def Do_Align(self):
"""
Main Function. Use this function will finish align work, useful for module using
Returns
-------
Align Properties(Dic):
Property of this alignment, including useful path and useful names.
"""
start_time = time.time() # Processing Start time
self.Before_Run_Average()
self.Align_Cores()
self.After_Align_Average()
finish_time = time.time()
time_cost = finish_time-start_time
print('Alignment Done, time cost = '+str(time_cost) +'s')
# Output a dictionary, coding
Align_Properties = {}
Align_Properties['all_save_folders'] = self.all_save_folders
all_tif_name = []
for i in range(len(self.Aligned_frame_folders)):
current_tif_list = OS_Tools.Get_File_Name(self.Aligned_frame_folders[i],file_type = '.tif')
all_tif_name.append(current_tif_list)
Align_Properties['all_tif_name'] = all_tif_name
return Align_Properties
def Global_Averagor(all_folder_list, sub_folders=r'\Results\Affined_Frames'):
'''
Average global graph from all subgraphs.
Parameters
----------
all_folder_list : TYPE
DESCRIPTION.
sub_folders : TYPE, optional
DESCRIPTION. The default is r'\Results\Affined_Frames'.
Returns
-------
global_averaged_graph : TYPE
DESCRIPTION.
'''
all_folders = lt.List_Annex(all_folder_list, [sub_folders])
all_tif_name = []
for i in range(len(all_folders)):
current_tif_name = ot.Get_File_Name(all_folders[i])
all_tif_name.extend(current_tif_name)
global_averaged_graph = Average_From_File(all_tif_name)
return global_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 One_Key_Frame_Graphs(
data_folder,
sub_dic,
show_clip=3,
alinged_sub_folder=r'\Results\Final_Aligned_Frames',
Stim_Align_sub_folder=r'\Results\Stim_Frame_Align.pkl'):
result_folder = data_folder + r'\Results'
graph_save_folder = result_folder + r'\Only_Frame_SubGraphs'
OS_Tools.mkdir(result_folder)
OS_Tools.mkdir(graph_save_folder)
stim_path = data_folder + Stim_Align_sub_folder
stim_dic = OS_Tools.Load_Variable(stim_path)
all_tif_name = OS_Tools.Get_File_Name(data_folder + alinged_sub_folder)
graph_num = len(sub_dic)
all_sub_graph_names = list(sub_dic.keys())
for i in range(graph_num):
current_name = all_sub_graph_names[i]
current_a = Frame_ID_Extractor(stim_dic, sub_dic[current_name][0])
current_b = Frame_ID_Extractor(stim_dic, sub_dic[current_name][1])
current_sub_graph, current_t_graph, current_info_dic = Single_Subgraph_Generator(
all_tif_name, current_a, current_b)
current_sub_graph = Graph_Tools.Clip_And_Normalize(
current_sub_graph, show_clip)
current_t_graph = Graph_Tools.Clip_And_Normalize(
current_t_graph, show_clip)
# Save graphs
Graph_Tools.Show_Graph(current_sub_graph, current_name + '_Sub_Graph',
graph_save_folder)
Graph_Tools.Show_Graph(current_t_graph, current_name + '_t_Graph',
graph_save_folder)
OS_Tools.Save_Variable(graph_save_folder, current_name + r'_Sub_Info',
current_info_dic, '.info')
return True
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 One_Key_Stim_Maps(data_folder,
cell_folder,
sub_dic,
have_blank=None,
alinged_sub_folder=r'\Results\Aligned_Frames',
Stim_Align_sub_folder=r'\Results\Stim_Frame_Align.pkl'):
'''
1 key generate stim map. Befor using this, you need to :
1.align graphs
2.give cell file path.
3.Finish stim frame align.
'''
result_folder = data_folder + r'\Results'
stim_path = data_folder + Stim_Align_sub_folder
cell_path = OS_Tools.Get_File_Name(cell_folder, '.cell')[0]
cell_dic = OS_Tools.Load_Variable(cell_path)
# Then generate spiketrain
stim_train = OS_Tools.Load_Variable(stim_path)['Original_Stim_Train']
all_tif_name = OS_Tools.Get_File_Name(data_folder + alinged_sub_folder)
cell_information = cell_dic['All_Cell_Information']
if have_blank != None:
warnings.warn(
'Have blank is detected automatically, this API is useless now.',
FutureWarning)
have_blank = (0 in stim_train)
if have_blank == True:
F_train, dF_F_train = Spike_Train_Generator(all_tif_name,
cell_information,
Base_F_type='nearest_0',
stim_train=stim_train)
else:
print('No blank, use previous ISI to calculate trains')
F_train, dF_F_train = Spike_Train_Generator(all_tif_name,
cell_information,
Base_F_type='before_ISI',
stim_train=stim_train)
# Then save F and dF/F trains
OS_Tools.Save_Variable(result_folder, 'F_Trains', F_train)
OS_Tools.Save_Variable(result_folder, 'dF_F_Trains', dF_F_train)
