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[FIX] stock_orderpoint_generator: stock history initial value

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With initial wrong values we can carry a computing error to the
subsequent moves.
This commit is contained in:
david
2021-02-23 13:44:22 +01:00
committed by sergiocorato
parent 84ac97ff75
commit 7404d7bc2a
2 changed files with 16 additions and 8 deletions
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20
stock_orderpoint_generator/models/product.py
View File

@@ -4,6 +4,7 @@
# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html) # License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html)
from collections import OrderedDict from collections import OrderedDict
from datetime import timedelta
from odoo import api, fields, models from odoo import api, fields, models
@@ -49,8 +50,13 @@ class ProductProduct(models.Model):
# default the compute the stock value anyway to default the value # default the compute the stock value anyway to default the value
# for products with no moves for the given period # for products with no moves for the given period
initial_stock = {} initial_stock = {}
# Compute the second before the given date so we don't duplicate
# history values in case the given hour is the same than the one
# of the first move
from_date_stock = from_date - timedelta(seconds=1)
to_date_stock = to_date + timedelta(seconds=1)
initial_stock = self.with_context(location=location)._compute_quantities_dict( initial_stock = self.with_context(location=location)._compute_quantities_dict(
False, False, False, to_date=from_date or to_date False, False, False, to_date=from_date_stock or to_date_stock
) )
product_moves_dict = {} product_moves_dict = {}
for move in moves: for move in moves:
@@ -76,19 +82,21 @@ class ProductProduct(models.Model):
# we can compute the stock historical values from the moves # we can compute the stock historical values from the moves
# sequence so we can exploit it statisticaly # sequence so we can exploit it statisticaly
product_moves = OrderedDict(sorted(product_moves.items())) product_moves = OrderedDict(sorted(product_moves.items()))
stock = False product_moves_dict[product.id]["stock_history"] = [
prod_initial_stock.get("qty_available", 0)
]
stock = 0
first_item = product_moves[next(iter(product_moves))] first_item = product_moves[next(iter(product_moves))]
if from_date: if from_date:
stock = prod_initial_stock.get("qty_available") stock = prod_initial_stock.get("qty_available")
if not stock: first_item["stock"] = stock + first_item["prod_qty"]
stock = first_item["prod_qty"] stock = first_item["stock"]
first_item["stock"] = stock
iter_moves = iter(product_moves) iter_moves = iter(product_moves)
next(iter_moves, None) next(iter_moves, None)
for date in iter_moves: for date in iter_moves:
stock += product_moves[date]["prod_qty"] stock += product_moves[date]["prod_qty"]
product_moves[date]["stock"] = stock product_moves[date]["stock"] = stock
product_moves_dict[product.id]["stock_history"] = [ product_moves_dict[product.id]["stock_history"] += [
v["stock"] for k, v in product_moves.items() v["stock"] for k, v in product_moves.items()
] ]
return product_moves_dict return product_moves_dict

4
stock_orderpoint_generator/tests/test_orderpoint_generator.py
View File

@@ -318,7 +318,7 @@ class TestOrderpointGenerator(SavepointCase):
self.template.write( self.template.write(
{ {
"auto_min_qty": True, "auto_min_qty": True,
"auto_min_date_start": "2019-01-01 00:00:00", "auto_min_date_start": "2019-01-01 01:30:00",
"auto_min_date_end": "2019-02-01 00:00:00", "auto_min_date_end": "2019-02-01 00:00:00",
"auto_min_qty_criteria": "max", "auto_min_qty_criteria": "max",
} }
@@ -368,7 +368,7 @@ class TestOrderpointGenerator(SavepointCase):
# Auto min max over a shorter period # Auto min max over a shorter period
self.template.write( self.template.write(
{ {
"auto_max_date_start": "2019-01-01 02:00:00", "auto_max_date_start": "2019-01-01 02:30:00",
"auto_max_date_end": "2019-01-01 03:00:00", "auto_max_date_end": "2019-01-01 03:00:00",
"auto_min_date_start": "2019-01-01 04:00:00", "auto_min_date_start": "2019-01-01 04:00:00",
"auto_min_date_end": "2019-01-01 06:00:00", "auto_min_date_end": "2019-01-01 06:00:00",