def test2(self):
visit_stats = VisitStatistics.get_stats(
datetime.date(2005,03,21),
datetime.date(2007,03,25),
Sex.FEMALE,
8.2, 45, 74,
Measured.RECUMBENT, None)
# BMI
util.assertNear(self, 15.3, visit_stats.body_mass_index, 0.1)
# Weight-for-length-or-height
zandp = visit_stats.get_zandp('weight_for_length_or_height')
util.assertNear(self, -1.00, zandp.zscore, 0.01)
util.assertNear(self, 15.9, zandp.percentile, 0.1)
# Weight-for-age
zandp = visit_stats.get_zandp('weight_for_age')
util.assertNear(self, -2.87, zandp.zscore, 0.01)
util.assertNear(self, 0.2, zandp.percentile, 0.1)
# Length-or-height for age
zandp = visit_stats.get_zandp('length_or_height_for_age')
util.assertNear(self, -3.87, zandp.zscore, 0.01)
self.assertTrue(util.isNaN(zandp.percentile))
# BMI-for-age
zandp = visit_stats.get_zandp('body_mass_index_for_age')
util.assertNear(self, -0.33, zandp.zscore, 0.01)
util.assertNear(self, 37.2, zandp.percentile, 0.1)
# HC-for-age
zandp = visit_stats.get_zandp('head_circumference_for_age')
util.assertNear(self, -1.58, zandp.zscore, 0.01)
util.assertNear(self, 5.8, zandp.percentile, 0.1)
def export_csv_line(self, patient):
'''A line of CSV values representing this visit and patient.
It is in the same order as export_csv_header.
Use the patient passed in so that we can load patients in bulk.
'''
assert patient.key() == self.parent_key()
patient_line = util.csv_line(Visit._patient_prop_names,
Patient.properties(),
patient)
visit_line = util.csv_line(Visit._visit_prop_names,
Visit.properties(),
self)
visit_stats = self.get_visit_statistics()
if visit_stats:
visit_stats_line = util.csv_line(Visit._visit_stat_prop_names,
VisitStatistics.properties(),
self.get_visit_statistics())
else:
visit_stat_props = []
for dummy in range(len(Visit.visit_stats_expanded_names())):
visit_stat_props.append('')
visit_stats_line = ",".join(visit_stat_props)
return patient_line + "," + visit_line + "," + visit_stats_line
def testBmiNotNan(self):
#tombomist is too old for most calculations, but BMI should be calculated
visit_stats = VisitStatistics.get_stats(
datetime.date(1995,03,21),
datetime.date(2007,03,25),
Sex.MALE,
30, 49.5, 140,
Measured.STANDING, None)
util.assertNear(self, 15.3, visit_stats.body_mass_index, 0.1)
def testZeroDayStats(self):
# Age is 0 days, whcih is still valid.
# Equivalent to AA2 in the anthrotest.csv
visit_stats = VisitStatistics.get_stats(
datetime.date(2009,11,27),
datetime.date(2009,11,27),
Sex.FEMALE,
4, 35, 49,
Measured.RECUMBENT, None)
# Length-or-height for age
zandp = visit_stats.get_zandp('length_or_height_for_age')
util.assertNear(self, -0.08, zandp.zscore, 0.01)
util.assertNear(self, 46.8, zandp.percentile, 0.1)
def testLengthOrHeightForAgeNotNan(self):
# Age is right on the border, but still valid.
# Equivalent to AA2 in the anthrotest.csv.
visit_stats = VisitStatistics.get_stats(
datetime.date(2002,03,15),
datetime.date(2007,04,14),
Sex.MALE,
10, 50, 65.6,
Measured.RECUMBENT, None)
# Length-or-height for age
zandp = visit_stats.get_zandp('length_or_height_for_age')
util.assertNear(self, -9.76, zandp.zscore, 0.01)
self.assertTrue(util.isNaN(zandp.percentile))
def testHCforAgeNaN(self):
# Head circumference is calculated slightly differently from other
# stats in a way that should make it come out NaN.
# This is "computeFinalZScore" from zscoreOtherRestricted.
# Equivalent to AA8 in the anthrotest.csv.
visit_stats = VisitStatistics.get_stats(
datetime.date(2007,12,14),
datetime.date(2008,02,29),
Sex.FEMALE,
8, 24.99, 63,
Measured.STANDING, None)
# HC-for-age
zandp = visit_stats.get_zandp('head_circumference_for_age')
self.assertTrue(util.isNaN(zandp.zscore))
self.assertTrue(util.isNaN(zandp.percentile))
def visit_stats_expanded_names():
'''Expand Visit._visit_stat_props.
