def test_luna_patches_3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]

    # pid = '1.3.6.1.4.1.14519.5.2.1.6279.6001.138080888843357047811238713686'
    # luna_data_paths = [pathfinder.LUNA_DATA_PATH + '/%s.mhd' % pid]
    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        # img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')
        print id

        annotations = id2zyxd[id]
        print annotations
        for zyxd in annotations:
            img_out, mask = config().data_prep_function_train(img,
                                                              pixel_spacing=pixel_spacing,
                                                              p_transform=config().p_transform,
                                                              p_transform_augment=config().p_transform_augment,
                                                              patch_center=zyxd,
                                                              luna_annotations=annotations,
                                                              luna_origin=origin)
            try:
                plot_slice_3d_2(img_out, mask, 0, id)
                plot_slice_3d_2(img_out, mask, 1, id)
                plot_slice_3d_2(img_out, mask, 2, id)
            except:
                pass
        print '------------------------------------------'

def test_luna3d_2():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = [
        '/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']

    for k, p in enumerate(luna_data_paths):
        id = os.path.basename(p).replace('.mhd', '')
        print id
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        lung_mask = lung_segmentation.segment_HU_scan(img)
        annotations = id2zyxd[id]
        x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
                                                                                       pixel_spacing=pixel_spacing,
                                                                                       p_transform=p_transform,
                                                                                       luna_annotations=annotations,
                                                                                       p_transform_augment=None,
                                                                                       luna_origin=origin,
                                                                                       lung_mask=lung_mask,
                                                                                       world_coord_system=True)

        y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')

        for zyxd in annotations_tf:
            plot_slice_3d_3(x, lung_mask_out, y, 0, id, idx=zyxd)
            plot_slice_3d_3(x, lung_mask_out, y, 1, id, idx=zyxd)
            plot_slice_3d_3(x, lung_mask_out, y, 2, id, idx=zyxd)

def test_luna3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]

    # luna_data_paths = [
    #     pathfinder.LUNA_DATA_PATH + '/1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886.mhd']

    luna_data_paths = [
        '/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']
    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        id = os.path.basename(p).replace('.mhd', '')
        print id

        annotations = id2zyxd[id]

        img_out, mask, annotations_out = config().data_prep_function(img,
                                                                     pixel_spacing=pixel_spacing,
                                                                     luna_annotations=annotations,
                                                                     luna_origin=origin)

        mask[mask == 0.] = 0.1
        print annotations_out
        for zyxd in annotations_out:
            plot_slice_3d_2(img_out, mask, 0, id, idx=zyxd)
            plot_slice_3d_2(img_out, mask, 1, id, idx=zyxd)
            plot_slice_3d_2(img_out, mask, 2, id, idx=zyxd)

def build_segmentation_model(l_in):
    metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
    metadata_path = utils.find_model_metadata(metadata_dir, patch_segmentation_config.__name__.split('.')[-1])
    metadata = utils.load_pkl(metadata_path)

    model = patch_segmentation_config.build_model(l_in=l_in, patch_size=p_transform['patch_size'])
    nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
    return model

def test3():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    id2mm_shape = utils.load_pkl(image_dir + '/pid2mm.pkl')
    s = [(key, value) for (key, value) in sorted(id2mm_shape.items(), key=lambda x: x[1][0])]
    for i in xrange(5):
        print s[i]
    print '--------------------------'
    for i in xrange(1,6):
        print s[-i]

def test1():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_1/'
    utils.auto_make_dir(image_dir)

    sys.stdout = logger.Logger(image_dir + '/%s.log' % 'test1_log')
    sys.stderr = sys.stdout

    patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
    print len(patient_data_paths)

    for k, p in enumerate(patient_data_paths):
        pid = utils_lung.extract_pid_dir(p)
        try:
            sid2data, sid2metadata = utils_lung.get_patient_data(p)
            sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
            sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
            sid2position = utils_lung.slice_location_finder(sid2metadata)

            try:
                slice_thickness_pos = np.abs(sid2metadata[sids_sorted[0]]['ImagePositionPatient'][2] -
                                             sid2metadata[sids_sorted[1]]['ImagePositionPatient'][2])
            except:
                print 'This patient has no ImagePosition!'
                slice_thickness_pos = 0.
            try:
                slice_thickness_loc = np.abs(
                    sid2metadata[sids_sorted[0]]['SliceLocation'] - sid2metadata[sids_sorted[1]]['SliceLocation'])
            except:
                print 'This patient has no SliceLocation!'
                slice_thickness_loc = 0.

