summaryListFile = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Excel_Sheets\Test_Comparison_2.xlsx' # Set Background and Normalization Mask Paths background_path = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Anatomical_Masks\Background_Mask\Bkg_No_Fish.tif' normalizer_path = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Anatomical_Masks\Background_Mask\Background_C_Fos_Brain_Area.tif' #--------------------------------------------------------------------------- #--------------------------------------------------------------------------- # Read summary list cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist( summaryListFile, normalized=False) num_files = len(cfos_paths) # Load masks normalizer_stack = SZCFOS.load_mask(normalizer_path, transpose=True) num_normalizer_voxels = np.sum(np.sum(np.sum(normalizer_stack))) background_stack = SZCFOS.load_mask(background_path, transpose=True) num_background_voxels = np.sum(np.sum(np.sum(background_stack))) # ------------------------------------------------------------------ # Normalization # ------------------------------------------------------------------ # Measure cFOS in Mask (normalize to "background") background_values = np.zeros(num_files) normalizer_values = np.zeros(num_files) for i in range(num_files):
summaryListFile = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Excel_Sheets\Test.xlsx' # Set Mask Path mask_path = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Anatomical_Masks\Diencephalon_Area_1_Caudal_Hypothalamus.tif' #--------------------------------------------------------------------------- #--------------------------------------------------------------------------- # Read summary list cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist( summaryListFile, normalized=True) behaviour_metrics = np.array(behaviour_metrics) num_files = len(cfos_paths) # Load masks mask_stack = SZCFOS.load_mask(mask_path, transpose=True) num_mask_voxels = np.sum(np.sum(np.sum(mask_stack))) # ------------------------------------------------------------------ # cFos Analysis # ------------------------------------------------------------------ # Measure (normalized) cFOS in Mask ROI cFos_values = np.zeros(num_files) for i in range(num_files): # Load original (warped) cFos stack cfos_data, cfos_affine, cfos_header = SZCFOS.load_nii(cfos_paths[i], normalized=True) # Measure average signal level in mask ROI
# Set Background Path #background_path = base_path + r'\Masks\Diencephalon_Area_10_DIL.tif' #background_path = base_path + r'\Masks\Diencephalon_Area_8.tif' background_path = base_path + r'\Masks\Tectum.labels.tif' #--------------------------------------------------------------------------- #--------------------------------------------------------------------------- # Read folder list folder_names, test_ROIs, stim_ROIs, cfos_names, NS_names, S_names, fish_numbers = SZCFOS.read_folderlist( base_path, folderListFile) num_folders = len(folder_names) # Load background mask background_stack = SZCFOS.load_mask(background_path) num_background_voxels = np.sum(np.sum(np.sum(background_stack))) # ------------------------------------------------------------------ # Start Analysis # Analyze Behaviour for each folder (BPS and SPI for now) group_values = np.zeros(num_folders) bps_values = np.zeros(num_folders) spi_values = np.zeros(num_folders) vpi_values = np.zeros(num_folders) dist_values = np.zeros(num_folders) for i in range(num_folders): # Retrieve group ID # - From folder list name
cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist( summaryListFile, normalized=True) cfos_paths = np.array(cfos_paths) behaviour_metrics = np.array(behaviour_metrics) # Assign metrics/paths for each group group_correct_id = (behaviour_metrics[:, 0] == group) group_metric_in_range = (behaviour_metrics[:, 2] > VPI_min) * (behaviour_metrics[:, 2] <= VPI_max) group_indices = np.where(group_correct_id * group_metric_in_range)[0].astype( np.uint) cfos_paths = cfos_paths[group_indices] n = len(group_indices) # Load ROI mask roi_stack = SZCFOS.load_mask(roi_path, transpose=True) num_roi_voxels = np.sum(np.sum(np.sum(roi_stack))) # ------------------------------------------------------------------ # cFos Analysis # ------------------------------------------------------------------ # Measure (normalized) cFOS in Mask ROI cFos_values = np.zeros(n) for i in range(n): # Load original (warped) cFos stack cfos_data, cfos_affine, cfos_header = SZCFOS.load_nii(cfos_paths[i], normalized=True) # Measure average signal level in mask ROI