Stock analytics script report (#15630)
* Stock analytics script report * Codacy Issue Fixes * Codacy Issue Fixes * Removed Trailing Whitespaces * Code cleaning and optimization * Minor Changes * Code cleaning and spacing * Added link for stock analytics in stock.py * Refactoring and code cleaning * Codacy issue fixes
This commit is contained in:
Deepesh Garg
Nabin Hait
parent
a057f4c2a0
commit
84483ff776
@ -218,10 +218,10 @@ def get_data():
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"doctype": "Item Price",
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},
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{
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"type": "page",
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"name": "stock-analytics",
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"label": _("Stock Analytics"),
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"icon": "fa fa-bar-chart"
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"type": "report",
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"is_query_report": True,
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"name": "Stock Analytics",
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"doctype": "Stock Entry"
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},
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{
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"type": "report",
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0
erpnext/stock/report/stock_analytics/__init__.py
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0
erpnext/stock/report/stock_analytics/__init__.py
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136
erpnext/stock/report/stock_analytics/stock_analytics.js
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136
erpnext/stock/report/stock_analytics/stock_analytics.js
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@ -0,0 +1,136 @@
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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["Stock Analytics"] = {
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"filters": [
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{
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fieldname: "item_group",
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label: __("Item Group"),
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fieldtype: "Link",
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options:"Item Group",
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default: "",
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},
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{
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fieldname: "item_code",
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label: __("Item"),
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fieldtype: "Link",
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options:"Item",
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default: "",
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},
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{
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fieldname: "value_quantity",
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label: __("Value Or Qty"),
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fieldtype: "Select",
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options: [
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{ "value": "Value", "label": __("Value") },
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{ "value": "Quantity", "label": __("Quantity") }
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],
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default: "Value",
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reqd: 1
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},
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{
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fieldname: "brand",
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label: __("Brand"),
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fieldtype: "Link",
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options:"Brand",
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default: "",
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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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default: "",
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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.defaults.get_global_default("year_start_date"),
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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.defaults.get_global_default("year_end_date"),
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reqd: 1
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},
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{
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fieldname: "range",
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label: __("Range"),
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fieldtype: "Select",
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options: [
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{ "value": "Weekly", "label": __("Weekly") },
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{ "value": "Monthly", "label": __("Monthly") },
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{ "value": "Quarterly", "label": __("Quarterly") },
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{ "value": "Yearly", "label": __("Yearly") }
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],
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default: "Monthly",
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reqd: 1
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}
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],
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"formatter": function(value, row, column, data) {
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if(!value && (column.fieldname == 'brand' || column.fieldname == 'uom')){
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value = ""
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}
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if(Number(value)){
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value = value.toFixed(2)
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}
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return value;
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},
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get_datatable_options(options) {
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return Object.assign(options, {
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checkboxColumn: true,
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events: {
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onCheckRow: function(data) {
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row_name = data[2].content;
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row_values = data.slice(6).map(function (column) {
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return column.content;
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})
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entry = {
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'name':row_name,
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'values':row_values
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}
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let raw_data = frappe.query_report.chart.data;
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let new_datasets = raw_data.datasets;
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var found = false;
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for(var i=0; i < new_datasets.length;i++){
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if(new_datasets[i].name == row_name){
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found = true;
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new_datasets.splice(i,1);
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break;
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}
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}
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if(!found){
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new_datasets.push(entry);
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}
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let new_data = {
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labels: raw_data.labels,
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datasets: new_datasets
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}
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setTimeout(() => {
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frappe.query_report.chart.update(new_data)
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},200)
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setTimeout(() => {
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frappe.query_report.chart.draw(true);
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}, 800)
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frappe.query_report.raw_chart_data = new_data;
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},
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}
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})
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},
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}
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32
erpnext/stock/report/stock_analytics/stock_analytics.json
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32
erpnext/stock/report/stock_analytics/stock_analytics.json
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@ -0,0 +1,32 @@
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{
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"add_total_row": 0,
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"creation": "2018-10-08 12:11:32.133020",
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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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"modified": "2018-10-08 12:18:42.834270",
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"modified_by": "Administrator",
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"module": "Stock",
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"name": "Stock Analytics",
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"owner": "Administrator",
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"prepared_report": 0,
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"ref_doctype": "Stock Entry",
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"report_name": "Stock Analytics",
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"report_type": "Script Report",
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"roles": [
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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": "Stock User"
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},
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{
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"role": "Manufacturing User"
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}
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]
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}
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185
erpnext/stock/report/stock_analytics/stock_analytics.py
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erpnext/stock/report/stock_analytics/stock_analytics.py
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@ -0,0 +1,185 @@
