# Sum#

This example shows how to use the `pylops.Sum` operator to stack values along an axis of a multi-dimensional array

```import matplotlib.gridspec as pltgs
import matplotlib.pyplot as plt
import numpy as np

import pylops

plt.close("all")
```

Let’s start by defining a 2-dimensional data

```ny, nx = 5, 7
x = (np.arange(ny * nx)).reshape(ny, nx)
```

We can now create the operator and peform forward and adjoint

```Sop = pylops.Sum(dims=(ny, nx), axis=0)

y = Sop * x

gs = pltgs.GridSpec(1, 7)
fig = plt.figure(figsize=(7, 4))
ax = plt.subplot(gs[0, 0:3])
im = ax.imshow(x, cmap="rainbow", vmin=0, vmax=ny * nx)
ax.set_title("x", size=20, fontweight="bold")
ax.set_xticks(np.arange(nx - 1) + 0.5)
ax.set_yticks(np.arange(ny - 1) + 0.5)
ax.grid(linewidth=3, color="white")
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.axis("tight")
ax = plt.subplot(gs[0, 3])
ax.imshow(y[:, np.newaxis], cmap="rainbow", vmin=0, vmax=ny * nx)
ax.set_title("y", size=20, fontweight="bold")
ax.set_xticks([])
ax.set_yticks(np.arange(nx - 1) + 0.5)
ax.grid(linewidth=3, color="white")
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.axis("tight")
ax = plt.subplot(gs[0, 4:])
ax.imshow(xadj, cmap="rainbow", vmin=0, vmax=ny * nx)
ax.set_xticks(np.arange(nx - 1) + 0.5)
ax.set_yticks(np.arange(ny - 1) + 0.5)
ax.grid(linewidth=3, color="white")
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.axis("tight")
plt.tight_layout()
```

Note that since the Sum operator creates and under-determined system of equations (data has always lower dimensionality than the model), an exact inverse is not possible for this operator.

Total running time of the script: (0 minutes 0.220 seconds)

Gallery generated by Sphinx-Gallery