def _populate_set_companies(self, records):
_logger.info('Setting companies')
r_company = populate.Random('res.partner+company_has_partners')
r_partner = populate.Random('res.partner+partner_has_company')
r_company_pick = populate.Random('res.partner+partner_company_pick=')
companies = records.filtered(
lambda p: p.is_company and r_company.getrandbits(
1)) # 50% change to have partners
partners = records.filtered(
lambda p: not p.is_company and r_partner.getrandbits(
1)) # 50% change to have a company
companies_partners = collections.defaultdict(
lambda: self.env['res.partner'])
for partner in partners:
companies_partners[r_company_pick.choice(companies)] |= partner
# batching company write improves performances a lot (~40% faster for total partner creation).
for count, (company, partners) in enumerate(companies_partners.items(),
start=1):
if count % 100 == 0:
_logger.info('Setting company: %s/%s', count, len(companies))
partners.write({'parent_id': company.id})
partners._onchange_company_id()
def _populate_factories(self):
random = populate.Random('product_with_supplierinfo')
company_ids = self.env.registry.populated_models[
'res.company'][:COMPANY_NB_WITH_STOCK] + [False]
partner_ids = self.env.registry.populated_models['res.partner']
product_templates_ids = self.env['product.product'].browse(
self.env.registry.populated_models['product.product']
).product_tmpl_id.ids
product_templates_ids += self.env.registry.populated_models[
'product.template']
product_templates_ids = random.sample(
product_templates_ids, int(len(product_templates_ids) * 0.95))
def get_company_id(values, counter, random):
partner = self.env['res.partner'].browse(values['name'])
if partner.company_id:
return partner.company_id.id
return random.choice(company_ids)
def get_delay(values, counter, random):
# 5 % with huge delay (between 5 month and 6 month), otherwise between 1 and 10 days
if random.random() > 0.95:
return random.randint(150, 210)
return random.randint(1, 10)
return [
('name', populate.randomize(partner_ids)),
('company_id', populate.compute(get_company_id)),
('product_tmpl_id', populate.iterate(product_templates_ids)),
('product_name', populate.constant("SI-{counter}")),
('sequence', populate.randint(1, 10)),
('min_qty', populate.randint(0, 10)),
('price', populate.randint(10, 100)),
('delay', populate.compute(get_delay)),
]
def _compute_default_locations(iterator, field_name, model_name):
random = populate.Random('_compute_default_locations')
locations_by_company = dict(
groupby(internal_locations, key=lambda loc: loc.company_id.id))
locations_by_company = {
company_id: self.env['stock.location'].concat(*locations)
for company_id, locations in locations_by_company.items()
}
for values in iterator:
locations_company = locations_by_company[values['company_id']]
# TODO : choice only location child of warehouse.lot_stock_id
inter_location = random.choice(locations_company)
values['warehouse_id'] = inter_location.get_warehouse().id
if values['code'] == 'internal':
values['default_location_src_id'] = inter_location.id
values['default_location_dest_id'] = random.choice(
locations_company - inter_location).id
elif values['code'] == 'incoming':
values['default_location_dest_id'] = inter_location.id
elif values['code'] == 'outgoing':
values['default_location_src_id'] = inter_location.id
yield values
def _populate(self, size):
@lru_cache()
def search_coa_ids(currency_id):
return self.env['account.chart.template'].search([('currency_id', '=', currency_id)])
records = super()._populate(size)
_logger.info('Loading Chart Template')
default_chart_templates = self.env['account.chart.template'].search([], limit=1)
if not default_chart_templates:
# TODO install l10n_generic_coa ?
raise UserError(_(
"At least one localization is needed to be installed in order to populate the "
"database with accounting"
))
random = populate.Random('res.company+chart_template_selector')
