# -*- coding: utf-8 -*-
"""
Created on Mon Aug  1 14:44:28 2016

@author: poyu

Data Pre-processing for Feature Representation
"""
import os
import nibabel as nib
import numpy as np

#def load_dataset():

def normalized_3d(inputs):
    inputs=np.copy(inputs)
    x_dim, y_dim, z_dim = inputs.shape    
    number_non_zero = np.float32(np.count_nonzero(inputs))
    sum_inputs = np.float32(inputs.sum())
    mean_inputs = sum_inputs/number_non_zero
    input_nonzero = inputs[np.nonzero(inputs)]
    std_input = np.std(input_nonzero)
    for i in range(x_dim):
        for j in range(y_dim):
            for k in range(z_dim):
                if inputs[i,j,k] != 0 :
                    inputs[i,j,k] = (inputs[i,j,k]-mean_inputs)/std_input
    return inputs
    
def createDatasetFeatureRepresentation():
    
        
    boundary_t1 = [11, 167, 9, 207, 9, 153]
    
    t1_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "MR_T1" in name and name.endswith("nii")]
    
    t1_filepaths.sort()
    
    boundary_fa = [6, 84, 5, 104, 8, 80]
    
    fa_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_FA" in name and name.endswith("nii")]
    
    fa_filepaths.sort()  
      
    boundary_md = [6, 84, 5, 104, 8, 80]
    
    md_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_MD" in name and name.endswith("nii")]
    
    md_filepaths.sort()
    
    # =============================Train========================================= #
    
    train_subject = np.array([5, 11, 17, 22, 27, 3, 10, 16, 20, 26, 4, 7, 8, 13, 25])-1
    
    train_label = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
    
    train_t1 = np.zeros((train_subject.shape[0],156,198,144), dtype = np.float32)
    train_t1_scaled = train_t1
    
    train_fa = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
    train_fa_scaled = train_fa
    
    train_md = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
    train_md_scaled = train_md
    
    count_train = 0
    for sub in train_subject:
        
        img_t1 = nib.load(t1_filepaths[sub])
        t1_data = img_t1.get_data() 
        train_t1[count_train,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
        train_t1_scaled[count_train,:] = normalized_3d(train_t1[count_train,:])
        
        img_fa = nib.load(fa_filepaths[sub])
        fa_data = img_fa.get_data() 
        train_fa[count_train,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
        train_fa_scaled[count_train,:] = normalized_3d(train_fa[count_train,:])    
        
        img_md = nib.load(md_filepaths[sub])
        md_data = img_md.get_data() 
        train_md[count_train,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
        train_md_scaled[count_train,:] = normalized_3d(train_md[count_train,:])    
        
        count_train = count_train+1
    
    # ==============================Test========================================= #
    
    test_subject = np.array([1, 2, 6, 9, 12, 14, 15, 18, 19, 21, 23, 24])-1 
    
    test_t1 = np.zeros((test_subject.shape[0],156,198,144), dtype = np.float32)
    test_t1_scaled = test_t1
    
    test_fa = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
    test_fa_scaled = test_fa
    
    test_md = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
    test_md_scaled = test_md
    
    count_test = 0
    for sub in test_subject:
        
        img_t1 = nib.load(t1_filepaths[sub])
        t1_data = img_t1.get_data() 
        test_t1[count_test,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
        test_t1_scaled[count_test,:] = normalized_3d(test_t1[count_test,:] )
        img_fa = nib.load(fa_filepaths[sub])
        fa_data = img_fa.get_data() 
        test_fa[count_test,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
        test_fa_scaled[count_test,:] = normalized_3d(test_fa[count_test,:])
        img_md = nib.load(md_filepaths[sub])
        md_data = img_md.get_data() 
        test_md[count_test,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
        test_md_scaled[count_test,:] = normalized_3d(test_md[count_test,:])
        count_test = count_test+1
    
    print train_t1_scaled.shape, train_fa_scaled.shape, train_md_scaled.shape
    print test_t1_scaled.shape, test_fa_scaled.shape, test_md_scaled.shape
    
    if not os.path.exists(os.path.join(os.getcwd(), 'numpy_data')):
        os.makedirs(os.path.join(os.getcwd(), 'numpy_data'))
    #np.save('X_T1_train.npy', train_t1)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_train.npy'), train_t1_scaled)
    #np.save('X_FA_train.npy', train_fa)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_train.npy'), train_fa_scaled)
    #np.save('X_MD_train.npy', train_md)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_train.npy'), train_md_scaled)
    np.save(os.path.join(os.getcwd(),'numpy_data','y_train.npy'), train_label)
    
    #np.save('X_T1_test.npy', test_t1)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_test.npy'), test_t1_scaled)
    #np.save('X_FA_test.npy', test_fa)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_test.npy'), test_fa_scaled)
    #np.save('X_MD_test.npy', test_md)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_test.npy'), test_md_scaled)

def main():
    createDatasetFeatureRepresentation()
    
if __name__ == "__main__":
    main()
# -*- coding: utf-8 -*-
"""
Created on Mon Aug  1 14:44:28 2016

@author: poyu

Data Pre-processing for Feature Representation
"""
import os
import nibabel as nib
import numpy as np

