def aggregate_cases(self, product_cases, slugs):
cases_by_product = map_reduce(lambda c: [(c.product,)], data=product_cases, include_docs=True)
products = Product.view('_all_docs', keys=cases_by_product.keys(), include_docs=True)
def _sum(vals):
return sum(vals) if vals else None
def aggregate_product(cases):
data = [(c.current_stock_level, c.monthly_consumption) for c in cases if is_timely(c, 1000)]
total_stock = _sum([d[0] for d in data if d[0] is not None])
total_consumption = _sum([d[1] for d in data if d[1] is not None])
# exclude stock values w/o corresponding consumption figure from total months left calculation
consumable_stock = _sum([d[0] for d in data if d[0] is not None and d[1] is not None])
return {
'total_stock': total_stock,
'total_consumption': total_consumption,
'consumable_stock': consumable_stock,
}
status_by_product = dict((p, aggregate_product(cases)) for p, cases in cases_by_product.iteritems())
for p in sorted(products, key=lambda p: p.name):
stats = status_by_product[p._id]
months_left = SPPCase.months_of_stock_remaining(stats['consumable_stock'], stats['total_consumption'])
category = SPPCase.stock_category(stats['total_stock'], stats['total_consumption'], stats['consumable_stock'])
full_output = {
self.SLUG_PRODUCT_NAME: p.name,
self.SLUG_PRODUCT_ID: p._id,
self.SLUG_LOCATION_ID: self.active_location._id if self.active_location else None,
self.SLUG_LOCATION_LINEAGE: self.active_location.lineage if self.active_location else None,
self.SLUG_CURRENT_STOCK: stats['total_stock'],
self.SLUG_CONSUMPTION: stats['total_consumption'],
self.SLUG_MONTHS_REMAINING: months_left,
self.SLUG_CATEGORY: category,
}
yield dict((slug, full_output['slug']) for slug in slugs) if slugs else full_output
def get_prod_data(self):
startkey = [self.domain, self.active_location._id if self.active_location else None]
product_cases = SPPCase.view('commtrack/product_cases', startkey=startkey, endkey=startkey + [{}], include_docs=True)
cases_by_product = map_reduce(lambda c: [(c.product,)], data=product_cases, include_docs=True)
products = Product.view('_all_docs', keys=cases_by_product.keys(), include_docs=True)
def _sum(vals):
return sum(vals) if vals else None
def aggregate_product(cases):
data = [(c.current_stock_level, c.monthly_consumption) for c in cases if is_timely(c, 1000)]
total_stock = _sum([d[0] for d in data if d[0] is not None])
total_consumption = _sum([d[1] for d in data if d[1] is not None])
# exclude stock values w/o corresponding consumption figure from total months left calculation
consumable_stock = _sum([d[0] for d in data if d[0] is not None and d[1] is not None])
return {
'total_stock': total_stock,
'total_consumption': total_consumption,
'consumable_stock': consumable_stock,
}
status_by_product = dict((p, aggregate_product(cases)) for p, cases in cases_by_product.iteritems())
for p in sorted(products, key=lambda p: p.name):
stats = status_by_product[p._id]
months_left = SPPCase.months_of_stock_remaining(stats['consumable_stock'], stats['total_consumption'])
category = SPPCase.stock_category(stats['total_stock'], stats['total_consumption'], stats['consumable_stock'])
yield [
p.name,
stats['total_stock'],
stats['total_consumption'],
months_left,
category,
]
