tdce-basic / functions /data_extractor /emanufac_tdabc_extractor_class.py

Tin Theethawat Savastham

♻️ Strcuture code

0887820 over 1 year ago

15.1 kB

import pandas as pd
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)

	import pandas as pd
	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)