| Name | smfsb JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | Python code relating to the textbook, Stochastic modelling for systems biology, third edition |
| upload_time | 2024-09-08 15:26:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# smfsb for python
Python library for the book, [Stochastic modelling for systems biology, third edition](https://github.com/darrenjw/smfsb/). This library is a Python port of the R package associated with the book.
## Install
Latest stable version:
```bash
pip install smfsb
```
To upgrade already installed package:
```bash
pip install --upgrade smfsb
```
## Basic usage
Note that **the book**, and its associated [github repo](https://github.com/darrenjw/smfsb) is the main source of documentation for this library. The code in the book is in R, but the code in this library is supposed to mirror the R code, but in Python.
### Using a model built-in to the library
First, see how to simulate a built-in model:
```python
import smfsb
lv = smfsb.models.lv()
print(lv)
stepLv = lv.stepGillespie()
out = smfsb.simTs(lv.m, 0, 100, 0.1, stepLv)
```
Now, if `matplotlib` is installed, you can plot the output with
```python
import matplotlib.pyplot as plt
fig, axis = plt.subplots()
for i in range(2):
axis.plot(range(out.shape[0]), out[:,i])
axis.legend(lv.n)
fig.savefig("lv.pdf")
```
Standard python docstring documentation is available. Usage information can be obtained from the python REPL with commands like `help(smfsb.Spn)`, `help(smfsb.Spn.stepGillespie)` or `help(smfsb.simTs)`. This documentation is also available on [ReadTheDocs](https://python-smfsb.readthedocs.io/). The API documentation contains very minimal usage examples. For more interesting examples, see the [demos directory](https://github.com/darrenjw/python-smfsb/tree/main/demos).
### Creating and simulating a model
Next, let's create and simulate our own model by specifying a stochastic Petri net explicitly.
```python
import numpy as np
sir = smfsb.Spn(["S", "I", "R"], ["S->I", "I->R"],
[[1,1,0],[0,1,0]], [[0,2,0],[0,0,1]],
lambda x, t: np.array([0.3*x[0]*x[1]/200, 0.1*x[1]]),
[197, 3, 0])
stepSir = sir.stepPTS()
sample = smfsb.simSample(500, sir.m, 0, 20, stepSir)
fig, axis = plt.subplots()
axis.hist(sample[:,1],30)
axis.set_title("Infected at time 20")
plt.savefig("sIr.pdf")
```
### Reading and parsing models in SBML and SBML-shorthand
Note that you can read in SBML or SBML-shorthand models that have been designed for discrete stochastic simulation into a stochastic Petri net directly. To read and parse an SBML model, use
```python
m = smfsb.file2Spn("myModel.xml")
```
Note that if you are working with SBML models in Python using [libsbml](https://pypi.org/project/python-libsbml/), then there is also a function `model2Spn` which takes a libsbml model object.
To read and parse an SBML-shorthand model, use
```python
m = smfsb.mod2Spn("myModel.mod")
```
There is also a function `sh2Spn` which expects a python string containing a shorthand model. This is convenient for embedding shorthand models inside python scripts, and is particularly convenient when working with things like Jupyter notebooks. Below follows a complete session to illustrate the idea by creating and simulating a realisation from a discrete stochastic SEIR model.
```python
import smfsb
import numpy as np
seirSH = """
@model:3.1.1=SEIR "SEIR Epidemic model"
s=item, t=second, v=litre, e=item
@compartments
Pop
@species
Pop:S=100 s
Pop:E=0 s
Pop:I=5 s
Pop:R=0 s
@reactions
@r=Infection
S + I -> E + I
beta*S*I : beta=0.1
@r=Transition
E -> I
sigma*E : sigma=0.2
@r=Removal
I -> R
gamma*I : gamma=0.5
"""
seir = smfsb.sh2Spn(seirSH)
stepSeir = seir.stepGillespie()
out = smfsb.simTs(seir.m, 0, 40, 0.05, stepSeir)
import matplotlib.pyplot as plt
fig, axis = plt.subplots()
for i in range(len(seir.m)):
axis.plot(np.arange(0, 40, 0.05), out[:,i])
axis.legend(seir.n)
fig.savefig("seir.pdf")
```
A [collection of appropriate models](https://github.com/darrenjw/smfsb/tree/master/models) is associated with the book.
