def Partial_Average_From_File(data_folder,
start_frame,
stop_frame,
graph_type='.tif',
LP_Para=False,
HP_Para=False,
filter_method=False):
'''
Average specific part of graphs in the folder.
Parameters
----------
data_folder : (str)
Data folder.
start_frame : (int)
Start ID of frame selection.
stop_frame : (int)
Stop ID of frame selection.
graph_type : (str), optional
Frame dtype. The default is '.tif'.
LP_Para\HP_Para\filter_method : optional
Filter parameters. The default is False.
Returns
-------
Averaged_Graph : (2D Array)
Averaged graphs.
'''
all_tif_name = np.array(
OS_Tools.Get_File_Name(data_folder, file_type=graph_type))
used_tif_name = all_tif_name[start_frame:stop_frame]
Averaged_Graph = Graph_Tools.Average_From_File(used_tif_name, LP_Para,
HP_Para, filter_method)
return Averaged_Graph
def After_Align_Average(self):
"""
This Functin will generate after align average graph of Run and Global, and then save them.
Returns
-------
None.
"""
print('Aligning done. ')
self.After_Align_Graphs = {} # Initialize a dictionary, will record all aligned graphs averages and graph nums.
# Fill After Align Graph Dictionary first
total_graph_num = 0
for i in range(len(self.Aligned_frame_folders)):
current_run_names = OS_Tools.Get_File_Name(self.Aligned_frame_folders[i])
temp_average = Graph_Tools.Average_From_File(current_run_names) # This will generate an average graph with 'f8' formation.
current_graph_aligned = Graph_Tools.Clip_And_Normalize(temp_average,clip_std = 5)
Graph_Tools.Show_Graph(current_graph_aligned, 'Run_Average_After_Align', self.all_save_folders[i])
current_run_Frame_Num = len(current_run_names)
total_graph_num += current_run_Frame_Num
self.After_Align_Graphs[i] = (current_graph_aligned,current_run_Frame_Num)
global_average_after_align = np.zeros(np.shape(current_graph_aligned),dtype = 'f8')
# Then calculate global average in each run.
for i in range(len(self.all_save_folders)):
global_average_after_align += self.After_Align_Graphs[i][0].astype('f8')*self.After_Align_Graphs[i][1]/total_graph_num
global_average_after_align = Graph_Tools.Clip_And_Normalize(global_average_after_align,clip_std = 5)
# Then save global graph into each folder.
for i in range(len(self.all_save_folders)):
if i == 0:
Graph_Tools.Show_Graph(global_average_after_align, 'Global_Average_After_Align', self.all_save_folders[i])
else:
Graph_Tools.Show_Graph(global_average_after_align, 'Global_Average_After_Align', self.all_save_folders[i],show_time = 0)
def Least_Tremble_Average_Graph(data_folder,
average_prop=0.1,
cut_shape=(9, 9)):
all_tif_name = np.array(OS_Tools.Get_File_Name(data_folder))
_, frac_disps = Tremble_Evaluator(data_folder, cut_shape=cut_shape)
frac_num, frame_num, _ = frac_disps.shape
# Then calculate average center and least error graph.
frac_centers = np.zeros(shape=(frac_num, 2), dtype='f8')
for i in range(frac_num):
frac_centers[i, 0] = frac_disps[i, :, 0].mean()
frac_centers[i, 1] = frac_disps[i, :, 1].mean()
# And all frac_total movings
total_movings = np.zeros(frame_num, dtype='f8')
for i in range(frame_num):
c_dist = 0
for j in range(frac_num):
c_dist += (frac_centers[j][0] - frac_disps[j, i, 0])**2 + (
frac_centers[j][1] - frac_disps[j, i, 1])**2
total_movings[i] = c_dist
# Then find least props.
used_num = int(frame_num * average_prop)
if used_num < 300: # least num of average is set to 300 to avoid problem.
used_num = min(300, frame_num)
print('Average of most stable ' + str(used_num) + ' Frames.')
if used_num < 300: # meaning all frame used
graph_names = all_tif_name
else:
used_frame_ind = np.argpartition(total_movings, used_num)[0:used_num]
graph_names = all_tif_name[used_frame_ind]
averaged_graph = Graph_Tools.Average_From_File(graph_names)
return averaged_graph, graph_names
def Before_Run_Average(self):
"""
Generate global and per run average graph.
This part is automatic,output averaged graph in folder, return nothing.
