# Quickstart¶

This Jupyter notebook demonstrates how a hyperbolic tiling can be constructed and plotted using only very few lines of code.

Import tiling object from *hypertiling* library

```
[1]:
```

```
from hypertiling import HyperbolicTiling
```

Set parameters and generate the tiling

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[2]:
```

```
p = 7
q = 3
nlayers = 5
T = HyperbolicTiling(p,q,nlayers)
```

The size of the tiling can be queried like this

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[3]:
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```
print("Number of cells:", len(T))
```

```
Number of cells: 232
```

Import and use available script for a quick plot

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```
from hypertiling.graphics.plot import plot_tiling
import matplotlib.cm as cmap
import numpy as np
```

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```

```
colors = np.random.rand(len(T))
plot_tiling(T, colors, cmap=cmap.RdBu, edgecolor="k", linewidth=0.2);
```

By default, tilings are centered around a “cell”. They can also be centered around a vertex at the origin, using the following syntax

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[14]:
```

```
p = 4
q = 5
nlayers = 4
T = HyperbolicTiling(p, q, nlayers, center="vertex")
```

This example also demonstrates how keyword arguments like “clim” can easily be passed through the plot function

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[15]:
```

```
plot_tiling(T, np.random.rand(len(T)), cmap=cmap.OrRd, edgecolor="w", lw=1, clim=[-1,2]);
```

In the next example we demonstrate how one can easily iterate over the cells of a tiling, e.g. in order to access their attributes

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[16]:
```

```
T = HyperbolicTiling(7, 3, 7, center="cell")
```

Paint cells by number of layer in which they reside

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[17]:
```

```
colors = []
for t in T:
colors.append(t.layer)
plot_tiling(T, colors, cmap=cmap.GnBu, edgecolor="k", lw=0.2);
```

Finally, something which just looks nice :)

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[18]:
```

```
T = HyperbolicTiling(3, 12, 4, center="vertex")
```

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[19]:
```

```
plot_tiling(T, np.random.rand(len(T)), cmap=cmap.OrRd, edgecolor="w", lw=0.4, clim=[-1,2]);
```