You can see this package on [PyPI](https://pypi.org/project/smfsb/) or [GitHub](https://github.com/darrenjw/python-smfsb).
## Fast simulation and inference
If you like this library but find it a little slow, you should know that there is a [JAX](https://jax.readthedocs.io/) port of this package: [JAX-smfsb](https://github.com/darrenjw/jax-smfsb). It requires a JAX installalation, and the API is (very) slightly modified, but it has state-of-the-art performance for simulation and inference.
**Copyright (2023-2024) Darren J Wilkinson**
Raw data
{
"_id": null,
"home_page": null,
"name": "smfsb",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": "Darren Wilkinson <darrenjwilkinson@btinternet.com>",
"download_url": "https://files.pythonhosted.org/packages/da/ff/565c22ab130d408d969f1aeddf3f646943466a16999f980e9277ea9df724/smfsb-1.0.2.tar.gz",
"platform": null,
"description": "# smfsb for python\n\nPython library for the book, [Stochastic modelling for systems biology, third edition](https://github.com/darrenjw/smfsb/). This library is a Python port of the R package associated with the book.\n\n## Install\n\nLatest stable version:\n```bash\npip install smfsb\n```\nTo upgrade already installed package:\n```bash\npip install --upgrade smfsb\n```\n\n## Basic usage\n\nNote that **the book**, and its associated [github repo](https://github.com/darrenjw/smfsb) is the main source of documentation for this library. The code in the book is in R, but the code in this library is supposed to mirror the R code, but in Python.\n\n### Using a model built-in to the library\n\nFirst, see how to simulate a built-in model:\n```python\nimport smfsb\n\nlv = smfsb.models.lv()\nprint(lv)\nstepLv = lv.stepGillespie()\nout = smfsb.simTs(lv.m, 0, 100, 0.1, stepLv)\n```\nNow, if `matplotlib` is installed, you can plot the output with\n```python\nimport matplotlib.pyplot as plt\nfig, axis = plt.subplots()\nfor i in range(2):\n\taxis.plot(range(out.shape[0]), out[:,i])\n\naxis.legend(lv.n)\nfig.savefig(\"lv.pdf\")\n```\nStandard python docstring documentation is available. Usage information can be obtained from the python REPL with commands like `help(smfsb.Spn)`, `help(smfsb.Spn.stepGillespie)` or `help(smfsb.simTs)`. This documentation is also available on [ReadTheDocs](https://python-smfsb.readthedocs.io/). The API documentation contains very minimal usage examples. For more interesting examples, see the [demos directory](https://github.com/darrenjw/python-smfsb/tree/main/demos).\n\n### Creating and simulating a model\n\nNext, let's create and simulate our own model by specifying a stochastic Petri net explicitly.\n```python\nimport numpy as np\nsir = smfsb.Spn([\"S\", \"I\", \"R\"], [\"S->I\", \"I->R\"],\n\t[[1,1,0],[0,1,0]], [[0,2,0],[0,0,1]],\n\tlambda x, t: np.array([0.3*x[0]*x[1]/200, 0.1*x[1]]),\n\t[197, 3, 0])\nstepSir = sir.stepPTS()\nsample = smfsb.simSample(500, sir.m, 0, 20, stepSir)\nfig, axis = plt.subplots()\naxis.hist(sample[:,1],30)\naxis.set_title(\"Infected at time 20\")\nplt.savefig(\"sIr.pdf\")\n```\n\n### Reading and parsing models in SBML and SBML-shorthand\n\nNote that you can read in SBML or SBML-shorthand models that have been designed for discrete stochastic simulation into a stochastic Petri net directly. To read and parse an SBML model, use\n```python\nm = smfsb.file2Spn(\"myModel.xml\")\n```\nNote that if you are working with SBML models in Python using [libsbml](https://pypi.org/project/python-libsbml/), then there is also a function `model2Spn` which takes a libsbml model object.