Forward-time population genetic simulation in Python
Project description
This is the README for fwdpy11, which is a Python package for forward-time population genetic simulation. It uses fwdpp as its C++ back-end.
Build status
Main:
Development:
Conda status
Miscellaneous
Python code style:
Features
Pickle-able population objects
Parallel computation via multiprocessing or concurrent.futures.
Custom temporal samplers to analyze populations during a simulation may be written in pure Python.
Flexible interface for simulating models with multiple populations.
Documentation
The manual can be found here.
License
GPLv3 or later (See COPYING)
Supported Python version
fwdpy11 is written for Python 3. We will not modify the package to be compatible with Python 2.7.
Dependencies and installation
These topics are covered in the user manual:
People wishing to run fwpdy11 should see this section.
Those who need to build the package from source should look the developer’s guide.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for fwdpy11-0.19.6-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ce0b1832237d0c8d1d4c313b75835165d156ac3a80f2c24bed91435d77fd6e22 |
|
MD5 | e5e92473bf5f09f22f42b7816f9ce57e |
|
BLAKE2b-256 | 6522a67918877cbafb033e61917c7385b19d49ef30cbccbc64b55383d36daaf6 |
Hashes for fwdpy11-0.19.6-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 87e011f128cfe4d0f986915d9f4994c3cee50e15a73bec774270fa9e46f75fed |
|
MD5 | 824851921ae5a7054a689249d238083f |
|
BLAKE2b-256 | d52295edd87a9a65c550415b67b0f6ea87e1b7480a3665c07ecd10835989d106 |
Hashes for fwdpy11-0.19.6-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6634a794c70995b8d367ef0331691c58659408d73184636f58412eff3714695c |
|
MD5 | caec27b4b4118ed6efc0b744ab23f728 |
|
BLAKE2b-256 | c50276a941df102d60b5aba67848c8f3b0c198e2e1e4aea8447227dbc9847e6d |
Hashes for fwdpy11-0.19.6-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5275b37d45bba3815c624443540e6027c4644e778ba1ecbecfa85b3a944bb261 |
|
MD5 | a826f4a53044dd56ed0703ad6d4737b3 |
|
BLAKE2b-256 | 05361624f9df760b8dba68e1fa55b78d23d4d2764cff3723d5c8ed7c3b1450fa |
Hashes for fwdpy11-0.19.6-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 52ae46eb597335e7cca091033ae92cac083ba0d3f72c1e5ce4bdd21a7c504a7c |
|
MD5 | 556c573b57299168af3a0f00c8338f0e |
|
BLAKE2b-256 | bcc20b10fd194cd3df6ec552a6177b8dd0150a07c93e906dad668b1507c8f07b |
Hashes for fwdpy11-0.19.6-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 78fd87f0fd670158638cc1eee3c9db2ad1a4d3dfbb716d2a9a91bd1a961d138c |
|
MD5 | 29405c14eacab4b34a836faafc4fa6cc |
|
BLAKE2b-256 | 23677001d06b541f3ea4aa78cdd129872073683591225663d7e17fd968165f41 |
Hashes for fwdpy11-0.19.6-cp37-cp37m-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c48ad3d434be013a23acb8d07e7c708d984243055864095b2cd15de869703bee |
|
MD5 | 58bd44345068f0872bc5bb45129085b2 |
|
BLAKE2b-256 | 21c1e9fc5f54fa847240ac0537685a94d437c079cab1e2c92293f9e91894a2c3 |
Hashes for fwdpy11-0.19.6-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9256abca4bd2496f7ffd83ac3f568efc4d54d724b8e201597f782cbfb65cc1f9 |
|
MD5 | d7e79e86537603f1046c58ee8a5f4832 |
|
BLAKE2b-256 | 268cf10c525ac6c6912c14d55dacdf350e9976244bfc4b6678b677a407e5663a |