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# Copyright (c) 2013, 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
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from frappe import _, scrub
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from frappe.utils import getdate, flt
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from erpnext.stock.report.stock_balance.stock_balance import (get_items, get_stock_ledger_entries, get_item_details)
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from erpnext.accounts.utils import get_fiscal_year
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from six import iteritems
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def execute(filters=None):
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filters = frappe._dict(filters or {})
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columns = get_columns(filters)
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data = get_data(filters)
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chart = get_chart_data(columns)
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return columns, data, None, chart
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def get_columns(filters):
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columns = [
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{
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"label": _("Item"),
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"options":"Item",
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"fieldname": "name",
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"fieldtype": "Link",
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"width": 140
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},
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{
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"label": _("Item Name"),
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"options":"Item",
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"fieldname": "item_name",
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"fieldtype": "Link",
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"width": 140
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},
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{
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"label": _("Item Group"),
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"options":"Item Group",
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"fieldname": "item_group",
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"fieldtype": "Link",
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"width": 140
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},
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{
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"label": _("Brand"),
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"fieldname": "brand",
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"fieldtype": "Data",
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"width": 120
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},
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{
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"label": _("UOM"),
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"fieldname": "uom",
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"fieldtype": "Data",
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"width": 120
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}]
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ranges = get_period_date_ranges(filters)
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for dummy, end_date in ranges:
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period = get_period(end_date, filters)
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columns.append({
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"label": _(period),
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"fieldname":scrub(period),
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"fieldtype": "Float",
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"width": 120
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})
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return columns
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def get_period_date_ranges(filters):
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from dateutil.relativedelta import relativedelta
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from_date, to_date = getdate(filters.from_date), getdate(filters.to_date)
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increment = {
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"Monthly": 1,
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"Quarterly": 3,
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"Half-Yearly": 6,
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"Yearly": 12
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}.get(filters.range,1)
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periodic_daterange = []
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for dummy in range(1, 53, increment):
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if filters.range == "Weekly":
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period_end_date = from_date + relativedelta(days=6)
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else:
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period_end_date = from_date + relativedelta(months=increment, days=-1)
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if period_end_date > to_date:
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period_end_date = to_date
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periodic_daterange.append([from_date, period_end_date])
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from_date = period_end_date + relativedelta(days=1)
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if period_end_date == to_date:
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break
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return periodic_daterange
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def get_period(posting_date, filters):
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months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
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if filters.range == 'Weekly':
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period = "Week " + str(posting_date.isocalendar()[1])
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elif filters.range == 'Monthly':
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period = months[posting_date.month - 1]
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elif filters.range == 'Quarterly':
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period = "Quarter " + str(((posting_date.month-1)//3)+1)
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else:
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year = get_fiscal_year(posting_date, company=filters.company)
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period = str(year[2])
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return period
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def get_periodic_data(entry, filters):
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periodic_data = {}
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for d in entry:
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period = get_period(d.posting_date, filters)
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bal_qty = 0
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if d.voucher_type == "Stock Reconciliation":
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if periodic_data.get(d.item_code):
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bal_qty = periodic_data[d.item_code]["balance"]
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qty_diff = d.qty_after_transaction - bal_qty
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else:
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qty_diff = d.actual_qty
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if filters["value_quantity"] == 'Quantity':
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value = qty_diff
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else:
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value = d.stock_value_difference
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periodic_data.setdefault(d.item_code, {}).setdefault(period, 0.0)
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periodic_data.setdefault(d.item_code, {}).setdefault("balance", 0.0)
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periodic_data[d.item_code]["balance"] += value
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periodic_data[d.item_code][period] = periodic_data[d.item_code]["balance"]
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return periodic_data
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def get_data(filters):
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data = []
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items = get_items(filters)
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sle = get_stock_ledger_entries(filters, items)
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item_details = get_item_details(items, sle, filters)
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periodic_data = get_periodic_data(sle, filters)
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ranges = get_period_date_ranges(filters)
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for dummy, item_data in iteritems(item_details):
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row = {
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"name": item_data.name,
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"item_name": item_data.item_name,
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"item_group": item_data.item_group,
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"uom": item_data.stock_uom,
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"brand": item_data.brand,
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}
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total = 0
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for dummy, end_date in ranges:
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period = get_period(end_date, filters)
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amount = flt(periodic_data.get(item_data.name, {}).get(period))
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row[scrub(period)] = amount
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total += amount
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row["total"] = total
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data.append(row)
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return data
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def get_chart_data(columns):
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labels = [d.get("label") for d in columns[4:]]
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chart = {
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"data": {
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'labels': labels,
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'datasets':[
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{ "values": ['0' for d in columns[4:]] }
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]
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}
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}
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chart["type"] = "line"
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return chart
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