# Path: loom/format.py
# def load_encoder(encoder):
#     model = encoder['model']
#     if 'symbols' in encoder:
#         encode = encoder['symbols'].__getitem__
#     elif model == 'bb':
#         encode = BOOLEAN_SYMBOLS.__getitem__
#     else:
#         encode = loom.schema.MODELS[model].Value
#     return encode
# 
# Path: loom/test/util.py
# CLEANUP_ON_ERROR = int(os.environ.get('CLEANUP_ON_ERROR', 1))
# 
# Path: loom/test/util.py
# def for_each_dataset(fun):
#     @functools.wraps(fun)
#     def test_one(dataset):
#         paths = loom.store.get_paths(dataset, sample_count=2)
#         for key, path in loom.store.iter_paths(dataset, paths):
#             if not os.path.exists(path):
#                 raise ValueError(
#                     'missing {} at {},\n  first `python -m loom.datasets test`'
#                     .format(key, path))
#         kwargs = {'name': dataset}
#         kwargs.update(paths['query'])
#         kwargs.update(paths['consensus'])
#         kwargs.update(paths['samples'][0])
#         kwargs.update(paths['ingest'])
#         kwargs.update(paths)
#         fun(**kwargs)
# 
#     @functools.wraps(fun)
#     def test_all():
#         for dataset in loom.datasets.TEST_CONFIGS:
#             yield test_one, dataset
# 
#     return test_all
# 
# Path: loom/test/util.py
# def load_rows_csv(dirname):
#     filenames = os.listdir(dirname)
#     rows = [None]
#     for filename in filenames:
#         filename = os.path.join(dirname, filename)
#         with csv_reader(filename) as reader:
#             header = reader.next()
#             rows += reader
#     rows[0] = header
#     return rows


import os
import csv
import numpy
import pandas
import loom.preql
from itertools import izip
from StringIO import StringIO
from nose import SkipTest
from nose.tools import assert_almost_equal
from nose.tools import assert_equal
from nose.tools import assert_raises
from nose.tools import assert_true
from distributions.fileutil import tempdir
from distributions.io.stream import json_load
from distributions.io.stream import open_compressed
from distributions.io.stream import protobuf_stream_load
from distributions.tests.util import assert_close
from loom.format import load_encoder
from loom.test.util import CLEANUP_ON_ERROR
from loom.test.util import for_each_dataset
from loom.test.util import load_rows_csv
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


COUNT = 10


def make_fully_observed_row(rows_csv):
    rows = iter(load_rows_csv(rows_csv))
    header = rows.next()
    try:
        id_pos = header.index('_id')
    except ValueError:
        id_pos = None
    dense_row = ['' for _ in header]
    for row in rows:
        if not any(condition == '' for condition in dense_row):
            if id_pos is not None:
                dense_row.pop(id_pos)
            return dense_row

        for i, (condition, x) in enumerate(izip(dense_row, row)):
            if condition == '':
                dense_row[i] = x
    raise SkipTest('no dense row could be constructed')


def _check_predictions(rows_in, result_out, encoding):
    encoders = json_load(encoding)

# Path: loom/format.py
# def load_encoder(encoder):
#     model = encoder['model']
#     if 'symbols' in encoder:
#         encode = encoder['symbols'].__getitem__
#     elif model == 'bb':
#         encode = BOOLEAN_SYMBOLS.__getitem__
#     else:
#         encode = loom.schema.MODELS[model].Value
#     return encode
# 
# Path: loom/test/util.py
# CLEANUP_ON_ERROR = int(os.environ.get('CLEANUP_ON_ERROR', 1))
# 
# Path: loom/test/util.py
# def for_each_dataset(fun):
#     @functools.wraps(fun)
#     def test_one(dataset):
#         paths = loom.store.get_paths(dataset, sample_count=2)
#         for key, path in loom.store.iter_paths(dataset, paths):
#             if not os.path.exists(path):
#                 raise ValueError(
#                     'missing {} at {},\n  first `python -m loom.datasets test`'
#                     .format(key, path))
#         kwargs = {'name': dataset}
#         kwargs.update(paths['query'])
#         kwargs.update(paths['consensus'])
#         kwargs.update(paths['samples'][0])
#         kwargs.update(paths['ingest'])
#         kwargs.update(paths)
#         fun(**kwargs)
# 
#     @functools.wraps(fun)
#     def test_all():
#         for dataset in loom.datasets.TEST_CONFIGS:
#             yield test_one, dataset
# 
#     return test_all
# 
# Path: loom/test/util.py
# def load_rows_csv(dirname):
#     filenames = os.listdir(dirname)
#     rows = [None]
#     for filename in filenames:
#         filename = os.path.join(dirname, filename)
#         with csv_reader(filename) as reader:
#             header = reader.next()
#             rows += reader
#     rows[0] = header
#     return rows


# Path: loom/test/test_preql.py
import os
import csv
import numpy
import pandas
import loom.preql
from itertools import izip
from StringIO import StringIO
from nose import SkipTest
from nose.tools import assert_almost_equal
from nose.tools import assert_equal
from nose.tools import assert_raises
from nose.tools import assert_true
from distributions.fileutil import tempdir
from distributions.io.stream import json_load
from distributions.io.stream import open_compressed
from distributions.io.stream import protobuf_stream_load
from distributions.tests.util import assert_close
from loom.format import load_encoder
from loom.test.util import CLEANUP_ON_ERROR
from loom.test.util import for_each_dataset
from loom.test.util import load_rows_csv

# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


COUNT = 10


def make_fully_observed_row(rows_csv):
    rows = iter(load_rows_csv(rows_csv))
    header = rows.next()
    try:
        id_pos = header.index('_id')
    except ValueError:
        id_pos = None
    dense_row = ['' for _ in header]
    for row in rows:
        if not any(condition == '' for condition in dense_row):
            if id_pos is not None:
                dense_row.pop(id_pos)
            return dense_row

        for i, (condition, x) in enumerate(izip(dense_row, row)):
            if condition == '':
                dense_row[i] = x
    raise SkipTest('no dense row could be constructed')


def _check_predictions(rows_in, result_out, encoding):
    encoders = json_load(encoding)

    name_to_encoder = {e['name']: load_encoder(e) for e in encoders}