Matplotlib Colormap Normalizations

Introduction

In this lab, we will learn how to use Matplotlib to map colormaps onto data in non-linear ways. We will demonstrate the use of norm to create logarithmic, power-law, symmetric logarithmic, and custom normalizations. We will also learn how to use BoundaryNorm to provide boundaries for colors.

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.

Skills Graph

Lognorm

We will create a low hump with a spike coming out of the top, which needs to have the z/color axis on a log scale, so that we see both the hump and the spike.

N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]

Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
Z = Z1 + 50 * Z2

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolor(X, Y, Z,
                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),
                   cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='max')

pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[1], extend='max')

PowerNorm

We will create a power-law trend in X that partially obscures a rectified sine wave in Y. We will then remove the power-law using a PowerNorm.

X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]
Z1 = (1 + np.sin(Y * 10.)) * X**2

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1. / 2.),
                       cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='max')

pcm = ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[1], extend='max')

SymLogNorm

We will create two humps, one negative and one positive, with the positive hump having 5 times the amplitude. Linearly, we cannot see detail in the negative hump. We will logarithmically scale the positive and negative data separately using a SymLogNorm.

X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z = 5 * np.exp(-X**2 - Y**2)

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z,
                       norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
                                              vmin=-1.0, vmax=1.0, base=10),
                       cmap='RdBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='both')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z),
                       shading='nearest')
fig.colorbar(pcm, ax=ax[1], extend='both')

Custom Norm

We will create an example with a customized normalization. This example uses the previous example, and normalizes the negative data differently from the positive.

X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

class MidpointNormalize(colors.Normalize):
    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
        self.midpoint = midpoint
        super().__init__(vmin, vmax, clip)

    def __call__(self, value, clip=None):
        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z,
                       norm=MidpointNormalize(midpoint=0.),
                       cmap='RdBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='both')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z),
                       shading='nearest')
fig.colorbar(pcm, ax=ax[1], extend='both')

BoundaryNorm

We will provide boundaries for colors using BoundaryNorm.

fig, ax = plt.subplots(3, 1, figsize=(8, 8))
ax = ax.flatten()
bounds = np.linspace(-1, 1, 10)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[0].pcolormesh(X, Y, Z,
                       norm=norm,
                       cmap='RdBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')

bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')

pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z1),
                       shading='nearest')
fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')

plt.show()

Summary

In this lab, we learned how to use Matplotlib to map colormaps onto data in non-linear ways using various normalizations such as LogNorm, PowerNorm, SymLogNorm, and custom normalizations. We also learned how to use BoundaryNorm to provide boundaries for colors.