### Plot cmocean Colormap Gallery

Source: http://matplotlib.org/cmocean

Use this function to visualize all available cmocean colormaps. Ensure the 'plots' submodule is installed.

```python
import cmocean
cmocean.plots.plot_gallery()

```

--------------------------------

### Plotting and Quick Plotting with cmocean Gray Colormap

Source: http://matplotlib.org/cmocean

Demonstrates how to use the cmocean.plots.test and cmocean.plots.quick_plot functions with the 'gray' colormap. This is useful for generic sequential datasets.

```python
import cmocean
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 3))
ax = fig.add_subplot(1, 2, 1)
cmocean.plots.test(cmocean.cm.gray, ax=ax)
ax = fig.add_subplot(1, 2, 2)
cmocean.plots.quick_plot(cmocean.cm.gray, ax=ax)
```

--------------------------------

### Plotting cmocean Colormaps

Source: http://matplotlib.org/cmocean

Demonstrates how to plot cmocean colormaps using test and quick_plot functions. Requires importing cmocean and matplotlib.pyplot.

```python
import cmocean
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 3))
ax = fig.add_subplot(1, 2, 1)
cmocean.plots.test(cmocean.cm.thermal, ax=ax)
ax = fig.add_subplot(1, 2, 2)
cmocean.plots.quick_plot(cmocean.cm.algae, ax=ax)
```

--------------------------------

### Generating a Colormap Dictionary

Source: http://matplotlib.org/cmocean

Creates a dictionary representation of a specific colormap (e.g., cmocean.cm.matter) with a specified number of entries (N). This dictionary can be used to define custom colormaps.

```python
import cmocean

cmdict = cmocean.tools.get_dict(cmocean.cm.matter, N=9)
```

--------------------------------

### Plot cmocean Colormap Lightness

Source: http://matplotlib.org/cmocean

Visualize the lightness of cmocean colormaps to understand their perceptual properties. Requires the 'plots' submodule.

```python
import cmocean
cmocean.plots.plot_lightness()

```

--------------------------------

### Plotting Reversed cmocean Colormaps

Source: http://matplotlib.org/cmocean

Demonstrates how to plot both a standard colormap (e.g., cmocean.cm.gray) and its reversed version (cmocean.cm.gray_r) side-by-side for comparison. Requires importing cmocean and matplotlib.pyplot.

```python
import cmocean
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 3))
ax = fig.add_subplot(1, 2, 1)
cmocean.plots.test(cmocean.cm.gray, ax=ax)
ax = fig.add_subplot(1, 2, 2)
cmocean.plots.test(cmocean.cm.gray_r, ax=ax)
fig.tight_layout()
```

--------------------------------

### Visualize Viscm Properties of a Colormap

Source: http://matplotlib.org/cmocean

Examine specific colormap properties like grayscale printing, perceptual deltas, and color blindness viewability using the viscm tool. Set saveplot=True to save the visualization.

```python
import cmocean
cmocean.plots.wrap_viscm(cmocean.cm.haline, saveplot=False)

```

--------------------------------

### Crop One End of a Colormap by Percent

Source: http://matplotlib.org/cmocean

Utilize `cmocean.tools.crop_by_percent` to remove a percentage from either the maximum ('max') or minimum ('min') end of a colormap. This is helpful when certain data ranges, like super-saturated conditions, are not relevant.

```python
import cmocean
import matplotlib.pyplot as plt

cmap = cmocean.cm.oxy
fig, axes = plt.subplots(1, 2, figsize=(8,4))
A = np.random.randint(0, 101, (10,10))
mappable = axes[0].pcolormesh(A, vmin=0, vmax=100, cmap=cmap)
axes[0].set_title('Values go to super-saturated')
fig.colorbar(mappable, ax=axes[0])

newcmap = cmocean.tools.crop_by_percent(cmap, 20, which='max', N=None)
A[A>80] = 80
mappable = axes[1].pcolormesh(A, vmin=0, vmax=80, cmap=newcmap)
axes[1].set_title('Values are all\nbelow super-saturated')
fig.colorbar(mappable, ax=axes[1])
```

