pygrowup 0.5.1

Calculate z-scores of anthropometric measurements based on the WHO Child Growth Standards

pygrowup calculates z-scores for the following anthropometric indicators:
weight-for-age, length/height-for-age, and weight-for-length/height
based on the WHO Child Growth Standards:
http://www.who.int/childgrowth/standards/en/
http://www.who.int/entity/childgrowth/standards/technical_report/en/index.html

and can optionally use CDC growth standards:
http://www.cdc.gov/growthcharts

pygrowup avoids floating-point operations to eliminate the unwanted rounding
that muddles the precision of some of the igrowup implementations:
http://docs.sun.com/source/806-3568/ncg_goldberg.html

REQUIREMENTS:
Python 2.5 or later
Additionally, Python 2.5 requires installation of simplejson library:
http://pypi.python.org/pypi/simplejson

INSTALLATION:
pip install pygrowup

EXAMPLE USAGE:
from pygrowup.pygrowup import *

# Height adjustments are part of the WHO specification (see section 5.1)
# to correct for recumbent vs standing measurements,
# but none of the existing software seems to implement this.
# default is false so values are closer to those produced
# by igrowup software
#
# WHO specs include adjustments (see Chapter 7) to z-scores of weight-based
# indicators that are greater than +/- 3 SDs. These adjustments
# correct for right skewness and avoid making assumptions about
# the distribution of data beyond the limits of the observed values.
#
# However, when calculating z-scores in a live data collection
# situation, z-scores greater than +/- 3 SDs are likely to indicate
# data entry or anthropometric measurement errors and should not
# be adjusted. Instead, these large z-scores should be used to
# identify poor data quality and/or entry errors.
# These z-score adjustments are appropriate only when there
# is confidence in data quality.
cg = childgrowth(adjust_height_data=False, adjust_weight_scores=False)

my_child = {'date_of_birth' : '100309', 'sex' : 'male',\
'weight' : '8.0', 'height' : '69.5'}

# use helper functions for formatting data

# transform '100309' into '2009-03-10'
valid_date = helpers.get_good_date(my_child['date_of_birth'])

# transform 'male' into 'M'
valid_gender = helpers.get_good_sex('male')

# calculate 9 months from '2009-03-10'
valid_age = helpers.date_to_age_in_months(valid_date[1])


# call zscore_for_measurement(indicator, measurement,\
# age_in_months, gender, height)
# optional height parameter is only necessary for weight-for-height

# calculate length/height-for-age zscore
lhfa_zscore_for_my_child = cg.zscore_for_measurement('lhfa',\
my_child['height'], valid_age, valid_gender)

# calculate weight-for-age zscore
wfa_zscore_for_my_child = cg.zscore_for_measurement('wfa',\
my_child['weight'], valid_age, valid_gender)

# calculate weight-for-length zscore
wfl_zscore_for_my_child = cg.zscore_for_measurement('wfl',\
my_child['weight'], valid_age, valid_gender, my_child['height'])