| Name | crazybin JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Much better than hexbins plots! Use any kind of tile to visualize data and pictures! |
| upload_time | 2024-05-03 21:52:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
hexbin
histogram
tiling
parquet
escher
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Crazybin
You think [hexbin](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hexbin.html) plots are a fancy way to visualize your data? Well, you can go much further... Check out Crazybin to bring your histograms to a whole new level!
Want an example? What about this double sine distribution visualized in the form of [reptiles](https://en.wikipedia.org/wiki/Reptiles_(M._C._Escher)) by [M. C. Escher](https://en.wikipedia.org/wiki/M._C._Escher)?
```python
import numpy as np
import matplotlib.pyplot as plt
from crazybin import crazybin
x=np.linspace(0,10,100)
y=np.linspace(0,10,100)
x,y=np.meshgrid(x,y)
x=x.ravel()
y=y.ravel()
weights=np.sin(x)*np.cos(y)
crazybin(x,y,weights, tile='reptile', cmap='jet', gridsize=4, edgecolor='black')
plt.show()
```
Crazybin provides two top-level methods: `crazybin` is the counterpart of [matplotlib.hexbin](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hexbin.html). `imshow` is similar to [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html). For example, with `imshow`, you can easily turn [Pointillism](https://en.wikipedia.org/wiki/Pointillism) into 'Hexillism'!
```python
import numpy as np
import matplotlib.pyplot as plt
from crazybin import imshow
image=plt.imread('images/grande_jatte_seurat.jpg')/255
imshow(image, tile='hex', gridsize=150)
plt.show()
```
## Usage
`crazybin` and `imshow` are (more or less) drop-in replacements of `matplotlib.hexbin` and `matplotlib.imshow`. For detailed information, check out their doc-strings. See also the examples in the [examples folder](./examples/).
```python
from crazybin import crazybin, imshow
```
Crazybin can also be used in an object oriented style. See [Custom Tiles](#tile-object).
## Tiles
Possible keywords for the `tile` argument are:
### Regular Tiles
- "hex": Regular hexagon
- "hex_rhomb": Composition of a regular hexagon, triangles and rhombs/squares.
- "reptile": Composition of three lizard shaped tiles inspired by M.C. Escher
- "frog": Composition of four frog shaped tiles inspired by M.C. Escher.
### Irregular Tiles
- "pen_rhomb": P3 penrose tiling, consisting of two rhombs with different angles.
### Custom Tiles
#### Via JSON File
Regular tiles can be read from files with `json` format. `v1` and `v2` contain the lattice vectors, defining the translation directions of the tiles. Each tile consists of one or more 'atoms', which are arbitrary shapes which make up the tile.
<details>
<summary>Example JSON for a hexagon</summary>
```json
{
"v1": [
1.5000000000000002,
0.8660254037844379
],
"v2": [
5.551115123125783e-17,
1.7320508075688774
],
"atoms": [
[
[
-0.49999999999999994,
0.8660254037844387
],
[
-1.0,
2.1460752085336256e-16
],
[
-0.5000000000000002,
-0.8660254037844385
],
[
0.49999999999999994,
-0.8660254037844387
],
[
1.0,
-6.031855794721673e-16
],
[
0.5,
0.8660254037844387
],
[
-0.49999999999999994,
0.8660254037844387
]
]
]
}
```
</details>
JSON files can be specified by providing a filename with `.json` extension to the tile keyword arguments.
```python
imshow(image, tile='path/to/hex.json', gridsize=150)
```
#### Tile Object
Grids consist of `Tile` objects, which can be instantiated from a list of `shapely.Polygon` objects. For a regular parquet, you need to specify two grid vectors via a `Grid` object, defining the translations of the tiles.
```python
import numpy as np
from shapely import Polygon
import matplotlib.pyplot as plt
from crazybin import Tile, Grid, TileImage, RegularParquetFactory
grid=Grid([0,1],[1,0])
tile=Tile([Polygon([(0,0), (1,0), (1,1), (0,1)])])
fac=RegularParquetFactory(tile, grid)
image=np.random.rand(10,10)
im=TileImage(image, fac, gridsize=10)
im.plot()
plt.show()
```
## Credits
The implementation of the penrose tiling generator was inspired by [this blog article](https://preshing.com/20110831/penrose-tiling-explained/). The "reptile" and "frog" patterns were inspired by [this](https://www.geogebra.org/m/CUdKaHeC#material/vnb3vpsy) and [this](https://www.geogebra.org/m/CUdKaHeC#material/pzjk5fru) [Geogebra tesselation projects](https://www.geogebra.org/m/CUdKaHeC). Artist images were taken from Wikipedia.
