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 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 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
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_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',
#%% 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'
)
Standard_Stim_Processor(r'G:\Test_Data\2P\201211_L76_2P\1-012',
All0_Graph[:,:,2] += ROI_boulder_prev
All0_Graph_Annotate = np.clip(All0_Graph,0,255).astype('u1')
Graph_Tools.Show_Graph(All0_Graph_Annotate, 'All-0_Annotate', save_folder)
#%% Last, RG-Lum
RG_Graph = cv2.imread(r'I:\Test_Data\2P\201211_L76_2P\1-014\Results\Subtraction_Graphs\RG-Lum_SubGraph.tif').astype('f8')
RG_Graph[:,:,2] += ROI_boulder_prev
RG_Graph_Annotate = np.clip(RG_Graph,0,255).astype('u1')
Graph_Tools.Show_Graph(RG_Graph_Annotate, 'RG_Annotate', save_folder)
#%% Next job, video compare.
import My_Wheels.Video_Writer as Video_Writer
roi_spon_folder = r'I:\Test_Data\2P\201222_L76_2P\1-001\Results\Aligned_Frames'
Video_Writer.Video_From_File(roi_spon_folder,(325,324),fps = 30,cut_boulder = [0,0,0,0])
full_frame_folder = r'I:\Test_Data\2P\201211_L76_2P\1-001\Results\Aligned_Frames'
Video_Writer.Video_From_File(full_frame_folder,(492,492),fps = 15,cut_boulder = [10,10,10,10])
#%% Then find a exp cell of ROI and full graph.
roi_cell_dic = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201222_L76_2P\1-008\Results\Global_Morpho','Global_Morpho.cell')
ROI_G8_F_Train = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201222_L76_2P\1-010\Results\F_Trains.pkl')
ROI_G8_Stim_Dic = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201222_L76_2P\1-010\Results\Stim_Frame_Align.pkl')
full_cell_dic = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201211_L76_2P\1-010\Results\Global_Morpho\Global_Morpho.cell')
full_G8_F_Train = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201211_L76_2P\1-010\Results\F_train.pkl')
full_Stim_Dic = OS_Tools.Load_Variable(r'I:\Test_Data\2P\201211_L76_2P\1-010\Results\Stim_Frame_Align.pkl')
#%% Run01 Bright and Low
aligned_folder = r'I:\Test_Data\2P\201222_L76_2P\1-001\Results\Aligned_Frames'
save_path = r'I:\Test_Data\2P\201222_L76_2P\1-001\Results'
bright_graph,_ = Selector.Intensity_Selector(aligned_folder,list_write= False)
bright_graph = np.clip(bright_graph.astype('f8')*32,0,65535).astype('u2')
Graph_Tools.Show_Graph(bright_graph, 'Brightest_Graph', save_path)
low_graph,_ = Selector.Intensity_Selector(aligned_folder,list_write= False,mode = 'smallest')
low_graph = np.clip(low_graph.astype('f8')*32,0,65535).astype('u2')
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 12 10:34:23 2021
@author: ZR
"""
from Standard_Aligner import Standard_Aligner
Sa = Standard_Aligner(r'K:\Test_Data\2P\210708_L76_2P',
list(range(1, 21)),
final_base='1-017')
Sa.One_Key_Aligner()
from My_Wheels.Stim_Frame_Align import One_Key_Stim_Align
One_Key_Stim_Align(r'K:\Test_Data\2P\210708_L76_2P\210709_L76_2P_stimuli')
from My_Wheels.Standard_Stim_Processor import One_Key_Frame_Graphs
from My_Wheels.Standard_Parameters.Sub_Graph_Dics import Sub_Dic_Generator
OD_Para = Sub_Dic_Generator('OD_2P')
One_Key_Frame_Graphs(r'K:\Test_Data\2P\210708_L76_2P\1-015', OD_Para)
G8_Para = Sub_Dic_Generator('G8+90')
One_Key_Frame_Graphs(r'K:\Test_Data\2P\210708_L76_2P\1-018', G8_Para)
RG_Para = Sub_Dic_Generator('RGLum4')
One_Key_Frame_Graphs(r'K:\Test_Data\2P\210708_L76_2P\1-020', RG_Para)
import My_Wheels.OS_Tools_Kit as ot
all_cell_dic = ot.Load_Variable(
r'K:\Test_Data\2P\210629_L76_2P\L76_210629A_All_Cells.ac')
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
#%%Get F and dF trains here.
cell_folder = r'H:\Test_Data\2P\210112_L76_2P\1-007\Results\All_Stim'
from Standard_Parameters.Sub_Graph_Dics import Sub_Dic_Generator
from Standard_Stim_Processor import One_Key_Stim_Maps
# Run07,OD
OD_Para = Sub_Dic_Generator('OD_2P')
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-007', cell_folder, OD_Para)
# Run08_G8
G8_Para = Sub_Dic_Generator('G8+90')
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-008', cell_folder, G8_Para)
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-009', cell_folder, G8_Para)
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-011', cell_folder, G8_Para)
RG_Para = Sub_Dic_Generator('RGLum4')
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-014', cell_folder, RG_Para)
One_Key_Stim_Maps(r'E:\Test_Data\2P\210112_L76_2P\1-015', cell_folder, RG_Para)
C7D8_Para = Sub_Dic_Generator('Color7Dir8')
One_Key_Stim_Maps(r'H:\Test_Data\2P\210112_L76_2P\1-013', cell_folder, C7D8_Para,have_blank = False)
S3D8_Para = Sub_Dic_Generator('Shape3Dir8')
One_Key_Stim_Maps(r'H:\Test_Data\2P\210112_L76_2P\1-016', cell_folder, S3D8_Para,have_blank = False)
#%% Then calculate spon cell response.
cell_dic = OS_Tools.Load_Variable(r'H:\Test_Data\2P\210112_L76_2P\1-007\Results\All_Stim\All_Stim.cell')
all_cell_mask = cell_dic['Cell_Graph'][:,:,0]
from My_Wheels.Average_Intensity_Calculator import AI_Calculator
frame_series = AI_Calculator(r'H:\Test_Data\2P\210112_L76_2P\1-001\Results\Aligned_Frames')
cell_series = AI_Calculator(r'H:\Test_Data\2P\210112_L76_2P\1-001\Results\Aligned_Frames',masks = all_cell_mask)
#%% Then calculate
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.')
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.
from My_Wheels.Standard_Stim_Processor import Standard_Stim_Processor
from My_Wheels.Standard_Parameters.Sub_Graph_Dics import Sub_Dic_Generator