| Name | nodemodel JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Generates and processes a graph based on a dictionary of functions, using the function names and their argument names to define the relationships. |
| upload_time | 2024-09-26 21:20:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
graph
model
node
function
relationship
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
nodemodel
=========
.. image:: https://github.com/krzysztof-pankow/nodemodel/actions/workflows/quality.yml/badge.svg
:target: https://github.com/krzysztof-pankow/nodemodel/actions?query=workflow%3Atest
.. image:: https://codecov.io/github/krzysztof-pankow/nodemodel/graph/badge.svg?token=00PQHNZH4W
:target: https://codecov.io/github/krzysztof-pankow/nodemodel
.. image:: https://readthedocs.org/projects/nodemodel/badge/?version=latest
:target: https://nodemodel.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue
:target: https://pypi.python.org/pypi/nodemodel
Nodemodel is a small Python package for generating and computing a graph based on a dictionary of functions. It uses the function names and their argument names to define relationships within the graph.
- **Source:** https://github.com/krzysztof-pankow/nodemodel
- **Tutorial:** https://nodemodel.readthedocs.io/en/latest
Main Features
-------------
- **Simple Interface** – Use a single class, ``Model``, to generate a graph of functions, and its method ``compute`` to compute the graph on a dictionary.
- **Organizes Your Code** – Add a ``@node`` decorator to your functions and load them with the ``load_nodes`` function for easy management.
- **Supports Conditional Functions** – Easily specify that the computation of a function depends on modified inputs or the modified results of other functions.
- **Lightweight** – ``nodemodel`` only depends on the ``networkx`` package; everything else is pure Python.
- **Efficient** – Avoids copying data and executes all functions iteratively to maximize performance.
Example: Basic
--------------
.. code-block:: python
from nodemodel import Model
# Define functions:
def d(a):
return a * 10
def c(a, b):
return a + b
def b(y):
return y + 1
def a(x):
return x + 1
# Initialize the model with the defined functions
m = Model({"a": a, "b": b, "c": c, "d": d})
# Compute values for the given inputs
result = m.compute({"x": 1, "y": 2})
print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'd': 20, 'c': 5}
# Compute only a part of the model
result = m.submodel("d").compute({"x": 1, "y": 2})
print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'd': 20}
Example: Conditional functions
------------------------------
.. code-block:: python
#"c" will be calculated with "x" forced to 100
c.forced_nodes = {"x":100}
#"d" will be calculated with "a" forced to "b"
d.forced_nodes = {"a":("node","b")}
# Reinitialize the model:
m = Model({"a": a, "b": b, "c": c,"d": d})
#Compute the model on a dictionary:
result = m.compute({"x": 1, "y": 2})
print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'c': 104, 'd': 30}
Please notice that only "c" and "d" values changed after computing the model.
Example: Node decorators
------------------------
Suppose we have the following file structure:
.. code-block:: text
my_model/
├── __init__.py
├── c_and_d_code.py
├── a_and_b/
│ ├── __init__.py
│ └── a_and_b_code.py
We will place the example functions in these files:
**c_and_d_code.py**
.. code-block:: python
from nodemodel import node
@node(x=100)
def c(a, b):
return a + b
@node(a=("node","b"))
def d(a):
return a * 10
**a_and_b_code.py**
.. code-block:: python
from nodemodel import node
@node
def a(x):
return x + 1
@node
def b(y):
return y + 1
Now we can load and execute these functions using the `nodemodel` package:
.. code-block:: python
from nodemodel import Model, load_nodes
# Import all functions with a @node decorator from the "my_model" directory
nodes = load_nodes("my_model")
# Initialize the model with the loaded functions
m = Model(nodes)
#Compute the model on a dictionary:
result = m.compute({"x": 1, "y": 2})
print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'c': 104, 'd': 30}
Example: Using nodemodel with Pandas
------------------------------------
Nodemodel can be useful for working with different data structures.
