_f: begin, end_f: end})
            for begin, end in scale
        ]