def get_item_sales_data():
date_data = get_date_from_request(request)
id_item = request.args(0)
raw_data = db((db.sale.id_bag == db.bag.id)
& (db.bag_item.id_bag == db.bag.id)
& (db.bag_item.id_item == id_item)
& (db.sale.id_store.belongs(date_data.stores))
& (db.sale.created_on >= date_data.start_date)
& (db.sale.created_on < date_data.end_date)).iterselect(
db.bag_item.quantity,
db.sale.created_on,
orderby=db.sale.created_on)
groups = analysis_utils.group_items(raw_data, date_data.start_date,
date_data.end_date,
date_data.time_step,
lambda r: r.sale.created_on)
rr = analysis_utils.reduce_groups(
groups,
dict(qty_total=dict(func=lambda r: float(r.bag_item.quantity))))
data = rr['qty_total']['results']
total_sales = DQ(sum(rr['qty_total']['results']), True, True)
chart_data = chart_data_template_for(
date_data.time_mode,
[dataset_format(T('Sold quantity'), data, ACCENT_COLOR)],
date_data.start_date)
return dict(chart_data=chart_data,
current_date=date_data.start_date,
next_date=date_data.next_date,
prev_date=date_data.prev_date,
title=date_data.title,
total_sales=total_sales)
def get_item_sales_data():
date_data = get_date_from_request(request)
id_item = request.args(0)
raw_data = db(
(db.sale.id_bag == db.bag.id) &
(db.bag_item.id_bag == db.bag.id) &
(db.bag_item.id_item == id_item) &
(db.sale.id_store.belongs(date_data.stores)) &
(db.sale.created_on >= date_data.start_date) &
(db.sale.created_on < date_data.end_date)
).iterselect(
db.bag_item.quantity, db.sale.created_on, orderby=db.sale.created_on
)
groups = analysis_utils.group_items(
raw_data, date_data.start_date, date_data.end_date, date_data.time_step,
lambda r : r.sale.created_on
)
rr = analysis_utils.reduce_groups(groups, dict(
qty_total=dict(func=lambda r : float(r.bag_item.quantity))
))
data = rr['qty_total']['results']
total_sales = DQ(sum(rr['qty_total']['results']), True, True)
chart_data = chart_data_template_for(
date_data.time_mode,
[dataset_format(T('Sold quantity'), data, ACCENT_COLOR)],
date_data.start_date
)
return dict(
chart_data=chart_data,
current_date=date_data.start_date,
next_date=date_data.next_date,
prev_date=date_data.prev_date,
title=date_data.title,
total_sales=total_sales
)
def get_day_sales_data():
""" Returns relevant data for the specified day
args: [year, month, day]
"""
date_data = get_date_from_request(request)
wallet_opt = get_wallet_payment_opt()
sales_total = 0
datasets = []
for store_id in date_data.stores:
data = db(
(db.payment.id_sale == db.sale.id) &
(db.sale.id_store == store_id) &
(db.payment.id_payment_opt != wallet_opt.id) &
(db.payment.created_on >= date_data.start_date) &
(db.payment.created_on < date_data.end_date)
).iterselect(db.payment.ALL, orderby=db.sale.created_on)
payments_groups = analysis_utils.group_items(
data, date_data.start_date, date_data.end_date,
date_data.time_step
)
rr = analysis_utils.reduce_groups(
payments_groups, dict(
payments_total=dict(
func=lambda r : float(r.amount - r.change_amount)
)
)
)
store_color = similar_color(ACCENT_COLOR, store_id * 100)
sales_data = rr['payments_total']['results']
dataset = dataset_format(
T("Store %s") % store_id, sales_data, store_color
)
dataset['store_id'] = store_id
datasets.append(dataset)
sales_total += DQ(sum(sales_data), True)
chart_sales_data = chart_data_template_for(
date_data.time_mode, datasets, date_data.start_date
)
# important averages
avg_sale_total = db.sale.total.avg()
avg_sale_volume = db.sale.quantity.avg()
total_items_sold = db.sale.quantity.sum()
sales_data = db(
(db.sale.id_store.belongs(date_data.stores)) &
(db.sale.is_done == True) &
(db.sale.created_on >= date_data.start_date) &