# At last, calculate Maps.
Standard_Stim_Processor(data_folder,
stim_path,
sub_dic,
cell_method=cell_path,
spike_train_path=result_folder +
r'\dF_F_Trains.pkl')
def __init__(self,all_folders):
self.all_folders = all_folders
self.all_save_folders = List_Op.List_Annex(self.all_folders,['Results'])
self.Aligned_frame_folders = List_Op.List_Annex(self.all_save_folders,['Aligned_Frames'])
for i in range(len(self.all_save_folders)):
OS_Tools.mkdir(self.all_save_folders[i])
OS_Tools.mkdir(self.Aligned_frame_folders[i])
self.Before_Align_Tif_Name = []
for i in range(len(self.all_folders)):
current_run_tif = OS_Tools.Get_File_Name(self.all_folders[i])
self.Before_Align_Tif_Name.append(current_run_tif)
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 Cell_Find(run_folder):
output_folder = run_folder+r'\Results'
aligned_frame_folder = output_folder+r'\Aligned_Frames'
all_tif_name = OS_Tools.Get_File_Name(aligned_frame_folder)
Stim_Frame_Dic = OS_Tools.Load_Variable(output_folder,'Stim_Frame_Align.pkl')
on_off_graph,Finded_Cells = On_Off_Cell_Finder(all_tif_name, Stim_Frame_Dic,shape_boulder=[20,20,20,35],filter_method = 'Gaussian',LP_Para = ((5,5),1.5))
cell_folder = output_folder+r'\Cells'
OS_Tools.Save_Variable(cell_folder, 'Finded_Cells', Finded_Cells,'.cell')
Graph_tools.Show_Graph(on_off_graph, 'on-off_graph', cell_folder)
all_keys = list(Finded_Cells.keys())
all_keys.remove('All_Cell_Information')
for i in range(len(all_keys)):
Graph_tools.Show_Graph(Finded_Cells[all_keys[i]], all_keys[i], cell_folder)
return True
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')
def Tremble_Evaluator(data_folder,
ftype='.tif',
boulder_ignore=20,
cut_shape=(9, 9),
mask_thres=0):
all_file_name = OS_Tools.Get_File_Name(data_folder, ftype)
template = cv2.imread(all_file_name[0], -1)
origin_dtype = template.dtype
graph_shape = template.shape
graph_num = len(all_file_name)
origin_graph_matrix = np.zeros(shape=graph_shape + (graph_num, ),
dtype=origin_dtype)
for i in range(graph_num):
origin_graph_matrix[:, :, i] = cv2.imread(all_file_name[i], -1)
average_graph = origin_graph_matrix.mean(axis=2).astype('u2')
# Show schematic of cutted graph.
schematic, _, _, _ = Graph_Cutter(average_graph, boulder_ignore, cut_shape)
# Then,save cutted graphs into dics.
cutted_graph_dic = {}
fracture_num = cut_shape[0] * cut_shape[1]
for i in range(fracture_num): # initialize cut dics.
cutted_graph_dic[i] = []
for i in range(graph_num): # Cycle all graphs
current_graph = origin_graph_matrix[:, :, i]
_, _, _, cutted_graphs = Graph_Cutter(current_graph, boulder_ignore,
cut_shape)
for j in range(fracture_num): # save each fracture
cutted_graph_dic[j].append(cutted_graphs[j])
# Calculate graph center of each fracture trains. Use weighted center.
all_frac_center = np.zeros(shape=(fracture_num, graph_num, 2), dtype='f8')
for i in range(fracture_num):
current_frac = cutted_graph_dic[i]
for j in range(graph_num):
current_graph = current_frac[j]
if mask_thres == 'otsu':
thres = filters.threshold_otsu(current_graph)
elif (type(mask_thres) == int or type(mask_thres) == float):
thres = mask_thres
else:
raise IOError('Invalid mask threshold.')