# Load the a chart of accounts matching the currency of the company for the 3 first created companies
# We are loading an existing CoA and not populating it because:
# * it reflects best real use cases.
# * it allows checking reports by localization
# * the config is complete with try_loading(), no need to adapt when the model changes
# * it is way easier :-)
# We are loading only for 3 companies because:
# * It takes a few hundreds of a second to create account.move records in batch.
# Because we want to have a lot of entries for at least one company (in order to test
# reports, functions and widgets performances for instance), we can't afford to do it for
# a lot of companies.
# * it would be useless to have entries for all the companies, we can already do everything with
# entries in only a few (but multiple) companies.
# Note that we can still populate some new records on top of the CoA if it makes sense,
# like account.journal for instance.
for company in records[:3]:
chart_templates_cur = search_coa_ids(company.currency_id.id) or default_chart_templates
random.choice(chart_templates_cur).with_company(company.id).try_loading()
return records
def _populate(self, size):
res = super()._populate(size)
# Set alternative workcenters
_logger.info("Set alternative workcenters")
# Valid workcenters by company_id (the workcenter without company can be the alternative of all workcenter)
workcenters_by_company = defaultdict(OrderedSet)
for workcenter in res:
workcenters_by_company[workcenter.company_id.id].add(workcenter.id)
workcenters_by_company = {
company_id: self.env['mrp.workcenter'].browse(workcenters)
for company_id, workcenters in workcenters_by_company.items()
}
workcenters_by_company = {
company_id: workcenters
| workcenters_by_company.get(False, self.env['mrp.workcenter'])
for company_id, workcenters in workcenters_by_company.items()
}
random = populate.Random('set_alternative_workcenter')
for workcenter in res:
nb_alternative = max(
random.randint(0, 3),
len(workcenters_by_company[workcenter.company_id.id]) - 1)
if nb_alternative > 0:
alternatives_workcenter_ids = random.sample(
(workcenters_by_company[workcenter.company_id.id] -
workcenter).ids, nb_alternative)
workcenter.alternative_workcenter_ids = [
(6, 0, alternatives_workcenter_ids)
]
return res
def _populate_factories(self):
# Take a subset (50%) of bom to have some of then without any operation
random = populate.Random('byproduct_subset_bom')
boms_ids = self.env.registry.populated_models['mrp.bom']
boms_ids = random.sample(boms_ids, int(len(boms_ids) * 0.5))
boms = self.env['mrp.bom'].search(
[('id', 'in', self.env.registry.populated_models['mrp.bom'])],
order='sequence, product_id, id')
product_manu_ids = OrderedSet()
for bom in boms:
if bom.product_id:
product_manu_ids.add(bom.product_id.id)
else:
for product_id in bom.product_tmpl_id.product_variant_ids:
product_manu_ids.add(product_id.id)
product_manu = self.env['product.product'].browse(product_manu_ids)
# product_no_manu is products which don't have any bom (leaves in the BoM trees)
product_no_manu = self.env['product.product'].browse(
self.env.registry.populated_models['product.product']
) - product_manu
product_no_manu_ids = product_no_manu.ids
def get_product_uom_id(values, counter, random):
return self.env['product.product'].browse(
values['product_id']).uom_id.id
return [
('bom_id', populate.iterate(boms_ids)),
('product_id', populate.randomize(product_no_manu_ids)),
('product_uom_id', populate.compute(get_product_uom_id)),
('product_qty', populate.randint(1, 10)),
]
def _compute_contact(iterator, *args):
r = populate.Random('res.partner+contact_generator_selector')
for values in iterator:
if values['partner_id']:
yield {**values}
else:
fn = r.choice(tools._p_forename_groups)
mn = r.choices(
[False] + tools._p_middlename_groups,
weights=[1] +
[1 / (len(tools._p_middlename_groups) or 1)] *
len(tools._p_middlename_groups))[0]
sn = r.choice(tools._p_surname_groups)
mn_wseparator = f' "{mn}" '
contact_name = f'{fn}{mn_wseparator}{sn}'
country_id = values['country_id']
if country_id not in phones_per_country.keys():
country_id = False
mobile = r.choice(phones_per_country[country_id])
phone = r.choice(phones_per_country[country_id])
yield {
**values,
'contact_name': contact_name,
'mobile': mobile,
'phone': phone,
}
def confirm_prototype_mo(sample_ratio):
# Confirm X % of prototype MO
random = populate.Random('confirm_prototype_mo')
