def run_svc(X, y, k, n_perm, model, fileName):
y_rand = np.random.permutation(y)
predict, dec, y_sorted, weight = model.main_svc_rfe_cv(X, y_rand, k)
# # write h5py
# write_h5py(fileName,'perm'+str(n_perm),['predict','dec','y_sorted','weight'],\
# [predict,dec,y_sorted,weight])
# write mat
write_mat(os.path.join(fileName,str(n_perm)),\
dataset_name=['predict','dec','y_sorted','weight'],\
dataset=[predict,dec,y_sorted,weight])
def run_svc(self, X, y, n_perm):
# print('we have processing {} permutation'.format(n_perm))
y_rand = np.random.permutation(y)
predict,dec,y_sorted,weight=\
self.model.main_svc_rfe_cv(X,y_rand,self.k)
# write mat
write_mat(os.path.join(self.fileName,str(n_perm)),\
dataset_name=['predict','dec','y_sorted','weight'],\
dataset=[predict,dec,y_sorted,weight])
def load_allmat(sel):
# 多线程
s = time.time()
print('loading all mat...\n')
# 判断是否有FC mat文件
if os.path.exists(os.path.join(sel.save_path, sel.feature + '.mat')):
sel.mat = pd.DataFrame(
read_mat(os.path.join(sel.save_path, sel.feature + '.mat'),
None))
print(
'Already have {}\nloaded all mat!\nrunning time={:.2f}'.format(
sel.feature + '.mat',
time.time() - s))
else:
sel.all_mat = os.listdir(sel.file_path)
all_mat_path = [
os.path.join(sel.file_path, all_mat_)
for all_mat_ in sel.all_mat
]
cores = multiprocessing.cpu_count()
if sel.n_processess > cores:
sel.n_processess = cores - 1
len_all = len(all_mat_path)
sel.mat = pd.DataFrame([])
# 特征用std还是mean
if sel.feature == 'mean':
ith = 1
elif sel.feature == 'std':
ith = 0
elif sel.feature == 'staticFC':
ith = 0
else:
print('###还未添加其他衡量dFC的指标,默认使用std###\n')
ith = 0
# load mat...
with ThreadPoolExecutor(sel.n_processess) as executor:
for i, all_mat_ in enumerate(all_mat_path):
task = executor.submit(sel.load_onemat_and_processing, i,
all_mat_, len_all, s)
sel.mat = pd.concat(
[sel.mat, pd.DataFrame(task.result()[ith]).T], axis=0)
# 保存后处理后的mat文件
if sel.if_save_post_mat:
write_mat(fileName=os.path.join(sel.save_path,
sel.feature + '.mat'),
dataset_name=sel.feature,
dataset=np.mat(sel.mat.values))
print('saved all {} mat!\n'.format(sel.feature))
Transform the .1D file to .mat file
TODO: to other file type
Args:
file_path: .1D file path
Return:
data: .mat file
"""
data = pd.read_csv(file_path)
data = data.values
data = [list(d)[0].split('\t') for d in data]
data =[np.array(d, dtype=np.float).T for d in data]
data = pd.DataFrame(data)
data = pd.DataFrame(data)
return data
if __name__ == '__main__':
dir = r'F:\Data\ASD\Outputs\dparsf\filt_global\rois_dosenbach160'
save_path = r'F:\Data\ASD\Outputs\dparsf\filt_global\rois_dosenbach160_m'
file_name = os.listdir(dir)
file_path = [os.path.join(dir, fn) for fn in file_name]
nfile = len(file_path)
for i, file in enumerate(zip(file_path, file_name)):
print(f'{i+1}/{nfile}')
data = read_1D(file[0])
fn = os.path.join(save_path, file[1].split('.')[0] + '.mat')
write_mat(fileName=fn, dataset_name='timeseries', dataset=data.values)