mask = (current_graph > thres).astype(int)
properties = regionprops(mask, current_graph)
current_mc = properties[0].weighted_centroid
all_frac_center[i, j, :] = current_mc #In sequence YX
return schematic, all_frac_center
def AI_Calculator(graph_folder, start_frame=0, end_frame=-1, masks='No_Mask'):
'''
This function is used to calculate average intensity variation. Masks can be given to calculate cells
Parameters
----------
graph_folder : (str)
All graphs folder.
start_frame : (int,optional)
Start frame num. The default is 0.
end_frame : (int,optional)
End frame. The default is -1.
masks : (2D_Array,optional)
2D arrays. Input will be binary, so be careful. The default is None.
Returns
-------
intensity_series : (Array)
Return average intensity.
'''
#initialize
all_tif_name = np.array(OS_Tools.Get_File_Name(graph_folder))
used_tif_name = all_tif_name[start_frame:end_frame]
frame_Num = len(used_tif_name)
intensity_series = np.zeros(frame_Num, dtype='f8')
graph_shape = np.shape(cv2.imread(used_tif_name[0], -1))
#calculate mask
if type(masks) == str:
masks = np.ones(graph_shape, dtype='bool')
elif masks.dtype != 'bool':
masks = masks > (masks // 2)
pix_num = masks.sum()
#calculate ai trains
for i in range(frame_Num):
current_graph = cv2.imread(used_tif_name[i], -1)
masked_graph = current_graph * masks
current_ai = masked_graph.sum() / pix_num
intensity_series[i] = current_ai
return intensity_series
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
# -*- 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 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
r'G:\Test_Data\2P\201111_L76_LM\1-002',
r'G:\Test_Data\2P\201111_L76_LM\1-003',
r'G:\Test_Data\2P\201111_L76_LM\1-009'
]
for i in range(3):
Cell_Find(run_list[i])
#%% Calculate spike train of all finded cells.
from My_Wheels.Spike_Train_Generator import Spike_Train_Generator
run_list = [
r'G:\Test_Data\2P\201111_L76_LM\1-002',
r'G:\Test_Data\2P\201111_L76_LM\1-003',
r'G:\Test_Data\2P\201111_L76_LM\1-009'
]
for i in range(3):
cell_dic = OS_Tools.Load_Variable(run_list[i]+r'\Results\Cells\Finded_Cells.cell')
all_tif_name = OS_Tools.Get_File_Name(run_list[i]+r'\Results\Aligned_Frames')
stim_train = OS_Tools.Load_Variable(run_list[i]+r'\Results\Stim_Frame_Align.pkl')['Original_Stim_Train']
F_train,dF_F_train = Spike_Train_Generator(all_tif_name, cell_dic['All_Cell_Information'])
OS_Tools.Save_Variable(run_list[i]+r'\Results\Cells', 'F_train', F_train)
OS_Tools.Save_Variable(run_list[i]+r'\Results\Cells', 'dF_F_train', dF_F_train)
#%% Calculate subgraph one by one.
from My_Wheels.Standard_Parameters.Sub_Graph_Dics import Sub_Dic_Generator
from My_Wheels.Standard_Stim_Processor import Standard_Stim_Processor
G8_Subdic = Sub_Dic_Generator('G8+90')
Standard_Stim_Processor(r'G:\Test_Data\2P\201111_L76_LM\1-002',
stim_folder = r'G:\Test_Data\2P\201111_L76_LM\1-002\Results\Stim_Frame_Align.pkl',
sub_dic = G8_Subdic,
tuning_graph=False,
cell_method = r'G:\Test_Data\2P\201111_L76_LM\1-002\Results\Cells\Finded_Cells.cell',
spike_train_path=r'G:\Test_Data\2P\201111_L76_LM\1-002\Results\Cells\dF_F_train.pkl',
Cell_Find_And_Plot(r'G:\Test_Data\2P\201211_L76_2P\1-001\Results', 'Global_Average_After_Align.tif', 'Global_Morpho',find_thres= 1.5,shape_boulder = [20,20,30,20])
#%% Then calculate the stim train of each stim series.
