def nsigtf_real(*args):
nargin = len(args)
if nargin < 4:
raiseValueError('Not enough input arguments')
# iscell
if iscell(c) == 0:
if np.ndims(c) == 2:
N, chan_len = c.shape
CH = 1
#
c = mat2cell(c.',chan_len,ones(1,N)).')
else:
N, chan_len, CH = c.shape
ctemp = mat2cell(np.permute(c,[2,1,3]),chan_len,np.ones((1,N)),np.ones((1,CH)))
c = np.permute(ctemp,[2,3,1])
else:
CH, N = c.shape
posit = np.cumsum(shift)
NN = posit[-1]
posit = posit - shift[1]
fr = np.zeros((NN,CH))
for ii in range(N):
Lg = len(g[ii])
win_range = (posit[ii]+(-np.floor(Lg/2):math.ceil(Lg/2)-1)) % NN + 1
temp = np.fft(c[ii], [], 1) * len(c[ii])
if phasemode == 'global':
fsNewBins = c[ii].shape[0]
fkBins = posit[ii]
displace = fkBins - np.floor(fkBins/fsNewBins) * fsNewBins
temp = np.roll(temp, -displace)
#
temp = temp[]
fr[win_range,:] = fr[win_Range,:] + temp * g[ii][Lg-np.floor(Lg/2)+1:Lg, 1:np.ceil(Lg/2)]
nyqBin = np.floor(Ls/2) + 1
#
fr[nyqBin+1:end] = conj( fr(nyqBin - (~logical(mod(Ls,2))) : -1 : 2) )
fr = np.real(np.fft.ifft(fr))
return fr
def generate_files_sony(old_subj_data, savepath):
num_subj = len(old_subj_data)
num_cond = len(old_subj_data[0])
subj_data = [[dcpy(empty_map) for i in range(num_cond)]
for j in range(num_subj)]
clip_size = old_subj_data[0][0]['trials'].shape[2] / 2
num_electrodes = old_subj_data[0][0]['trials'].shape[1]
for subj_ind in range(num_subj):
for cond in range(num_cond):
conds = conds_map[cond]
old_entry = old_subj_data[subj_ind][cond]
for i in range(2):
clip = range(clip_size * i, clip_size * (i + 1))
entry = subj_data[subj_ind][conds[i]]
entry['trials'] = concat(entry['trials'],
old_entry['trials'][:, :, clip])
if not os.path.isdir(savepath):
os.makedirs(savepath)
dats = []
dats_det = []
for cond in range(num_cond):
print 'Generating condition', cond + 1, 'files...'
dats.append(np.zeros((0, clip_size, num_electrodes)))
dats_det.append(np.zeros((0, clip_size, num_electrodes)))
for subj in range(num_subj):
trials = subj_data[subj][cond]['trials']
dats[-1] = concat(dats[-1], permute(trials, axes=(0, 2, 1)))
savename = os.path.join(savepath, cond_names_map[cond])
savemat(savename, {'dat': dats[-1]})
dats_det[-1] = detrend(dats[-1], axis=1)
savename = os.path.join(savepath, cond_names_map[cond] + '_det')
savemat(savename, {'dat': dats_det[-1]})
def invariant_perturbation(equation_system, constants, variables, operators):
permuted_system = np.permute(equation_system)
equation = equation_system.join(',')
all_vars = [chr(i) for i in range(97, 123)]
for var in variables():
replacement = np.random.choice(all_vars)
all_vars.remove(replacement)
equation = equation.replace(var, replacement)
for i, char in enumerate(equation):
if char in ['+', '-']:
left_begin = EquivalenceDataset.get_expression_subtree_suffix(equation, i - 1, operators)
right_end = EquivalenceDataset.get
def decode_segmap(mask, unk_label=255):
"""Decode segmentation label prediction as RGB images
Args:
mask (torch.tensor): class map with dimensions (B, M,N), where the value at
a given location is the integer denoting the class index.