Returns
-------
None.
"""
print('Averaging before align...')
#Get Run Average First
self.Before_Align_Dics = {}# Define a dictionary to save before align graphs.
total_graph_num = 0 # counter of graph numbers
for i in range(len(self.Before_Align_Tif_Name)):
run_graph_num = len(self.Before_Align_Tif_Name[i])# How many graphs in this run
total_graph_num += run_graph_num
current_run_average = Graph_Tools.Average_From_File(self.Before_Align_Tif_Name[i])
current_run_average = Graph_Tools.Clip_And_Normalize(current_run_average,clip_std = 5)
self.Before_Align_Dics[i] = (current_run_average,run_graph_num) # Write Current dict as a Tuple.
Graph_Tools.Show_Graph(current_run_average, 'Run_Average',self.all_save_folders[i])# Show and save Run Average.
# Then Use Weighted average method to generate global tif. This methos can be faster than original ones.
global_average_graph = np.zeros(shape = np.shape(self.Before_Align_Dics[0][0]),dtype = 'f8')# Base on shape of graph
for i in range(len(self.Before_Align_Tif_Name)):
global_average_graph += self.Before_Align_Dics[i][0].astype('f8')*self.Before_Align_Dics[i][1]/total_graph_num
global_average_graph = Graph_Tools.Clip_And_Normalize(global_average_graph,clip_std = 5)
# At last, save global average graph in every run folders.
for i in range(len(self.all_save_folders)):
Graph_Tools.Show_Graph(global_average_graph, 'Global_Average', self.all_save_folders[i],show_time = 0)
self.Align_Base = global_average_graph # Define Base of Alignment, use this to do the job.
def Intensity_Selector(data_folder,
graph_type='.tif',
mode='biggest',
propotion=0.05,
list_write=True):
'''
Select frames have biggest or smallest a.i., and generate average graphs.
Parameters
----------
data_folder : (str)
Data folder.
graph_type : (str), optional
Data type of . The default is '.tif'.
mode : ('biggest' or 'smallest'), optional
Type of frame selection. The default is 'biggest'.
propotion : (float), optional
Propotion of graph selection. The default is 0.05.
list_write : (bool), optional
Whether we write down graph intensity data. The default is True.
Returns
-------
averaged_graph : (2D Array)
Averaged graph of selected frames.
selected_graph_name : (ND List)
List of selected graph names.
'''
all_graph_name = np.array(
OS_Tools.Get_File_Name(data_folder, file_type=graph_type))
graph_Num = len(all_graph_name)
bright_data = np.zeros(graph_Num, dtype='f8')
for i in range(graph_Num):
current_graph = cv2.imread(all_graph_name[i], -1)
bright_data[i] = np.mean(current_graph)
# write bright data if required.
if list_write == True:
OS_Tools.Save_Variable(data_folder, 'brightness_info', bright_data)
# Then select given mode frames.
used_graph_num = int(graph_Num * propotion)
if mode == 'biggest':
used_graph_id = np.argpartition(bright_data,
-used_graph_num)[-used_graph_num:]
elif mode == 'smallest':
used_graph_id = np.argpartition(bright_data,
used_graph_num)[0:used_graph_num]
selected_graph_name = all_graph_name[used_graph_id]
averaged_graph = Graph_Tools.Average_From_File(selected_graph_name)
return averaged_graph, selected_graph_name
def Tremble_Calculator_From_File(
data_folder,
graph_type='.tif',
cut_shape=(8, 8),
boulder=20,
base_method='former',
base=[],
):
'''
Calculate align tremble from graph. This program is used to evaluate align quality.
Parameters
----------
data_folder : (str)
Data folder of graphs.
graph_type : (str),optional
Extend name of input grahp. The default is '.tif'.
cut_shape : (turple), optional
Shape of fracture cut. Proper cut will . The default is (10,5).
boulder : (int),optional
Boulder of graph. Cut and not used in following calculation.The default is 20.
base_method : ('average'or'former'or'input'), optional
Method of bais calculation. The default is 'former'.
'average' bais use all average; 'former' bais use fomer frame; 'input' bais need to be given.
base : (2D_NdArray), optional
If move_method == 'input', base should be given here. The default is [].
Returns
-------
mass_center_maps(Graph)
A plotted graph, showing movement trace of mass center.
tremble_plots : (List)
List of all fracture graph tremble list.
tremble_information : (Dic)
Dictionary of tramble informations.
