mirror of
https://gitlab.com/flectra-community/mis-builder.git
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97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
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# Copyright 2020 ACSONE SA/NV
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# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).
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from flectra import _, api, fields, models
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from flectra.exceptions import UserError
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from flectra.fields import Date
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from .mis_kpi_data import intersect_days
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class ProRataReadGroupMixin(models.AbstractModel):
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_name = "prorata.read_group.mixin"
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_description = "Adapt model with date_from/date_to for pro-rata temporis read_group"
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date_from = fields.Date(required=True)
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date_to = fields.Date(required=True)
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date = fields.Date(
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compute=lambda self: None,
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search="_search_date",
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help=(
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"Dummy field that adapts searches on date "
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"to searches on date_from/date_to."
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),
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)
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def _search_date(self, operator, value):
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if operator in (">=", ">"):
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return [("date_to", operator, value)]
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elif operator in ("<=", "<"):
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return [("date_from", operator, value)]
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raise UserError(
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_("Unsupported operator %s for searching on date") % (operator,)
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)
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@api.model
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def _intersect_days(self, item_dt_from, item_dt_to, dt_from, dt_to):
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return intersect_days(item_dt_from, item_dt_to, dt_from, dt_to)
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@api.model
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def read_group(
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self, domain, fields, groupby, offset=0, limit=None, orderby=False, lazy=True
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):
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"""Override read_group to perform pro-rata temporis adjustments.
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When read_group is invoked with a domain that filters on
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a time period (date >= from and date <= to, or
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date_from <= to and date_to >= from), adjust the accumulated
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values pro-rata temporis.
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"""
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date_from = None
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date_to = None
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assert isinstance(domain, list)
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for domain_item in domain:
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if isinstance(domain_item, (list, tuple)):
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field, op, value = domain_item
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if field == "date" and op == ">=":
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date_from = value
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elif field == "date_to" and op == ">=":
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date_from = value
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elif field == "date" and op == "<=":
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date_to = value
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elif field == "date_from" and op == "<=":
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date_to = value
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if (
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date_from is not None
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and date_to is not None
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and not any(":" in f for f in fields)
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):
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dt_from = Date.from_string(date_from)
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dt_to = Date.from_string(date_to)
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res = {}
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sum_fields = set(fields) - set(groupby)
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read_fields = set(fields + ["date_from", "date_to"])
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for item in self.search(domain).read(read_fields):
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key = tuple(item[k] for k in groupby)
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if key not in res:
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res[key] = {k: item[k] for k in groupby}
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res[key].update({k: 0.0 for k in sum_fields})
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res_item = res[key]
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for sum_field in sum_fields:
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item_dt_from = Date.from_string(item["date_from"])
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item_dt_to = Date.from_string(item["date_to"])
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i_days, item_days = self._intersect_days(
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item_dt_from, item_dt_to, dt_from, dt_to
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)
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res_item[sum_field] += item[sum_field] * i_days / item_days
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return res.values()
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return super(ProRataReadGroupMixin, self).read_group(
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domain,
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fields,
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groupby,
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offset=offset,
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limit=limit,
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orderby=orderby,
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lazy=lazy,
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)
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