--- a
+++ b/experiments/bleed_exp/old_preprocessing.py
@@ -0,0 +1,181 @@
+#!/usr/bin/env python
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+'''
+This preprocessing script loads nrrd files obtained by the data conversion tool: https://github.com/MIC-DKFZ/LIDC-IDRI-processing/tree/v1.0.1
+After applying preprocessing, images are saved as numpy arrays and the meta information for the corresponding patient is stored
+as a line in the dataframe saved as info_df.pickle.
+'''
+
+import os
+import SimpleITK as sitk
+import numpy as np
+from multiprocessing import Pool
+import pandas as pd
+import numpy.testing as npt
+from skimage.transform import resize
+import subprocess
+import pickle
+
+import configs
+cf = configs.configs()
+
+
+
+def resample_array(src_imgs, src_spacing, target_spacing):
+
+    src_spacing = np.round(src_spacing, 3)
+    target_shape = [int(src_imgs.shape[ix] * src_spacing[::-1][ix] / target_spacing[::-1][ix]) for ix in range(len(src_imgs.shape))]
+    for i in range(len(target_shape)):
+        try:
+            assert target_shape[i] > 0
+        except:
+            raise AssertionError("AssertionError:", src_imgs.shape, src_spacing, target_spacing)
+
+    img = src_imgs.astype(float)
+    resampled_img = resize(img, target_shape, order=1, clip=True, mode='edge').astype('float32')
+
+    return resampled_img
+
+#Convert .nii.gz to .nrrd
+
+def pp_patient(inputs):
+
+    #read image
+    ix, path = inputs
+    pid = path.split('/')[-1]
+    img = sitk.ReadImage(os.path.join(path,'venous.nii.gz'))
+    img_arr = sitk.GetArrayFromImage(img)
+    print('processing {}'.format(pid), img.GetSpacing(), img_arr.shape)
+    img_arr = resample_array(img_arr, img.GetSpacing(), cf.target_spacing)
+    img_arr = np.clip(img_arr, -1200, 600)
+    #img_arr = (1200 + img_arr) / (600 + 1200) * 255  # a+x / (b-a) * (c-d) (c, d = new)
+    img_arr = img_arr.astype(np.float32)
+    img_arr = (img_arr - np.mean(img_arr)) / np.std(img_arr).astype(np.float16)
+
+    #Open Characteristics File
+    df = pd.read_csv(os.path.join(cf.root_dir, 'raw_characteristics.csv'), sep=',',converters={'PatientID': lambda x: str(x)})
+    df = df[df.PatientID == pid]
+
+    #Make Masks Array, Grab Mask ID Per Patient
+    final_rois = np.zeros_like(img_arr, dtype=np.uint8)
+    mal_labels = []
+    roi_ids = set([ii.split('.')[0].split('_')[0] for ii in os.listdir(path) if 'mask.nii.gz' in ii])
+    rix = 1
+    for rid in roi_ids:
+
+        #Grab Mask Paths and Nodule IDs
+        roi_id_paths = [ii for ii in os.listdir(path) if 'mask.nii' in ii]
+        print ("ROI ID Paths:"+str(roi_id_paths))
+        nodule_ids = [ii.split('.')[0].split('_')[0].lstrip("0") for ii in roi_id_paths]
+        print ("Nodule ID:"+str(nodule_ids))
+
+        #Grab Severity Value From Characteristics file
+        rater_labels = [df[df.ROI_ID == int(ii)].Severity.values[0] for ii in nodule_ids]
+        print ("Rater Labels:"+str(rater_labels))
+
+        ##Take Mean Severity Value
+        #rater_labels.extend([0] * (4-len(rater_labels)))
+        #mal_label = np.mean([ii for ii in rater_labels if ii > -1])
+        mal_label = rater_labels
+        mal_list = mal_label
+        print ("#############Mal Label: "+str(mal_list))
+        ##Read Mask Paths
+        #roi_rater_list = []
+        # for rp in roi_id_paths:
