__all__ = ['COPYRIGHT','TITLE','SOURCE','DESCRSHORT','DESCRLONG','NOTE', 'load']

"""US Capital Punishment dataset."""

__docformat__ = 'restructuredtext'

COPYRIGHT   = """Used with express permission from the original author,
who retains all rights."""
TITLE       = "US Capital Punishment Dataset"
SOURCE      = """
Jeff Gill's `Generalized Linear Models: A Unifited Approach

http://jgill.wustl.edu/research/books.html
"""

DESCRSHORT  = """Number of state executions in 1997"""

DESCRLONG   = """
This data describes the number of times capital punishment is implemented
at the state level for the year 1997.  The outcome variable is the number of
executions.  There were executions in 17 states.
Included in the data are explanatory variables for median per capita income
in dollars, the percent of the population classified as living in poverty,
the percent of Black citizens in the population, the rate of violent
crimes per 100,000 residents for 1996, a dummy variable indicating
whether the state is in the South, and (an estimate of) the proportion
of the population with a college degree of some kind.

The original source files are included in /cpunish/src/

/cpunish/cpunish.csv contains the cleaned data for the example
in a comma-delimited file
"""

NOTE        = """
Number of Observations: 17.
Number of Variables: 7
Variable name definitions:
    EXECUTIONS: Executions in 1996
    INCOME: Median per capita income in 1996 dollars
    PERPOVERTY: Percent of the population classified as living in poverty
    PERBLACK: Percent of black citizens in the population
    VC100k96: Rate of violent crimes per 100,00 residents for 1996
    SOUTH: SOUTH == 1 indicates a state in the South
    DEGREE: An esimate of the proportion of the state population with a
        college degree of some kind

State names are included in the data file, though not returned by load.
"""

from numpy import recfromtxt, column_stack, array
from scikits.statsmodels.datasets import Dataset
from os.path import dirname, abspath

def load():
    """
    Load the cpunish data and return a Dataset class.

    Returns
    -------
    Dataset instance:
        See DATASET_PROPOSAL.txt for more information.
    """
    filepath = dirname(abspath(__file__))
    data = recfromtxt(open(filepath + '/cpunish.csv', 'rb'), delimiter=",",
            names=True, dtype=float, usecols=(1,2,3,4,5,6,7))
    names = list(data.dtype.names)
    endog = array(data[names[0]], dtype=float)
    endog_name = names[0]
    exog = column_stack(data[i] for i in names[1:]).astype(float)
    exog_name = names[1:]
    dataset = Dataset(data=data, names=names, endog=endog, exog=exog,
            endog_name=endog_name, exog_name=exog_name)
    return dataset