For example, with `pandas` DataFrames:
.. code-block:: python
import pandas as pd
df = pd.DataFrame({"x": [1, 2, 3],"y": [2, 3, 4]})
df = df.to_dict(orient="series")
result = pd.DataFrame(m.compute(df))
print(result)
x y a b c d
0 1 2 2 3 104 30
1 2 3 3 4 105 40
2 3 4 4 5 106 50
Installation
------------
You can install `nodemodel` using `pip`:
.. code-block:: bash
pip install nodemodel
Raw data
{
"_id": null,
"home_page": null,
"name": "nodemodel",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "graph, model, node, function, relationship",
"author": null,
"author_email": "Krzysztof Pankow <kpankow@dinga.ai>",
"download_url": "https://files.pythonhosted.org/packages/e3/c6/81f60987cd0fe87958c1e4bd758daae27aea855ed7e99ef74dc448fb191a/nodemodel-0.1.4.tar.gz",
"platform": null,
"description": "nodemodel\n=========\n\n.. image:: https://github.com/krzysztof-pankow/nodemodel/actions/workflows/quality.yml/badge.svg\n :target: https://github.com/krzysztof-pankow/nodemodel/actions?query=workflow%3Atest\n\n.. image:: https://codecov.io/github/krzysztof-pankow/nodemodel/graph/badge.svg?token=00PQHNZH4W\n :target: https://codecov.io/github/krzysztof-pankow/nodemodel\n\n.. image:: https://readthedocs.org/projects/nodemodel/badge/?version=latest\n :target: https://nodemodel.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue\n :target: https://pypi.python.org/pypi/nodemodel\n\n\nNodemodel is a small Python package for generating and computing a graph based on a dictionary of functions. It uses the function names and their argument names to define relationships within the graph.\n\n- **Source:** https://github.com/krzysztof-pankow/nodemodel\n- **Tutorial:** https://nodemodel.readthedocs.io/en/latest\n\nMain Features\n-------------\n- **Simple Interface** \u2013 Use a single class, ``Model``, to generate a graph of functions, and its method ``compute`` to compute the graph on a dictionary.\n- **Organizes Your Code** \u2013 Add a ``@node`` decorator to your functions and load them with the ``load_nodes`` function for easy management.\n- **Supports Conditional Functions** \u2013 Easily specify that the computation of a function depends on modified inputs or the modified results of other functions.\n- **Lightweight** \u2013 ``nodemodel`` only depends on the ``networkx`` package; everything else is pure Python.\n- **Efficient** \u2013 Avoids copying data and executes all functions iteratively to maximize performance.\n\nExample: Basic\n--------------\n\n.. code-block:: python\n\n from nodemodel import Model\n\n # Define functions:\n def d(a):\n return a * 10\n\n def c(a, b):\n return a + b\n\n def b(y):\n return y + 1\n\n def a(x):\n return x + 1\n\n # Initialize the model with the defined functions\n m = Model({\"a\": a, \"b\": b, \"c\": c, \"d\": d})\n\n # Compute values for the given inputs\n result = m.compute({\"x\": 1, \"y\": 2})\n print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'd': 20, 'c': 5}\n\n # Compute only a part of the model\n result = m.submodel(\"d\").compute({\"x\": 1, \"y\": 2})\n print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'd': 20}\n\nExample: Conditional functions\n------------------------------\n\n.. code-block:: python\n\n #\"c\" will be calculated with \"x\" forced to 100\n c.forced_nodes = {\"x\":100}\n\n #\"d\" will be calculated with \"a\" forced to \"b\"\n d.forced_nodes = {\"a\":(\"node\",\"b\")}\n\n # Reinitialize the model:\n m = Model({\"a\": a, \"b\": b, \"c\": c,\"d\": d})\n\n #Compute the model on a dictionary:\n result = m.compute({\"x\": 1, \"y\": 2})\n print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'c': 104, 'd': 30}\n\nPlease notice that only \"c\" and \"d\" values changed after computing the model.