mo_ids = moves.raw_material_production_id.ids
mo_to_confirm = self.env['mrp.production'].browse(
random.sample(mo_ids, int(len(mo_ids) * 0.8)))
_logger.info("Confirm %d of prototype MO" % len(mo_to_confirm))
mo_to_confirm.action_confirm()
def confirm_bom_mo(sample_ratio):
# Confirm X % of prototype MO
random = populate.Random('confirm_bom_mo')
mo_ids = productions.filtered('bom_id').ids
mo_to_confirm = self.env['mrp.production'].browse(
random.sample(mo_ids, int(len(mo_ids) * 0.8)))
_logger.info("Confirm %d MO with BoM" % len(mo_to_confirm))
mo_to_confirm.action_confirm()
def start_inventory_sample(sample_ratio):
random = populate.Random('start_inventory_sample')
inventories_to_start = self.browse(
random.sample(inventories.ids,
int(len(inventories.ids) * sample_ratio)))
# Start empty to let the stock.inventory.line populate create lines
inventories_to_start.start_empty = True
for inventory in inventories_to_start:
inventory.action_start()
def confirm_sale_order(sample_ratio):
# Confirm sample_ratio * 100 % of picking
random = populate.Random('confirm_sale_order')
order_ids = so_line.order_id.ids
orders_to_confirm = self.env['sale.order'].browse(
random.sample(order_ids, int(len(order_ids) * sample_ratio)))
_logger.info("Confirm %d sale orders", len(orders_to_confirm))
orders_to_confirm.action_confirm()
return orders_to_confirm
def _attach_to_record(iterator, field_name, model_name):
random = populate.Random('_attach_to_record')
fields, weights = self._populate_attach_record_weight()
fields_generator = self._populate_attach_record_generator()
for values in iterator:
field = random.choices(fields, weights)[0]
values[field] = next(fields_generator[field])
yield values
def confirm_pickings(sample_ratio):
# Confirm sample_ratio * 100 % of picking
random = populate.Random('confirm_pickings')
picking_ids = moves.picking_id.ids
picking_to_confirm = self.env['stock.picking'].browse(
random.sample(picking_ids,
int(len(picking_ids) * sample_ratio)))
_logger.info("Confirm %d of pickings" % len(picking_to_confirm))
picking_to_confirm.action_confirm()
def _compute_address(iterator, *args):
r = populate.Random('res.partner+address_generator_selector')
for values in iterator:
if values['partner_id']:
yield {**values}
else:
address_gen = r.choice(address_generators)
address_values = next(address_gen)
yield {**values, **address_values}
def _populate(self, size):
p_tmpl_ids = self.env.registry.populated_models["product.template"]
ptavs = self.search([('product_tmpl_id', 'in', p_tmpl_ids)])
# ptavs are automatically created when specifying attribute lines on product templates.
rand = populate.Random("ptav_extra_price_generator")
for ptav in ptavs:
if rand.random() < 0.50: # 50% of having a extra price
ptav.price_extra = rand.randrange(500) * rand.random()
return ptavs
def validate_inventory_sample(sample_ratio):
# Validate the inventory adjustment, useful to have a stock at beginning
inventories_ids = inventory_lines.inventory_id.ids
random = populate.Random('validate_inventory')
inventories_to_done = self.env['stock.inventory'].browse(
random.sample(inventories_ids,
int(len(inventories_ids) * sample_ratio)))
_logger.info("Validate %d inventory adjustment" %
len(inventories_to_done))
for inventory in inventories_to_done:
inventory.action_validate()
def _compute_locations(iterator, field_name, model_name):
locations_out = cross_company_locations.filtered_domain([
('usage', '=', 'customer')
])
locations_in = cross_company_locations.filtered_domain([
('usage', '=', 'supplier')
])
locations_internal = locations_companies.filtered_domain([
('usage', '=', 'internal')
])
locations_by_company = dict(
groupby(locations_companies,
key=lambda loc: loc.company_id.id))
locations_by_company = {
com: self.env['stock.location'].concat(*locs)
for com, locs in locations_by_company.items()
}
random = populate.Random('_compute_locations')
for values in iterator:
picking_type = self.env['stock.picking.type'].browse(
values['picking_type_id'])
source_loc = picking_type.default_location_src_id
dest_loc = picking_type.default_location_dest_id
locations_company = locations_by_company[
picking_type.company_id.id]
if not source_loc or random.random() > 0.8:
if picking_type.code == 'incoming':
source_loc = random.choice(locations_in)
elif picking_type.code == 'outgoing':
source_loc = random.choice(locations_internal
& locations_company)
elif picking_type.code == 'internal':
source_loc = random.choice(locations_internal
