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match_ROIs_test.py
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match_ROIs_test.py
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import numpy as np
import logging
import time
import imp
import os
from scipy import sparse
import matplotlib.pyplot as plt
from scipy.io import loadmat
from scipy.io import savemat
import h5py
import hdf5storage
import caiman as cm
from caiman.base.rois import register_ROIs
#imp.load_source("register_ROIs","../CaImAn/caiman/base/rois.py")
logging.basicConfig(format=
"%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s]"\
"[%(process)d] %(message)s",
level=logging.ERROR)
def compare_old_vs_new(pathMouse,sessions=None,std=None,thr_cost=0.7,pl=False):
#pathResults_old = 'resultsCNMF_MF1_LK1.mat'
pathResults_new = 'results_OnACID.mat'
pathSave = 'matching_old_new.mat'
nS = sessions[1]-sessions[0]+1
path_new = '%sSession01/%s' % (pathMouse,pathResults_new)
f = loadmat(path_new)
Cn0 = f['Cn']
pathResults_old = 'backup/save_final/footprints.mat'
path_old = '%s%s' % (pathMouse,pathResults_old)
t_start = time.time()
for s in range(sessions[0],sessions[1]+1):
t_start_s = time.time()
print('---------- Now matching session %d ----------'%s)
dims = (512,512)
pathFigDir = '%sSession%02d/pics/' % (pathMouse,s)
#path_old = '%sSession%02d/%s' % (pathMouse,s,pathResults_old)
path_new = '%sSession%02d/%s' % (pathMouse,s,pathResults_new)
svname = '%sSession%02d/%s' % (pathMouse,s,pathSave)
print(path_old)
print(path_new)
f = h5py.File(path_old,'r')
#A_old = sparse.csc_matrix((f['A2']['data'], f['A2']['ir'], f['A2']['jc']))
A_old = sparse.vstack([sparse.csc_matrix((f[A]['data'], f[A]['ir'], f[A]['jc']),shape=(512,512)).transpose().reshape(512*512) if 'data' in list(f[A].keys()) else sparse.csc_matrix((1,512*512)) for A in f[f['footprints/session/ROI'][s-1][0]]['A'][0]]).T.asformat('csc')
f.close()
f = loadmat(path_new)
A_new = f['A']#.reshape(-1,dims[0],dims[1]).transpose(2,1,0).reshape(dims[0]*dims[1],-1)
Cn = f['Cn']
#N = A_old.shape[1]
#A_old.resize(dims[0]*dims[1],N)
print(A_old.shape[1])
print(A_new.shape[1])
#return A_old, A_new
[matched_ROIs1, matched_ROIs2, non_matched1, non_matched2, performance, _, scores, shifts] = cm.base.rois.register_ROIs(A_old, A_new, dims,
template1=Cn0, template2=Cn,
std=std, cr=(15,15),
thresh_cost=thr_cost, max_dist=8, max_thr=0.01, plot_results=pl)
print(performance)
results = dict(matched_ROIs1=matched_ROIs1,
matched_ROIs2=matched_ROIs2,
non_matched1=non_matched1,
non_matched2=non_matched2,
performance=performance,
scores=scores,
shifts=np.array(shifts))
if os.path.exists(svname):
os.remove(svname)
savemat(svname, results)
if pl:
if not os.path.exists(pathFigDir):
os.mkdir(pathFigDir)
plt.savefig('%smatching_old_vs_new2.png'%pathFigDir)
plt.close('all')
print('---------- finished matching session %d.\t time taken: %s ----------'%(s,str(time.time()-t_start_s)))
print('---------- all done. Overall time taken: %s ----------'%str(time.time()-t_start))
return
def match_ROIs_test(pathMouse,sessions=None,thr_cost=0.7,std=(2,2),w=1/3,OnACID=True,pl=False):
print('should remove deleted ones')
if OnACID:
suffix = '_OnACID'
else:
suffix = ''
pathMatching = '%smatching' % pathMouse
if not os.path.exists(pathMatching):
os.mkdir(pathMatching)
if std is None:
pathSave = '%smatching/results_matching_multi_std=0_thr=%d_w=%d%s.mat'%(pathMouse,thr_cost*100,int(w*100),suffix)
else:
pathSave = '%smatching/results_matching_multi_std=%d_thr=%d_w=%d%s.mat'%(pathMouse,std[0],thr_cost*100,int(w*100),suffix)
print(pathSave)
if isinstance(pathMouse,str):
assert isinstance(sessions,tuple), 'Please provide the numbers of sessions as a tuple of start and end session to be matched'
if OnACID:
pathResults = 'results_OnACID.mat'
else:
pathResults = 'resultsCNMF_MF1_LK1.mat'
path = [('%sSession%02d/%s' % (pathMouse,i,pathResults)) for i in range(sessions[0],sessions[1]+1)]
nS = len(path)
A = [[]]*nS
if not OnACID:
pathResults = 'results_OnACID.mat'
path = '%sSession01/%s' % (pathMouse,pathResults)
f = loadmat(path)
Cn = [f['Cn']]
pathResults_old = 'backup/save_final/footprints.mat'
path = '%s%s' % (pathMouse,pathResults_old)
print("bla")
print(path)
f = h5py.File(path)
for s in range(nS):
A[s] = sparse.vstack([sparse.csc_matrix((f[A]['data'], f[A]['ir'], f[A]['jc']),shape=(512,512)).transpose().reshape(512*512) if 'data' in list(f[A].keys()) else sparse.csc_matrix((1,512*512)) for A in f[f['footprints/session/ROI'][s][0]]['A'][0]]).T.asformat('csc')
f.close()
else:
Cn = [[]]*nS
for s in range(nS):
#print(path[s])
if OnACID:
if not os.path.exists(path[s]):
print("File %s does not exist. Skipping..."%path[s])
continue
f = loadmat(path[s])
A[s] = f['A']
Cn[s] = f['Cn']
#else:
#f = h5py.File(path[s],'r')
#Cn[s] = f['Cn'].value.transpose()
#A[s] = sparse.csc_matrix((f['A2']['data'], f['A2']['ir'], f['A2']['jc']),shape=(np.prod(Cn[s].shape),f['C2'].shape[-1]))
#f.close()
A[s] = A[s].astype(np.float32)
print('# ROIs: '+str(A[s].shape[1]))
print("Start matching")
t_start = time.time()
if nS == 2:
[matched_ROIs1, matched_ROIs2, non_matched1, non_matched2, performance, _, scores, shifts] = cm.base.rois.register_ROIs(A[0], A[1], Cn[0].shape, template1=Cn[0], template2=Cn[1],
std=std, cr=(15,15), max_dist=8, thresh_cost=thr_cost,
plot_results=pl,use_opt_flow=False)
print(performance)
print("Time taken: %s" % str(time.time()-t_start))
pathSave = pathMouse + 'matching/results_matching_single.mat'
results = dict(matched_ROIs1=matched_ROIs1,
matched_ROIs2=matched_ROIs2,
non_matched1=non_matched1,
non_matched2=non_matched2,
performance=performance,
scores=scores,
shifts=np.array(shifts))
savemat(pathSave, results)
return matched_ROIs1, matched_ROIs2, non_matched1, non_matched2, performance, scores, shifts
else:
[A_union, assignments, matchings, scores, shifts] = cm.base.rois.register_multisession(A, Cn[0].shape, templates=Cn,
thresh_cost=thr_cost, max_dist=8, max_thr=0.01, std=std,
plot_results=pl)
print("Time taken: %s" % str(time.time()-t_start))
#pathSave = '%smatching/results_matching_multi_std=%d_thr=%d_w=%d.mat'%(pathMouse,std[0],thr_cost*100,int(w*100))
results = dict(A_union=sparse.csc_matrix(A_union),
assignments=assignments,
matchings=matchings,
scores=scores,
shifts=shifts)
savemat(pathSave, results)
return A_union, assignments, matchings, scores, shifts
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/34/",sessions=(1,22),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/35/",sessions=(1,22),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/65/",sessions=(1,44),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/66/",sessions=(1,45),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/72/",sessions=(1,44),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/243/",sessions=(1,71),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);
#_ = match_ROIs_test("/media/wollex/Analyze_AS3/Data/244/",sessions=(1,44),std=None,thr_cost=0.7,w=1/3,OnACID=True,pl=False);