Data type of tremble_information:
'''
all_tif_name = OS_Tools.Get_File_Name(data_folder, file_type=graph_type)
average_graph = Graph_Tools.Average_From_File(all_tif_name)
tremble_information = {}
#1. Get base graph first.
if base_method == 'input':
base_graph = base
elif base_method == 'average':
base_graph = average_graph
elif base_method == 'former':
base_graph = cv2.imread(all_tif_name[0], -1) # First input graph.
else:
raise IOError('Invalid Base Method, check please.\n')
_,Frame_Stim_Dic = Stim_Frame_Align(stim_folder,frame_thres = frame_thres,jmp_step = jmp_step)
#%% Forth, generate Morpho graph and find cell.
cell_Dic = Cell_Find_And_Plot(save_folder, 'Run_Average_After_Align.tif', 'Morpho_Cell')
cell_mask = (cell_Dic['Cell_Graph'][:,:,0])>0
#%% Fifth, calculate RF reaction.
RF_Data = np.zeros(shape = (5,5,2),dtype = 'f8')# use 5*5 matrix, set 0 are frames, set 1 are cells
loc_ids = np.array([1,26,51,76,101,126,151,176,201,226,251,276])
for i in range(5):# i as vector1
for j in range(5):# j as vector2
start_id = i*5+j
current_keys = loc_ids+start_id
current_loc_frame_id = []
for k in range(len(current_keys)):
current_loc_frame_id.extend(Frame_Stim_Dic[current_keys[k]])
current_loc_graph_name = aligned_all_tif_name[current_loc_frame_id]
current_graph = Graph_Tools.Average_From_File(current_loc_graph_name)
all_cells = current_graph*cell_mask
RF_Data[i,j,0] = current_graph.mean()
RF_Data[i,j,1] = all_cells.mean()
# then sub average value.
frame_average = RF_Data[:,:,0].min()
cell_average = RF_Data[:,:,1].min()
prop_RF_Data = np.zeros(shape = (5,5,2),dtype = 'f8')
prop_RF_Data[:,:,0] = RF_Data[:,:,0]/frame_average -1
prop_RF_Data[:,:,1] = RF_Data[:,:,1]/cell_average -1
OS_Tools.Save_Variable(save_folder, 'Origin_RF_Data', RF_Data)
#%% Then do Spline interpolation here.
import pylab as pl
# Show graph first.
x = np.array([1,2,3,4,5])
y = np.array([1,2,3,4,5])
def Translation_Alignment(all_folders,
base_mode='global',
input_base=np.array([[0, 0], [0, 0]]),
align_range=20,
align_boulder=20,
before_average=True,
average_std=5,
big_memory_mode=False,
save_aligned_data=False,
graph_shape=(512, 512),
timer=True):
'''
This function will align all tif graphs in input folders. Only translation transaction here. Affine transformation need further discussion.
Parameters
----------
all_folders:(list)
List of all tif folders, elements are strs.
base_mode:('global',int,'input',optional. The default is 'global')
How to select base frame. 'global': use global average as base. int: use average of specific run as base. 'input':Manually input base graph, need to be a 2D-Ndarray.
input_base:(2D-Ndarray,optional. The default is none.)
If base_mode = 'input', input_base must be given. This will be the base for alignment.
align_range:(int,optional. The default is 20)
Max pixel of alignment.
align_boulder:(int,optional. The default is 20)
boulder cut for align. For different graph size, this variable shall be change.
before_average:(bool,optional. The default is True)
Whether before average is done. It can be set False to save time, on this case base graph shall be given.
average_std:(float,optional. The default is 5)
How much std you want for average graph generation. Different std can effect graph effect.
big_memory_mode:(bool,optional. The default is False)
If memory is big enough, use this mode is faster.
save_aligned_data:(bool,optional. The default is False)
Can be true only in big memory mode. This will save all aligned graph in a single 4D-Ndarray file.Save folder is the first folder.
graph_shape:(2-element-turple,optional. The default is (512,512))
Shape of graphs. All input graph must be in same shape.
timer:(bool,optional. The default is True)
Show runtime of function and each procedures.
Returns
-------
bool
Whether new folder is generated.
