def integrate_datasets(data_norm,
data_target,
path_xlsx_output,
integrator=None,
df_prepro=None,
df_norm=None):
"""
Integrates the supplier dataset into the target schema.
INPUT:
- data_norm: preprocessed and normalised supplier dataset, must be in wide format
- data_target: target dataset
- path_xlsx_output: full path where to save the final xlsx file
OUTPUT:
- pandas dataframe
"""
if integrator is None:
integrator = Integrator(path_normalised_file=None,
path_target_file=None,
path_prepro_file=None,
path_xlsx_output=path_xlsx_output,
path_integr_output=None)
# renames the columns in the normalised dataframe so they can be appended to the target dataframe
data = integrator.rename_columns(data_norm)
# drops the columns in the normalised dataframe that have no match in the target dataframe
data = integrator.drop_columns(data, data_target)
# appends the supplier dataset to the target dataset
data = integrator.append_supplier_to_target(data, data_target)
# saves all the files in this task in one single xlsx file with each step in a different sheet
integrator.save_xlsx(data, df_prepro=df_prepro, df_norm=df_norm)
return data
def main(path_normalised_file,
path_target_file,
path_prepro_file,
path_xlsx_output,
path_integr_output=None):
# call object
integr = Integrator(path_normalised_file=path_normalised_file,
path_target_file=path_target_file,
path_prepro_file=path_prepro_file,
path_xlsx_output=path_xlsx_output,
path_integr_output=path_integr_output)
# load input dataframe
data = integr.load_normalised()
# load target dataframe
data_target = integr.load_target()
data = integrate_datasets(data_norm=data,
data_target=data_target,
path_xlsx_output=path_xlsx_output,
integrator=integr)
# write output csv file
if path_integr_output: data.to_csv(path_integr_output, index=True)
def main():
# Ingreso de datos
h_max = float(input("Altura máxima de agua: "))
h = float(input("Altura actual de agua: "))
# Comprobar condiciones de la altura ingresada
if (h < 0):
h = 0 # h = 0 cuando este sea menor a 0
elif (h > h_max):
h = h_max # h = h_max cuando h supere el limite
integrator = Integrator(0, h) #Clase integradora
V = integrator.integrate() #Se integra la funcion pi * x^2
u = integrator.bisection(
-1 * h, h, 1e-6
) #Algoritmo root-finding | Metodo de Bisección con tolerancia de 1e-6
V_u = V + u #Volumen total de la presa
# FUNCION PARA DIBUJAR LA GRAFICA
x = np.linspace(-5, 5, 100)
plt.plot(x, integrator.function_value(x))
plt.grid()
# Valor del volumen cuando la altura es la máxima
integrator_max = Integrator(0, h_max)
v_max = integrator_max.integrate()
# Comprobar si V(u) supera a V(h_max) entonces se vuelve
# a realizar el calculo con u = h_max ó u = 0
if (V_u > v_max):
V_u = V + h_max
if (V_u < 0):
V_u = V
# RESULTADOS
print(f"V(u): {round(V_u, 5)}, V_max(h_max): {round(v_max,5)}")
plt.show()
order_id= uuid.uuid1()
comment1=" "
comment2=""
chromecast_unit_price = 150
chromecast_quantity = 2
chromecast_subtotal = chromecast_unit_price*chromecast_quantity
organza_unit_price = 50
organza_quantity = 2
organza_subtotal = organza_unit_price* organza_quantity
client = Integrator(namespace, wsdl, apiVersion, merchantEmail, merchantKey, serviceType, integrationMode)
chromecast = client.buildOrderItem("001", "chromecast", chromecast_unit_price, chromecast_quantity, chromecast_subtotal)
organza = client.buildOrderItem("002", "organza", organza_unit_price, organza_quantity,organza_subtotal)
order_items = client.buildAPIObject('ArrayOfOrderItem')
order_items.OrderItem.append(chromecast)
order_items.OrderItem.append(organza)
# print order_items
#
sub_total=chromecast_subtotal+ organza_subtotal
shipping_cost=30
tax_amount=0
total= sub_total + shipping_cost+ tax_amount