from My_Wheels.Stim_Frame_Align import Stim_Frame_Align
all_stim_folder = [
r'G:\Test_Data\2P\201211_L76_2P\201211_L76_2P_stimuli\Run10_2P_G8',
r'G:\Test_Data\2P\201211_L76_2P\201211_L76_2P_stimuli\Run12_2P_OD8_auto',
r'G:\Test_Data\2P\201211_L76_2P\201211_L76_2P_stimuli\Run14_2P_RGLum4',
]
for i in range(3):
_,current_stim_dic = Stim_Frame_Align(all_stim_folder[i])
OS_Tools.Save_Variable(all_stim_folder[i], 'Stim_Frame_Align', current_stim_dic)
#%% Then calculate spike train of different runs.
from My_Wheels.Spike_Train_Generator import Spike_Train_Generator
#Cycle basic stim map. this maps have
for i,index in enumerate([1,2,4]):
current_aligned_tif_name = OS_Tools.Get_File_Name(all_run_folder[index]+r'\Results\Aligned_Frames')
current_stim = OS_Tools.Load_Variable(all_stim_folder[i],file_name='Stim_Frame_Align.pkl')['Original_Stim_Train']
current_cell_info = OS_Tools.Load_Variable(all_run_folder[index]+r'\Results\Global_Morpho\Global_Morpho.cell')['All_Cell_Information']
F_train,dF_F_train = Spike_Train_Generator(current_aligned_tif_name, current_cell_info,Base_F_type= 'nearest_0',stim_train = current_stim)
OS_Tools.Save_Variable(all_run_folder[index]+r'\Results', 'F_train', F_train)
OS_Tools.Save_Variable(all_run_folder[index]+r'\Results', 'dF_F_train', dF_F_train)
#%% Then calculate standard stim map.
from My_Wheels.Standard_Stim_Processor import Standard_Stim_Processor
from My_Wheels.Standard_Parameters.Sub_Graph_Dics import Sub_Dic_Generator
Standard_Stim_Processor(r'G:\Test_Data\2P\201211_L76_2P\1-010',
r'G:\Test_Data\2P\201211_L76_2P\1-010\Results\Stim_Frame_Align.pkl',
Sub_Dic_Generator('G8+90'),
cell_method = r'G:\Test_Data\2P\201211_L76_2P\1-010\Results\Global_Morpho\Global_Morpho.cell',
spike_train_path=r'G:\Test_Data\2P\201211_L76_2P\1-010\Results\dF_F_train.pkl'
)
def Standard_Cell_Processor(
animal_name,
date,
day_folder,
cell_file_path,
#average_graph_path, # not necessary.
run_id_lists,
location='A', # For runs have
Stim_Frame_Align_name='_All_Stim_Frame_Infos.sfa',
#Stim_Frame_Align_subfolder = r'\Results\Stim_Frame_Align.pkl',# API changed.
align_subfolder=r'\Results\Aligned_Frames',
response_head_extend=3,
response_tail_extend=3,
base_frame=[0, 1, 2],
filter_para=(0.02, False)):
# Folder and name initialization
print('Just make sure average and cell find is already done.')
cell_dic = ot.Load_Variable(cell_file_path)
cell_info = cell_dic['All_Cell_Information']
cell_name_prefix = animal_name + '_' + str(date) + location + '_'
all_cell_num = len(cell_info)
all_run_subfolders = lt.List_Annex([day_folder],
lt.Run_Name_Producer_2P(run_id_lists))
save_folder = day_folder
all_Stim_Frame_Align = ot.Load_Variable(day_folder + r'\\' +
Stim_Frame_Align_name)
# Set cell data formats.
all_cell_list = []
for i in range(all_cell_num):
current_cell_name = cell_name_prefix + ot.Bit_Filler(i, 4)
current_cell_dic = {}
current_cell_dic['Name'] = current_cell_name
current_cell_dic['Cell_Info'] = cell_info[i]
# Cycle all runs for F and dF trains.
current_cell_dic['dF_F_train'] = {}
current_cell_dic['F_train'] = {}
current_cell_dic['Raw_CR_trains'] = {}
current_cell_dic['CR_trains'] = {}
all_cell_list.append(current_cell_dic)
# Then cycle all runs, fill in
for i in range(len(all_run_subfolders)):
current_runid = 'Run' + (all_run_subfolders[i][-3:]
) # Use origin run id to avoid bugs.
current_all_tif_name = ot.Get_File_Name(
all_run_subfolders[i] + align_subfolder, '.tif')
current_Stim_Frame_Align = all_Stim_Frame_Align[current_runid]
if current_Stim_Frame_Align == None or len(
current_Stim_Frame_Align
) == 302: # meaning this run is spon or RF25.
