def obtener_progresion_lineal(AnioPrediccion, body):
#def obtener_progresion_lineal_anio_cantidad(AnioPrediccion, body):
"""
Obtener la prediccion para un año dado el año a predecir y los datos de varios años anteriores
Obtener la prediccion para un año dado el año a predecir y los datos de varios años anteriores
:param AnioPrediccion: Año para predecir la cantidad
:type AnioPrediccion: str
:param body: Datos de entrada obtenidos previamente
:type body: list | bytes
:rtype: int
"""
prediccionCantidad = -1
if connexion.request.is_json:
body = [Body1.from_dict(d) for d in connexion.request.get_json()]
lista = list()
print(body)
for item in body:
if (hasattr(item, 'anio') and hasattr(item, 'cantidad')):
tupla = (item.anio, item.cantidad)
lista.append(tupla)
prediccionCantidad = tensorflow.ObtenerProgresionLineal(
lista, int(AnioPrediccion))
return int(prediccionCantidad)
def test_towdata_put(self):
"""Test case for towdata_put
Uploads live data to the server for predicition
"""
update_file = "C:/Users/21ale/Documents/CLASE/TFG/TFG-proyect/TFG-web/server/swagger_server/test/test-files/updateTest.csv"
test_file = "C:/Users/21ale/Documents/CLASE/TFG/TFG-proyect/TFG-web/server/swagger_server/test/test-files/appendedUpdateTest.csv"
retrieved_file = "C:/Users/21ale/Documents/CLASE/TFG/TFG-proyect/TFG-web/server/swagger_server/database/savefile.csv"
with open(update_file, 'r') as myfile:
data = myfile.read()
body = Body1(1, data)
response = self.client.open(
'/towdata',
method='PUT',
data=json.dumps(body),
content_type='application/json')
default_controller.towdata_put(body.csv)
md5hasher = FileHash("md5")
expected_hash = md5hasher.hash_file(test_file)
obtained_hash = md5hasher.hash_file(retrieved_file)
self.assertEqual(expected_hash, obtained_hash)
def test_add_party(self):
"""Test case for add_party
Добавить заявку на участие в мероприятие
"""
body = Body1()
response = self.client.open('/v2/event',
method='PUT',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_feedback(self):
"""Test case for feedback
良いか悪いかをユーザーが判断
"""
body = Body1()
response = self.client.open('/api/feedback',
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_towdata_post(self):
"""Test case for towdata_post
uploads data to train and predict over
"""
body = Body1()
response = self.client.open('/towdata',
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_get_trainings_search_results(self):
"""Test case for get_trainings_search_results
Get trainings search results
"""
body = Body1()
response = self.client.open('/trainings/search',
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_update_sm_context(self):
"""Test case for update_sm_context
Update SM Context
"""
body = Body1()
response = self.client.open(
'/nsmf-pdusession/v1/sm-contexts/{smContextRef}/modify'.format(sm_context_ref='sm_context_ref_example'),
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def add_party(body): # noqa: E501
"""Добавить заявку на участие в мероприятие
# noqa: E501
:param body: Подать заявку на участие в мероприятиях от добровольцев России
:type body: dict | bytes
:rtype: None
"""
if connexion.request.is_json:
body = Body1.from_dict(connexion.request.get_json()) # noqa: E501
return 'do some magic!'
def test_create_station(self):
"""Test case for create_station
create a new station
"""
body = Body1()
response = self.client.open(
'/ayhanyunt12/MeteorolojiAPI/1.0.0/station',
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_add_pet(self):
"""
Test case for add_pet
Add a new pet to the store
"""
body = Body1()
response = self.client.open('/v2/pet',
method='POST',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
"Response body is : " + response.data.decode('utf-8'))
def test_catalog_product_website_link_repository_v1_save_put(self):
"""
Test case for catalog_product_website_link_repository_v1_save_put
"""
body = Body1()
response = self.client.open(
'/v2/v1/products/{sku}/websites'.format(sku='sku_example'),
method='PUT',
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
"Response body is : " + response.data.decode('utf-8'))
def towdata_post(body): # noqa: E501
"""uploads data to train and predict over
The body should be in .csv format so the server can read it # noqa: E501
:param body:
:type body: dict | bytes
:rtype: None
"""
body = Body1.from_dict(connexion.request.get_json()) # noqa: E501
SaveData.save(SaveData, body.csv)
return "Done!"
def catalog_product_website_link_repository_v1_save_put(sku, body=None):
"""
catalog_product_website_link_repository_v1_save_put
Assign a product to the website
:param sku:
:type sku: str
:param body:
:type body: dict | bytes
:rtype: bool
"""
if connexion.request.is_json:
body = Body1.from_dict(connexion.request.get_json())
return 'do some magic!'
