"""California housing dataset.

The original database is available from StatLib

    http://lib.stat.cmu.edu/

The data contains 20,640 observations on 9 variables.


References
----------

Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297.

"""
# Authors: Peter Prettenhofer
# License: BSD 3 clause

from io import BytesIO
from os.path import join, exists
from os import makedirs
from zipfile import ZipFile
try:
    # Python 2
    from urllib2 import urlopen
except ImportError:
    # Python 3+
    from urllib.request import urlopen

import numpy as np

from .base import get_data_home, Bunch
from ..externals import joblib


DATA_URL = "http://lib.stat.cmu.edu/modules.php?op=modload&name=Downloads&"\
           "file=index&req=getit&lid=83"
TARGET_FILENAME = "cal_housing.pkz"

# Grab the module-level docstring to use as a description of the
# dataset
MODULE_DOCS = __doc__


def fetch_california_housing(data_home=None, download_if_missing=True):
    """Loader for the California housing dataset from StatLib.

    Parameters
    ----------
    data_home : optional, default: None
        Specify another download and cache folder for the datasets. By default
        all scikit learn data is stored in '~/scikit_learn_data' subfolders.

    download_if_missing: optional, True by default
        If False, raise a IOError if the data is not locally available
        instead of trying to download the data from the source site.

    Notes
    ------

    This dataset consists of 20,640 samples and 9 features.
    """
    data_home = get_data_home(data_home=data_home)
    if not exists(data_home):
        makedirs(data_home)
    if not exists(join(data_home, TARGET_FILENAME)):
        print('downloading Cal. housing from %s to %s' % (DATA_URL, data_home))
        fhandle = urlopen(DATA_URL)
        buf = BytesIO(fhandle.read())
        zip_file = ZipFile(buf)
        try:
            cadata_fd = zip_file.open('cadata.txt', 'r')
            cadata = BytesIO(cadata_fd.read())
            # skip the first 27 lines (documentation)
            cal_housing = np.loadtxt(cadata, skiprows=27)
            joblib.dump(cal_housing, join(data_home, TARGET_FILENAME),
                        compress=6)
        finally:
            zip_file.close()
    else:
        cal_housing = joblib.load(join(data_home, TARGET_FILENAME))

    feature_names = ["MedInc", "HouseAge", "AveRooms", "AveBedrms",
                     "Population", "AveOccup", "Latitude", "Longitude"]

    target, data = cal_housing[:, 0], cal_housing[:, 1:]

    # avg rooms = total rooms / households
    data[:, 2] /= data[:, 5]

    # avg bed rooms = total bed rooms / households
    data[:, 3] /= data[:, 5]

    # avg occupancy = population / housholds
    data[:, 5] = data[:, 4] / data[:, 5]

    # target in units of 100,000
    target = target / 100000.0

    return Bunch(data=data,
                 target=target,
                 feature_names=feature_names,
                 DESCR=MODULE_DOCS)