            jonas_slicethick = []
            for i in xrange(len(sids_sorted_jonas) - 1):
                s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
                jonas_slicethick.append(s)

            full_img = np.stack([data_transforms.ct2normHU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
            del sid2data, sid2metadata
            print np.min(full_img), np.max(full_img)
            # spacing = sid2metadata[sids_sorted[0]]['PixelSpacing']
            # spacing = [slice_thickness, spacing[0], spacing[1]]
            # resampled_image, _ = resample(full_img, spacing)
            plot_2d(full_img, axis=0, pid=pid + 'ax0', img_dir=image_dir)
            plot_2d(full_img, axis=1, pid=pid + 'ax1', img_dir=image_dir)
            plot_2d(full_img, axis=2, pid=pid + 'ax2', img_dir=image_dir)
            print k, pid, full_img.shape, slice_thickness_pos, slice_thickness_loc, set(jonas_slicethick)
            del full_img
        except:
            print 'exception!!!', pid

def test_luna3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = [
        '/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']

    candidates = utils.load_pkl(
        '/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')

    candidates = candidates[:4]
    print candidates
    print '--------------'
    print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']

    for k, p in enumerate(luna_data_paths):
        id = os.path.basename(p).replace('.mhd', '')
        print id
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        lung_mask = lung_segmentation.segment_HU_scan_ira(img)
        print np.min(lung_mask), np.max(lung_mask)
        x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
                                                                                       pixel_spacing=pixel_spacing,
                                                                                       p_transform=p_transform,
                                                                                       luna_annotations=candidates,
                                                                                       p_transform_augment=None,
                                                                                       luna_origin=origin,
                                                                                       lung_mask=lung_mask,
                                                                                       world_coord_system=False)

        print np.min(lung_mask_out), np.max(lung_mask_out)

        plot_slice_3d_2(x, lung_mask_out, 0, id)
        plot_slice_3d_2(x, lung_mask_out, 1, id)
        plot_slice_3d_2(x, lung_mask_out, 2, id)

        # for zyxd in annotations_tf:
        #     plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
        #     plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
        #     plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)

        for i in xrange(136, x.shape[1]):
            plot_slice_3d_2(x, lung_mask_out, 1, str(id) + str(i), idx=np.array([200, i, 200]))

def test_dsb():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_1/'
    utils.auto_make_dir(image_dir)

    patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
    print len(patient_data_paths)
    patient_data_paths = [pathfinder.DATA_PATH + '/01de8323fa065a8963533c4a86f2f6c1']

    for k, p in enumerate(patient_data_paths):
        pid = utils_lung.extract_pid_dir(p)
        # sid2data, sid2metadata = utils_lung.get_patient_data(p)
        # sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
        # sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
        # sid2position = utils_lung.slice_location_finder(sid2metadata)
        #
        # jonas_slicethick = []
        # for i in xrange(len(sids_sorted_jonas) - 1):
        #     s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
        #     jonas_slicethick.append(s)
        #
        # img = np.stack([data_transforms.ct2HU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
        # xx = (jonas_slicethick[0],
        #       sid2metadata[sids_sorted[0]]['PixelSpacing'][0],
        #       sid2metadata[sids_sorted[0]]['PixelSpacing'][1])
        # pixel_spacing = np.asarray(xx)

        img, pixel_spacing = utils_lung.read_dicom_scan(p)
        mask = lung_segmentation.segment_HU_scan_ira(img)
        print pid
        print pixel_spacing
        print '===================================='

        img_out, transform_matrix, mask_out = data_transforms.transform_scan3d(img,
                                                                               pixel_spacing=pixel_spacing,
                                                                               p_transform=config().p_transform,
                                                                               p_transform_augment=None,
                                                                               lung_mask=mask)

