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import requests
import numpy as np
import pickle
class EManufacTDABCExtractor:
def __init__(self, api_url, api_key, profile_id, costed_place):
self.api_url = api_url
self.api_key = api_key
self.process_df = pd.DataFrame()
self.material_usage_df = pd.DataFrame()
self.original_material_usage_df = pd.DataFrame()
self.original_employee_usage_df = pd.DataFrame()
self.original_process_df = pd.DataFrame()
self.capital_cost_df = pd.DataFrame()
self.employee_usage_df = pd.DataFrame()
self.original_capital_cost_df = pd.DataFrame()
self.profile_id = profile_id # Profile For TDABC
self.original_procedure_profile_id = "" # Profile For Factory Current Method
self.profile_element_name_for_material = "ต้นทุนวัตถุดิบ"
self.costed_place = costed_place
self.running_no_start = ""
self.running_no_end = ""
def set_running_no_margin(self, start, end):
self.running_no_start = start
self.running_no_end = end
print("Setting Successfully")
def fetch_material_usage(self, start_date, end_date, limit, page=1):
url = f"{self.api_url}/cost-estimation/on-type"
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
querystring = {
"startDate": start_date,
"endDate": end_date,
"size": limit,
"page": page,
"profile": self.profile_id,
"elementType": "MATERIAL",
"placeRestricNotify": "true",
"correctPlaceOnly": "true",
"extractOriginalLot": "true",
"place": self.costed_place,
"specifyProfileElement": self.profile_element_name_for_material,
"runningNoStart": self.running_no_start,
"runningNoEnd": self.running_no_end,
}
response = requests.get(url, headers=headers, params=querystring)
try:
material_data = response.json()["rows"]
except Exception as e:
print("Error in fetch material", e)
material_data = []
self.material_usage_df = pd.DataFrame(material_data)
self.original_material_usage_df = pd.DataFrame(material_data)
with open("material.pickle", "wb") as handle:
pickle.dump(self.material_usage_df, handle)
return (self.material_usage_df,)
def fetch_employee_usage(self, start_date, end_date, limit, page=1):
url = f"{self.api_url}/cost-estimation/on-type"
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
querystring = {
"startDate": start_date,
"endDate": end_date,
"size": limit,
"page": page,
"profile": self.profile_id,
"elementType": "LABOR",
"placeRestricNotify": "true",
"correctPlaceOnly": "true",
"place": self.costed_place,
"merged": "true",
"runningNoStart": self.running_no_start,
"runningNoEnd": self.running_no_end,
}
response = requests.get(url, headers=headers, params=querystring)
try:
employee_data = response.json()["rows"]
except Exception as e:
print("Error in fetch employee", e)
employee_data = []
self.employee_usage_df = pd.DataFrame(employee_data)
self.original_employee_usage_df = pd.DataFrame(employee_data)
with open("employee.pickle", "wb") as handle:
pickle.dump(self.employee_usage_df, handle)
return (self.employee_usage_df,)
def fetch_capital_cost_usage(self, start_date, end_date, limit, page=1):
url = f"{self.api_url}/cost-estimation/on-type"
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
querystring = {
"startDate": start_date,
"endDate": end_date,
"size": limit,
"page": page,
"profile": self.profile_id,
"elementType": "CAPITAL_COST",
"placeRestricNotify": "true",
"correctPlaceOnly": "true",
"place": self.costed_place,
"merged": "true",
"splitCostDriver": "true",
"runningNoStart": self.running_no_start,
"runningNoEnd": self.running_no_end,
}
response = requests.get(url, headers=headers, params=querystring)
try:
capital_cost_data = response.json()["rows"]
except Exception as e:
print("Error in fetch capital cost", e)
capital_cost_data = []
self.capital_cost_df = pd.DataFrame(capital_cost_data)
self.original_capital_cost_df = pd.DataFrame(capital_cost_data)
with open("capital.pickle", "wb") as handle:
pickle.dump(self.capital_cost_df, handle)
return (self.capital_cost_df,)
def change_profile_element_for_material(self, new_element_name):