--------------------------------

### Accessing cmocean Colormap Dictionary

Source: http://matplotlib.org/cmocean

Loads a dictionary containing all available colormap instances from the cmocean.cm module. This dictionary can be used to access colormaps by name.

```python
import cmocean

cmocean.cm.cmap_d
```

--------------------------------

### Crop Colormap by Data Values

Source: http://matplotlib.org/cmocean

Employ `cmocean.tools.crop` to clip a colormap based on the minimum and maximum data values intended for plotting. This ensures the colormap range accurately reflects the data's extent, useful for combined datasets like bathymetry and topography.

```python
import cmocean
import matplotlib.pyplot as plt

cmap = cmocean.cm.topo
fig, axes = plt.subplots(1, 2, figsize=(8,4))
A = np.random.randint(-50, 201, (10,10))
mappable = axes[0].pcolormesh(A, vmin=-200, vmax=200, cmap=cmap)
axes[0].set_title('No values<-50, but still\nshow possibility in colorbar')
fig.colorbar(mappable, ax=axes[0])

newcmap = cmocean.tools.crop(cmap, -50, 200, 0)
mappable = axes[1].pcolormesh(A, vmin=-50, vmax=200, cmap=newcmap)
axes[1].set_title('Colorbar only shows color\nrange used by data')
fig.colorbar(mappable, ax=axes[1])
```

--------------------------------

### Crop Both Ends of a Colormap by Percent

Source: http://matplotlib.org/cmocean

Use `cmocean.tools.crop_by_percent` to remove a specified percentage from both ends of a colormap. This is useful for reducing the lightness range and excluding extreme values.

```python
import cmocean
import matplotlib.pyplot as plt

cmap = cmocean.cm.tarn
fig, axes = plt.subplots(1, 2, figsize=(8,4))
A = np.random.randint(-5, 6, (10,10))
mappable = axes[0].pcolormesh(A, cmap=cmap)
axes[0].set_title('Full diverging colormap')
fig.colorbar(mappable, ax=axes[0])

newcmap = cmocean.tools.crop_by_percent(cmap, 30, which='both', N=None)
mappable = axes[1].pcolormesh(A, cmap=newcmap)
axes[1].set_title('Same colormap,\n30% removed from each end')
fig.colorbar(mappable, ax=axes[1])
```

--------------------------------

### Using Registered cmocean Colormaps in Matplotlib

Source: http://matplotlib.org/cmocean

Illustrates how cmocean colormaps are registered with matplotlib, allowing them to be called directly by their registered name (e.g., 'cmo.amp'). Requires importing cmocean and matplotlib.pyplot, and numpy.

```python
import cmocean
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(4, 3))
ax = fig.add_subplot(111)
Z = np.random.randn(10,10)
ax.pcolormesh(Z, cmap='cmo.amp')
```

--------------------------------

### Lightening cmocean Colormaps

Source: http://matplotlib.org/cmocean

Shows how to lighten a colormap using the cmocean.tools.lighten() function with an alpha value. This is useful for overlaying contours or lines on plots. Requires importing cmocean, cmocean.cm, and matplotlib.pyplot, and numpy.

```python
import cmocean
import cmocean.cm as cmo
import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(8, 3))
ax = fig.add_subplot(1, 2, 1)
Z = np.random.randn(10,10)
ax.pcolormesh(Z, cmap=cmo.matter)

ax = fig.add_subplot(1, 2, 2)
lightcmap = cmocean.tools.lighten(cmo.matter, 0.5)
ax.pcolormesh(Z, cmap=lightcmap)
fig.tight_layout()
```

--------------------------------

### Accessing cmocean Colormap Names

Source: http://matplotlib.org/cmocean

Retrieves a list of all available colormap names from the cmocean.cm module. This is useful for iterating through or selecting specific colormaps.

```python
import cmocean

cmocean.cm.cmapnames
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.