        for i in xrange(100, img_out.shape[0], 5):
            plot_slice_3d_2(img_out, mask_out, 0, str(pid) + str(i), idx=np.array([i, 200, 200]))

        plot_slice_3d_2(img_out, mask_out, 0, pid, idx=np.array(img_out.shape) / 2)
        plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 4)
        plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 8)

def test2():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
    print len(luna_data_paths)
    pid2mm_shape = {}

    for k, p in enumerate(luna_data_paths):
        img, origin, spacing = utils_lung.read_mhd(p)
        id = os.path.basename(p).replace('.mhd', '')
        mm_shape = img.shape * spacing
        pid2mm_shape[id] = mm_shape
        print k, id, mm_shape
        if k % 50 == 0:
            print 'Saved'
            utils.save_pkl(pid2mm_shape, image_dir + '/pid2mm.pkl')

    utils.save_pkl(pid2mm_shape, image_dir + '/pid2mm.pkl')

def build_model():
    metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
    metadata_path = utils.find_model_metadata(metadata_dir, patch_class_config.__name__.split('.')[-1])
    metadata = utils.load_pkl(metadata_path)

    print 'Build model'
    model = patch_class_config.build_model()
    all_layers = nn.layers.get_all_layers(model.l_out)
    num_params = nn.layers.count_params(model.l_out)
    print '  number of parameters: %d' % num_params
    print string.ljust('  layer output shapes:', 36),
    print string.ljust('#params:', 10),
    print 'output shape:'
    for layer in all_layers:
        name = string.ljust(layer.__class__.__name__, 32)
        num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
        num_param = string.ljust(num_param.__str__(), 10)
        print '    %s %s %s' % (name, num_param, layer.output_shape)

    nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
    return model

def test1():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
    print len(luna_data_paths)
    print id2zyxd.keys()

    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')

        for nodule_zyxd in id2zyxd.itervalues():
            zyx = np.array(nodule_zyxd[:3])
            voxel_coords = utils_lung.world2voxel(zyx, origin, pixel_spacing)
            diameter_mm = nodule_zyxd[-1]
            radius_px = diameter_mm / pixel_spacing[1] / 2.
            roi_radius = (radius_px, radius_px)
            slice = img[voxel_coords[0], :, :]
            slice_prev = img[voxel_coords[0] - 1, :, :]
            slice_next = img[voxel_coords[0] + 1, :, :]
            roi_center_yx = (voxel_coords[1], voxel_coords[2])
            mask = data_transforms.make_2d_mask(slice.shape, roi_center_yx, roi_radius, masked_value=0.1)
            plot_2d(slice, mask, id, image_dir)

            plot_2d_4(slice, slice_prev, slice_next, mask, id, image_dir)

            a = [{'center': roi_center_yx, 'diameter_mm': diameter_mm}]
            p_transform = {'patch_size': (256, 256),
                           'mm_patch_size': (360, 360)}
            slice_patch, mask_patch = data_transforms.luna_transform_slice(slice, a, pixel_spacing[1:],
                                                                           p_transform, None)
            plot_2d(slice_patch, mask_patch, id, image_dir)

def test1():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    # sys.stdout = logger.Logger(image_dir + '/test_luna.log')
    # sys.stderr = sys.stdout

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
    print len(luna_data_paths)
    print id2zyxd.keys()

    for k, p in enumerate(luna_data_paths):
        img, origin, spacing = utils_lung.read_mhd(p)
        img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')
        for roi in id2zyxd[id]:
            zyx = np.array(roi[:3])
            voxel_coords = utils_lung.world2voxel(zyx, origin, spacing)
            print spacing
            radius_mm = roi[-1] / 2.
            radius_px = radius_mm / spacing[1]
            print 'r in pixels =', radius_px
            # roi_radius = (32.5, 32.5)
            roi_radius = (radius_px, radius_px)
            slice = img[voxel_coords[0], :, :]
            roi_center_yx = (voxel_coords[1], voxel_coords[2])
            # print slice.shape, slice_resample.shape
            mask = make_circular_mask(slice.shape, roi_center_yx, roi_radius)
            plot_2d(slice, mask, id, image_dir)