& locations_company)
if not dest_loc or random.random() > 0.8:
if picking_type.code == 'incoming':
dest_loc = random.choice(locations_internal
& locations_company)
elif picking_type.code == 'outgoing':
dest_loc = random.choice(locations_out)
elif picking_type.code == 'internal':
# Need at most 2 internal locations
dest_loc = random.choice((locations_internal
& locations_company) -
source_loc)
values['location_id'] = source_loc.id
values['location_dest_id'] = dest_loc.id
yield values
def _populate_set_manager(self, employees):
manager_ids = []
rand = populate.Random('hr.employee+manager_generator')
for employee in employees:
if rand.random() >= 0.85: # 15% of employees are managers
manager_ids.append(employee.id)
for employee in employees:
manager = rand.choice(manager_ids)
if manager != employee.id:
employee.parent_id = manager
def set_barcode_variant(sample_ratio):
random = populate.Random('barcode_product_template')
product_variants_ids = res.product_variant_ids.ids
product_variants_ids = random.sample(
product_variants_ids,
int(len(product_variants_ids) * sample_ratio))
product_variants = self.env['product.product'].browse(
product_variants_ids)
_logger.info('Set barcode on product variants (%s)',
len(product_variants))
for product in product_variants:
product.barcode = "BARCODE-PT-%s" % product.id
def get_custom_values(iterator, field_group, model_name):
r = populate.Random('%s+fields+%s' % (model_name, field_group))
for _, values in enumerate(iterator):
attribute_id = values.get("attribute_id")
if attribute_id in color_attribute_ids:
values["html_color"] = r.choice([
"#FFFFFF", "#000000", "#FFC300", "#1BC56D", "#FFFF00",
"#FF0000"
], )
elif not r.getrandbits(4):
values["is_custom"] = True
yield values
def _populate_set_manager(self, employees):
manager_ids = defaultdict(list)
rand = populate.Random('hr.employee+manager_generator')
for employee in employees:
# 15% of employees are managers, at least one per company
if rand.random() >= 0.85 or not manager_ids.get(
employee.company_id):
manager_ids[employee.company_id].append(employee.id)
for employee in employees:
manager = rand.choice(manager_ids[employee.company_id])
if manager != employee.id:
employee.parent_id = manager
def get_target_info(iterator, field_name, model_name):
random = populate.Random("pricelist_target")
for values in iterator:
# If product population is updated to consider multi company
# the company of product would have to be considered
# for product_id & product_tmpl_id
applied_on = values["applied_on"]
if applied_on == "0_product_variant":
values["product_id"] = random.choice(product_ids)
elif applied_on == "1_product":
values["product_tmpl_id"] = random.choice(p_tmpl_ids)
elif applied_on == "2_product_category":
values["categ_id"] = random.choice(categ_ids)
yield values
def _compute_picking_values(iterator, field_name, model_name):
random = populate.Random('_compute_picking_values')
for values in iterator:
if values.get('picking_id'):
picking = self.env['stock.picking'].browse(
values['picking_id'])
values['picking_id'] = picking.id
values['location_id'] = picking.location_id.id
values['location_dest_id'] = picking.location_dest_id.id
values['name'] = picking.name
values['date'] = picking.scheduled_date
values['company_id'] = picking.company_id.id
if picking.picking_type_id.code == 'incoming':
values['price_unit'] = random.randint(1, 100)
yield values
def _populate_set_parent_departments(self, departments, size):
parent_ids = []
rand = populate.Random('hr.department+parent_generator')
for dept in departments:
if rand.random() > 0.3:
parent_ids.append(dept.id)
parent_children = defaultdict(lambda: self.env['hr.department'])
for dept in departments:
parent = rand.choice(parent_ids)
if parent < dept.id:
parent_children[parent] |= dept
for parent, children in parent_children.items():
children.write({'parent_id': parent})
def get_company_info(iterator, field_name, model_name):
random = populate.Random("sale_order_company")
for values in iterator:
cid = values.get("company_id")
valid_partner_ids = partners_ids_by_company[cid]
valid_user_ids = user_ids_by_company[cid]
valid_pricelist_ids = pricelist_ids_by_company[cid]
values.update({
"partner_id":
random.choice(valid_partner_ids),
"user_id":
random.choice(valid_user_ids),
"pricelist_id":
random.choice(valid_pricelist_ids),
})
yield values
def _populate_factories(self):
product_ids = self.env['product.product'].browse(
