Metadata-Version: 1.1
Name: pygrowup
Version: 0.7b0
Summary: Calculate z-scores of anthropometric measurements based on WHO and CDC child growth standards
Home-page: http://github.com/ewheeler/pygrowup
Author: Evan Wheeler
Author-email: evanmwheeler@gmail.com
License: BSD
Download-URL: https://github.com/ewheeler/pygrowup/archive/0.7b0.tar.gz
Description: ========
        pygrowup
        ========
        
        pygrowup calculates z-scores for the following anthropometric indicators:
        
        * weight-for-age
        
        * length/height-for-age
        
        * weight-for-length/height
        
        * head-circumference-for-age
        
        * body-mass-index-for-age
        
        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 http://pypi.python.org/pypi/simplejson
        
        
        INSTALLATION
        ============
        `pip install pygrowup`
        
        
        EXAMPLE USAGE
        =============
        
        Typical usage might look like this::
        
            #!/usr/bin/env python
        
            from pygrowup import Calculator
            # helpers contains optional utilities for formatting dates, etc
            from pygrowup import helpers
        
            # 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.
            #
            # In this example, Calculator is initialized with its default values
            # (i.e., ``calculator = Calculator()`` would do the same thing).
            # The ``include_cdc`` option will enable CDC measurements for children >5 years.
            calculator = Calculator(adjust_height_data=False, adjust_weight_scores=False,
                                   include_cdc=False, logger_name='pygrowup',
                                   log_level='INFO')
        
            # for a timeless example, lets pick a birthdate nine months ago
            import datetime
            great_day = datetime.datetime.utcnow().date()
            nine_months_ago = great_day - datetime.timedelta(days=(9 * 30.4374))
        
            # nine months ago in an odd, ambiguous string format
            dob = nine_months_ago.strftime("%d%m%y")
        
            my_child = {'date_of_birth' : dob, 'sex' : 'male', 'weight' : '8.0', 'height' : '69.5'}
        
            # optionally use helper functions for formatting data
        
            # transform something like '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 valid_date
            valid_age = helpers.date_to_age_in_months(valid_date[1])
        
        
            # calculate length/height-for-age zscore
            lhfa_zscore_for_my_child = calculator.lhfa(my_child['height'], valid_age, valid_gender)
        
            # calculate weight-for-age zscore
            wfa_zscore_for_my_child = calculator.wfa(my_child['weight'], valid_age, valid_gender)
        
            # calculate weight-for-length zscore
            # optional height parameter is only necessary for weight-for-height
            # and weight-for-length
            wfl_zscore_for_my_child = calculator.wfl(my_child['weight'], valid_age, valid_gender, my_child['height'])
        
            # Note: for backwards compatibility you may still make calls to:
            wfl_zscore_for_my_child = calculator.zscore_for_measurement('wfl', my_child['weight'], valid_age, valid_gender, my_child['height'])
        
        
        EXCEPTIONS
        ==========
        
        caller should watch for:
        
        * `AssertionError` raised when caller provides inappropriate parameters
        
        as well as more specific errors (all subclasses of `RuntimeError`):
        
        * `InvalidMeasurement` raised when measurement is invalid for requested indicator
        
        * `InvalidAge` raised when age is invalid for requested indicator
        
        * `DataNotFound` raised when WHO/CDC data is not found for the requested observation (e.g., box-cox, median, coeffeciant of vairance for age)
        
        * `DataError` raised when an error occurs while loading WHO/CDC data into memory
        
        
        TESTING
        =======
        
        install nose to execute tests:
        `pip install nose`
        
        the included tests use example anthropometric data taken from
        demonstration data shipped with WHO's igrowup software.
        pygrowup performs the same calculations and compares the results
        to the WHO results.
        please see the sofware licence agreement for WHO's igrowup, which
        is the souce of the test data files:
        http://www.who.int/childgrowth/software/license2.pdf
        
        currently, 5 cases fail to produce results within 1 standard deviation
        of the WHO resuts. I believe these discrepencies are due to WHO's use
        of floating point arithmetic in their igrowup software, which leads to less
        precise calculations compared to pygrowup. In the absence of any other
        trusted test data, please be aware that no claims are made to the
        accuracy or reliability of pygroup's calculations.
        
        to run the tests:
        `$ nosetests tests.py`
        
        
        DEVELOPING
        ==========
        
        The source WHO .txt tables can be easily converted to json with the help of
        two amazing python utilities:
        
        * The Pyed Piper https://code.google.com/p/pyp/
        
        * csvkit http://pypi.python.org/pypi/csvkit
        
        heres an example one-liner that changes the source .txt from tsv
        to csv (with `pyp`) and then to json (with csvkit's `csvjson`)
        `$ cat bmi_girls_2_5_zscores.txt | pyp "p.replace('\t', ',')" | csvjson > bmifa_girls_2_5_zscores.json`
        
Platform: UNKNOWN
Classifier: Intended Audience :: Healthcare Industry
Classifier: Programming Language :: Python :: 2.5
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
