def plot():
if request.method == "POST":
data = request.get_json()
if data['product'] == "Example Product":
header = ['IIP', 'INI', 'D', 'FIP',
'FNI', 'LS', 'AVG', 'ORD', 'LT']
df_A = DataFrame(index=np.arange(52), columns=header).fillna(1)
price = 18
order_cost = 200
else:
product_A = ProductDB.get_product(data['product'])
policy = data['policy']
periods = int(data['periods'])
p1 = int(data['p1'])
p2 = int(data['p2'])
df_A = make_data(product_A,
policy={'method': policy,
'param1': p1,
'param2': p2},
periods=periods)
price = product_A.price
order_cost = product_A.order_cost
carrying_costs = int(df_A['AVG'].sum() * price * (0.21/52))
stock_out_costs = int(df_A['LS'].sum() * price)
ordering_costs = int(df_A.ORD[df_A.ORD > 0].count() * order_cost)
step_data = [{"PERIOD": int(x), "FIP": int(y)} for x, y in zip(df_A.index, df_A["FIP"])]
pie_data = [{"TipoCosto": "Carrying", "Costo": carrying_costs},
{"TipoCosto": "Stock-Out", "Costo": stock_out_costs},
{"TipoCosto": "Ordering", "Costo": ordering_costs}]
data = [step_data, pie_data]
return json.dumps(data)
import unittest
import pandas as pd
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)