'''
time_tic_start = time.time()
#%% Step1, generate folders and file cycle.
all_save_folders = List_Op.List_Annex(all_folders, ['Results'])
Aligned_frame_folders = List_Op.List_Annex(all_save_folders,
['Aligned_Frames'])
for i in range(len(all_save_folders)):
OS_Tools.mkdir(all_save_folders[i])
OS_Tools.mkdir(Aligned_frame_folders[i])
Before_Align_Tif_Name = []
for i in range(len(all_folders)):
current_run_tif = OS_Tools.Get_File_Name(all_folders[i])
Before_Align_Tif_Name.append(current_run_tif)
#%% Step2, Generate average map before align.
if before_average == True:
print('Before run averaging ...')
Before_Align_Dics = {
} # This is the dictionary of all run averages. Keys are run id.
total_graph_num = 0 # Counter of graph numbers.
for i in range(len(Before_Align_Tif_Name)):
current_run_graph_num = len(Before_Align_Tif_Name[i])
total_graph_num += current_run_graph_num
current_run_average = Graph_Tools.Average_From_File(
Before_Align_Tif_Name[i])
current_run_average = Graph_Tools.Clip_And_Normalize(
current_run_average, clip_std=average_std)
Before_Align_Dics[i] = (
current_run_average, current_run_graph_num
) # Attention here, data recorded as turple.
Graph_Tools.Show_Graph(
current_run_average, 'Run_Average',
all_save_folders[i]) # Show and save Run Average.
# Then Use Weighted average method to generate global tif.
global_average_graph = np.zeros(shape=np.shape(
Before_Align_Dics[0][0]),
dtype='f8') # Base on shape of graph
for i in range(len(Before_Align_Tif_Name)):
global_average_graph += Before_Align_Dics[i][0].astype(
'f8') * Before_Align_Dics[i][1] / total_graph_num
global_average_graph = Graph_Tools.Clip_And_Normalize(
global_average_graph, clip_std=average_std)
# Then save global average in each run folder.
if len(all_folders) > 1:
for i in range(len(Before_Align_Tif_Name)):
Graph_Tools.Show_Graph(global_average_graph,
'Global_Average',
all_save_folders[i],
show_time=0)
else:
print('Only One run, no global average.')
else:
print('Before average Skipped.')
time_tic_average0 = time.time()
#%% Step3, Core Align Function.
print('Aligning...')
if base_mode == 'global':
base = global_average_graph
elif base_mode == 'input':
base = input_base
elif type(base_mode) == int:
base = Before_Align_Dics[base_mode][0]
else:
raise IOError('Invalid base mode.')
# In big memory mode, save aligned_data in a dictionary file.
if big_memory_mode == True:
All_Aligned_Frame = {}
for i in range(len(Before_Align_Tif_Name)):
All_Aligned_Frame[i] = np.zeros(
shape=(graph_shape + (len(Before_Align_Tif_Name[i]), )),
dtype='u2') # Generate empty graph matrix.
for i in range(len(Before_Align_Tif_Name)): # Cycle all runs
for j in range(len(
Before_Align_Tif_Name[i])): # Cycle all graphs in current run
current_graph = cv2.imread(Before_Align_Tif_Name[i][j],
-1) # Read in current graph.
_, _, current_aligned_graph = Alignment(base,
current_graph,
boulder=align_boulder,
align_range=align_range)
graph_name = Before_Align_Tif_Name[i][j].split(
'\\')[-1][:-4] # Ignore extend name'.tif'.
Graph_Tools.Show_Graph(current_aligned_graph,
graph_name,
Aligned_frame_folders[i],
show_time=0)
if big_memory_mode == True:
All_Aligned_Frame[i][:, :, j] = current_aligned_graph
print('Align Finished, generating average graphs...')
time_tic_align_finish = time.time()
#%% Step4, After Align Average
After_Align_Graphs = {}
if big_memory_mode == True: # Average can be faster.
temp_global_average_after_align = np.zeros(shape=graph_shape,
dtype='f8')
for i in range(len(All_Aligned_Frame)):
current_run_average = Graph_Tools.Clip_And_Normalize(
np.mean(All_Aligned_Frame[i], axis=2),
clip_std=average_std) # Average run graphs, in type 'u2'
After_Align_Graphs[i] = (current_run_average,
len(All_Aligned_Frame[i][0, 0, :]))
temp_global_average_after_align += After_Align_Graphs[i][0].astype(
'f8') * After_Align_Graphs[i][1] / total_graph_num
global_average_after_align = Graph_Tools.Clip_And_Normalize(
temp_global_average_after_align, clip_std=average_std)
else: # Traditional ways.