current_run_Fs, current_run_dF_Fs = Spike_Train_Generator(
current_all_tif_name, cell_info, 'most_unactive', None)
else:
current_run_stim_train = current_Stim_Frame_Align[
'Original_Stim_Train']
if 0 in current_run_stim_train: # having 0
current_run_Fs, current_run_dF_Fs = Spike_Train_Generator(
current_all_tif_name,
cell_info,
Base_F_type='nearest_0',
stim_train=current_run_stim_train)
else:
current_run_Fs, current_run_dF_Fs = Spike_Train_Generator(
current_all_tif_name,
cell_info,
Base_F_type='before_ISI',
stim_train=current_run_stim_train)
# Then put trains above into each cell files.
for j in range(all_cell_num):
all_cell_list[j]['dF_F_train'][current_runid] = current_run_dF_Fs[
j]
all_cell_list[j]['F_train'][current_runid] = current_run_Fs[j]
# Then, we generate Condition Reaction Train for each cell and each condition.
if current_Stim_Frame_Align == None:
all_cell_list[j]['CR_trains'][current_runid] = None
all_cell_list[j]['Raw_CR_trains'][current_runid] = None
else:
for j in range(all_cell_num):
all_cell_list[j]['CR_trains'][current_runid], all_cell_list[j][
'Raw_CR_trains'][
current_runid] = Single_Condition_Train_Generator(
current_run_Fs[j], current_Stim_Frame_Align,
response_head_extend, response_tail_extend,
base_frame, filter_para)
# Till now, all cell data of all runs is saved in 'all_cell_list'.
# Last part, saving files. All cells in one file, dtype = dic.
all_cell_dic = {}
for i in range(all_cell_num):
all_cell_dic[all_cell_list[i]['Name']] = all_cell_list[i]
ot.Save_Variable(save_folder,
'_' + animal_name + '_' + date + location + '_All_Cells',
all_cell_dic, '.ac')
return True
import My_Wheels.List_Operation_Kit as List_Tools
import My_Wheels.Graph_Operation_Kit as Graph_Tools
import My_Wheels.OS_Tools_Kit as OS_Tools
import cv2
data_folder = [r'E:\Test_Data\2P\201222_L76_2P']
run_list = [
'1-001', # Spon
'1-008', # OD
'1-010', # G8
'1-011', # RGLum4
'1-014' # Spon After
]
all_runs = List_Tools.List_Annex(data_folder, run_list)
#%% Add 3 list for run01 to fit ROI change.
run_1 = all_runs[0]
run1_all_tif = OS_Tools.Get_File_Name(run_1)
save_path = run_1 + r'\shape_extended'
OS_Tools.mkdir(save_path)
for i in range(len(run1_all_tif)):
current_graph = cv2.imread(run1_all_tif[i], -1)
extended_graph = Graph_Tools.Boulder_Extend(
current_graph, [0, 0, 0, 3]) # 3 pix on the right.
current_graph_name = run1_all_tif[i].split('\\')[-1]
Graph_Tools.Show_Graph(extended_graph,
current_graph_name,
save_path,
show_time=0)
#%% Then align Run01_Spon.
from My_Wheels.Translation_Align_Function import Translation_Alignment
Translation_Alignment([all_runs[0] + r'\shape_extended'],
graph_shape=(325, 324))
def Video_From_File(data_folder,
plot_range=(0, 9999),
graph_size=(472, 472),
file_type='.tif',
fps=15,
gain=20,
LP_Gaussian=([5, 5], 1.5),
frame_annotate=True,
cut_boulder=[20, 20, 20, 20]):
'''
Write all files in a folder as a video.
Parameters
----------
data_folder : (std)
Frame folder. All frame in this folder will be write into video. Dtype shall be u2 or there will be a problem.
graph_size : (2-element-turple), optional
Frame size AFTER cut. The default is (472,472).
file_type : (str), optional
Data type of graph file. The default is '.tif'.
fps : (int), optional
Frame per second. The default is 15.
gain : (int), optional
Show gain. The default is 20.
LP_Gaussian : (turple), optional
LP Gaussian Filter parameter. Only do low pass. The default is ([5,5],1.5).
frame_annotate : TYPE, optional
Whether we annotate frame number on it. The default is True.
cut_boulder : TYPE, optional
Boulder cut of graphs, UDLR. The default is [20,20,20,20].
