Basic 1d upsampling

In this example we demonstrate how to employ the utility functions from symjax.tensor.interpolation which can be used for upsampling

import matplotlib.pyplot as plt

import symjax
import symjax.tensor as T
import numpy as np

w = T.Placeholder((3,), "float32", name="w")
w_interp1 = T.interpolation.upsample_1d(w, repeat=4, mode="nearest")
w_interp2 = T.interpolation.upsample_1d(
    w, repeat=4, mode="linear", boundary_condition="mirror"
)
w_interp3 = T.interpolation.upsample_1d(
    w, repeat=4, mode="linear", boundary_condition="periodic"
)
w_interp4 = T.interpolation.upsample_1d(w, repeat=4)

f = symjax.function(w, outputs=[w_interp1, w_interp2, w_interp3, w_interp4])

samples = f(np.array([1, 2, 3]))
fig = plt.figure(figsize=(6, 6))
plt.subplot(411)
plt.plot(samples[0], "xg", linewidth=3, markersize=15)
plt.plot([0, 5, 10], [1, 2, 3], "ok", alpha=0.5)
plt.title("nearest-periodic")
plt.xticks([])

plt.subplot(412)
plt.plot(samples[1], "xg", linewidth=3, markersize=15)
plt.plot([0, 5, 10], [1, 2, 3], "ok", alpha=0.5)
plt.title("linear-mirror")
plt.xticks([])

plt.subplot(413)
plt.plot(samples[2], "xg", linewidth=3, markersize=15)
plt.plot([0, 5, 10], [1, 2, 3], "ok", alpha=0.5)
plt.title("linear-periodic")
plt.xticks([])

plt.subplot(414)
plt.plot(samples[3], "xg", linewidth=3, markersize=15)
plt.plot([0, 5, 10], [1, 2, 3], "ok", alpha=0.5)
plt.title("constant-0")

plt.tight_layout()