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# Colormap Collections
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CMasher provides 53 scientifically designed colormaps organized by type. All colormaps are perceptually uniform, color-vision deficiency friendly, and available in both normal and reversed versions. Cyclic colormaps also include shifted variants.
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## Capabilities
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### Sequential Colormaps
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Colormaps that transition smoothly from one color to another, ideal for representing ordered data with a natural progression.
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```python { .api }
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# Available sequential colormaps (37 total)
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cmrcm.amber       # ListedColormap
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cmrcm.amethyst    # ListedColormap  
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cmrcm.apple       # ListedColormap
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cmrcm.arctic      # ListedColormap
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cmrcm.bubblegum   # ListedColormap
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cmrcm.chroma      # ListedColormap
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cmrcm.cosmic      # ListedColormap
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cmrcm.dusk        # ListedColormap
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cmrcm.eclipse     # ListedColormap
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cmrcm.ember       # ListedColormap
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cmrcm.emerald     # ListedColormap
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cmrcm.fall        # ListedColormap
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cmrcm.flamingo    # ListedColormap
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cmrcm.freeze      # ListedColormap
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cmrcm.gem         # ListedColormap
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cmrcm.ghostlight  # ListedColormap
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cmrcm.gothic      # ListedColormap
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cmrcm.horizon     # ListedColormap
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cmrcm.jungle      # ListedColormap
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cmrcm.lavender    # ListedColormap
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cmrcm.lilac       # ListedColormap
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cmrcm.neon        # ListedColormap
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cmrcm.neutral     # ListedColormap
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cmrcm.nuclear     # ListedColormap
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cmrcm.ocean       # ListedColormap
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cmrcm.pepper      # ListedColormap
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cmrcm.rainforest  # ListedColormap
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cmrcm.sapphire    # ListedColormap
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cmrcm.savanna     # ListedColormap
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cmrcm.sepia       # ListedColormap
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cmrcm.sunburst    # ListedColormap
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cmrcm.swamp       # ListedColormap
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cmrcm.torch       # ListedColormap
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cmrcm.toxic       # ListedColormap
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cmrcm.tree        # ListedColormap
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cmrcm.tropical    # ListedColormap
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cmrcm.voltage     # ListedColormap
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```
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#### Usage Example
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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import cmasher.cm as cmrcm
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# Use sequential colormap for heatmap
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data = np.random.rand(10, 10)
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plt.imshow(data, cmap=cmrcm.rainforest)
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plt.colorbar(label='Values')
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plt.title('Sequential Colormap Example')
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plt.show()
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```
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### Diverging Colormaps
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Colormaps with a neutral central color and contrasting colors at the extremes, perfect for data with a meaningful midpoint or zero value.
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```python { .api }
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# Available diverging colormaps (12 total)
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cmrcm.fusion      # ListedColormap
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cmrcm.guppy       # ListedColormap
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cmrcm.holly       # ListedColormap
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cmrcm.iceburn     # ListedColormap
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cmrcm.pride       # ListedColormap
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cmrcm.prinsenvlag # ListedColormap
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cmrcm.redshift    # ListedColormap
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cmrcm.seaweed     # ListedColormap
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cmrcm.viola       # ListedColormap
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cmrcm.waterlily   # ListedColormap
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cmrcm.watermelon  # ListedColormap
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cmrcm.wildfire    # ListedColormap
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```
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#### Usage Example
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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import cmasher.cm as cmrcm
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# Use diverging colormap for correlation matrix
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data = np.random.randn(10, 10)
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correlation = np.corrcoef(data)
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plt.imshow(correlation, cmap=cmrcm.iceburn, vmin=-1, vmax=1)
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plt.colorbar(label='Correlation')
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plt.title('Diverging Colormap Example')
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plt.show()
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```
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### Cyclic Colormaps
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Colormaps that wrap around seamlessly, suitable for periodic data like angles, phases, or time-of-day.
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```python { .api }
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# Available cyclic colormaps (4 total)
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cmrcm.copper     # ListedColormap
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cmrcm.emergency  # ListedColormap
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cmrcm.infinity   # ListedColormap
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cmrcm.seasons    # ListedColormap
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```
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#### Usage Example
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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import cmasher.cm as cmrcm
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# Use cyclic colormap for angular data
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theta = np.linspace(0, 2*np.pi, 100)
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r = np.linspace(0, 1, 50)
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T, R = np.meshgrid(theta, r)
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data = np.sin(3*T)
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plt.subplot(projection='polar')
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plt.pcolormesh(T, R, data, cmap=cmrcm.seasons)
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plt.colorbar(label='Phase')
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plt.title('Cyclic Colormap Example')
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plt.show()
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```
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### Reversed and Shifted Versions
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All colormaps have reversed versions (with `_r` suffix) and cyclic colormaps have shifted versions (with `_s` suffix).
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```python { .api }
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# Reversed versions
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cmrcm.rainforest_r  # ListedColormap - reversed rainforest
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cmrcm.iceburn_r     # ListedColormap - reversed iceburn
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cmrcm.seasons_r     # ListedColormap - reversed seasons
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# Shifted versions (cyclic only)
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cmrcm.seasons_s     # ListedColormap - shifted seasons
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cmrcm.copper_s      # ListedColormap - shifted copper
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cmrcm.emergency_s   # ListedColormap - shifted emergency
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cmrcm.infinity_s    # ListedColormap - shifted infinity
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# Reversed + shifted versions (cyclic only)
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cmrcm.seasons_s_r   # ListedColormap - reversed shifted seasons
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cmrcm.copper_s_r    # ListedColormap - reversed shifted copper
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cmrcm.emergency_s_r # ListedColormap - reversed shifted emergency
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cmrcm.infinity_s_r  # ListedColormap - reversed shifted infinity
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```
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### Colormap Access Patterns
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```python { .api }
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# Direct access to individual colormaps
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import cmasher.cm as cmrcm
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colormap = cmrcm.rainforest
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# Access via matplotlib
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import matplotlib.pyplot as plt
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plt.imshow(data, cmap='cmr.rainforest')
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# Access via cmasher utility
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import cmasher as cmr
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import matplotlib as mpl
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colormap = mpl.colormaps['cmr.rainforest']
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```
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### Colormap Collections
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```python { .api }
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import cmasher.cm as cmrcm
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# All colormaps dictionary
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cmrcm.cmap_d: dict[str, ListedColormap]
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# Dictionary with all colormap objects keyed by name
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# Categorized colormaps dictionary  
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cmrcm.cmap_cd: dict[str, dict[str, ListedColormap]]
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# Nested dictionary organized by type:
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# - cmrcm.cmap_cd['sequential']: dict[str, ListedColormap]
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# - cmrcm.cmap_cd['diverging']: dict[str, ListedColormap] 
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# - cmrcm.cmap_cd['cyclic']: dict[str, ListedColormap]
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# - cmrcm.cmap_cd['qualitative']: dict[str, ListedColormap]
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# - cmrcm.cmap_cd['misc']: dict[str, ListedColormap]
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```
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#### Usage Example
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```python
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import cmasher.cm as cmrcm
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# Get all sequential colormaps
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sequential_cmaps = cmrcm.cmap_cd['sequential']
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print(f"Available sequential colormaps: {len(sequential_cmaps)}")
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# Iterate through all diverging colormaps
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for name, cmap in cmrcm.cmap_cd['diverging'].items():
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    print(f"Diverging colormap: {name}")
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# Access specific colormap from collection
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ocean_cmap = cmrcm.cmap_d['ocean']
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```