Returns
-------
bool
True if function processed.
'''
all_tif_name = OS_Tools.Get_File_Name(path=data_folder,
file_type=file_type)
start_frame = plot_range[0]
end_frame = min(plot_range[1], len(all_tif_name))
all_tif_name = all_tif_name[start_frame:end_frame]
graph_num = len(all_tif_name)
video_writer = cv2.VideoWriter(data_folder + r'\\Video.mp4',
cv2.VideoWriter_fourcc('X', 'V', 'I', 'D'),
fps, graph_size, 0)
#video_writer = cv2.VideoWriter(data_folder+r'\\Video.avi',-1,fps,graph_size,0)
for i in range(graph_num):
raw_graph = cv2.imread(all_tif_name[i], -1).astype('f8')
# Cut graph boulder.
raw_graph = Graph_Tools.Graph_Cut(raw_graph, cut_boulder)
# Do gain then
gained_graph = np.clip(raw_graph.astype('f8') * gain / 256, 0,
255).astype('u1')
# Then do filter, then
if LP_Gaussian != False:
u1_writable_graph = Filters.Filter_2D(gained_graph, LP_Gaussian,
False)
else:
u1_writable_graph = gained_graph
if frame_annotate == True:
cv2.putText(u1_writable_graph, 'Stim ID = ' + str(i), (250, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255), 1)
video_writer.write(u1_writable_graph)
del video_writer
return True
def Affine_Aligner_Gaussian(data_folder,
base_graph,
window_size=1,
max_point=50000,
good_match_prop=0.3,
dist_lim=120,
match_checker=1,
sector_num=4,
write_file=False,
save_folder='Default'):
if save_folder == 'Default':
save_folder = data_folder + r'\Results'
aligned_tif_folder = save_folder + r'\Affined_Frames'
OS_Tools.mkdir(save_folder)
OS_Tools.mkdir(aligned_tif_folder)
all_tif_name = OS_Tools.Get_File_Name(data_folder)
graph_num = len(all_tif_name)
graph_shape = cv2.imread(all_tif_name[0], -1).shape
height, width = graph_shape
origin_tif_matrix = np.zeros(shape=graph_shape + (graph_num, ), dtype='u2')
# Read in all tif name.
for i in range(graph_num):
origin_tif_matrix[:, :, i] = cv2.imread(all_tif_name[i], -1)
# Then get window slipped average graph.
if window_size == 1:
slipped_average_matrix = origin_tif_matrix
else:
slipped_average_matrix = Filters.Window_Average(
origin_tif_matrix, window_size=window_size)
# Use slip average to get deformation parameters.
aligned_tif_matrix = np.zeros(shape=origin_tif_matrix.shape, dtype='u2')
h_dic = {} # Deformation parameters
for i in range(graph_num):
target = slipped_average_matrix[:, :, i]
_, current_h = Affine_Core_Point_Equal(target,
base_graph,
max_point=max_point,
good_match_prop=good_match_prop,
sector_num=sector_num,
dist_lim=dist_lim,
match_checker=match_checker)
h_dic[i] = current_h
current_deformed_graph = cv2.warpPerspective(
origin_tif_matrix[:, :, i], current_h, (width, height))
Graph_Tools.Show_Graph(current_deformed_graph,
all_tif_name[i].split('\\')[-1],
aligned_tif_folder,
show_time=0,
graph_formation='')
aligned_tif_matrix[:, :, i] = current_deformed_graph
OS_Tools.Save_Variable(save_folder, 'Deform_H', h_dic)
if write_file == True:
OS_Tools.Save_Variable(save_folder, 'Affine_Aligned_Graphs',
aligned_tif_matrix)
# At last, generate average graphs
graph_before_align = origin_tif_matrix.mean(axis=2).astype('u2')
graph_after_align = aligned_tif_matrix.mean(axis=2).astype('u2')
graph_before_align = Graph_Tools.Clip_And_Normalize(graph_before_align,
clip_std=5)
graph_after_align = Graph_Tools.Clip_And_Normalize(graph_after_align,
clip_std=5)
Graph_Tools.Show_Graph(graph_before_align, 'Graph_Before_Affine',
save_folder)
Graph_Tools.Show_Graph(graph_after_align, 'Graph_After_Affine',
save_folder)
return True
def Standard_Stim_Processor(data_folder,
stim_folder,
sub_dic,
alinged_sub_folder=r'\Results\Aligned_Frames',
show_clip=3,
tuning_graph=False,
cell_method='Default',
filter_method='Gaussian',
LP_Para=((5, 5), 1.5),
HP_Para=False,
spike_train_path='Default',
spike_train_filter_para=(False, False),
spike_train_filter_method=False):
'''
Generate subtraction graph, cell graph and tuning graphs if requred.
