brotherton-erpnext/erpnext/selling/page/sales_funnel/sales_funnel.py

# Copyright (c) 2018, Frappe Technologies Pvt. Ltd. and Contributors
# License: GNU General Public License v3. See license.txt


import frappe
import pandas as pd
from frappe import _

from erpnext.accounts.report.utils import convert


def validate_filters(from_date, to_date, company):
	if from_date and to_date and (from_date >= to_date):
		frappe.throw(_("To Date must be greater than From Date"))

	if not company:
		frappe.throw(_("Please Select a Company"))


@frappe.whitelist()
def get_funnel_data(from_date, to_date, company):
	validate_filters(from_date, to_date, company)

	active_leads = frappe.db.sql(
		"""select count(*) from `tabLead`
		where (date(`creation`) between %s and %s)
		and company=%s""",
		(from_date, to_date, company),
	)[0][0]

	opportunities = frappe.db.sql(
		"""select count(*) from `tabOpportunity`
		where (date(`creation`) between %s and %s)
		and opportunity_from='Lead' and company=%s""",
		(from_date, to_date, company),
	)[0][0]

	quotations = frappe.db.sql(
		"""select count(*) from `tabQuotation`
		where docstatus = 1 and (date(`creation`) between %s and %s)
		and (opportunity!="" or quotation_to="Lead") and company=%s""",
		(from_date, to_date, company),
	)[0][0]

	converted = frappe.db.sql(
		"""select count(*) from `tabCustomer`
		JOIN `tabLead` ON `tabLead`.name = `tabCustomer`.lead_name
		WHERE (date(`tabCustomer`.creation) between %s and %s)
		and `tabLead`.company=%s""",
		(from_date, to_date, company),
	)[0][0]

	return [
		{"title": _("Active Leads"), "value": active_leads, "color": "#B03B46"},
		{"title": _("Opportunities"), "value": opportunities, "color": "#F09C00"},
		{"title": _("Quotations"), "value": quotations, "color": "#006685"},
		{"title": _("Converted"), "value": converted, "color": "#00AD65"},
	]


@frappe.whitelist()
def get_opp_by_lead_source(from_date, to_date, company):
	validate_filters(from_date, to_date, company)

	opportunities = frappe.get_all(
		"Opportunity",
		filters=[
			["status", "in", ["Open", "Quotation", "Replied"]],
			["company", "=", company],
			["transaction_date", "Between", [from_date, to_date]],
		],
		fields=["currency", "sales_stage", "opportunity_amount", "probability", "source"],
	)

	if opportunities:
		default_currency = frappe.get_cached_value("Global Defaults", "None", "default_currency")

		cp_opportunities = [
			dict(
				x,
				**{
					"compound_amount": (
						convert(x["opportunity_amount"], x["currency"], default_currency, to_date)
						* x["probability"]
						/ 100
					)
				}
			)
			for x in opportunities
		]

		df = (
			pd.DataFrame(cp_opportunities)
			.groupby(["source", "sales_stage"], as_index=False)
			.agg({"compound_amount": "sum"})
		)

		result = {}
		result["labels"] = list(set(df.source.values))
		result["datasets"] = []

		for s in set(df.sales_stage.values):
			result["datasets"].append(
				{"name": s, "values": [0] * len(result["labels"]), "chartType": "bar"}
			)

		for row in df.itertuples():
			source_index = result["labels"].index(row.source)

			for dataset in result["datasets"]:
				if dataset["name"] == row.sales_stage:
					dataset["values"][source_index] = row.compound_amount

		return result

	else:
		return "empty"


@frappe.whitelist()
def get_pipeline_data(from_date, to_date, company):
	validate_filters(from_date, to_date, company)

	opportunities = frappe.get_all(
		"Opportunity",
		filters=[
			["status", "in", ["Open", "Quotation", "Replied"]],
			["company", "=", company],
			["transaction_date", "Between", [from_date, to_date]],
		],
		fields=["currency", "sales_stage", "opportunity_amount", "probability"],
	)

	if opportunities:
		default_currency = frappe.get_cached_value("Global Defaults", "None", "default_currency")

		cp_opportunities = [
			dict(
				x,
				**{
					"compound_amount": (
						convert(x["opportunity_amount"], x["currency"], default_currency, to_date)
						* x["probability"]
						/ 100
					)
				}
			)
			for x in opportunities
		]

		df = (
			pd.DataFrame(cp_opportunities)
			.groupby(["sales_stage"], as_index=True)
			.agg({"compound_amount": "sum"})
			.to_dict()
		)

		result = {}
		result["labels"] = df["compound_amount"].keys()
		result["datasets"] = []
		result["datasets"].append(
			{"name": _("Total Amount"), "values": df["compound_amount"].values(), "chartType": "bar"}
		)

		return result

	else:
		return "empty"