Each ZscoreAndPercentileProperty gets two slots: x_zscore, x_percentile'''
visit_stat_props = []
vsprops = VisitStatistics.properties()
for prop in Visit._visit_stat_prop_names:
# HACK(dan): Expand ZscoreAndPercentileProperty names into 2:
# x_zscore, x_percentile
# This is a hack because the printing actually takes place in
# util.csv_line()
if isinstance(vsprops[prop], ZscoreAndPercentileProperty):
visit_stat_props.append('%s_zscore' % prop)
visit_stat_props.append('%s_percentile' % prop)
else:
visit_stat_props.append(prop)
return visit_stat_props
def testFile(self):
lineNum = 0
for row in csv.DictReader(open('growthcalc/anthrotest.csv')):
lineNum += 1
# The first fields are the visit
name = row['name']
sex = row['sex']
try:
dateOfBirth = datetime.datetime.strptime(row['dateOfBirth'], "%m/%d/%Y")
except:
dateOfBirth = None
try:
dateOfVisit = datetime.datetime.strptime(row['dateOfVisit'], "%m/%d/%Y")
except:
dateOfVisit = None
weight = float(row['weight'])
height = float(row['lengthOrHeight'])
headCircumference = float(row['headCircumference'])
measured = row['measured']
if row['oedema'] == "TRUE":
oedema = True
elif row['oedema'] == "FALSE":
oedema = False
visit_stats = VisitStatistics.get_stats(dateOfBirth,
dateOfVisit,
sex,
weight,
headCircumference,
height,
measured,
oedema,
None)
testScore = float(row['bmi'])
eps = float(row['bmi-eps'])
self.assertTrue(util.isNanOrNear(
testScore, visit_stats.body_mass_index, eps))
testScore = float(row['weight-for-length-or-height'])
eps = float(row['weight-for-length-or-height-eps'])
zandp = visit_stats.get_zandp('weight_for_length_or_height')
util.assertIsNanOrNear(self, testScore, zandp.zscore, eps)
testScore = float(row['weight-for-length-or-height-%'])
eps = float(row['weight-for-length-or-height-%-eps'])
util.assertIsNanOrNear(self, testScore, zandp.percentile, eps)
testScore = float(row['weight-for-age'])
eps = float(row['weight-for-age-eps'])
zandp = visit_stats.get_zandp('weight_for_age')
util.assertIsNanOrNear(self, testScore, zandp.zscore, eps)
testScore = float(row['weight-for-age-%'])
eps = float(row['weight-for-age-%-eps'])
util.assertIsNanOrNear(self, testScore, zandp.percentile, eps)
testScore = float(row['length-or-height-for-age'])
eps = float(row['length-or-height-for-age-eps'])
zandp = visit_stats.get_zandp('length_or_height_for_age')
self.assertTrue(util.isNanOrNear(testScore, zandp.zscore, eps))
testScore = float(row['length-or-height-for-age-%'])
eps = float(row['length-or-height-for-age-%-eps'])
self.assertTrue(util.isNanOrNear(testScore, zandp.percentile, eps))
testScore = float(row['bmi-for-age'])
eps = float(row['bmi-for-age-eps'])
zandp = visit_stats.get_zandp('body_mass_index_for_age')
self.assertTrue(util.isNanOrNear(testScore, zandp.zscore, eps))
testScore = float(row['bmi-for-age-%'])
eps = float(row['bmi-for-age-%-eps'])
self.assertTrue(util.isNanOrNear(testScore, zandp.percentile, eps))
testScore = float(row['head-circumference-for-age'])
eps = float(row['head-circumference-for-age-eps'])
zandp = visit_stats.get_zandp('head_circumference_for_age')
self.assertTrue(util.isNanOrNear(testScore, zandp.zscore, eps))
testScore = float(row['head-circumference-for-age-%'])
eps = float(row['head-circumference-for-age-%-eps'])
self.assertTrue(util.isNanOrNear(testScore, zandp.percentile, eps))
self.assertEquals(lineNum, 23)