Merge pull request #21724 from rohitwaghchaure/production-forecasting
feat: production forecasting using exponential smoothing method
This commit is contained in:
commit
df78e29957
@ -19,7 +19,7 @@ from six import itervalues
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from erpnext.accounts.doctype.accounting_dimension.accounting_dimension import get_accounting_dimensions, get_dimension_with_children
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def get_period_list(from_fiscal_year, to_fiscal_year, period_start_date, period_end_date, filter_based_on, periodicity, accumulated_values=False,
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company=None, reset_period_on_fy_change=True):
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company=None, reset_period_on_fy_change=True, ignore_fiscal_year=False):
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"""Get a list of dict {"from_date": from_date, "to_date": to_date, "key": key, "label": label}
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Periodicity can be (Yearly, Quarterly, Monthly)"""
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@ -67,8 +67,9 @@ def get_period_list(from_fiscal_year, to_fiscal_year, period_start_date, period_
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# if a fiscal year ends before a 12 month period
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period.to_date = year_end_date
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period.to_date_fiscal_year = get_fiscal_year(period.to_date, company=company)[0]
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period.from_date_fiscal_year_start_date = get_fiscal_year(period.from_date, company=company)[1]
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if not ignore_fiscal_year:
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period.to_date_fiscal_year = get_fiscal_year(period.to_date, company=company)[0]
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period.from_date_fiscal_year_start_date = get_fiscal_year(period.from_date, company=company)[1]
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period_list.append(period)
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@ -43,10 +43,11 @@
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"docstatus": 0,
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"doctype": "Desk Page",
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"extends_another_page": 0,
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"hide_custom": 0,
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"idx": 0,
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"is_standard": 1,
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"label": "Manufacturing",
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"modified": "2020-05-19 14:05:59.100891",
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"modified": "2020-05-20 11:50:20.029056",
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"modified_by": "Administrator",
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"module": "Manufacturing",
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"name": "Manufacturing",
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@ -88,6 +89,17 @@
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"stats_filter": "{ \n \"status\": [\"not in\", [\"Completed\"]]\n}",
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"type": "DocType"
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},
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{
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"label": "Dashboard",
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"link_to": "Manufacturing",
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"restrict_to_domain": "Manufacturing",
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"type": "Dashboard"
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},
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{
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"label": "Forecasting",
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"link_to": "Exponential Smoothing Forecasting",
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"type": "Report"
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},
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{
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"label": "Work Order Summary",
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"link_to": "Work Order Summary",
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@ -95,10 +107,14 @@
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"type": "Report"
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},
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{
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"label": "Dashboard",
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"link_to": "Manufacturing",
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"restrict_to_domain": "Manufacturing",
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"type": "Dashboard"
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"label": "BOM Stock Report",
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"link_to": "BOM Stock Report",
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"type": "Report"
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},
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{
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"label": "Production Planning Report",
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"link_to": "Production Planning Report",
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"type": "Report"
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}
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]
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}
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@ -0,0 +1,97 @@
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// Copyright (c) 2016, Frappe Technologies Pvt. Ltd. and contributors
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// For license information, please see license.txt
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/* eslint-disable */
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frappe.query_reports["Exponential Smoothing Forecasting"] = {
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"filters": [
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{
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"fieldname":"company",
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"label": __("Company"),
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"fieldtype": "Link",
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"options": "Company",
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"reqd": 1,
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"default": frappe.defaults.get_user_default("Company")
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},
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{
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"fieldname":"from_date",
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"label": __("From Date"),
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"fieldtype": "Date",
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"default": frappe.datetime.get_today(),
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"reqd": 1
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},
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{
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"fieldname":"to_date",
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"label": __("To Date"),
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"fieldtype": "Date",
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"default": frappe.datetime.add_months(frappe.datetime.get_today(), 12),
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"reqd": 1
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},
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{
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"fieldname":"based_on_document",
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"label": __("Based On Document"),
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"fieldtype": "Select",
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"options": ["Sales Order", "Delivery Note", "Quotation"],
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"default": "Sales Order",
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"reqd": 1
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},
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{
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"fieldname":"based_on_field",
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"label": __("Based On"),
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"fieldtype": "Select",
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"options": ["Qty", "Amount"],
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"default": "Qty",
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"reqd": 1
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},
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{
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"fieldname":"no_of_years",
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"label": __("Based On Data ( in years )"),
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"fieldtype": "Select",
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"options": [3, 6, 9],
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"default": 3,
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"reqd": 1
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},
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{
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"fieldname": "periodicity",
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"label": __("Periodicity"),
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"fieldtype": "Select",
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"options": [
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{ "value": "Monthly", "label": __("Monthly") },
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{ "value": "Quarterly", "label": __("Quarterly") },
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{ "value": "Half-Yearly", "label": __("Half-Yearly") },
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{ "value": "Yearly", "label": __("Yearly") }
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],
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"default": "Yearly",
