samila
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Generative Art Generator
Table of contents
- Overview
- Installation
- Usage
- Mathematical Details
- Try Samila in Your Browser
- Issues & Bug Reports
- Dependencies
- Social Media
- Contribution
- References
- Acknowledgments
- Authors
- License
- Show Your Support
- Changelog
- Code of Conduct
Overview
Samila is a generative art generator written in Python, Samila let's you create arts based on many thousand points. The position of every single point is calculated by a formula, which has random parameters. Because of the random numbers, every image looks different.
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Github Stars |
Branch | master | dev |
CI |
Code Quality |
Installation
Source code
- Download Version 0.8 or Latest Source
- Run
pip install -r requirements.txt
orpip3 install -r requirements.txt
(Need root access) - Run
python3 setup.py install
orpython setup.py install
(Need root access)
PyPI
- Check Python Packaging User Guide
- Run
pip install samila==0.8
orpip3 install samila==0.8
(Need root access)
Easy install
- Run
easy_install --upgrade samila
(Need root access)
Usage
Magic
>>> import matplotlib.pyplot as plt
>>> from samila import GenerativeImage
>>> g = GenerativeImage()
>>> g.generate()
>>> g.plot()
>>> plt.show()
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Basic
>>> import random
>>> import math
>>> def f1(x, y):
result = random.uniform(-1,1) * x**2 - math.sin(y**2) + abs(y-x)
return result
>>> def f2(x, y):
result = random.uniform(-1,1) * y**3 - math.cos(x**2) + 2*x
return result
>>> g = GenerativeImage(f1, f2)
>>> g.generate()
>>> g.plot()
>>> g.seed
188781
>>> plt.show()
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Projection
>>> from samila import Projection
>>> g = GenerativeImage(f1, f2)
>>> g.generate()
>>> g.plot(projection=Projection.POLAR)
>>> g.seed
829730
>>> plt.show()
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- Supported projections :
RECTILINEAR
,POLAR
,AITOFF
,HAMMER
,LAMBERT
,MOLLWEIDE
andRANDOM
- Default projection is
RECTILINEAR
Range
>>> g = GenerativeImage(f1, f2)
>>> g.generate(start=-2*math.pi, step=0.01, stop=0)
>>> g.plot()
>>> g.seed
234752
>>> plt.show()
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Color
>>> g = GenerativeImage(f1, f2)
>>> g.generate()
>>> g.plot(color="yellow", bgcolor="black", projection=Projection.POLAR)
>>> g.seed
1018273
>>> plt.show()

-
Supported colors are available in
VALID_COLORS
list -
color
andbgcolor
parameters supported formats:- Color name (example:
color="yellow"
) - RGB/RGBA (example:
color=(0.1,0.1,0.1)
,color=(0.1,0.1,0.1,0.1)
) - Hex (example:
color="#eeefff"
) - Random (example:
color="random"
) - Complement (example:
color="complement", bgcolor="blue"
) - Transparent (example:
bgcolor="transparent"
)
- Color name (example:
⚠️ Transparent mode is only available for background
Regeneration
>>> g = GenerativeImage(f1, f2)
>>> g.generate(seed=1018273)
>>> g.plot(projection=Projection.POLAR)
>>> plt.show()

NFT.storage
Upload generated image directly to NFT.storage
>>> g.nft_storage(api_key="YOUR_API_KEY")
{'status': True, 'message': 'FILE_LINK'}
Save image
Save generated image
>>> g.save_image(file_adr="test.png")
{'status': True, 'message': 'FILE_PATH'}
Save generated image in higher resolutions
>>> g.save_image(file_adr="test.png", depth=5)
{'status': True, 'message': 'FILE_PATH'}
Save data
Save generated image data
>>> g.save_data(file_adr="data.json")
{'status': True, 'message': 'FILE_PATH'}
So you can load it into a GenerativeImage
instance later by
>>> g = GenerativeImage(data=open('data.json', 'r'))
Data structure:
{
"plot": {
"projection": "polar",
"bgcolor": "black",
"color": "snow",
"spot_size": 0.01
},
"matplotlib_version": "3.0.3",
"data1": [
0.3886741692042526,
22.57390286376703,
-0.1646310981668766,
66.23632344600155
],
"data2": [
-0.14588750183600108,
20.197945942677833,
0.5485453260942901,
-589.3284610518896
]
}
Save config
Save generated image config. It contains string formats of functions which is also human readable.
>>> g.save_config(file_adr="config.json")
{'status': True, 'message': 'FILE_PATH'}
So you can load it into a GenerativeImage
instance later by
>>> g = GenerativeImage(config=open('config.json', 'r'))
Config structure:
{
"matplotlib_version": "3.0.3",
"generate": {
"seed": 379184,
"stop": 3.141592653589793,
"step": 0.01,
"start": -3.141592653589793
},
"f2": "random.uniform(-1,1)*math.cos(x*(y**3))+random.uniform(-1,1)*math.ceil(y-x)",
"f1": "random.uniform(-1,1)*math.ceil(y)-random.uniform(-1,1)*y**2+random.uniform(-1,1)*abs(y-x)",
"plot": {
"color": "snow",
"bgcolor": "black",
"projection": "polar",
"spot_size": 0.01
}
}
Mathematical details
Samila is simply a transformation between a square-shaped space from the Cartesian coordinate system to any arbitrary coordination like Polar coordinate system.
Example

We have set of points in the first space (left square) which can be define as follow:

And bellow functions are used for transformation:
>>> def f1(x, y):
result = random.uniform(-1,1) * x**2 - math.sin(y**2) + abs(y-x)
return result
>>> def f2(x, y):
result = random.uniform(-1,1) * y**3 - math.cos(x**2) + 2*x
return result

here we uses Projection.POLAR
so later space will be the polar space and we have:
>>> g = GenerativeImage(f1, f2)
>>> g.generate(seed=10)
>>> g.plot(projection=Projection.POLAR)


Try Samila in your browser!
Samila can be used online in interactive Jupyter Notebooks via the Binder or Colab services! Try it out now! :
- Check
examples
folder
Issues & bug reports
Just fill an issue and describe it. We'll check it ASAP!
- Please complete the issue template
You can also join our discord server
Dependencies
master | dev |
Social media
References
1- Schönlieb, Carola-Bibiane, and Franz Schubert. "Random simulations for generative art construction–some examples." Journal of Mathematics and the Arts 7.1 (2013): 29-39.
2- Create Generative Art with R
3- NFT.storage : Free decentralized storage and bandwidth for NFTs
Acknowledgments
This project was funded through the Next Step Microgrant, a program established by Protocol Labs.
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