Parameters
----------
data_folder : (str)
Run folder.
stim_folder : (str)
Stim file folder or Frame_Stim_Align File folder. Pre align is advised.
sub_dic : (Dic)
Subtraction dicionary. This can be generated from My_Wheels.Standard_Parameters
show_clip : (float), optional
Clip of graph show. The default is 3.
tuning_graph : (bool), optional
Whether we generate tuning graph of each cells. The default is False.
cell_method : (str), optional
Cell find method. You can input cell file path here. The default is 'Default'.
filter_method : (str), optional
False to skip filter. Kernel function of graph filtering. The default is 'Gaussian'.
LP_Para : (turple), optional
False to skip. Low pass filter of graph. The default is ((5,5),1.5).
HP_Para : (turple), optional
False to skip. High pass filter of graph. Big HP can be very slow!. The default is False.
spike_train_path : (str), optional
Path of spike train.'Default' will generate spike train directly. The default is 'Default'.
spike_train_filter_para : (turple), optional
Signal filter bandpass propotion of spike train. Please be sure if you need this. The default is (False,False).
spike_train_filter_method : (str), optional
False to skip. Method of signal filtering. The default is False.
Returns
-------
None.
'''
# Path Cycle.
from Cell_Find_From_Graph import On_Off_Cell_Finder
work_folder = data_folder + r'\Results'
OS_Tools.mkdir(work_folder)
aligned_frame_folder = data_folder + alinged_sub_folder
OS_Tools.mkdir(aligned_frame_folder)
# Step1, align graphs. If already aligned, just read
if not os.listdir(aligned_frame_folder): # if this is a new folder
print('Aligned data not found. Aligning here..')
Translation_Alignment([data_folder])
aligned_all_tif_name = np.array(
OS_Tools.Get_File_Name(aligned_frame_folder)
) # Use numpy array, this is easier for slice.
# Step2, get stim fram align matrix. If already aligned, just read in aligned dictionary.
file_detector = len(stim_folder.split('.'))
if file_detector == 1: # Which means input is a folder
print('Frame Stim not Aligned, aligning here...')
from My_Wheels.Stim_Frame_Align import Stim_Frame_Align
_, Frame_Stim_Dic = Stim_Frame_Align(stim_folder)
else: # Input is a file
Frame_Stim_Dic = OS_Tools.Load_Variable(stim_folder)
# Step3, get cell information
if cell_method == 'Default': # meaning we will use On-Off graph to find cell.
print('Cell information not found. Finding here..')
cell_dic = On_Off_Cell_Finder(aligned_all_tif_name,
Frame_Stim_Dic,
filter_method=filter_method,
LP_Para=LP_Para,
HP_Para=HP_Para)
else:
cell_dic = OS_Tools.Load_Variable(cell_method)
# Step4, calculate spike_train.
if spike_train_path != 'Default':
dF_F_train = OS_Tools.Load_Variable(spike_train_path)
else: # meaning we need to calculate spike train from the very begining.
_, dF_F_train = Spike_Train_Generator(
aligned_all_tif_name,
cell_dic['All_Cell_Information'],
Base_F_type='nearest_0',
stim_train=Frame_Stim_Dic['Original_Stim_Train'],
LP_Para=LP_Para,
HP_Para=HP_Para,
filter_method=filter_method)
#Step5, filt spike trains.
if spike_train_filter_method != False: # Meaning we need to do train filter.
