def test_extract_ft():
data = {"entreprise": {"raison_sociale": "blablabar"}}
assert helpers.extract_ft(models.Data(data)) == "blablabar"
data = {
"entreprise": {
"raison_sociale": "blablabar",
"ues": {
"nom": "nom ues"
}
}
}
assert helpers.extract_ft(models.Data(data)) == "blablabar nom ues"
data = {
"entreprise": {
"raison_sociale": "blablabar",
"ues": {
"nom":
"nom ues",
"entreprises": [
{
"siren": "123456789",
"raison_sociale": "entreprise une"
},
{
"siren": "123456780",
"raison_sociale": "entreprise deux"
},
],
},
}
}
assert (helpers.extract_ft(models.Data(data)) ==
"blablabar nom ues entreprise une entreprise deux")
async def search(q, verbose=False):
rows = await db.declaration.search(q)
for row in rows:
data = models.Data(row)
print(f"{data.siren} | {data.year} | {data.company}")
if verbose:
print(row)
def test_compute_augmentations_note():
data = models.Data({
"déclaration": {},
"indicateurs": {
"augmentations_et_promotions": {
"résultat": 5,
"résultat_nombre_salariés": 6,
}
},
})
helpers.compute_notes(data)
assert data["indicateurs"]["augmentations_et_promotions"]["note"] == 25
assert (data["indicateurs"]["augmentations_et_promotions"]
["note_nombre_salariés"] == 15)
assert (data["indicateurs"]["augmentations_et_promotions"]
["note_en_pourcentage"] == 25)
data = models.Data({
"déclaration": {},
"indicateurs": {
"augmentations_et_promotions": {
"résultat": 5.05,
"résultat_nombre_salariés": 2,
}
},
})
helpers.compute_notes(data)
assert data["indicateurs"]["augmentations_et_promotions"]["note"] == 35
assert (data["indicateurs"]["augmentations_et_promotions"]
["note_nombre_salariés"] == 35)
assert (data["indicateurs"]["augmentations_et_promotions"]
["note_en_pourcentage"] == 15)
data["indicateurs"]["augmentations_et_promotions"][
"non_calculable"] = "egvi40pcet"
helpers.compute_notes(data)
assert not data["indicateurs"]["augmentations_et_promotions"].get("note")
assert not data["indicateurs"]["augmentations_et_promotions"].get(
"note_nombre_salariés")
assert not data["indicateurs"]["augmentations_et_promotions"].get(
"note_en_pourcentage")
async def factory(
siren="123456782",
year=2020,
owner="*****@*****.**",
company="Total Recall",
departement="26",
region="84",
grade=26,
uid=str(uuid.uuid1()),
compute_notes=False,
modified_at=None,
**data,
):
data.setdefault("entreprise", {})
data.setdefault("déclaration", {})
data.setdefault("déclarant", {})
data.setdefault("indicateurs", {})
data.setdefault("id", uid)
data["entreprise"].setdefault("raison_sociale", company)
data["entreprise"].setdefault("département", departement)
data["entreprise"].setdefault("région", region)
data["entreprise"].setdefault("siren", siren)
data["entreprise"].setdefault("effectif", {
"tranche": "50:250",
"total": 149
})
data["déclaration"].setdefault("année_indicateurs", year)
data["déclaration"].setdefault("index", grade)
data["déclaration"].setdefault("fin_période_référence", "2019-12-31")
data["déclarant"].setdefault("email", owner)
data["déclarant"].setdefault("prénom", "Martin")
data["déclarant"].setdefault("nom", "Martine")
data["indicateurs"].setdefault("rémunérations", {"mode": "csp"})
data["indicateurs"].setdefault("congés_maternité", {})
if data["entreprise"]["effectif"]["tranche"] == "50:250":
data["indicateurs"].setdefault("augmentations_et_promotions", {})
else:
data["indicateurs"].setdefault("augmentations", {})
data["indicateurs"].setdefault("promotions", {})
if compute_notes:
helpers.compute_notes(models.Data(data))
await db.declaration.put(siren,
year,
owner,
data,
modified_at=modified_at)
await db.ownership.put(siren, owner)
return data
def test_compute_augmentations_hp_note_with_correction_measures_but_equality():
data = models.Data({
"déclaration": {},
"indicateurs": {
"rémunérations": {
"note": 40,
"résultat": 0.0,
"population_favorable": "femmes",
},
"augmentations": {
"résultat": 4.0,
"population_favorable": "hommes",
},
},
})
helpers.compute_notes(data)
assert data["indicateurs"]["augmentations"]["note"] == 10
def ues_data(sheet, data):
data = models.Data(data)
entreprises = data.path("entreprise.ues.entreprises")
if not entreprises:
return
region = constants.REGIONS.get(data.path("entreprise.région"))
departement = constants.DEPARTEMENTS.get(
data.path("entreprise.département"))
adresse = data.path("entreprise.adresse")
cp = data.path("entreprise.code_postal")
commune = data.path("entreprise.commune")
tranche = EFFECTIF.get(data.path("entreprise.effectif.tranche"))
nom = data.path("entreprise.ues.nom")
rows = [[
data.year,
region,
departement,
adresse,
cp,
commune,
tranche,
nom,
data.siren,
data.company,
data.path("entreprise.raison_sociale"),
data.siren,
]]
for ues in entreprises or []:
rows.append([
data.year,
region,
departement,
adresse,
cp,
commune,
tranche,
nom,
data.siren,
data.company,
ues["raison_sociale"],
ues["siren"],
])
for row in rows:
sheet.append(clean_cell(cell) for cell in row)
def test_compute_augmentations_note_with_correction_measures_but_equality():
data = models.Data({
"déclaration": {},
"indicateurs": {
"rémunérations": {
"résultat": 0,
"population_favorable": "hommes"
},
"augmentations_et_promotions": {
"résultat": 5,
"résultat_nombre_salariés": 6,
"population_favorable": "femmes",
},
},
})
helpers.compute_notes(data)
# rémuénrations.résultat == 0, this means equality, so whatever the value of
# population_favorable, we do not follow it
assert data["indicateurs"]["augmentations_et_promotions"]["note"] == 25
def test_compute_augmentations_note_with_correction_measures():
data = models.Data({
"déclaration": {},
"indicateurs": {
"rémunérations": {
"résultat": 5,
"population_favorable": "hommes"
},
"augmentations_et_promotions": {
"résultat": 5,
"résultat_nombre_salariés": 6,
"population_favorable": "femmes",
},
},
})
helpers.compute_notes(data)
# Maximal note because this indicateur is favourable for the opposition population
# of rémunérations indicateur
assert data["indicateurs"]["augmentations_et_promotions"]["note"] == 35
async def resend_receipts(*sirens,
recipient=None,
year=constants.CURRENT_YEAR):
"""Resend receipt for a list of sirens in the current year"""
for siren in sirens:
record = await db.declaration.get(siren, year)
data = models.Data(record.get("data"))
url = config.DOMAIN + data.uri
recipient_ = recipient or record["declarant"]
try:
emails.success.send(
recipient_,
url=url,
modified_at=record["modified_at"],
**data,
)
except Exception as ex:
print(ex)
print(data)
continue
SMALL_COMPANY = models.Data({
"id": "1234",
"source": "formulaire",
"déclaration": {
"date": "2020-11-04T10:37:06+00:00",
"année_indicateurs": 2019,
"fin_période_référence": "2019-12-31",
"index": 65,
},
"déclarant": {
"email": "*****@*****.**",
"prénom": "Foo",
"nom": "Bar",
"téléphone": "+33123456789",
},
"entreprise": {
"raison_sociale": "FooBar",
"siren": "514027945",
"code_naf": "47.25Z",
"code_postal": "12345",
"région": "76",
"département": "12",
"adresse": "12, rue des adresses",
"commune": "Y",
"effectif": {
"total": 312,
"tranche": "50:250"
},
},
"indicateurs": {
"promotions": {},
"augmentations": {},
"rémunérations": {
"mode":
"csp",
"note":
38,
"résultat":
1.95,
"catégories": [
{
"nom": "tranche 0",
"tranches": {}
},
{
"nom": "tranche 1",
"tranches": {
"50:": -8.6,
":29": 4.6,
"30:39": -0.7,
"40:49": 5.2,
},
},
{
"nom": "tranche 2",
"tranches": {
"50:": 1.9,
":29": 2.2,
"30:39": 1.1,
"40:49": 2.2,
},
},
{
"nom": "tranche 3",
"tranches": {
"50:": 22.5,
":29": 7.6,
"30:39": 2.9,
"40:49": 6.4,
},
},
],
"population_favorable":
"hommes",
},
"congés_maternité": {
"non_calculable": "absrcm"
},
"hautes_rémunérations": {
"note": 0,
"résultat": 0,
"population_favorable": "hommes",
},
"augmentations_et_promotions": {
"note": 0,
"résultat": 34.4888,
"note_en_pourcentage": 0,
"population_favorable": "hommes",
"note_nombre_salariés": 0,
"résultat_nombre_salariés": 11.4,
},
},
})