def plot_trans_ts(json_data, kernel, bias, use_padding, stride):
dff = pd.read_json(json_data, orient='values').to_numpy().flatten()
original_length = dff.shape[0]
kernel = np.array(json.loads(kernel)['dila_kernel'])
bias = float(bias)
stride = int(stride)
if use_padding:
# add padding
padlen = len(kernel) // 2
ts = np.zeros(len(dff) + 2 * padlen)
ts[padlen:(padlen + len(dff))] = dff
dff = ts
transformed = utls.apply_kernel(dff, kernel, bias, stride)
layout = {
'title': {
'text': 'Transformed time series'
},
'xaxis': {
'range': [0, original_length]
}
}
ppv = utls.ppv(transformed)
max_value = np.max(transformed)
min_value = np.min(transformed)
return go.Figure(
data=[go.Scatter(y=transformed)], layout=layout
), f"ppv = {ppv:.2f}", f"min = {min_value:.2f}", f"max = {max_value:.2f}"
def gabor(im, W, angles):
(x, y) = im.size
im_load = im.load()
freqs = frequency.freq(im, W, angles)
print "computing local ridge frequency done"
gauss = utils.gauss_kernel(3)
utils.apply_kernel(freqs, gauss)
for i in range(1, x / W - 1):
for j in range(1, y / W - 1):
kernel = gabor_kernel(W, angles[i][j], freqs[i][j])
for k in range(0, W):
for l in range(0, W):
im_load[i * W + k, j * W + l] = utils.apply_kernel_at(
lambda x, y: im_load[x, y], kernel, i * W + k,
j * W + l)
return im
def gabor(im, W, angles):
(x, y) = im.size
im_load = im.load()
freqs = frequency.freq(im, W, angles)
print "computing local ridge frequency done"
gauss = utils.gauss_kernel(3)
utils.apply_kernel(freqs, gauss)
for i in range(1, x / W - 1):
for j in range(1, y / W - 1):
kernel = gabor_kernel(W, angles[i][j], freqs[i][j])
for k in range(0, W):
for l in range(0, W):
im_load[i * W + k, j * W + l] = utils.apply_kernel_at(
lambda x, y: im_load[x, y],
kernel,
i * W + k,
j * W + l)
return im