def feedback(body): # noqa: E501
"""良いか悪いかをユーザーが判断
# noqa: E501
:param body:
:type body: dict | bytes
:rtype: None
"""
if connexion.request.is_json:
body = Body1.from_dict(connexion.request.get_json()) # noqa: E501
if rslt := redis.get(body.url):
with open(f"{__main__.DATA_PATH}/feedback.csv", mode="a") as f:
f.writelines([
body.url, ", ",
str(body.user_evaluation), ", ",
rslt.decode(), "\n"
])
def obtener_outliers_inliers_anios_cantidad(AnioInicio, AnioFin, Metodo,
body): #OK
"""
Obtener los valores fuera de lo comun dado unos valores iniciales y unos valores a tratar
Obtener los valores fuera de lo comun dado unos valores iniciales y unos valores a tratar
:param AnioInicio: Año inicial de la matriz de datos
:type AnioInicio: str
:param AnioFin: Año final de la matriz de datos, Año final de la matriz de datos, año desde el cual obtenemos los datos a probar
:type AnioFin: str
:param Metodo: Metodo a usar para obtener los outliers
:type Metodo: str
:param body: Datos de entrada obtenidos previamente
:type body: list | bytes
:rtype: Dict[str, int]
"""
if connexion.request.is_json:
body = [Body1.from_dict(d) for d in connexion.request.get_json()]
listaLabels = list()
listaLabels.append('Anio')
listaLabels.append('Cantidad')
listaValores, listaValoresAComprobar, listaValoresCentrales = conversor.separarValoresBody(
body, AnioFin)
# print(listaValoresAComprobar)
matriz, listaColumnas = conversor.ConvertirTuplasToMatriz(
listaValores, listaLabels)
# print(matriz)
listaValoresOutliers, listaValoresInliers = outliers.ObtenerOutliersDadaMatrizAniosYTipo(
matriz, AnioInicio, AnioFin, listaValoresAComprobar, listaLabels,
Metodo)
outliers.MostrarOutliersMedianteEnvolturaElipticaDadosDatos(
matriz, AnioInicio, AnioFin, listaValoresAComprobar, listaLabels,
listaColumnas)
return conversor.ObtenerJSONDeListasOutliersInliers(
listaValoresInliers, listaValoresOutliers, listaLabels,
listaValoresCentrales)
def get_trainings_search_results(body=None): # noqa: E501
"""Search employee' results
Get training search results # noqa: E501
:param body:
:type body: dict | bytes
:rtype: Trainings
"""
if connexion.request.is_json:
body = Body1().from_dict(connexion.request.get_json())
#body = Body1.from_dict(connexion.request.get_json()) # noqa: E501
#接続情報の作成
engine = sqlalchemy.create_engine(
'postgresql://*****:*****@wakupy-db.csw5cdbrfmzu.ap-northeast-1.rds.amazonaws.com/omurice'
)
# クエリの作成
query = "SELECT * FROM public.m_training WHERE 1=1"
#検索条件にtraining_nameが指定された場合
if not body.training_name:
pass
else:
query += " AND training_name like '%%{}%%'".format(body.training_name)
#検索条件にtraining_classが指定された場合(チェックボックス)
if not body.training_class:
pass
else:
for index, training_class in enumerate(body.training_class):
if index == 0:
query += " AND training_class in ('{}'".format(training_class)
else:
query += " ,'{}'".format(training_class)
query += ")"
#検索条件にskill_classが指定された場合(チェックボックス)
if not body.skill_class:
pass
else:
for index, skill in enumerate(body.skill_class):
if index == 0:
query += " AND skill_class in ('{}'".format(skill)
else:
query += " ,'{}'".format(skill)
query += ")"
#検索条件にit_levelが指定された場合(チェックボックス)
if not body.it_level:
pass
else:
for index, it_level in enumerate(body.it_level):
if index == 0:
query += " AND it_level in ('{}'".format(it_level)
else:
query += " ,'{}'".format(it_level)
query += ")"
#検索条件にrecruitment_num_fromが指定された場合
if not body.recruitment_num_from:
pass
else:
query += " AND recruitment_numbers >= '{}'".format(
body.recruitment_num_from)
#検索条件にrecruitment_num_toが指定された場合
if not body.recruitment_num_to:
pass
else:
query += " AND recruitment_numbers <= '{}'".format(
body.recruitment_num_to)
#検索条件にnew_flgが指定された場合(チェックボックス)
if not body.new_flg:
pass
else:
for index, new_flg in enumerate(body.new_flg):
if index == 0:
query += " AND new_training_flg in ('{}'".format(new_flg)
else:
query += " ,'{}'".format(new_flg)
query += ")"
#検索条件にselection_flgが指定された場合(チェックボックス)
if not body.selection_flg:
pass
else:
for index, selection_flg in enumerate(body.selection_flg):
if index == 0:
query += " AND selection_flg in ('{}'".format(selection_flg)
else:
query += " ,'{}'".format(selection_flg)
query += ")"
#検索条件にstart_dateが指定された場合
if not body.start_date:
pass
else:
query += " AND tstart_date = '{}'".format(body.start_date)
#検索条件にend_dateが指定された場合
if not body.end_date:
pass
else:
query += " AND end_date = '{}'".format(body.end_date)
#クエリの実行
result = engine.execute(query)
#結果を格納するリストの作成
trainings = []
trainings_inner = TrainingsInner()
for row in result:
trainings_inner.training_name = row['training_name']
trainings_inner.training_class = row['training_class']
trainings_inner.skill_class = row['skill_class']
trainings_inner.it_level = row['it_level']
trainings_inner.recruitment_num = row['recruitment_numbers']
trainings_inner.participation_num = row['participation_numbers']
trainings_inner.new_flg = row['new_training_flg']
trainings_inner.selection_flg = row['selection_flg']
trainings_inner.start_date = row['start_date']
trainings_inner.end_date = row['end_date']
trainings.append(copy.copy(trainings_inner))
#接続クローズ
result.close()
return trainings