import cPickle as pickle

import string

import sys

import time

from itertools import izip

import numpy as np

from datetime import datetime, timedelta

import utils

import utils_lung

import logger

import buffering

import pathfinder

import data_iterators

candidates_config = 'dsb_c3_s5_p8a1'

predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)

candidates_path = predictions_dir + '/%s' % candidates_config

id2candidates_path = utils_lung.get_candidates_paths(candidates_path)

train_valid_ids = utils.load_pkl(pathfinder.VALIDATION_SPLIT_PATH)

train_pids, valid_pids, test_pids = train_valid_ids['training'], train_valid_ids['validation'], train_valid_ids['test']

all_pids = train_pids + valid_pids + test_pids

data_iterator = data_iterators.DSBPixelSpacingsGenerator(pathfinder.DATA_PATH, id2candidates_path, all_pids)

z = []

y = []

x = []

pixel_spacings = {}

# use buffering.buffered_gen_threaded()

for idx, (pid, pixel_spacing) in enumerate(data_iterator.generate()):

    print idx, pid, pixel_spacing

    z.append(pixel_spacing[0])

    y.append(pixel_spacing[1])

    x.append(pixel_spacing[2])

    pixel_spacings[pid] = pixel_spacing

utils.save_pkl(pixel_spacings, 'pixel_spacings_dsb.pkl')

print 'z', min(z), max(z)

print 'y', min(y), max(y)

print 'x', min(x), max(x)