# Set Groups to include
group = 2
# Spatial smoothing factor
smooth_factor = 4
# Set analysis path
analysis_folder = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Analysis\G2_VPI'
#analysis_folder = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Comparison'
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Read summary list
cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist(
summaryListFile, normalized=True, change_base_path=True)
cfos_paths = np.array(cfos_paths)
behaviour_metrics = np.array(behaviour_metrics)
# Assign metrics/paths for group
group_indices = np.where(behaviour_metrics[:, 0] == group)[0].astype(np.uint)
cfos_paths = cfos_paths[group_indices]
n = len(cfos_paths)
# ------------------------------------------------------------------
# Measure voxel-wise correlations
# ------------------------------------------------------------------
# Extract relevant metric (explanatory variable)
metric = behaviour_metrics[:, selected_metric]
VPI_max_A = 1.1 VPI_min_B = -1.1 VPI_max_B = 1.1 # Spatial smoothing factor smooth_factor = 4; # Set analysis path analysis_folder = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Analysis\G2vG4' #analysis_folder = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Comparison' #--------------------------------------------------------------------------- #--------------------------------------------------------------------------- # Read summary list cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist(summaryListFile, normalized=True, change_base_path=True) cfos_paths = np.array(cfos_paths) behaviour_metrics = np.array(behaviour_metrics) # Assign metrics/paths for each group group_A_correct_id = (behaviour_metrics[:,0] == group_A) group_B_correct_id = (behaviour_metrics[:,0] == group_B) group_A_metric_in_range = (behaviour_metrics[:,2] > VPI_min_A) * (behaviour_metrics[:,2] <= VPI_max_A) group_B_metric_in_range = (behaviour_metrics[:,2] > VPI_min_B) * (behaviour_metrics[:,2] <= VPI_max_B) group_A_indices = np.where(group_A_correct_id * group_A_metric_in_range)[0].astype(np.uint) group_B_indices = np.where(group_B_correct_id * group_B_metric_in_range)[0].astype(np.uint) cfos_paths_A = cfos_paths[group_A_indices] cfos_paths_B = cfos_paths[group_B_indices]
# Set Folder List
#folderListFile = base_path + r'\Folder_list\wt_list.txt'
folderListFile = base_path + r'\Folder_list\isolation.txt'
#folderListFile = base_path + r'\Folder_list\No_SC.txt'
# 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)
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Set Summary List
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)
# 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\BRAIN_DAPI_MASK_FINAL2.nii'
mask_path = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\C-Fos_Groups\BRAIN_DAPI_MASK_FINAL_ARK.nii'
# Use the normalized stacks?
normalized = True
# Set analysis path
analysis_folder = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Analysis\PCA'
#analysis_folder = r'\\128.40.155.187\data\D R E O S T I L A B\Isolation_Experiments\Social_Brain_Areas_Analisys\Comparison'
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Load mask
mask_data, mask_affine, mask_header = SZCFOS.load_nii(mask_path,
normalized=True)
mask_data = mask_data[:, :, :, 0]
mask_planes = mask_data[:, :, analysis_planes]
num_mask_voxels = np.sum(np.sum(mask_planes))
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Read summary list
cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist(
summaryListFile, normalized=True, change_base_path=True)
cfos_paths = np.array(cfos_paths)
behaviour_metrics = np.array(behaviour_metrics)
n = len(cfos_paths)
# Extract z-planes from stacks and pre-process for PCA
import SZ_analysis as SZA
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Set Summary List
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):
import SZ_cfos as SZCFOS
import SZ_summary as SZS
import SZ_analysis as SZA
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Set Summary List
#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'
summaryListFile = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Test_all_ARK.xlsx'
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Read summary list
cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist(
summaryListFile, normalized=False, change_base_path=True)
num_files = len(cfos_paths)
# ------------------------------------------------------------------
# Normalization
# ------------------------------------------------------------------
# Subtract histogram offset and scale (divide) by mode
for i in range(num_files):
# Read histogram npz
histogram_file = os.path.dirname(cfos_paths[i]) + r'\voxel_histogram.npz'
npzfile = np.load(histogram_file)
histogram = npzfile['histogram']
bin_centers = npzfile['bin_centers']
offset = npzfile['offset']
# Set Input stack #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_1.xlsx' stackFolder = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Analysis\G1vG2spp' stackFile = stackFolder + r'\Diff_Stack.nii.gz' # 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\BRAIN_DAPI_MASK_FINAL2.nii' mask_path = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\C-Fos_Groups\BRAIN_DAPI_MASK_FINAL_ARK.nii' mask_slice_range_start = 0 mask_slice_range_stop = 251 #--------------------------------------------------------------------------- #--------------------------------------------------------------------------- # Load mask mask_data, mask_affine, mask_header = SZCFOS.load_nii(mask_path, normalized=True) mask_data = mask_data[:,:,:,0] mask_data[:,:,:mask_slice_range_start] = 0 mask_data[:,:,mask_slice_range_stop:] = 0 num_mask_voxels = np.sum(np.sum(np.sum(mask_data))) # Load stack cfos_data, cfos_affine, cfos_header = SZCFOS.load_nii(stackFile, normalized=True) masked_values = cfos_data[mask_data == 1] n_stack_rows = np.size(cfos_data, 0) n_stack_cols = np.size(cfos_data, 1) n_stack_slices = np.size(cfos_data, 2) display_data = np.zeros((n_stack_rows, n_stack_cols, n_stack_slices), dtype = np.float32) # Compute stats min_val = np.min(cfos_data[:])
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Set Summary List
#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'
summaryListFile = r'C:\Users\adamk\Dropbox\Adam_Ele\Last_Hope\Test_all_ARK.xlsx'
# Use the normalized stacks?
normalized = True
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Read summary list
cfos_paths, behaviour_metrics, metric_labels = SZCFOS.read_summarylist(summaryListFile, normalized=True, change_base_path=True)
num_files = len(cfos_paths)
# ------------------------------------------------------------------
# Load Histograms
# ------------------------------------------------------------------
plt.figure()
start = 0
#stop = 10
stop = num_files
for i in range(start, stop, 1):
# Read histogram npz
if(normalized):
histogram_file = os.path.dirname(cfos_paths[i]) + r'\voxel_histogram_normalized.npz'