#def load_dataset():

def normalized_3d(inputs):
    inputs=np.copy(inputs)
    x_dim, y_dim, z_dim = inputs.shape    
    number_non_zero = np.float32(np.count_nonzero(inputs))
    sum_inputs = np.float32(inputs.sum())
    mean_inputs = sum_inputs/number_non_zero
    input_nonzero = inputs[np.nonzero(inputs)]
    std_input = np.std(input_nonzero)
    for i in range(x_dim):
        for j in range(y_dim):
            for k in range(z_dim):
                if inputs[i,j,k] != 0 :
                    inputs[i,j,k] = (inputs[i,j,k]-mean_inputs)/std_input
    return inputs
    
def createDatasetFeatureRepresentation():
    
        
    boundary_t1 = [11, 167, 9, 207, 9, 153]
    
    t1_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "MR_T1" in name and name.endswith("nii")]
    
    t1_filepaths.sort()
    
    boundary_fa = [6, 84, 5, 104, 8, 80]
    
    fa_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_FA" in name and name.endswith("nii")]
    
    fa_filepaths.sort()  
      
    boundary_md = [6, 84, 5, 104, 8, 80]
    
    md_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_MD" in name and name.endswith("nii")]
    
    md_filepaths.sort()
    
    # =============================Train========================================= #
    
    train_subject = np.array([5, 11, 17, 22, 27, 3, 10, 16, 20, 26, 4, 7, 8, 13, 25])-1
    
    train_label = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
    
    train_t1 = np.zeros((train_subject.shape[0],156,198,144), dtype = np.float32)
    train_t1_scaled = train_t1
    
    train_fa = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
    train_fa_scaled = train_fa
    
    train_md = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
    train_md_scaled = train_md
    
    count_train = 0
    for sub in train_subject:
        
        img_t1 = nib.load(t1_filepaths[sub])
        t1_data = img_t1.get_data() 
        train_t1[count_train,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
        train_t1_scaled[count_train,:] = normalized_3d(train_t1[count_train,:])
        
        img_fa = nib.load(fa_filepaths[sub])
        fa_data = img_fa.get_data() 
        train_fa[count_train,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
        train_fa_scaled[count_train,:] = normalized_3d(train_fa[count_train,:])    
        
        img_md = nib.load(md_filepaths[sub])
        md_data = img_md.get_data() 
        train_md[count_train,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
        train_md_scaled[count_train,:] = normalized_3d(train_md[count_train,:])    
        
        count_train = count_train+1
    
    # ==============================Test========================================= #
    
    test_subject = np.array([1, 2, 6, 9, 12, 14, 15, 18, 19, 21, 23, 24])-1 
    
    test_t1 = np.zeros((test_subject.shape[0],156,198,144), dtype = np.float32)
    test_t1_scaled = test_t1
    
    test_fa = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
    test_fa_scaled = test_fa
    
    test_md = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
    test_md_scaled = test_md
    
    count_test = 0
    for sub in test_subject:
        
        img_t1 = nib.load(t1_filepaths[sub])
        t1_data = img_t1.get_data() 
        test_t1[count_test,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
        test_t1_scaled[count_test,:] = normalized_3d(test_t1[count_test,:] )
        img_fa = nib.load(fa_filepaths[sub])
        fa_data = img_fa.get_data() 
        test_fa[count_test,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
        test_fa_scaled[count_test,:] = normalized_3d(test_fa[count_test,:])
        img_md = nib.load(md_filepaths[sub])
        md_data = img_md.get_data() 
        test_md[count_test,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
        test_md_scaled[count_test,:] = normalized_3d(test_md[count_test,:])
        count_test = count_test+1
    
    print train_t1_scaled.shape, train_fa_scaled.shape, train_md_scaled.shape
    print test_t1_scaled.shape, test_fa_scaled.shape, test_md_scaled.shape
    
    if not os.path.exists(os.path.join(os.getcwd(), 'numpy_data')):
        os.makedirs(os.path.join(os.getcwd(), 'numpy_data'))
    #np.save('X_T1_train.npy', train_t1)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_train.npy'), train_t1_scaled)
    #np.save('X_FA_train.npy', train_fa)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_train.npy'), train_fa_scaled)
    #np.save('X_MD_train.npy', train_md)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_train.npy'), train_md_scaled)
    np.save(os.path.join(os.getcwd(),'numpy_data','y_train.npy'), train_label)
    
    #np.save('X_T1_test.npy', test_t1)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_test.npy'), test_t1_scaled)
    #np.save('X_FA_test.npy', test_fa)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_test.npy'), test_fa_scaled)
    #np.save('X_MD_test.npy', test_md)
    np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_test.npy'), test_md_scaled)

def main():
    createDatasetFeatureRepresentation()
    
if __name__ == "__main__":
    main()
# -*- coding: utf-8 -*- Created on Mon Aug  1 14:44:28 2016  @author: poyu  Data Pre-processing for Feature Representation #def load_dataset(): # =============================Train========================================= # # ==============================Test========================================= # #np.save('X_T1_train.npy', train_t1) #np.save('X_FA_train.npy', train_fa) #np.save('X_MD_train.npy', train_md) #np.save('X_T1_test.npy', test_t1) #np.save('X_FA_test.npy', test_fa) #np.save('X_MD_test.npy', test_md)