added code for calculating the linear regression weights

utils/data_utils.py

@@ -119,6 +119,10 @@ def data_test_train_validation_split(data_folder_name, test_percentage, subject_
     else:
         subDirectoryList, _ = directory_reader(data_directory, subject_number)
 
+    ###
+    ### For subject in the directory list, calculate the wd-wf pairs byh calling function
+    ###
+
     subDirectoryList = np.array(subDirectoryList)
     create_folder(data_folder_name)
 
@@ -609,4 +613,58 @@ def load_and_preprocess_targets(target_path, mean_mask_path):
     target = Image(target_path[0]).data[:,:,:,0]
     target_demeaned = np.subtract(target, Image(mean_mask_path).data[:,:,:,0])
 
-    return target, target_demeaned
\ No newline at end of file
+    return target, target_demeaned
+
+def regression_weight_calculator(data_directory, subject):
+    """ Calculator for linear regression weights
+
+    This function cals the calculator for the weights required for peforming linear regression
+
+    Args:
+        data_directory (str): A string containing the address of the required directory.
+        subject (str): Path to the relevant subject's data file
+
+    Returns:
+        w_dMRI (float): Linear regression weight for dMRI data
+        w_rsfMRI (flat): Linear regression weight for rsfMRI data
+    """
+
+    w_dMRI = weight_calculator(data_directory, subject, data_type='dmri')
+    w_rsfMRI = weight_calculator(data_directory, subject, data_type='fmri')
+
+    return w_dMRI, w_rsfMRI
+
+def weight_calculator(data_directory, subject, data_type):
+    """ Calculator for linear regression weights
+
+    This function calcualtes the weights required for peforming linear regression
+
+    Args:
+        data_directory (str): A string containing the address of the required directory.
+        subject (str): Path to the relevant subject's data file
+        data_type (str): Flag indicating the data type
+
+    Returns:
+        weigth (float): Linear regressiong weight. 
+    """
+
+    if data_type == 'dmri':
+        mean_path = 'utils/mean_tractsNormSummed_downsampled.nii.gz'
+        data_path = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
+        mean_volume = Image(mean_path).data
+        subject_path = os.path.join(os.path.expanduser("~"), data_directory, subject, data_path)
+        subject_volume, _ = resampleToPixdims(Image(dMRI_subject_path), (2,2,2))
+    elif data_type = 'fmri':
+        mean_path = 'utils/mean_dr_stage2.nii.gz'
+        data_path = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
+        mean_volume = Image(mean_path).data[:,:,:,0]
+        subject_path = os.path.join(os.path.expanduser("~"), data_directory, subject, data_path)
+        subject_volume = Image(rsfMRI_subject_path).data[:,:,:,0]
+
+    x = np.reshape(mean_volume, -1)
+    y = np.reshape(subject_volume, -1)
+    x_matrix = np.vstack((np.ones(len(x)), x)).T
+    beta_hat = np.linalg.inv(x_matrix.T.dot(x_matrix)).dot(x_matrix.T).dot(y)
+    w = beta_hat[1]
+
+    return w
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