+        rp = roi_id_paths[0]
+        roi = sitk.ReadImage(os.path.join(cf.raw_data_dir, pid, rp))
+        roi_arr = sitk.GetArrayFromImage(roi).astype(np.uint8)
+        roi_arr = resample_array(roi_arr, roi.GetSpacing(), cf.target_spacing)
+        assert roi_arr.shape == img_arr.shape, [roi_arr.shape, img_arr.shape, pid, roi.GetSpacing()]
+        
+        for ix in range(len(img_arr.shape)):
+            npt.assert_almost_equal(roi.GetSpacing()[ix], img.GetSpacing()[ix])
+        #roi_rater_list.append(roi_arr)
+
+        final_rois = roi_arr
+
+        # roi_rater_list.extend([np.zeros_like(roi_rater_list[-1])]*(4-len(roi_id_paths)))
+        # roi_raters = np.array(roi_rater_list)
+        # roi_raters = np.mean(roi_raters, axis=0)
+        # roi_raters[roi_raters < 0.5] = 0
+
+        # if np.sum(roi_raters) > 0:
+        #     mal_labels.append(mal_label)
+        #     final_rois[roi_raters >= 0.5] = rix
+        #     rix += 1
+        # else:
+        #     # indicate rois suppressed by majority voting of raters
+        #     print('suppressed roi!', roi_id_paths)
+        #     with open(os.path.join(cf.pp_dir, 'suppressed_rois.txt'), 'a') as handle:
+        #         handle.write(" ".join(roi_id_paths))
+
+    #Generate Foreground Slice Indices
+    final_rois = np.around(final_rois)
+    fg_slices = [ii for ii in np.unique(np.argwhere(final_rois != 0)[:, 0])]
+    
+    #Make Array From Severity 
+    #mal_labels = np.array(mal_label)
+
+    if mal_list[0] == [0]:
+        mal_labels_assert_test = []
+    else:
+        mal_labels_assert_test = mal_list
+
+    print ("Print Malignancy Labels:"+str(mal_list))
+    print ("Print Unique Values in ROI Array:"+str(len(np.unique(final_rois))))
+
+
+    assert len(mal_labels_assert_test) + 1 == len(np.unique(final_rois)), [len(mal_labels), np.unique(final_rois), pid]
+
+    np.save(os.path.join(cf.pp_dir, '{}_rois.npy'.format(pid)), final_rois)
+    np.save(os.path.join(cf.pp_dir, '{}_img.npy'.format(pid)), img_arr)
+
+    with open(os.path.join(cf.pp_dir, 'meta_info_{}.pickle'.format(pid)), 'wb') as handle:
+        meta_info_dict = {'pid': pid, 'class_target': mal_list, 'spacing': img.GetSpacing(), 'fg_slices': fg_slices}
+        print (meta_info_dict)
+        pickle.dump(meta_info_dict, handle)
+
+
+
+def aggregate_meta_info(exp_dir):
+
+    files = [os.path.join(exp_dir, f) for f in os.listdir(exp_dir) if 'meta_info' in f]
+    df = pd.DataFrame(columns=['pid', 'class_target', 'spacing', 'fg_slices'])
+    for f in files:
+        with open(f, 'rb') as handle:
+            df.loc[len(df)] = pickle.load(handle)
+
+    df.to_pickle(os.path.join(exp_dir, 'info_df.pickle'))
+    print ("aggregated meta info to df with length", len(df))
+
+
+if __name__ == "__main__":
+
+    paths = [os.path.join(cf.raw_data_dir, ii) for ii in os.listdir(cf.raw_data_dir) if not ii.startswith('.')]
+
+    if not os.path.exists(cf.pp_dir):
+        os.makedirs(cf.pp_dir)
+
+    # pool = Pool(processes=1)
+    # p1 = pool.map(pp_patient, enumerate(paths), chunksize=1)
+    # pool.close()
+    # pool.join()
+    for i in enumerate(paths):
+        pp_patient(i)
+
+    aggregate_meta_info(cf.pp_dir)
+    subprocess.call('cp {} {}'.format(os.path.join(cf.pp_dir, 'info_df.pickle'), os.path.join(cf.pp_dir, 'info_df_bk.pickle')), shell=True)