self.profile_element_name_for_material = new_element_name
print("Success Changing")
# Profile For Current Factory Cost Estimation Method
def change_profile_for_original_procedure(self, profile_id):
self.original_procedure_profile_id = profile_id
print("Success Changing")
# Get Data From Current Factory Cost Estimation Method as a Reference or Result
# Of our new Profile using TDABC
def fetch_process_data(self, start_date, end_date, limit, page=1):
url = f"{self.api_url}/cost-estimation"
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
querystring = {
"startDate": start_date,
"endDate": end_date,
"size": limit,
"page": page,
"profile": self.original_procedure_profile_id,
"hideResultList": "true",
"placeRestricNotify": "true",
"correctPlaceOnly": "true",
"runningNoStart": self.running_no_start,
"runningNoEnd": self.running_no_end,
"place": self.costed_place,
}
response = requests.get(url, headers=headers, params=querystring)
process_data = response.json()["rows"]
self.process_df = pd.DataFrame(process_data)
self.original_process_df = pd.DataFrame(process_data)
with open("process.pickle", "wb") as handle:
pickle.dump(self.process_df, handle)
return (self.process_df,)
def load_from_pickle(self):
try:
with open("material.pickle", "rb") as handle:
material_usage_df = pickle.load(handle)
self.material_usage_df = material_usage_df
self.original_material_usage_df = material_usage_df
except:
print("Error loading material Pickle")
try:
with open("employee.pickle", "rb") as handle:
employee_usage_df = pickle.load(handle)
self.employee_usage_df = employee_usage_df
self.original_employee_usage_df = employee_usage_df
except:
print("Error loading employee Pickle")
try:
with open("capital.pickle", "rb") as handle:
capital_cost_df = pickle.load(handle)
self.capital_cost_df = capital_cost_df
self.original_capital_cost_df = capital_cost_df
except:
print("Error loading capital Pickle")
try:
with open("process.pickle", "rb") as handle:
process_df = pickle.load(handle)
self.process_df = process_df
self.original_process_df = process_df
except:
print("Error loading Process Pickle")
# self.capital_cost_df = capital_cost_df
print("Loaded from pickle Successfully")
def get_process_list(self):
return self.process_df
def get_material_usage(self):
return self.material_usage_df
def get_employee_usage(self):
return self.employee_usage_df
def get_capital_cost(self):
return self.capital_cost_df
def load_material_usage(self, material_usage_df):
self.material_usage_df = material_usage_df
def load_employee_usage(self, employee_usage_df):
self.employee_usage_df = employee_usage_df
def load_capital_cost(self, capital_cost_df):
self.capital_cost_df = capital_cost_df
def load_process(self, process_df):
self.process_df = process_df
def load_original_process(self, process_df):
self.original_process_df = process_df
def load_original_material_usage(self, material_usage_df):
self.original_material_usage_df = material_usage_df
def load_original_employee_usage(self, employee_usage_df):
self.original_employee_usage_df = employee_usage_df
def load_original_capital_cost(self, capital_cost_df):
self.original_capital_cost_df = capital_cost_df
def adjust_material_usage(self):
new_material_usage_df = pd.DataFrame() # self.material_usage_df.copy()
new_material_usage_df["_id"] = self.original_material_usage_df["material_id"]
new_material_usage_df["process_id"] = self.original_material_usage_df[
"process_id"
]
new_material_usage_df["name"] = self.original_material_usage_df["material_name"]
new_material_usage_df["amount"] = self.original_material_usage_df[
"used_quantity"
]
new_material_usage_df["unit_cost"] = self.original_material_usage_df[
"unit_cost"
]
# TODO: Update in EManufac Code to pick the purchase date instead
new_material_usage_df["date"] = self.original_material_usage_df["used_date"]
self.material_usage_df = new_material_usage_df
def adjust_employee_usage(self):
new_employee_usage_df = pd.DataFrame()