temp_global_average_after_align = np.zeros(shape=graph_shape,
dtype='f8')
for i in range(len(Aligned_frame_folders)):
current_run_names = OS_Tools.Get_File_Name(
Aligned_frame_folders[i])
current_run_average = Graph_Tools.Average_From_File(
current_run_names)
current_run_average = Graph_Tools.Clip_And_Normalize(
current_run_average, clip_std=average_std)
After_Align_Graphs[i] = (current_run_average,
len(current_run_names))
temp_global_average_after_align += After_Align_Graphs[i][0].astype(
'f8') * After_Align_Graphs[i][1] / total_graph_num
global_average_after_align = Graph_Tools.Clip_And_Normalize(
temp_global_average_after_align, clip_std=average_std)
# After average, save aligned graph in each save folder.
for i in range(len(all_save_folders)):
current_save_folder = all_save_folders[i]
Graph_Tools.Show_Graph(After_Align_Graphs[i][0],
'Run_Average_After_Align', current_save_folder)
if i == 0: # Show global average only once.
global_show_time = 5000
else:
global_show_time = 0
if len(all_folders) > 1:
Graph_Tools.Show_Graph(global_average_after_align,
'Global_Average_After_Align',
current_save_folder,
show_time=global_show_time)
time_tic_average1 = time.time()
#%% Step5, save and timer
if save_aligned_data == True:
OS_Tools.Save_Variable(all_save_folders[0], 'All_Aligned_Frame_Data',
All_Aligned_Frame)
if timer == True:
whole_time = time_tic_average1 - time_tic_start
before_average_time = time_tic_average0 - time_tic_start
align_time = time_tic_align_finish - time_tic_average0
after_average_time = time_tic_average1 - time_tic_align_finish
print('Total Time = ' + str(whole_time) + ' s.')
print('Before Average Time = ' + str(before_average_time) + ' s.')
print('Align Time = ' + str(align_time) + ' s.')
print('After Average Time = ' + str(after_average_time) + ' s.')
return True
def Tremble_Calculator_From_File(data_folder,
graph_type='.tif',
cut_shape=(10, 5),
boulder=20,
move_method='former',
base=[],
center_method='weight'):
'''
Calculate align tremble from graph. This program is used to evaluate align quality.
Parameters
----------
data_folder : (str)
Data folder of graphs.
graph_type : (str),optional
Extend name of input grahp. The default is '.tif'.
cut_shape : (turple), optional
Shape of fracture cut. Proper cut will . The default is (10,5).
boulder : (int),optional
Boulder of graph. Cut and not used in following calculation.The default is 20.
move_method : ('average'or'former'or'input'), optional
Method of bais calculation. The default is 'former'.
'average' bais use all average; 'former' bais use fomer frame; 'input' bais need to be given.
base : (2D_NdArray), optional
If move_method == 'input', base should be given here. The default is [].
center_method : ('weight' or 'binary'), optional
Method of center find. Whether we use weighted intense.The default is 'weight'.
Returns
-------
mass_center_maps(Graph)
A plotted graph, showing movement trace of mass center.
tremble_plots : (List)
List of all fracture graph tremble list.
tremble_information : (Dic)
Dictionary of tramble informations.
Data type of tremble_information:
keys:frame ID
data are lists, every element in list indicate a fracture grpah, ID in cut graph.
list elements are turples, each turple[0] are move vector, turple[1] as move distance.
'''
all_tif_name = OS_Tools.Get_File_Name(data_folder, graph_type)
average_graph = Graph_Tools.Average_From_File(all_tif_name)
tremble_information = {}
# get base of align first.
if move_method == 'average':
base_graph = average_graph
elif move_method == 'input':
base_graph = base
elif move_method == 'former':
base_graph = cv2.imread(all_tif_name[0],
-1) # Use first frame as base.