Returns:
(np.ndarray): colored image of shape (BM, BN, 3)
"""
mask[mask == unk_label] == 0
mask = mask.numpy()
cmap = get_pascal_labels()
cmap_exp = cmap[..., None]
colored = cmap[mask].squeeze()
grid = make_grid(torch.tensor(colored).permute(0, 3, 1, 2))
return np.permute(grid, (1, 2, 0))
def generate_data(path, savepath):
subjs = [item for item in os.listdir(path) if '.' not in item]
num_cond = get_num_conds(os.path.join(path, subjs[0]))
subj_data = [[dcpy(empty_map) for i in range(num_cond)]
for j in range(len(subjs))]
for subj_ind in xrange(len(subjs)):
subj = subjs[subj_ind]
print 'Adding subject', subj
subj_path = os.path.join(path, subj)
files = [item for item in os.listdir(subj_path) if 'Raw' in item]
for data_ind in xrange(len(files)):
data_file = files[data_ind]
cond = int(data_file[data_file.rfind('c00') + 3]) - 1
data = loadmat(os.path.join(subj_path, data_file))
raw_data = data['RawTrial'][:, electrodes]
num_TP = raw_data.shape[0] / data['NmbEpochs']
raw_data = permute(raw_data.reshape(num_TP, data['NmbEpochs'],
len(electrodes)),
axes=(1, 2, 0))
valid_epochs = [i for i in range(data['NmbEpochs']) if \
np.sum(data['IsEpochOK'][i]) > num_channels/2]
raw_data = raw_data[valid_epochs, :, :]
num_epochs = raw_data.shape[0]
if num_epochs:
entry = subj_data[subj_ind][cond]
entry['groups'].append(num_epochs)
entry['trials'] = concat(entry['trials'], raw_data)
raw_data_mean = np.mean(raw_data, axis=0)[None, :]
entry['mean_trials'] = concat(entry['mean_trials'],
raw_data_mean)
for cond in xrange(num_cond):
entry = subj_data[subj_ind][cond]
entry['groups'] = np.squeeze(entry['groups'])
entry['trials'] = np.squeeze(entry['trials'])
entry['mean_trials'] = np.squeeze(entry['mean_trials'])
if not os.path.isdir(savepath):
os.makedirs(savepath)
savemat(os.path.join(savepath, 'subj_data'), {'subj_data': subj_data})
return subj_data
def perturbImage(img, pmesh):
mr = 89
mc = 69
tr = 100
tc = 100
mg = 5
[mh, mw,c] = img.shape
mx = np.linspace(mh, 1, mr)
my = np.linspace(1, mw, mc)
[my,mx] = np.meshgrid(mx,my)
smesh = np.concatenate((mx,my),axis=1)
px = pmesh[:, 0]
py = pmesh[:, 1]
minx = np.min(px)
maxx = np.max(px)
miny = np.min(py)
maxy = np.max(py)
px = (px - minx) / (maxx - minx)
py = (py - miny) / (maxy - miny)
px = px * (tc - 2 * mg - 1) + 1 + mg
py = py * (tr - 2 * mg - 1) + 1 + mg
pmesh = np.concatenate((px, py),axis=0);
print(pmesh.shape)
fm = localWarp(pmesh, smesh, tr, tc, mr, mc);
fm = np.reshape(fm, 2, tr, tc);
fm = np.permute(fm, [2, 3, 1]);
fm = np.concatenate((fm[2 : end, :, :], fm[1, :, :]),axis=0)
fm = np.concatenate((fm[:, 2 : end, :], fm[:, 1, :]),axis = 1)
pimg = imgMeshWarp(img, fm)
pv = pmesh
pv = np.reshape(pv, mc, mr, 2)
pv = pv[:, end : -1 : 1, :]
pv = np.reshape(pv, (-1, 2))
return pimg
def save_np_img(fname, x):
if x.shape[0] == 1:
x = np.tile(x, (3, 1, 1))
img = Image.fromarray((np.permute(x, (1, 2, 0)) * 255.0).astype(np.uint8),
'RGB')
img.save(fname)
def randomize_position(pos):
# Find non indel species
I = np.where(pos != '-')
Iperm = np.permute(I)
return pos[Iperm]
def shuffle_random(x, ind):
return np.permute(x)