(db.sale.created_on < date_data.end_date)
).select(avg_sale_total, avg_sale_volume, total_items_sold).first()
avg_sale_volume = DQ(sales_data[avg_sale_volume] or 0, True)
avg_sale_total = DQ(sales_data[avg_sale_total] or 0, True)
# average items sell price
avg_item_price = DQ(sales_total / (sales_data[total_items_sold] or 1), True)
total_items_sold = DQ(sales_data[total_items_sold] or 0, True)
expenses = db.bag_item.total_buy_price.sum()
expenses = db(
(db.sale.id_bag == db.bag.id) &
(db.bag_item.id_bag == db.bag.id) &
(db.sale.id_store.belongs(date_data.stores)) &
(db.sale.created_on >= date_data.start_date) &
(db.sale.created_on < date_data.end_date)
).select(expenses).first()[expenses]
expenses = DQ(expenses or 0, True)
profit = sales_total - expenses
return dict(
chart_data=chart_sales_data,
current_date=date_data.start_date,
next_date=date_data.next_date,
prev_date=date_data.prev_date,
title=date_data.title,
avg_sale_total=avg_sale_total,
avg_sale_volume=avg_sale_volume,
total_items_sold=total_items_sold,
avg_item_price=avg_item_price,
sales_total=sales_total,
profit=profit
)
def get_day_sales_data():
""" Returns relevant data for the specified day
args: [year, month, day]
"""
date_data = get_date_from_request(request)
wallet_opt = get_wallet_payment_opt()
sales_total = 0
datasets = []
for store_id in date_data.stores:
data = db((db.payment.id_sale == db.sale.id)
& (db.sale.id_store == store_id)
& (db.payment.id_payment_opt != wallet_opt.id)
& (db.payment.created_on >= date_data.start_date)
& (db.payment.created_on < date_data.end_date)).iterselect(
db.payment.ALL, orderby=db.sale.created_on)
payments_groups = analysis_utils.group_items(data,
date_data.start_date,
date_data.end_date,
date_data.time_step)
rr = analysis_utils.reduce_groups(
payments_groups,
dict(payments_total=dict(
func=lambda r: float(r.amount - r.change_amount))))
store_color = similar_color(ACCENT_COLOR, store_id * 100)
sales_data = rr['payments_total']['results']
dataset = dataset_format(
T("Store %s") % store_id, sales_data, store_color)
dataset['store_id'] = store_id
datasets.append(dataset)
sales_total += DQ(sum(sales_data), True)
chart_sales_data = chart_data_template_for(date_data.time_mode, datasets,
date_data.start_date)
# important averages
avg_sale_total = db.sale.total.avg()
avg_sale_volume = db.sale.quantity.avg()
total_items_sold = db.sale.quantity.sum()
sales_data = db((db.sale.id_store.belongs(date_data.stores))
& (db.sale.is_done == True)
& (db.sale.created_on >= date_data.start_date)
& (db.sale.created_on < date_data.end_date)).select(
avg_sale_total, avg_sale_volume,
total_items_sold).first()
avg_sale_volume = DQ(sales_data[avg_sale_volume] or 0, True)
avg_sale_total = DQ(sales_data[avg_sale_total] or 0, True)
# average items sell price
avg_item_price = DQ(sales_total / (sales_data[total_items_sold] or 1),
True)
total_items_sold = DQ(sales_data[total_items_sold] or 0, True)
expenses = db.bag_item.total_buy_price.sum()
expenses = db((db.sale.id_bag == db.bag.id)
& (db.bag_item.id_bag == db.bag.id)
& (db.sale.id_store.belongs(date_data.stores))
& (db.sale.created_on >= date_data.start_date)
& (db.sale.created_on < date_data.end_date)).select(
expenses).first()[expenses]
expenses = DQ(expenses or 0, True)
profit = sales_total - expenses
return dict(chart_data=chart_sales_data,
current_date=date_data.start_date,
next_date=date_data.next_date,
prev_date=date_data.prev_date,
title=date_data.title,
avg_sale_total=avg_sale_total,
avg_sale_volume=avg_sale_volume,
total_items_sold=total_items_sold,
avg_item_price=avg_item_price,
sales_total=sales_total,
profit=profit)