def demand(self):
if self.demand_dist == "Constant":
return self.demand_p1
elif self.demand_dist == "Normal":
return make_distribution(
np.random.normal,
self.demand_p1,
self.demand_p2)()
elif self.demand_dist == "Triangular":
return make_distribution(
np.random_triangular,
self.demand_p1,
self.demand_p2,
self.demand_p3
)()
def lead_time(self):
if self.leadtime_dist == "Constant":
return self.leadtime_p1
elif self.leadtime_dist == "Normal":
return make_distribution(
np.random.normal,
self.leadtime_p1,
self.leadtime_p2)()
if self.leadtime_dist == "Triangular":
return make_distribution(
np.random.triangular,
self.leadtime_p1,
self.leadtime_p2,
self.leadtime_p3
)()
import numpy as np
from functions import make_data, Product, make_distribution
# Product with constants distributions
product_A = Product(name="Product_A", demand_dist=8000, lead_time_dist=1, initial_inventory=24000, price=18)
df_A = make_data(product_A, policy={"method": "Qs", "param1": 20000, "param2": 10000}, periods=52)
# Product with a normal demand and triangular lead_time
# Set the seed so we can get the same sequence of number
np.random.seed(42)
product_B = Product(
name="Product_B",
demand_dist=make_distribution(np.random.normal, 8082, 783),
lead_time_dist=make_distribution(np.random.triangular, 1, 1, 2),
initial_inventory=24000,
price=18,
)
df_B = make_data(product_B, policy={"method": "Qs", "param1": 20000, "param2": 10000}, periods=52)
class TestDataframe(unittest.TestCase):
def test_dataframeA_type(self):
"make_data() return a dataframe"
self.assertEqual(type(df_A), pd.DataFrame)
def test_dataframeB_type(self):
"make_data() return a dataframe"