\n\nExample: Node decorators\n------------------------\n\nSuppose we have the following file structure:\n\n.. code-block:: text\n\n my_model/\n \u251c\u2500\u2500 __init__.py\n \u251c\u2500\u2500 c_and_d_code.py\n \u251c\u2500\u2500 a_and_b/\n \u2502 \u251c\u2500\u2500 __init__.py\n \u2502 \u2514\u2500\u2500 a_and_b_code.py\n\nWe will place the example functions in these files:\n\n**c_and_d_code.py**\n\n.. code-block:: python\n\n from nodemodel import node\n\n @node(x=100)\n def c(a, b):\n return a + b\n\n @node(a=(\"node\",\"b\"))\n def d(a):\n return a * 10\n\n**a_and_b_code.py**\n\n.. code-block:: python\n\n from nodemodel import node\n\n @node\n def a(x):\n return x + 1\n\n @node\n def b(y):\n return y + 1\n\nNow we can load and execute these functions using the `nodemodel` package:\n\n.. code-block:: python\n\n from nodemodel import Model, load_nodes\n\n # Import all functions with a @node decorator from the \"my_model\" directory\n nodes = load_nodes(\"my_model\")\n\n # Initialize the model with the loaded functions\n m = Model(nodes)\n\n #Compute the model on a dictionary:\n result = m.compute({\"x\": 1, \"y\": 2})\n print(result) # Output: {'x': 1, 'y': 2, 'a': 2, 'b': 3, 'c': 104, 'd': 30}\n\nExample: Using nodemodel with Pandas\n------------------------------------\n\nNodemodel can be useful for working with different data structures.\nFor example, with `pandas` DataFrames:\n\n.. code-block:: python\n\n import pandas as pd\n df = pd.DataFrame({\"x\": [1, 2, 3],\"y\": [2, 3, 4]})\n\n df = df.to_dict(orient=\"series\")\n result = pd.DataFrame(m.compute(df))\n print(result)\n\n x y a b c d\n 0 1 2 2 3 104 30\n 1 2 3 3 4 105 40\n 2 3 4 4 5 106 50\n\nInstallation\n------------\nYou can install `nodemodel` using `pip`:\n\n.. code-block:: bash\n\n pip install nodemodel\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Generates and processes a graph based on a dictionary of functions, using the function names and their argument names to define the relationships.",
"version": "0.1.4",
"project_urls": {
"Homepage": "https://github.com/krzysztof-pankow/nodemodel",
"Source": "https://github.com/krzysztof-pankow/nodemodel"
},
"split_keywords": [
"graph",
" model",
" node",
" function",
" relationship"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "04f7eb2aa81ac26dbcd2ee89163a1ac106b3bd9a6d896a8fd14085a88bee67bb",
"md5": "0af849d52b8e25ca237ca79fa3a792cb",
"sha256": "e476695c7fe81f6065d7682096c766c13cae3178b06197eb7ae75a21ed7281a7"
},
"downloads": -1,
"filename": "nodemodel-0.1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0af849d52b8e25ca237ca79fa3a792cb",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 11630,
"upload_time": "2024-09-26T21:20:52",
"upload_time_iso_8601": "2024-09-26T21:20:52.197574Z",
"url": "https://files.pythonhosted.org/packages/04/f7/eb2aa81ac26dbcd2ee89163a1ac106b3bd9a6d896a8fd14085a88bee67bb/nodemodel-0.1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e3c681f60987cd0fe87958c1e4bd758daae27aea855ed7e99ef74dc448fb191a",
"md5": "3ade2a7832bc4d88fdcc5021ce1227ca",
"sha256": "fac1151d8841703ecda76874e17c582af5fae56ee6e2cebc78e7abd653f6d23e"
},
"downloads": -1,
"filename": "nodemodel-0.1.4.tar.gz",
"has_sig": false,
"md5_digest": "3ade2a7832bc4d88fdcc5021ce1227ca",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 14394,
"upload_time": "2024-09-26T21:20:53",
"upload_time_iso_8601": "2024-09-26T21:20:53.481242Z",
"url": "https://files.pythonhosted.org/packages/e3/c6/81f60987cd0fe87958c1e4bd758daae27aea855ed7e99ef74dc448fb191a/nodemodel-0.1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-26 21:20:53",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "krzysztof-pankow",
"github_project": "nodemodel",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "nodemodel"
}
