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# Python Matplotlib | Triangular Grid Interpolation

## Introduction

In this lab, you will learn how to use Python's Matplotlib library to interpolate data from a triangular grid to a quad grid. We will start by creating a triangulation and then interpolate the data using linear and cubic methods. Finally, we will plot the results.

### VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

## Create Triangulation

The first step is to create a triangulation using the given x, y, and triangles data. We will then plot the triangulation.

``````# Create triangulation.
x = np.asarray([0, 1, 2, 3, 0.5, 1.5, 2.5, 1, 2, 1.5])
y = np.asarray([0, 0, 0, 0, 1.0, 1.0, 1.0, 2, 2, 3.0])
triangles = [[0, 1, 4], [1, 2, 5], [2, 3, 6], [1, 5, 4], [2, 6, 5], [4, 5, 7],
[5, 6, 8], [5, 8, 7], [7, 8, 9]]
triang = mtri.Triangulation(x, y, triangles)

# Plot the triangulation.
plt.triplot(triang, 'ko-')
plt.title('Triangular grid')
plt.show()
``````

## Interpolate Data using Linear Method

The second step is to interpolate the data using the linear method. We will create a regularly-spaced quad grid and then use the LinearTriInterpolator method to interpolate the data. Finally, we will plot the interpolated data.

``````# Interpolate to regularly-spaced quad grid.
z = np.cos(1.5 * x) * np.cos(1.5 * y)
xi, yi = np.meshgrid(np.linspace(0, 3, 20), np.linspace(0, 3, 20))

# Interpolate using linear method.
interp_lin = mtri.LinearTriInterpolator(triang, z)
zi_lin = interp_lin(xi, yi)

# Plot the interpolated data.
plt.contourf(xi, yi, zi_lin)
plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
plt.title("Linear interpolation")
plt.show()
``````

## Interpolate Data using Cubic Method

The third step is to interpolate the data using the cubic method. We will use the CubicTriInterpolator method with the kind parameter set to 'geom' or 'min_E'. Finally, we will plot the interpolated data.

``````# Interpolate using cubic method with kind=geom.
interp_cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')
zi_cubic_geom = interp_cubic_geom(xi, yi)

# Plot the interpolated data.
plt.contourf(xi, yi, zi_cubic_geom)
plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
plt.title("Cubic interpolation, kind='geom'")
plt.show()

# Interpolate using cubic method with kind=min_E.
interp_cubic_min_E = mtri.CubicTriInterpolator(triang, z, kind='min_E')
zi_cubic_min_E = interp_cubic_min_E(xi, yi)

# Plot the interpolated data.
plt.contourf(xi, yi, zi_cubic_min_E)
plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
plt.title("Cubic interpolation, kind='min_E'")
plt.show()
``````

## Summary

In this lab, we learned how to use Python's Matplotlib library to interpolate data from a triangular grid to a quad grid. We started by creating a triangulation and then interpolated the data using linear and cubic methods. Finally, we plotted the results.

π Practice Now: Interpolation From Triangular to Quad Grid