\n\nTo read and parse an SBML-shorthand model, use\n```python\nm = smfsb.mod2Spn(\"myModel.mod\")\n```\nThere is also a function `sh2Spn` which expects a python string containing a shorthand model. This is convenient for embedding shorthand models inside python scripts, and is particularly convenient when working with things like Jupyter notebooks. Below follows a complete session to illustrate the idea by creating and simulating a realisation from a discrete stochastic SEIR model.\n```python\nimport smfsb\nimport numpy as np\n\nseirSH = \"\"\"\n@model:3.1.1=SEIR \"SEIR Epidemic model\"\n s=item, t=second, v=litre, e=item\n@compartments\n Pop\n@species\n Pop:S=100 s\n Pop:E=0 s\t \n Pop:I=5 s\n Pop:R=0 s\n@reactions\n@r=Infection\n S + I -> E + I\n beta*S*I : beta=0.1\n@r=Transition\n E -> I\n sigma*E : sigma=0.2\n@r=Removal\n I -> R\n gamma*I : gamma=0.5\n\"\"\"\n\nseir = smfsb.sh2Spn(seirSH)\nstepSeir = seir.stepGillespie()\nout = smfsb.simTs(seir.m, 0, 40, 0.05, stepSeir)\n\nimport matplotlib.pyplot as plt\nfig, axis = plt.subplots()\nfor i in range(len(seir.m)):\n\taxis.plot(np.arange(0, 40, 0.05), out[:,i])\n\naxis.legend(seir.n)\nfig.savefig(\"seir.pdf\")\n```\n\n\nA [collection of appropriate models](https://github.com/darrenjw/smfsb/tree/master/models) is associated with the book.\n\n\n\nYou can see this package on [PyPI](https://pypi.org/project/smfsb/) or [GitHub](https://github.com/darrenjw/python-smfsb).\n\n\n## Fast simulation and inference\n\nIf you like this library but find it a little slow, you should know that there is a [JAX](https://jax.readthedocs.io/) port of this package: [JAX-smfsb](https://github.com/darrenjw/jax-smfsb). It requires a JAX installalation, and the API is (very) slightly modified, but it has state-of-the-art performance for simulation and inference.\n\n\n**Copyright (2023-2024) Darren J Wilkinson**\n\n\n",
"bugtrack_url": null,
"license": null,
"summary": "Python code relating to the textbook, Stochastic modelling for systems biology, third edition",
"version": "1.0.2",
"project_urls": {
"Documentation": "https://python-smfsb.readthedocs.io/",
"Homepage": "https://github.com/darrenjw/python-smfsb",
"Issues": "https://github.com/darrenjw/python-smfsb/issues"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "38da00911b58cbe6cd968f12810f8472583adf6961430cee34437142d32dd139",
"md5": "d5640c8adb48e92ad3c94a14ad87c5c7",
"sha256": "8b4dae9ed6f4c4d4bd2d5b0a97f9b1925038a9a27e16cc279bb55f2c656f81f8"
},
"downloads": -1,
"filename": "smfsb-1.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "d5640c8adb48e92ad3c94a14ad87c5c7",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 25829,
"upload_time": "2024-09-08T15:26:41",
"upload_time_iso_8601": "2024-09-08T15:26:41.419712Z",
"url": "https://files.pythonhosted.org/packages/38/da/00911b58cbe6cd968f12810f8472583adf6961430cee34437142d32dd139/smfsb-1.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "daff565c22ab130d408d969f1aeddf3f646943466a16999f980e9277ea9df724",
"md5": "d93ae61cf7630435df589ffe70882b5c",
"sha256": "116b12d42788a469f392cea9ee3e1cba2d0a5bf6b0014ed4c94d1fa82e83ffb7"
},
"downloads": -1,
"filename": "smfsb-1.0.2.tar.gz",
"has_sig": false,
"md5_digest": "d93ae61cf7630435df589ffe70882b5c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 7085630,
"upload_time": "2024-09-08T15:26:46",
"upload_time_iso_8601": "2024-09-08T15:26:46.388446Z",
"url": "https://files.pythonhosted.org/packages/da/ff/565c22ab130d408d969f1aeddf3f646943466a16999f980e9277ea9df724/smfsb-1.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-08 15:26:46",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "darrenjw",
"github_project": "python-smfsb",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "smfsb"
}