<img src="https://github.com/Ockenfuss/crazybin/blob/main/examples/images/frogs_gaussian.jpg" width="30%"></img> <img src="https://github.com/Ockenfuss/crazybin/blob/main/examples/images/hex_rhomb.jpg" width="30%"></img> <img src="https://github.com/Ockenfuss/crazybin/blob/main/examples/images/great_wave.jpg" width="30%"></img>
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"description": "# Crazybin\n\nYou think [hexbin](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hexbin.html) plots are a fancy way to visualize your data? Well, you can go much further... Check out Crazybin to bring your histograms to a whole new level!\n\nWant an example? What about this double sine distribution visualized in the form of [reptiles](https://en.wikipedia.org/wiki/Reptiles_(M._C._Escher)) by [M. C. Escher](https://en.wikipedia.org/wiki/M._C._Escher)?\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom crazybin import crazybin\n\nx=np.linspace(0,10,100)\ny=np.linspace(0,10,100)\nx,y=np.meshgrid(x,y)\nx=x.ravel()\ny=y.ravel()\nweights=np.sin(x)*np.cos(y)\n\ncrazybin(x,y,weights, tile='reptile', cmap='jet', gridsize=4, edgecolor='black')\nplt.show()\n```\n\n\n\nCrazybin provides two top-level methods: `crazybin` is the counterpart of [matplotlib.hexbin](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hexbin.html). `imshow` is similar to [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html). For example, with `imshow`, you can easily turn [Pointillism](https://en.wikipedia.org/wiki/Pointillism) into 'Hexillism'!\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom crazybin import imshow\n\nimage=plt.imread('images/grande_jatte_seurat.jpg')/255\nimshow(image, tile='hex', gridsize=150)\nplt.show()\n```\n\n\n\n## Usage\n`crazybin` and `imshow` are (more or less) drop-in replacements of `matplotlib.hexbin` and `matplotlib.imshow`. For detailed information, check out their doc-strings. See also the examples in the [examples folder](./examples/).\n```python\nfrom crazybin import crazybin, imshow\n```\nCrazybin can also be used in an object oriented style. See [Custom Tiles](#tile-object).\n\n## Tiles\nPossible keywords for the `tile` argument are:\n### Regular Tiles\n- \"hex\": Regular hexagon\n- \"hex_rhomb\": Composition of a regular hexagon, triangles and rhombs/squares.\n- \"reptile\": Composition of three lizard shaped tiles inspired by M.C. Escher\n- \"frog\": Composition of four frog shaped tiles inspired by M.C. Escher.\n\n### Irregular Tiles\n- \"pen_rhomb\": P3 penrose tiling, consisting of two rhombs with different angles.\n\n### Custom Tiles\n#### Via JSON File\nRegular tiles can be read from files with `json` format. `v1` and `v2` contain the lattice vectors, defining the translation directions of the tiles. Each tile consists of one or more 'atoms', which are arbitrary shapes which make up the tile.\n<details>\n <summary>Example JSON for a hexagon</summary>\n\n```json\n{\n \"v1\": [\n 1.5000000000000002,\n 0.8660254037844379\n ],\n \"v2\": [\n 5.551115123125783e-17,\n 1.7320508075688774\n ],\n \"atoms\": [\n [\n [\n -0.49999999999999994,\n 0.8660254037844387\n ],\n [\n -1.0,\n 2.1460752085336256e-16\n ],\n [\n -0.5000000000000002,\n -0.8660254037844385\n ],\n [\n 0.49999999999999994,\n -0.8660254037844387\n ],\n [\n 1.0,\n -6.031855794721673e-16\n ],\n [\n 0.5,\n 0.8660254037844387\n ],\n [\n -0.49999999999999994,\n 0.8660254037844387\n ]\n ]\n ]\n}\n```\n</details>\n\n\nJSON files can be specified by providing a filename with `.json` extension to the tile keyword arguments.\n```python\nimshow(image, tile='path/to/hex.json', gridsize=150)\n```\n\n#### Tile Object\nGrids consist of `Tile` objects, which can be instantiated from a list of `shapely.Polygon` objects. For a regular parquet, you need to specify two grid vectors via a `Grid` object, defining the translations of the tiles. \n```python\nimport numpy as np\nfrom shapely import Polygon\nimport matplotlib.pyplot as plt\nfrom crazybin import Tile, Grid, TileImage, RegularParquetFactory\ngrid=Grid([0,1],[1,0])\ntile=Tile([Polygon([(0,0), (1,0), (1,1), (0,1)])])\nfac=RegularParquetFactory(tile, grid)\nimage=np.random.rand(10,10)\nim=TileImage(image, fac, gridsize=10)\nim.plot()\nplt.show()\n```\n\n## Credits\nThe implementation of the penrose tiling generator was inspired by [this blog article](https://preshing.com/20110831/penrose-tiling-explained/). The \"reptile\" and \"frog\" patterns were inspired by [this](https://www.geogebra.org/m/CUdKaHeC#material/vnb3vpsy) and [this](https://www.geogebra.org/m/CUdKaHeC#material/pzjk5fru) [Geogebra tesselation projects](https://www.geogebra.org/m/CUdKaHeC). Artist images were taken from Wikipedia.\n\n<img src=\"https://github.com/Ockenfuss/crazybin/blob/main/examples/images/frogs_gaussian.jpg\" width=\"30%\"></img> <img src=\"https://github.com/Ockenfuss/crazybin/blob/main/examples/images/hex_rhomb.jpg\" width=\"30%\"></img> <img src=\"https://github.com/Ockenfuss/crazybin/blob/main/examples/images/great_wave.jpg\" width=\"30%\"></img>\n",
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