# cycle all graph to generate tremble plots.
for i in range(len(all_tif_name)):
# Process input graph, get cell
current_graph = cv2.imread(all_tif_name[i], -1)
processed_cell_graph = None
#Cut Graph as described
_, _, _, cutted_current_graph = Graph_Cutter(processed_cell_graph,
boulder, cut_shape)
_, _, _, cutted_base = Graph_Cutter(base_graph, boulder, cut_shape)
# Renew base if former mode.
if move_method == 'former':
base_graph = cv2.imread(all_tif_name[i], -1)
# Then cycle all cutted_fracture, to calculate movement of every fracture graph.
current_frame_move_list = []
for j in range(len(cutted_current_graph)):
temp_graph_part = cutted_current_graph[j]
temp_base_part = cutted_base[j]
temp_graph_center, _ = Graph_Tools.Graph_Center_Calculator(
temp_graph_part, center_mode=center_method)
temp_base_center, _ = Graph_Tools.Graph_Center_Calculator(
temp_base_part, center_mode=center_method)
temp_tremble_vector, temp_tremble_dist = Calculator.Vector_Calculate(
temp_base_center, temp_graph_center)
current_frame_move_list.append(
(temp_tremble_vector, temp_tremble_dist))
tremble_information[i] = current_frame_move_list
# Then, plot mass center plots. This will show change of mass center position.
if move_method == 'input':
print('No Mass Center plot Generated.')
mass_center_maps = False
elif move_method == 'average': # If average, use current location
mass_center_maps = []
for i in range(len(tremble_information[0])): # Cycle all fracture
fig = plt.figure()
ax = plt.subplot()
for j in range(len(tremble_information)): # Cycle all frame
current_point = tremble_information[j][i][0]
ax.scatter(current_point[1], current_point[0], alpha=1, s=5)
mass_center_maps.append(fig)
plt.close()
elif move_method == 'former':
mass_center_maps = []
for i in range(len(tremble_information[0])): # Cycle all fracture
fig = plt.figure()
ax = plt.subplot()
current_point = (0, 0)
for j in range(len(tremble_information)): # Cycle all frame
current_point = (current_point[0] +
tremble_information[j][i][0][0],
current_point[1] +
tremble_information[j][i][0][1])
ax.scatter(current_point[1], current_point[0], alpha=1, s=5)
mass_center_maps.append(fig)
plt.close()
# At last, plot tremble dist plots. Each fracture have a plot.
tremble_plots = {}
for i in range(len(tremble_information[0])): # Cycle all fractures
current_tremble_plot = []
for j in range(len(tremble_information)): # Cycle all frame
current_dist = tremble_information[j][i][1]
current_tremble_plot.append(current_dist)
tremble_plots[i] = np.asarray(current_tremble_plot)
return mass_center_maps, tremble_plots, tremble_information
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 27 13:41:07 2020
@author: ZR
Codes to process L76 Data
"""
import My_Wheels.Graph_Operation_Kit as Graph_Tools
import My_Wheels.OS_Tools_Kit as OS_Tools
#%% Cell1, Average Graph.
graph_folder = r'I:\Test_Data\201023_L76_LM\1-003'
save_path = graph_folder + r'\Results'
OS_Tools.mkdir(save_path)
all_tif_name = OS_Tools.Get_File_Name(graph_folder)
average_graph = Graph_Tools.Average_From_File(all_tif_name)
norm_average_graph = Graph_Tools.Clip_And_Normalize(average_graph, clip_std=3)
Graph_Tools.Show_Graph(norm_average_graph, 'Average_Graph', save_path)
#%% Then Calculate Runs
graph_folder = r'I:\Test_Data\201023_L76_LM\1-013'
import My_Wheels.Translation_Align_Function as Align
Align.Translation_Alignment([graph_folder])
#%% Align Stim and Frame
import My_Wheels.Stim_Frame_Align as Stim_Frame_Align
stim_folder = r'I:\Test_Data\201023_L76_LM\201023_L76_LM_Stimulus\Run13_RGLum4'
Frame_Stim_Sequence, Frame_Stim_Dictionary = Stim_Frame_Align.Stim_Frame_Align(
stim_folder)
aligned_tif_name = OS_Tools.Get_File_Name(
r'I:\Test_Data\201023_L76_LM\1-013\Results\Aligned_Frames')
#%% Generate On-Off Map
on_id = []
def Single_Subgraph_Generator(all_tif_name,
A_IDs,
B_IDs,
filter_method='Gaussian',
LP_Para=((5, 5), 1.5),
HP_Para=False,
t_map=True,
t_sig=1):
'''
Generate single subtraction map of 2P data. A-B graph is generated.
Parameters
----------
all_tif_name : (list or nparray)
All graph name. Usually aligned tif name.
A_IDs : (list)
List of A ID.
B_IDs : (list)
List of B ID.
filter_method : (str), optional
Can be set False to skip. Filter used before and after subtraction. The default is 'Gaussian'.
LP_Para: (turple),optional
Can be set False to skip. Low pass filter parameter. The default is ((5,5),1.5).
