# Path: loom/util.py
# @contextlib.contextmanager
# def csv_reader(filename):
#     with open_compressed(filename, 'rb') as f:
#         yield csv.reader(f)


import os
import csv
import shutil
import random
import numpy
import numpy.random
import scipy
import scipy.misc
import scipy.ndimage
import loom.tasks
import loom.query
import loom.preql
import loom.store
import loom.datasets
import parsable
from StringIO import StringIO
from matplotlib import pyplot
from distributions.dbg.random import sample_discrete
from distributions.io.stream import open_compressed
from loom.util import csv_reader
    y = int(round((loom_y + 1.0) / Y_SCALE))
    return x, y


def to_loom_coordinates(image_x, image_y):
    x = image_x * X_SCALE - 1.0
    y = image_y * Y_SCALE - 1.0
    return x, y


def sample_from_image(image, row_count):
    image = -1.0 * image
    image -= image.min()
    x_pmf = image.sum(axis=1)
    y_pmfs = image.copy()
    for y_pmf in y_pmfs:
        y_pmf /= (y_pmf.sum() + 1e-8)

    for _ in xrange(row_count):
        x = sample_discrete(x_pmf)
        y = sample_discrete(y_pmfs[x])
        x += numpy.random.random() - 0.5
        y += numpy.random.random() - 0.5
        yield to_loom_coordinates(x, y)


def synthesize_search(name, image_pos):
    shape = IMAGE.shape
    image = IMAGE.reshape(shape[0], shape[1], 1).repeat(3, 2)
    image[image_pos] = [0, 255, 0]

# Path: loom/util.py
# @contextlib.contextmanager
# def csv_reader(filename):
#     with open_compressed(filename, 'rb') as f:
#         yield csv.reader(f)


# Path: examples/fox/main.py
import os
import csv
import shutil
import random
import numpy
import numpy.random
import scipy
import scipy.misc
import scipy.ndimage
import loom.tasks
import loom.query
import loom.preql
import loom.store
import loom.datasets
import parsable
from StringIO import StringIO
from matplotlib import pyplot
from distributions.dbg.random import sample_discrete
from distributions.io.stream import open_compressed
from loom.util import csv_reader

    y = int(round((loom_y + 1.0) / Y_SCALE))
    return x, y


def to_loom_coordinates(image_x, image_y):
    x = image_x * X_SCALE - 1.0
    y = image_y * Y_SCALE - 1.0
    return x, y


def sample_from_image(image, row_count):
    image = -1.0 * image
    image -= image.min()
    x_pmf = image.sum(axis=1)
    y_pmfs = image.copy()
    for y_pmf in y_pmfs:
        y_pmf /= (y_pmf.sum() + 1e-8)

    for _ in xrange(row_count):
        x = sample_discrete(x_pmf)
        y = sample_discrete(y_pmfs[x])
        x += numpy.random.random() - 0.5
        y += numpy.random.random() - 0.5
        yield to_loom_coordinates(x, y)


def synthesize_search(name, image_pos):
    shape = IMAGE.shape
    image = IMAGE.reshape(shape[0], shape[1], 1).repeat(3, 2)
    image[image_pos] = [0, 255, 0]

    with csv_reader(SAMPLES) as reader: