def __init__(self, rows, msh_price=None):
# Don't like this, but rows is being modified below, causing side effects
rows = deepcopy(rows)
if msh_price is not None:
# msh_price might be a Decimal, which max() thinks is larger than
# any float, so in order to compare properly with floats we need
# to convert msh_price to float as well
self.max_price = max(self.max_price, float(msh_price))
# print "input data = %s" % rows
(self.median_fob_price, self.median_landed_price) = \
utils.get_median_prices(rows)
# print "median prices = %s, %s" % (self.median_fob_price, self.median_landed_price)
for row in rows:
new_max_price = max(self.max_price, row['fob_price'],
row['landed_price'])
# logging.warning("max of %s, %s and %s is %s" %
# (self.max_price, row['fob_price'], row['landed_price'],
# new_max_price))
self.max_price = new_max_price
self.round_to_set_decimal_places(row, 'fob_price')
self.round_to_set_decimal_places(row, 'landed_price')
# scale up the value to between 1 and 10, then round up the first
# digit to 1, 2 or 5 to make nicer graph scales
rounded_up = self.max_price
scale_factor = 1
while rounded_up < 1:
rounded_up = rounded_up * 10
scale_factor = scale_factor * 10
while rounded_up > 10:
rounded_up = rounded_up / 10.0
scale_factor = scale_factor / 10.0
if rounded_up > 5:
rounded_up = 10.0
elif rounded_up > 2:
rounded_up = 5.0
elif rounded_up > 1:
rounded_up = 2.0
# else it's 1, which is fine
# scale back down
self.max_price = rounded_up / scale_factor
# generate the intervals on the graph scale
self.scale = []
for k in range(1, 11):
self.scale.append(k * (self.max_price / 10))
self.msh_price = msh_price
SarpamTable.__init__(self, rows)
def __init__(self, rows, msh_price=None):
# Don't like this, but rows is being modified below, causing side effects
rows = deepcopy(rows)
if msh_price is not None:
# msh_price might be a Decimal, which max() thinks is larger than
# any float, so in order to compare properly with floats we need
# to convert msh_price to float as well
self.max_price = max(self.max_price, float(msh_price))
# print "input data = %s" % rows
(self.median_fob_price, self.median_landed_price) = \
utils.get_median_prices(rows)
# print "median prices = %s, %s" % (self.median_fob_price, self.median_landed_price)
for row in rows:
new_max_price = max(self.max_price, row['fob_price'],
row['landed_price'])
# logging.warning("max of %s, %s and %s is %s" %
# (self.max_price, row['fob_price'], row['landed_price'],
# new_max_price))
self.max_price = new_max_price
self.round_to_set_decimal_places(row, 'fob_price')
self.round_to_set_decimal_places(row, 'landed_price')
# scale up the value to between 1 and 10, then round up the first
# digit to 1, 2 or 5 to make nicer graph scales
rounded_up = self.max_price
scale_factor = 1
while rounded_up < 1:
rounded_up = rounded_up * 10
scale_factor = scale_factor * 10
while rounded_up > 10:
rounded_up = rounded_up / 10.0
scale_factor = scale_factor / 10.0
if rounded_up > 5:
rounded_up = 10.0
elif rounded_up > 2:
rounded_up = 5.0
elif rounded_up > 1:
rounded_up = 2.0
# else it's 1, which is fine
# scale back down
self.max_price = rounded_up / scale_factor
# generate the intervals on the graph scale
self.scale = []
for k in range(1, 11):
self.scale.append(k * (self.max_price / 10))
self.msh_price = msh_price
SarpamTable.__init__(self, rows)
def test_none_values_ignored_when_calculating_median_landed_price_of_list(self):
price_list = [
{"landed_price": None, "fob_price": None},
{"landed_price": 0.09, "fob_price": None},
{"landed_price": None, "fob_price": None},
{"landed_price": 0.05, "fob_price": None},
{"landed_price": None, "fob_price": None},
{"landed_price": 0.14, "fob_price": None},
]
median = utils.get_median_prices(price_list)
self.assertAlmostEquals(0.09, median[1])
def test_none_values_ignored_when_calculating_median_landed_price_of_list(
self):
price_list = [{
'landed_price': None,
'fob_price': None
}, {
'landed_price': 0.09,
'fob_price': None
}, {
'landed_price': None,
'fob_price': None
}, {
'landed_price': 0.05,
'fob_price': None
}, {
'landed_price': None,
'fob_price': None
}, {
'landed_price': 0.14,
'fob_price': None
}]
median = utils.get_median_prices(price_list)
self.assertAlmostEquals(0.09, median[1])
