# Path: crowdastro/config.py
# SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))
# def get(*args, **kwargs):
# 
# Path: crowdastro/exceptions.py
# class CatalogueError(Exception):
#     pass


import argparse
import csv
import hashlib
import logging
import os
import astropy.io.fits
import astropy.utils.exceptions
import astropy.wcs
import h5py
import numpy
import scipy.spatial.distance
import sklearn.neighbors
from astropy.coordinates import SkyCoord
from astropy.io import ascii
from .config import config
from .exceptions import CatalogueError
    from . import rgz_data as data
    from . import rgz_data as data
    from . import rgz_data as data
    # and one for strings. We will have to preallocate the numeric table so that
    # we aren't storing huge amounts of image data in memory.

    # Strings.
    dtype = [('zooniverse_id', '<S{}'.format(MAX_ZOONIVERSE_ID_LENGTH)),
             ('name', '<S{}'.format(MAX_NAME_LENGTH))]
    string_data = numpy.array(list(zip(zooniverse_ids, names)), dtype=dtype)
    cdfs.create_dataset('string', data=string_data, dtype=dtype)

    # Numeric.
    image_size = (config['surveys']['atlas']['fits_width'] *
                  config['surveys']['atlas']['fits_height'])
    # RA, DEC, radio, (distance to SWIRE object added later)
    dim = (n_cdfs, 1 + 1 + image_size)
    numeric = cdfs.create_dataset('_numeric', shape=dim, dtype='float32')

    # Load image patches and store numeric data.
    with astropy.io.fits.open(
            config['data_sources']['atlas_{}_image'.format(field)],
            ignore_blank=True) as atlas_image:
        wcs = astropy.wcs.WCS(atlas_image[0].header).dropaxis(3).dropaxis(2)
        pix_coords = wcs.all_world2pix(coords, FITS_CONVENTION)
        assert pix_coords.shape[1] == 2
        logging.debug('Fetching %d ATLAS images.', len(pix_coords))
        for index, (x, y) in enumerate(pix_coords):
            radio = atlas_image[0].data[
                0, 0,  # stokes, freq
                int(y) - config['surveys']['atlas']['fits_height'] // 2:
                int(y) + config['surveys']['atlas']['fits_height'] // 2,
                int(x) - config['surveys']['atlas']['fits_width'] // 2:

# Path: crowdastro/config.py
# SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))
# def get(*args, **kwargs):
# 
# Path: crowdastro/exceptions.py
# class CatalogueError(Exception):
#     pass


# Path: crowdastro/import_data.py
import argparse
import csv
import hashlib
import logging
import os
import astropy.io.fits
import astropy.utils.exceptions
import astropy.wcs
import h5py
import numpy
import scipy.spatial.distance
import sklearn.neighbors
from astropy.coordinates import SkyCoord
from astropy.io import ascii
from .config import config
from .exceptions import CatalogueError
    from . import rgz_data as data
    from . import rgz_data as data
    from . import rgz_data as data

    # and one for strings. We will have to preallocate the numeric table so that
    # we aren't storing huge amounts of image data in memory.

    # Strings.
    dtype = [('zooniverse_id', '<S{}'.format(MAX_ZOONIVERSE_ID_LENGTH)),
             ('name', '<S{}'.format(MAX_NAME_LENGTH))]
    string_data = numpy.array(list(zip(zooniverse_ids, names)), dtype=dtype)
    cdfs.create_dataset('string', data=string_data, dtype=dtype)

    # Numeric.
    image_size = (config['surveys']['atlas']['fits_width'] *
                  config['surveys']['atlas']['fits_height'])
    # RA, DEC, radio, (distance to SWIRE object added later)
    dim = (n_cdfs, 1 + 1 + image_size)
    numeric = cdfs.create_dataset('_numeric', shape=dim, dtype='float32')

    # Load image patches and store numeric data.
    with astropy.io.fits.open(
            config['data_sources']['atlas_{}_image'.format(field)],
            ignore_blank=True) as atlas_image:
        wcs = astropy.wcs.WCS(atlas_image[0].header).dropaxis(3).dropaxis(2)
        pix_coords = wcs.all_world2pix(coords, FITS_CONVENTION)
        assert pix_coords.shape[1] == 2
        logging.debug('Fetching %d ATLAS images.', len(pix_coords))
        for index, (x, y) in enumerate(pix_coords):
            radio = atlas_image[0].data[
                0, 0,  # stokes, freq
                int(y) - config['surveys']['atlas']['fits_height'] // 2:
                int(y) + config['surveys']['atlas']['fits_height'] // 2,
                int(x) - config['surveys']['atlas']['fits_width'] // 2:

                int(x) + config['surveys']['atlas']['fits_width'] // 2]