for i in range(len(dF_F_train)):
dF_F_train[i] = My_Filter.Signal_Filter(dF_F_train,
spike_train_filter_method,
spike_train_filter_para)
# Step6, get each frame graph and cell graph.
all_graph_keys = list(sub_dic.keys())
for i in range(len(sub_dic)):
output_folder = work_folder + r'\Subtraction_Graphs'
current_key = all_graph_keys[i]
current_sub_list = sub_dic[current_key]
A_conds = current_sub_list[0] # condition of A graph
B_conds = current_sub_list[1] # condition of B graph
A_IDs = []
B_IDs = []
for i in range(len(A_conds)):
A_IDs.extend(Frame_Stim_Dic[A_conds[i]])
for i in range(len(B_conds)):
B_IDs.extend(Frame_Stim_Dic[B_conds[i]])
# Get frame maps.
current_sub_graph, current_t_graph, current_F_info = Single_Subgraph_Generator(
aligned_all_tif_name, A_IDs, B_IDs, filter_method, LP_Para,
HP_Para)
current_sub_graph = Graph_Tools.Clip_And_Normalize(
current_sub_graph, show_clip)
Graph_Tools.Show_Graph(current_sub_graph, current_key + '_SubGraph',
output_folder)
current_t_graph = Graph_Tools.Clip_And_Normalize(
current_t_graph, show_clip)
Graph_Tools.Show_Graph(current_t_graph, current_key + '_T_Graph',
output_folder)
OS_Tools.Save_Variable(output_folder,
current_key + '_Sub_Info',
current_F_info,
extend_name='.info')
# Get cell maps
cell_info = cell_dic['All_Cell_Information']
current_cell_sub_graph, current_cell_t_graph, current_cell_info = Single_Cellgraph_Generator(
dF_F_train, cell_info, show_clip, A_IDs, B_IDs)
Graph_Tools.Show_Graph(current_cell_sub_graph,
current_key + '_Cell_SubGraph', output_folder)
Graph_Tools.Show_Graph(current_cell_t_graph,
current_key + '_Cell_T_Graph', output_folder)
OS_Tools.Save_Variable(output_folder,
current_key + '_Cell_Info',
current_cell_info,
extend_name='.info')
#Step7, calculate cell tuning graph.
if tuning_graph == True:
print('Not finished yet.')
import My_Wheels.Graph_Operation_Kit as Graph_Tools
import numpy as np
import cv2
#%% First, read in config file.
# All Read in shall be in this part to avoid bugs = =
f = open('Config.punch','r')
config_info = f.readlines()
del f
frame_folder = config_info[3][:-1]# Remove '\n'
stim_folder = config_info[6][:-1]# Remove '\n'
cap_freq = float(config_info[9])
frame_thres = float(config_info[12])
#%% Second do graph align.
save_folder = frame_folder+r'\Results'
aligned_tif_folder = save_folder+r'\Aligned_Frames'
all_tif_name = OS_Tools.Get_File_Name(frame_folder)
graph_size = np.shape(cv2.imread(all_tif_name[0],-1))
Translation_Alignment([frame_folder],align_range = 10,align_boulder = 40,big_memory_mode=True,graph_shape = graph_size)
aligned_all_tif_name = np.array(OS_Tools.Get_File_Name(aligned_tif_folder))
#%% Third, Stim Frame Align
jmp_step = int(5000//cap_freq)
_,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
Frame_Stim_Dic)
#%%Cell Find from Run01 Morphology graph.
from My_Wheels.Cell_Find_From_Graph import Cell_Find_And_Plot
Cell_Find_And_Plot(r'G:\Test_Data\2P\201121_L76_LM\1-001\Results',
'Run_Average_After_Align.tif',
'Morpho',
find_thres=1.5)
#%% Calculate Spike Train of Run01 Morpho cell into each run.
from My_Wheels.Spike_Train_Generator import Spike_Train_Generator
all_run_folder = [
r'G:\Test_Data\2P\201121_L76_LM\1-002',
r'G:\Test_Data\2P\201121_L76_LM\1-003',
r'G:\Test_Data\2P\201121_L76_LM\1-004'
]
for i in range(3):
all_tif_name = OS_Tools.Get_File_Name(all_run_folder[i] +
r'\Results\Aligned_Frames')
cell_information = OS_Tools.Load_Variable(
all_run_folder[i] +
r'\Results\Morpho\Morpho.cell')['All_Cell_Information']
stim_train = OS_Tools.Load_Variable(
all_run_folder[i] +
r'\Results\Stim_Fram_Align.pkl')['Original_Stim_Train']
F_train, dF_F_train = Spike_Train_Generator(all_tif_name,
cell_information,
Base_F_type='nearest_0',
stim_train=stim_train)
OS_Tools.Save_Variable(all_run_folder[i] + r'\Results\Morpho', 'F_train',
F_train)
OS_Tools.Save_Variable(all_run_folder[i] + r'\Results\Morpho',
'dF_F_train', dF_F_train)
#%% Then Get graph of each run.