            slice_mm, _ = resample(slice, spacing[1:])
            roi_center_mm = tuple(int(r * ps) for r, ps in zip(roi_center_yx, spacing[1:]))
            mask_mm = make_circular_mask(slice_mm.shape, roi_center_mm, (radius_mm, radius_mm))
            plot_2d(slice_mm, mask_mm, id, image_dir)

def build_model():
    l_in = nn.layers.InputLayer((None, n_candidates_per_patient, 1,) + p_transform['patch_size'])
    l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
    l_target = nn.layers.InputLayer((batch_size,))

    base_n_filters = 128
    l = conv_prelu_layer(l_in_rshp, n_filters=base_n_filters)
    l = conv_prelu_layer(l, n_filters=base_n_filters)
    l = conv_prelu_layer(l, n_filters=base_n_filters)

    l = max_pool3d(l)

    l = conv_prelu_layer(l, n_filters=base_n_filters)
    l = conv_prelu_layer(l, n_filters=base_n_filters)
    l = conv_prelu_layer(l, n_filters=base_n_filters)
    l_enc = conv_prelu_layer(l, n_filters=base_n_filters)

    num_units_dense = 512
    l_d01 = dense_prelu_layer(l, num_units=512)
    l_d01 = nn.layers.ReshapeLayer(l_d01, (-1, n_candidates_per_patient, num_units_dense))
    l_d02 = dense_prelu_layer(l_d01, num_units=512)
    l_out = nn.layers.DenseLayer(l_d02, num_units=2,
                                 W=nn.init.Constant(0.),
                                 b=np.array([np.log((1397. - 362) / 1398), np.log(362. / 1397)], dtype='float32'),
                                 nonlinearity=nn.nonlinearities.softmax)

    metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
    metadata_path = utils.find_model_metadata(metadata_dir, 'luna_p8a1')
    metadata = utils.load_pkl(metadata_path)
    for p, pv in zip(nn.layers.get_all_params(l_enc), metadata['param_values']):
        if p.get_value().shape != pv.shape:
            raise ValueError("mismatch: parameter has shape %r but value to "
                             "set has shape %r" %
                             (p.get_value().shape, pv.shape))
        p.set_value(pv)

    return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)

def test_luna3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = [
        'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']

    candidates = utils.load_pkl(
        'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')

    candidates = candidates[:4]
    print candidates
    print '--------------'
    print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']

    for k, p in enumerate(luna_data_paths):
        id = os.path.basename(p).replace('.mhd', '')
        print id
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        lung_mask = lung_segmentation.segment_HU_scan_elias(img)
        x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
                                                                                       pixel_spacing=pixel_spacing,
                                                                                       p_transform=p_transform,
                                                                                       luna_annotations=candidates,
                                                                                       p_transform_augment=None,
                                                                                       luna_origin=origin,
                                                                                       lung_mask=lung_mask,
                                                                                       world_coord_system=False)

        for zyxd in annotations_tf:
            plot_slice_3d_2(x, lung_mask_out, 0, id, img_dir='./', idx=zyxd)
            plot_slice_3d_2(x, lung_mask_out, 1, id, img_dir='./', idx=zyxd)
            plot_slice_3d_2(x, lung_mask_out, 2, id, img_dir='./', idx=zyxd)

import pathfinder

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_props_patch', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print 'Build model'
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
all_params = nn.layers.get_all_params(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print '  number of parameters: %d' % num_params
print string.ljust('  layer output shapes:', 36),
print string.ljust('#params:', 10),

import nn_lung
from collections import namedtuple
from functools import partial
import lasagne.layers.dnn as dnn
import theano.tensor as T
import utils
import utils_lung
import os

# TODO: import correct config here
candidates_config = 'dsb_c3_s2_p8a1_ls_elias'

restart_from_save = None
rng = np.random.RandomState(42)

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)