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"reqd": 1
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},
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{
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"fieldname":"smoothing_constant",
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"label": __("Smoothing Constant"),
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"fieldtype": "Select",
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"options": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
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"reqd": 1,
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"default": 0.3
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},
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{
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"fieldname":"item_code",
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"label": __("Item Code"),
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"fieldtype": "Link",
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"options": "Item"
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},
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{
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"fieldname":"warehouse",
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"label": __("Warehouse"),
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"fieldtype": "Link",
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"options": "Warehouse",
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get_query: () => {
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var company = frappe.query_report.get_filter_value('company');
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if (company) {
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return {
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filters: {
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'company': company
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}
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};
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}
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}
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}
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]
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};
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@ -0,0 +1,40 @@
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{
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"add_total_row": 0,
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"creation": "2020-05-15 05:18:55.838030",
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"disable_prepared_report": 0,
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"disabled": 0,
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"docstatus": 0,
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"doctype": "Report",
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"idx": 0,
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"is_standard": "Yes",
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"letter_head": "",
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"modified": "2020-05-15 05:18:55.838030",
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"modified_by": "Administrator",
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"module": "Manufacturing",
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"name": "Exponential Smoothing Forecasting",
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"owner": "Administrator",
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"prepared_report": 0,
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"ref_doctype": "Sales Order",
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"report_name": "Exponential Smoothing Forecasting",
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"report_type": "Script Report",
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"roles": [
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{
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"role": "Manufacturing User"
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},
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{
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"role": "Stock User"
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},
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{
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"role": "Manufacturing Manager"
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},
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{
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"role": "Stock Manager"
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},
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{
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"role": "Sales Manager"
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},
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{
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"role": "Sales User"
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}
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]
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}
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@ -0,0 +1,222 @@
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# Copyright (c) 2020, Frappe Technologies Pvt. Ltd. and contributors
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# For license information, please see license.txt
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from __future__ import unicode_literals
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import frappe, erpnext
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from frappe import _
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from frappe.utils import flt, nowdate, add_years, cint, getdate
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from erpnext.accounts.report.financial_statements import get_period_list
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from erpnext.stock.doctype.warehouse.warehouse import get_child_warehouses
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def execute(filters=None):
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return ForecastingReport(filters).execute_report()
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class ExponentialSmoothingForecast(object):
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def forecast_future_data(self):
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for key, value in self.period_wise_data.items():
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forecast_data = []
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for period in self.period_list:
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forecast_key = "forecast_" + period.key
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if value.get(period.key) and not forecast_data:
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value[forecast_key] = flt(value.get("avg", 0)) or flt(value.get(period.key))
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# will be use to forecaset next period
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forecast_data.append([value.get(period.key), value.get(forecast_key)])
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elif forecast_data:
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previous_period_data = forecast_data[-1]
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value[forecast_key] = (previous_period_data[1] +
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flt(self.filters.smoothing_constant) * (
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flt(previous_period_data[0]) - flt(previous_period_data[1])
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)
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)
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class ForecastingReport(ExponentialSmoothingForecast):
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def __init__(self, filters=None):
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self.filters = frappe._dict(filters or {})
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self.data = []
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self.doctype = self.filters.based_on_document
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self.child_doctype = self.doctype + " Item"
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self.based_on_field = ("qty"
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if self.filters.based_on_field == "Qty" else "amount")
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self.fieldtype = "Float" if self.based_on_field == "qty" else "Currency"
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self.company_currency = erpnext.get_company_currency(self.filters.company)
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def execute_report(self):
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self.prepare_periodical_data()
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self.forecast_future_data()
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self.data = self.period_wise_data.values()
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self.add_total()
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columns = self.get_columns()
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charts = self.get_chart_data()
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summary_data = self.get_summary_data()
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return columns, self.data, None, charts, summary_data
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def prepare_periodical_data(self):
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self.period_wise_data = {}
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from_date = add_years(self.filters.from_date, cint(self.filters.no_of_years) * -1)
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self.period_list = get_period_list(from_date, self.filters.to_date,
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from_date, self.filters.to_date, None, self.filters.periodicity, ignore_fiscal_year=True)
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order_data = self.get_data_for_forecast() or []
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for entry in order_data:
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key = (entry.item_code, entry.warehouse)
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if key not in self.period_wise_data:
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self.period_wise_data[key] = entry
