def interp(self, m):
# q = data_extra[self.outs[0]]["|q|"]
# a = self.a()
p = self.point_value()
zeros = tf.zeros_like(m)
ones = tf.ones_like(m)
def poly_i(i, xi):
tmp = zeros
for j in range(i - 1, i + 1):
if j < 0 or j > self.interp_N - 1:
continue
r = ones
for k in range(j, j + 2):
if k == i:
continue
r = r * (m - xi[k]) / (xi[i] - xi[k])
r = tf.where((m >= xi[j]) & (m < xi[j + 1]), r, zeros)
tmp = tmp + r
return tmp
h = tf.stack(
[poly_i(i, self.points) for i in range(1, self.interp_N - 1)],
axis=-1,
)
h = tf.stop_gradient(h)
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
ret_r = tf.reduce_sum(h * p_r, axis=-1)
ret_i = tf.reduce_sum(h * p_i, axis=-1)
return tf.complex(ret_r, ret_i)
def interp(self, m):
zeros = tf.zeros_like(m)
p = self.point_value()
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
xi_m = self.h_matrix
x_m = spline_x_matrix(m, self.points)
x_m = tf.expand_dims(x_m, axis=-1)
m_xi = tf.reduce_sum(xi_m * x_m, axis=[-3, -2])
m_xi = tf.stop_gradient(m_xi)
ret_r = tf.reduce_sum(tf.cast(m_xi, p_r.dtype) * p_r, axis=-1)
ret_i = tf.reduce_sum(tf.cast(m_xi, p_i.dtype) * p_i, axis=-1)
return tf.complex(ret_r, ret_i)
def interp(self, m):
p = self.point_value()
ones = tf.ones_like(m)
zeros = tf.zeros_like(m)
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
h, b = get_matrix_interp1d3_v2(m, self.points)
h = tf.stop_gradient(h)
f = lambda x: tf.reshape(
tf.matmul(tf.cast(h, x.dtype), tf.reshape(x, (-1, 1))), b.shape
) + tf.cast(b, x.dtype)
ret_r = f(p_r)
ret_i = f(p_i)
return tf.complex(ret_r, ret_i)
def get_bin_index(self, m):
if self.fix_width:
m_min = tf.convert_to_tensor(self.points[0], m.dtype)
m_max = tf.convert_to_tensor(self.points[-1], m.dtype)
delta_width = (m_max - m_min) / (self.interp_N - 1)
bin_idx = tf.histogram_fixed_width_bins(
m,
[m_min - delta_width, m_max + delta_width],
nbins=self.interp_N + 1,
dtype=tf.dtypes.int64,
)
else:
dig = lambda x, y: tf.numpy_function(np.digitize, [x, y], tf.int64)
bin_idx = dig(m, self.points)
bin_idx = bin_idx - 1
# print(tf.reduce_max(bin_idx), tf.reduce_min(bin_idx))
bin_idx = tf.stop_gradient(bin_idx)
return bin_idx
def interp(self, m):
p = self.point_value()
ones = tf.ones_like(m)
zeros = tf.zeros_like(m)
def add_f(x, bl, br):
return tf.where((x > bl) & (x <= br), ones, zeros)
x_bin = tf.stack(
[
add_f(
m,
(self.points[i] + self.points[i + 1]) / 2,
(self.points[i + 1] + self.points[i + 2]) / 2,
) for i in range(self.interp_N - 2)
],
axis=-1,
)
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
x_bin = tf.stop_gradient(x_bin)
ret_r = tf.reduce_sum(x_bin * p_r, axis=-1)
ret_i = tf.reduce_sum(x_bin * p_i, axis=-1)
return tf.complex(ret_r, ret_i)