A library for declarative computation.

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# graffiti - a library for declarative computation

## Installation

`pip install graffiti`

*or*

1. `git clone https://github.com/SegFaultAX/graffiti`

1. `cd graffiti/`

1. To install: `python setup.py install`

1. To get development dependencies: `pip install -r requirements.txt`

1. To run tests: `nosetests`

1. To audit source: `python setup.py audit`

Check out my [blog post](http://mkbernard.com/blog/2014/06/graffiti-a-python-library-for-declarative-computation/)

for more background on the "why" of this project. Get in touch if you have any

comments or questions!

Inspired by Prismatic's Graph library (https://github.com/prismatic/plumbing)

## Overview

```python

from graffiti import Graph

stats_descriptor = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2

}

graph = Graph(stats_descriptor)

graph({ "xs": [1, 2, 3, 4, 5] })

#=> {'xs': [1, 2, 3, 4, 5], 'n': 5, 'm': 3, 'm2': 11, 'v': 2}

# if pydot is installed

graph.visualize()

```

## Tutorial

Consider the following perfectly reasonable Python code:

```python

def stats(xs):

n = len(xs)

m = sum(xs) / n

m2 = sum(x ** 2 for x in xs) / n

v = m2 - m ** 2

return {

"xs": xs,

"n": n,

"m": m,

"m2": m2,

"v": v,

}

```

The first computation is based on our input (xs), and each additional

computation builds up more values based on things we've computed already. It's

convenient to visualize this computation as a graph with the user inputs at the

root and values we're interested in at the leaves.

graffiti allows you to structure your computation in exactly that fashion:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2

}

graph = Graph(stats_graph)

graph({ "xs": range(100) })

```

graffiti finds the relationships between the nodes in your graph and determines

the optimal execution order. Leaves in the graph are computed lazily which means

we can choose to only evaluate the ones we're interested in:

```python

graph({ "xs": range(100) }, _keys={"m", "n"})

```

In this case, graffiti will only evaluate what's needed to compute m and n, but

not the rest of the graph. You can also build nested graphs with dependencies

across nesting levels:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

"order": {

"sorted": lambda xs: sorted(xs),

"reversed": lambda xs: sorted(xs, reverse=True)

},

}

graph = Graph(stats_graph)

graph({ "xs": range(100) })

graph({ "xs": range(10) }, _keys={"order"})

```

Again, none of the unecessary nodes will be computed. Nodes in the graph can

also contain optional arguments:

```python

graph = Graph({ "mul": lambda n, p=10: n * p })

graph({ "n": 10 }) # mul == 100

graph({ "n": 10, "p": 20 }) # mul == 200

```

If the optional key is provided as an input to the graph evaluator, it will

override the default value of the node. This makes it easy to play with

different values or settings as the computation flows through your pipeline.

Finally, graphs are resumable. Since a graph object takes a dict as input and

returns a dict as output, you can replay a previously generated dict as the

input to the next thereby reusing all previously computed values:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

}

graph = Graph(stats_graph)

v1 = graph({ "xs": [1,2,3] }, _keys={"n"}) # just n

v2 = graph(v1, _keys={"m"}) # v1 + just m

v3 = graph(v2) # the rest of the graph

```

This allows you to defer the evaluation of expensive keys until the moment

they're actually needed without duplicating previous computations.

graffiti also supports drawing the transitive graph of dependencies:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

"order": {

"sorted": lambda xs: sorted(xs),

"reversed": lambda xs: sorted(xs, reverse=True)

},

}

graph = Graph(stats_graph)

graph.visualize()

```

![](https://raw.githubusercontent.com/SegFaultAX/graffiti/master/example_graph.png)

Using graffiti allows you to structure your computation and the complex

interdependencies therein naturally and efficiently. Most importantly, your

computational pipeline is just data. That means it's easy to inspect, easy to

reason about, and easy to build tooling around.

This project is still under active development. Contact me on Twitter

@SegFaultAX if you have any questions, comments, or bug reports.

## License

Copyright 2014 Michael-Keith Bernard

Availabe under the MIT License. See LICENSE for full details.