HP_Para: (turple),optional
Can be set False to skip. High pass filter parameter. The default is False.
t_map : (bool), optional
Whether t map is generated. The default is True.
t_sig:(0~1),optional.
Threshold of significant t map. The default is 1.
Returns
-------
sub_graph : (2D array)
Subtraction dF/F graph. Origin data, clip and normalize shall be done before plot.
t_graph : (2D array)
T test graph.0-1 value normalized array. If t_map == False, this will be None.
F_info_Dics : (Dic)
Information dictionary. Including origin F & dF/F information of input graph.
'''
warnings.filterwarnings('ignore')
F_info_Dics = {}
all_tif_name = np.array(all_tif_name) # Change into nparray to slice.
A_Set_Graph_names = all_tif_name[A_IDs]
B_Set_Graph_names = all_tif_name[B_IDs]
# Calculate sub graph.
F_info_Dics['Graph_Shape'] = np.shape(cv2.imread(all_tif_name[0], -1))
F_info_Dics['Origin_Data_Type'] = str((cv2.imread(all_tif_name[0],
-1)).dtype)
F_info_Dics['Average_A_Graph'] = Graph_Tools.Average_From_File(
A_Set_Graph_names, LP_Para, HP_Para, filter_method)
F_info_Dics['Average_B_Graph'] = Graph_Tools.Average_From_File(
B_Set_Graph_names, LP_Para, HP_Para, filter_method)
F_info_Dics['dF_Map'] = (F_info_Dics['Average_A_Graph'].astype('f8') -
F_info_Dics['Average_B_Graph'].astype('f8'))
F_info_Dics['Average_dF_value'] = abs(
F_info_Dics['dF_Map']).mean() # Average dF value.
F_info_Dics['Average_dF/F_value'] = F_info_Dics['Average_dF_value'] / (
F_info_Dics['Average_B_Graph'].mean())
F_info_Dics['dF/F_Graph'] = np.nan_to_num(
F_info_Dics['dF_Map'] / F_info_Dics['Average_B_Graph'].astype('f8'))
sub_graph = F_info_Dics['dF_Map']
# Then calculate F value graph.
if t_map == False:
F_info_Dics['t_value_map'] = None
F_info_Dics['p_value_map'] = None
t_graph = None
else:
import random
sample_size = min(len(A_Set_Graph_names), len(B_Set_Graph_names))
selected_A_name = np.array(
random.sample(list(A_Set_Graph_names), sample_size))
selected_B_name = np.array(
random.sample(list(B_Set_Graph_names), sample_size))
A_graph_arrays = np.zeros(shape=(F_info_Dics['Graph_Shape'] +
(sample_size, )),
dtype='f8')
B_graph_arrays = np.zeros(shape=(F_info_Dics['Graph_Shape'] +
(sample_size, )),
dtype='f8')
# Then we will fill filtered data into graph.
# First, we will read in AB graphs together.
for i in range(sample_size):
current_a_graph = cv2.imread(selected_A_name[i], -1)
current_b_graph = cv2.imread(selected_B_name[i], -1)
if filter_method != False:
A_graph_arrays[:, :, i] = My_Filter.Filter_2D(
current_a_graph, LP_Para, HP_Para, filter_method)
B_graph_arrays[:, :, i] = My_Filter.Filter_2D(
current_b_graph, LP_Para, HP_Para, filter_method)
# After that, we calculate t and p value pix by pix.
t_value_graph = np.zeros(shape=F_info_Dics['Graph_Shape'], dtype='f8')
p_value_graph = np.zeros(shape=F_info_Dics['Graph_Shape'], dtype='f8')
from scipy.stats import ttest_rel
for i in range(F_info_Dics['Graph_Shape'][0]):
for j in range(F_info_Dics['Graph_Shape'][1]):
t_value_graph[i, j], p_value_graph[i, j] = ttest_rel(
A_graph_arrays[i, j, :], B_graph_arrays[i, j, :])
# avoid nan
t_graph_origin = np.nan_to_num(t_value_graph)
p_value_graph = np.nan_to_num(p_value_graph)
F_info_Dics['t_graph_origin'] = t_graph_origin
F_info_Dics['p_value_of_t_test'] = p_value_graph
t_graph = t_graph_origin * (p_value_graph < t_sig)
F_info_Dics['t_graph'] = t_graph
return sub_graph, t_graph, F_info_Dics