# ChewC
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
In short, this will be a GPU-enabled stochastic simulation for breeding
programs with an emphasis on cost-benefit-analysis for novel breeding
tools and creating a suitable interface for RL agents.
------------------------------------------------------------------------
We will also incorporate an emphasis on budget and costs associated with
each action to manage long-term breeding budgets. As well as model
theoretical tools in the plant breeder’s toolbox. e.g.
> a treatment which increases crossover rates
> a treatment which reduces flowering time
> a treatment which enables gene drive at select loci
Each treatment will cost \$\$ ultimately helping guide the
implementation in real-world breeding programs.
## Install
``` sh
pip install chewc
```
## How to use
First, define the genome of your crop
``` python
import torch
```
``` python
ploidy = 2
n_chr = 10
n_loci = 100
n_Ind = 333
g = Genome(ploidy, n_chr, n_loci)
population = Population()
population.create_random_founder_population(g, n_founders=n_Ind)
init_pop = population.get_dosages().float() # gets allele dosage for calculating trait values
# multi_traits
target_means = torch.tensor([0, 5, 20])
target_vars = torch.tensor([1, 1, 0.5]) # Note: I'm assuming you want a variance of 1 for the second trait
correlation_matrix = [
[1.0, 0.2, 0.58],
[0.2, 1.0, -0.37],
[0.58, -0.37, 1.0],
]
correlation_matrix = torch.tensor(correlation_matrix)
ta = TraitModule(g, population, target_means, target_vars, correlation_matrix,100)
ta(population.get_dosages()).shape
```
Created genetic map
torch.Size([333, 3])
Raw data
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"description": "# ChewC\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nIn short, this will be a GPU-enabled stochastic simulation for breeding\nprograms with an emphasis on cost-benefit-analysis for novel breeding\ntools and creating a suitable interface for RL agents.\n\n------------------------------------------------------------------------\n\nWe will also incorporate an emphasis on budget and costs associated with\neach action to manage long-term breeding budgets. As well as model\ntheoretical tools in the plant breeder\u2019s toolbox. e.g.\n\n> a treatment which increases crossover rates\n\n> a treatment which reduces flowering time\n\n> a treatment which enables gene drive at select loci\n\nEach treatment will cost \\$\\$ ultimately helping guide the\nimplementation in real-world breeding programs.\n\n## Install\n\n``` sh\npip install chewc\n```\n\n## How to use\n\nFirst, define the genome of your crop\n\n``` python\nimport torch\n```\n\n``` python\nploidy = 2\nn_chr = 10\nn_loci = 100\nn_Ind = 333\ng = Genome(ploidy, n_chr, n_loci)\npopulation = Population()\npopulation.create_random_founder_population(g, n_founders=n_Ind)\ninit_pop = population.get_dosages().float() # gets allele dosage for calculating trait values\n\n# multi_traits\ntarget_means = torch.tensor([0, 5, 20])\ntarget_vars = torch.tensor([1, 1, 0.5]) # Note: I'm assuming you want a variance of 1 for the second trait\ncorrelation_matrix = [\n [1.0, 0.2, 0.58],\n [0.2, 1.0, -0.37],\n [0.58, -0.37, 1.0],\n ]\ncorrelation_matrix = torch.tensor(correlation_matrix)\n\nta = TraitModule(g, population, target_means, target_vars, correlation_matrix,100)\nta(population.get_dosages()).shape\n```\n\n Created genetic map\n\n torch.Size([333, 3])\n",
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