new_employee_usage_df["_id"] = self.original_employee_usage_df[
"artifact_employee_id"
]
new_employee_usage_df["employee_id"] = self.original_employee_usage_df[
"artifact_employee_id"
]
new_employee_usage_df["process_id"] = self.original_employee_usage_df[
"process_id"
]
new_employee_usage_df["employee_name"] = self.original_employee_usage_df[
"artifact_employee_name"
]
new_employee_usage_df["amount"] = self.original_employee_usage_df[
"average_labor_amount"
]
new_employee_usage_df["date"] = self.original_employee_usage_df["receipt_date"]
# If more than 1 employee (unit cost is same) we group to one, and sum the duration
new_employee_usage_df["duration"] = (
self.original_employee_usage_df["artifact_minute_use"]
* self.original_employee_usage_df["average_labor_amount"]
)
# new_employee_usage_df["type"]
new_employee_usage_df["type"] = "daily"
new_employee_usage_df['day_amount'] = 1
try:
new_employee_usage_df["cost"] = self.original_employee_usage_df[
"average_daily_labor_cost"
]
except:
new_employee_usage_df["cost"] = 0
new_employee_usage_df = new_employee_usage_df.dropna(subset=["cost"])
self.employee_usage_df = new_employee_usage_df
def adjust_capital_cost(self):
new_capital_cost_df = pd.DataFrame()
try:
new_capital_cost_df["_id"] = self.original_capital_cost_df[
"artifact_cost_title"
]
new_capital_cost_df["process_id"] = self.original_capital_cost_df[
"process_id"
]
new_capital_cost_df["name"] = self.original_capital_cost_df[
"artifact_cost_title"
]
new_capital_cost_df["cost"] = self.original_capital_cost_df[
"artifact_capital_cost"
]
new_capital_cost_df["day_amount"] = self.original_capital_cost_df[
"average_day_amount"
]
new_capital_cost_df["hour_amount"] = self.original_capital_cost_df[
"average_hour_amount"
]
new_capital_cost_df["unit_cost"] = self.original_capital_cost_df[
"artifact_unit_cost"
]
new_capital_cost_df["duration"] = self.original_capital_cost_df[
"artifact_used_time"
]
new_capital_cost_df["date"] = self.original_capital_cost_df["receipt_date"]
# new_capital_cost_df["machine_hour"] = new_capital_cost_df["machineHour"]
# new_capital_cost_df["life_time"] = new_capital_cost_df["lifeTime"]
# new_capital_cost_df["day_per_month"] = new_capital_cost_df["dayPerMonth"]
zero_cost = new_capital_cost_df[new_capital_cost_df["cost"] == 0]
# Remove None machine usage
new_capital_cost_df = new_capital_cost_df.drop(zero_cost.index)
self.capital_cost_df = new_capital_cost_df
except Exception as e:
print("Error in adjust capital cost", e)
def adjust_process_df(self):
temp_process_df = self.original_process_df.copy()
temp_process_df = temp_process_df[temp_process_df["cost"] > 0]
self.process_df = temp_process_df
def save_material_csv(self, destination_folder_path="generated"):
self.material_usage_df.to_csv(
f"{destination_folder_path}/generated_material_usage.csv"
)
self.original_material_usage_df.to_csv(
f"{destination_folder_path}/original_material_usage.csv"
)
def save_process_csv(self, destination_folder_path="generated"):
self.process_df.to_csv(
f"{destination_folder_path}/generated_process_data.csv")
self.original_process_df.to_csv(
f"{destination_folder_path}/original_process_data.csv"
)
def save_employee_csv(self, destination_folder_path="generated"):
self.employee_usage_df.to_csv(
f"{destination_folder_path}/generated_employee_usage.csv"
)
self.original_employee_usage_df.to_csv(
f"{destination_folder_path}/original_employee_usage.csv"
)
def save_capital_csv(self, destination_folder_path="generated"):
self.capital_cost_df.to_csv(
f"{destination_folder_path}/generated_captial_cost.csv"
)
self.original_capital_cost_df.to_csv(
f"{destination_folder_path}/original_captial_cost.csv"
)
def save_csv(self, destination_folder_path="generated"):
self.save_process_csv(destination_folder_path)
self.save_material_csv(destination_folder_path)
self.save_employee_csv(destination_folder_path)
self.save_capital_csv(destination_folder_path)
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