self.env.registry.populated_models['product.product']).filtered(
lambda p: p.type in ('product', 'consu')).ids
random_products = populate.Random("move_product_sample")
product_ids = random_products.sample(product_ids,
int(len(product_ids) * 0.8))
def get_product_uom(values, counter, random):
return self.env['product.product'].browse(
values['product_id']).uom_id.id
def _attach_to_record(iterator, field_name, model_name):
random = populate.Random('_attach_to_record')
fields, weights = self._populate_attach_record_weight()
fields_generator = self._populate_attach_record_generator()
for values in iterator:
field = random.choices(fields, weights)[0]
values[field] = next(fields_generator[field])
yield values
def _compute_picking_values(iterator, field_name, model_name):
random = populate.Random('_compute_picking_values')
for values in iterator:
if values.get('picking_id'):
picking = self.env['stock.picking'].browse(
values['picking_id'])
values['picking_id'] = picking.id
values['location_id'] = picking.location_id.id
values['location_dest_id'] = picking.location_dest_id.id
values['name'] = picking.name
values['date'] = picking.scheduled_date
values['company_id'] = picking.company_id.id
if picking.picking_type_id.code == 'incoming':
values['price_unit'] = random.randint(1, 100)
yield values
return [
('product_id', populate.randomize(product_ids)),
('product_uom', populate.compute(get_product_uom)),
('product_uom_qty', populate.randint(1, 10)),
('sequence', populate.randint(1, 1000)),
('_attach_to_record', _attach_to_record),
('_compute_picking_values', _compute_picking_values),
]
def _populate_set_children_tasks(self, tasks, size):
_logger.info('Setting parent tasks')
rand = populate.Random('project.task+children_generator')
parents = self.env["project.task"]
for task in tasks:
if not rand.getrandbits(4):
parents |= task
parent_ids = parents.ids
tasks -= parents
parent_childs = collections.defaultdict(
lambda: self.env['project.task'])
for count, task in enumerate(tasks):
if not rand.getrandbits(4):
parent_childs[rand.choice(parent_ids)] |= task
for count, (parent, childs) in enumerate(parent_childs.items()):
if (count + 1) % 100 == 0:
_logger.info('Setting parent: %s/%s', count + 1,
len(parent_childs))
childs.write({'parent_id': parent})
def generate_address(iterator, *args):
address_generators = [
populate.chain_factories(address_factories, self._name)
for address_factories in address_factories_groups
]
# first, exhaust all address_generators
for adress_generator in address_generators:
for adress_values in adress_generator:
if adress_values['__complete']:
break
values = next(
iterator) # only consume main iterator if usefull
yield {**values, **adress_values}
# then, go pseudorandom between generators
r = populate.Random('res.partner+address_generator_selector')
for values in iterator:
adress_generator = r.choice(address_generators)
adress_values = next(adress_generator)
yield {**adress_values, **values}
def compute_product_location(iterator, field_name, model_name):
random = populate.Random('compute_product_location')
# To avoid create twice the same line inventory (avoid _check_no_duplicate_line) : product/location (limitation for lot)
# Use generator to avoid cartisian product in memory
generator_product_loc_dict = {}
for inventory in inventories:
# TODO: randomize cartesian product
generator_product_loc_dict[inventory.id] = cartesian_product(
random.sample(inventory.location_ids,
len(inventory.location_ids)),
random.sample(inventory.product_ids,
len(inventory.product_ids)))
for values in iterator:
loc_id, product = next(
generator_product_loc_dict[values['inventory_id']])
values['product_id'] = product.id
values['location_id'] = loc_id.id
yield values
def get_prices(iterator, field_name, model_name):
random = populate.Random("pricelist_prices")
for values in iterator:
# Fixed price, percentage, formula
compute_price = values["compute_price"]
if compute_price == "fixed":
# base = "list_price" = default
# fixed_price
values["fixed_price"] = random.randint(1, 1000)
elif compute_price == "percentage":
# base = "list_price" = default
# percent_price
values["percent_price"] = random.randint(1, 100)
else: # formula
# pricelist base not considered atm.
values["base"] = random.choice(
["list_price", "standard_price"])
values["price_discount"] = random.randint(0, 100)
# price_min_margin, price_max_margin
# price_round ??? price_discount, price_surcharge
yield values