## Installation

`pip install graffiti`

*or*

1. `git clone https://github.com/SegFaultAX/graffiti`

1. `cd graffiti/`

1. To install: `python setup.py install`

1. To get development dependencies: `pip install -r requirements.txt`

1. To run tests: `nosetests`

1. To audit source: `python setup.py audit`

Check out my [blog post](http://mkbernard.com/blog/2014/06/graffiti-a-python-library-for-declarative-computation/)

for more background on the "why" of this project. Get in touch if you have any

comments or questions!

Inspired by Prismatic's Graph library (https://github.com/prismatic/plumbing)

## Overview

```python

from graffiti import Graph

stats_descriptor = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2

}

graph = Graph(stats_descriptor)

graph({ "xs": [1, 2, 3, 4, 5] })

#=> {'xs': [1, 2, 3, 4, 5], 'n': 5, 'm': 3, 'm2': 11, 'v': 2}

# if pydot is installed

graph.visualize()

```

## Tutorial

Consider the following perfectly reasonable Python code:

```python

def stats(xs):

n = len(xs)

m = sum(xs) / n

m2 = sum(x ** 2 for x in xs) / n

v = m2 - m ** 2

return {

"xs": xs,

"n": n,

"m": m,

"m2": m2,

"v": v,

}

```

The first computation is based on our input (xs), and each additional

computation builds up more values based on things we've computed already. It's

convenient to visualize this computation as a graph with the user inputs at the

root and values we're interested in at the leaves.

graffiti allows you to structure your computation in exactly that fashion:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2

}

graph = Graph(stats_graph)

graph({ "xs": range(100) })

```

graffiti finds the relationships between the nodes in your graph and determines

the optimal execution order. Leaves in the graph are computed lazily which means

we can choose to only evaluate the ones we're interested in:

```python

graph({ "xs": range(100) }, _keys={"m", "n"})

```

In this case, graffiti will only evaluate what's needed to compute m and n, but

not the rest of the graph. You can also build nested graphs with dependencies

across nesting levels:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

"order": {

"sorted": lambda xs: sorted(xs),

"reversed": lambda xs: sorted(xs, reverse=True)

},

}

graph = Graph(stats_graph)

graph({ "xs": range(100) })

graph({ "xs": range(10) }, _keys={"order"})

```

Again, none of the unecessary nodes will be computed. Nodes in the graph can

also contain optional arguments:

```python

graph = Graph({ "mul": lambda n, p=10: n * p })

graph({ "n": 10 }) # mul == 100

graph({ "n": 10, "p": 20 }) # mul == 200

```

If the optional key is provided as an input to the graph evaluator, it will

override the default value of the node. This makes it easy to play with

different values or settings as the computation flows through your pipeline.

Finally, graphs are resumable. Since a graph object takes a dict as input and

returns a dict as output, you can replay a previously generated dict as the

input to the next thereby reusing all previously computed values:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

}

graph = Graph(stats_graph)

v1 = graph({ "xs": [1,2,3] }, _keys={"n"}) # just n

v2 = graph(v1, _keys={"m"}) # v1 + just m

v3 = graph(v2) # the rest of the graph

```

This allows you to defer the evaluation of expensive keys until the moment

they're actually needed without duplicating previous computations.

graffiti also supports drawing the transitive graph of dependencies:

```python

stats_graph = {

"n": lambda xs: len(xs),

"m": lambda xs, n: sum(xs) / n,

"m2": lambda xs, n: sum(x ** 2 for x in xs) / n,

"v": lambda m, m2: m2 - m ** 2,

"order": {

"sorted": lambda xs: sorted(xs),

"reversed": lambda xs: sorted(xs, reverse=True)

},

}

graph = Graph(stats_graph)

graph.visualize()

```

![](https://raw.githubusercontent.com/SegFaultAX/graffiti/master/example_graph.png)

Using graffiti allows you to structure your computation and the complex

interdependencies therein naturally and efficiently. Most importantly, your

computational pipeline is just data. That means it's easy to inspect, easy to

reason about, and easy to build tooling around.

This project is still under active development. Contact me on Twitter

@SegFaultAX if you have any questions, comments, or bug reports.

## License

Copyright 2014 Michael-Keith Bernard

Availabe under the MIT License. See LICENSE for full details.

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
---|---|---|---|

graffiti-0.1.2.tar.gz (9.6 kB) Copy SHA256 Checksum SHA256 | – | Source | Jun 10, 2014 |