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period_data = self.period_wise_data[key]
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for period in self.period_list:
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# check if posting date is within the period
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if (entry.posting_date >= period.from_date and entry.posting_date <= period.to_date):
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period_data[period.key] = period_data.get(period.key, 0.0) + flt(entry.get(self.based_on_field))
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for key, value in self.period_wise_data.items():
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list_of_period_value = [value.get(p.key, 0) for p in self.period_list]
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if list_of_period_value:
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value["avg"] = sum(list_of_period_value) / len(list_of_period_value)
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def get_data_for_forecast(self):
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cond = ""
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if self.filters.item_code:
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cond = " AND soi.item_code = %s" %(frappe.db.escape(self.filters.item_code))
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warehouses = []
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if self.filters.warehouse:
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warehouses = get_child_warehouses(self.filters.warehouse)
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cond += " AND soi.warehouse in ({})".format(','.join(['%s'] * len(warehouses)))
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input_data = [self.filters.from_date, self.filters.company]
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if warehouses:
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input_data.extend(warehouses)
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date_field = "posting_date" if self.doctype == "Delivery Note" else "transaction_date"
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return frappe.db.sql("""
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SELECT
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so.{date_field} as posting_date, soi.item_code, soi.warehouse,
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soi.item_name, soi.stock_qty as qty, soi.base_amount as amount
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FROM
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`tab{doc}` so, `tab{child_doc}` soi
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WHERE
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so.docstatus = 1 AND so.name = soi.parent AND
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so.{date_field} < %s AND so.company = %s {cond}
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""".format(doc=self.doctype, child_doc=self.child_doctype, date_field=date_field, cond=cond),
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tuple(input_data), as_dict=1)
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def add_total(self):
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total_row = {
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"item_code": _(frappe.bold("Total Quantity"))
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}
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for value in self.data:
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for period in self.period_list:
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forecast_key = "forecast_" + period.key
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if forecast_key not in total_row:
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total_row.setdefault(forecast_key, 0.0)
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if period.key not in total_row:
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total_row.setdefault(period.key, 0.0)
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total_row[forecast_key] += value.get(forecast_key, 0.0)
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total_row[period.key] += value.get(period.key, 0.0)
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self.data.append(total_row)
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def get_columns(self):
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columns = [{
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"label": _("Item Code"),
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"options": "Item",
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"fieldname": "item_code",
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"fieldtype": "Link",
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"width": 130
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}, {
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"label": _("Warehouse"),
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"options": "Warehouse",
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"fieldname": "warehouse",
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"fieldtype": "Link",
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"width": 130
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}]
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width = 150 if self.filters.periodicity in ['Yearly', "Half-Yearly", "Quarterly"] else 100
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for period in self.period_list:
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if (self.filters.periodicity in ['Yearly', "Half-Yearly", "Quarterly"]
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or period.from_date >= getdate(self.filters.from_date)):
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forecast_key = 'forecast_' + period.key
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columns.append({
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"label": _(period.label),
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"fieldname": forecast_key,
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"fieldtype": self.fieldtype,
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"width": width,
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"default": 0.0
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})
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return columns
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def get_chart_data(self):
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if not self.data: return
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labels = []
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self.total_demand = []
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self.total_forecast = []
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self.total_history_forecast = []
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self.total_future_forecast = []
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for period in self.period_list:
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forecast_key = "forecast_" + period.key
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labels.append(_(period.label))
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if period.from_date < getdate(self.filters.from_date):
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self.total_demand.append(self.data[-1].get(period.key, 0))
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self.total_history_forecast.append(self.data[-1].get(forecast_key, 0))
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else:
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self.total_future_forecast.append(self.data[-1].get(forecast_key, 0))
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self.total_forecast.append(self.data[-1].get(forecast_key, 0))
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return {
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"data": {
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"labels": labels,
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"datasets": [
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{
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"name": "Demand",
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"values": self.total_demand
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},
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{
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"name": "Forecast",
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"values": self.total_forecast
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}
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]
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},
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"type": "line"
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}
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def get_summary_data(self):
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return [
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{
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"value": sum(self.total_demand),
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"label": _("Total Demand (Past Data)"),
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"currency": self.company_currency,
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"datatype": self.fieldtype
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},
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{
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"value": sum(self.total_history_forecast),
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"label": _("Total Forecast (Past Data)"),
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"currency": self.company_currency,
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"datatype": self.fieldtype
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},
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{
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"value": sum(self.total_future_forecast),
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"indicator": "Green",
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"label": _("Total Forecast (Future Data)"),
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"currency": self.company_currency,
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"datatype": self.fieldtype
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}
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]
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