# transformations
p_transform = {'patch_size': (48, 48, 48),
               'mm_patch_size': (48, 48, 48),
               'pixel_spacing': (1., 1., 1.)
               }
p_transform_augment = {
    'translation_range_z': [-5, 5],
    'translation_range_y': [-5, 5],
    'translation_range_x': [-5, 5],
    'rotation_range_z': [-10, 10],
    'rotation_range_y': [-10, 10],
    'rotation_range_x': [-10, 10]

from configuration import config, set_configuration, set_subconfiguration
from pathfinder import METADATA_PATH, PREDICTIONS_PATH
import logger

NUM_PATIENTS = 1140

if not (3 <= len(sys.argv) <= 5):
    sys.exit("Usage: predict.py <config_name> <n_tta_iterations> <average: arithmetic|geometric>")

config_name = sys.argv[1]
n_tta_iterations = int(sys.argv[2]) if len(sys.argv) >= 3 else 100
mean = sys.argv[3] if len(sys.argv) >= 4 else "geometric"

print "Make %s tta predictions for %s set using %s mean" % (n_tta_iterations, "valid and test", mean)

metadata_dir = utils.get_dir_path("train", METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)
metadata = utils.load_pkl(metadata_path)
assert config_name == metadata["configuration"]
if "subconfiguration" in metadata:
    set_subconfiguration(metadata["subconfiguration"])
set_configuration(config_name)

# predictions paths
jonas_prediction_path = PREDICTIONS_PATH + "/ira_%s.pkl" % config().__name__
prediction_dir = utils.get_dir_path("predictions", METADATA_PATH)
valid_prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (
    metadata["experiment_id"],
    "valid",
    n_tta_iterations,
    mean,

import utils_heart
from configuration import config, set_configuration, set_subconfiguration
import pathfinder

if not (3 <= len(sys.argv) <= 5):
    sys.exit("Usage: predict.py <config_name> <set: train|valid|test> <n_tta_iterations> "
             "<average: arithmetic|geometric>")

config_name = sys.argv[1]
set = sys.argv[2] if len(sys.argv) >= 3 else 'valid'
n_tta_iterations = int(sys.argv[3]) if len(sys.argv) >= 4 else 1
mean = sys.argv[4] if len(sys.argv) >= 5 else 'geometric'

print 'Make %s tta predictions for %s set using %s mean' % (n_tta_iterations, set, mean)

metadata_dir = utils.get_dir_path('train', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)
metadata = utils.load_pkl(metadata_path)

assert config_name == metadata['configuration']
if 'subconfiguration' in metadata:
    set_subconfiguration(metadata['subconfiguration'])
set_configuration(config_name)

# predictions paths
prediction_dir = utils.get_dir_path('predictions', pathfinder.METADATA_PATH)
prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (metadata['experiment_id'], set, n_tta_iterations, mean)

# submissions paths
submission_dir = utils.get_dir_path('submissions', pathfinder.METADATA_PATH)
submission_path = submission_dir + "/%s-%s-%s-%s.csv" % (metadata['experiment_id'], set, n_tta_iterations, mean)

from datetime import datetime, timedelta
import utils
import logger
import buffering
from configuration import config, set_configuration
import pathfinder
import utils_plots
import data_iterators
import data_transforms

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt


predictions_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + 'dsb_scan_histograms'
utils.auto_make_dir(outputs_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']
print 'n train', len(train_pids)
print 'n valid', len(valid_pids)
print 'n test', len(test_pids)

all_pids = train_pids + valid_pids + test_pids

data_iterator = data_iterators.DSBDataGenerator(data_path=pathfinder.DATA_PATH, patient_pids=all_pids)

histograms = {}
bins = np.arange(-960,1700,40)

import utils_lung
import os
import evaluate_submission

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_class_dsb.py <configuration_name> <valid|test>")

config_name = sys.argv[1]
set_configuration('configs_class_dsb', config_name)

set = sys.argv[2] if len(sys.argv) == 3 else 'test'

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-%s.log' % (expid, set))
sys.stderr = sys.stdout

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
output_pkl_file = predictions_dir + '/%s-%s.pkl' % (expid, set)

submissions_dir = utils.get_dir_path('submissions', pathfinder.METADATA_PATH)