sleap-roots 0.0.7

Analysis tools for SLEAP-based plant root phenotyping.

sleap-roots

Analysis tools for SLEAP-based plant root phenotyping.

Installation

pip install sleap-roots

If you are using conda (recommended):

conda create -n sleap-roots python=3.9
conda activate sleap-roots
pip install sleap-roots

Usage

DicotPipeline

1. Computing traits for a single plant:

import sleap_roots as sr

plant = sr.Series.load(
    "tests/data/canola_7do/919QDUH.h5",
    primary_name="primary_multi_day",
    lateral_name="lateral_3_nodes"
)
pipeline = sr.DicotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)

2. Computing traits for a batch of plants:

import sleap_roots as sr

plant_paths = sr.find_all_series("tests/data/soy_6do")
plants = [
    sr.Series.load(
        plant_path,
        primary_name="primary_multi_day",
        lateral_name="lateral__nodes",
    ) for plant_path in plant_paths]

pipeline = sr.DicotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)

3. Computing individual traits:

import sleap_roots as sr
import numpy as np

plant = sr.Series.load(
    "tests/data/canola_7do/919QDUH.h5",
    primary_name="primary_multi_day",
    lateral_name="lateral_3_nodes"
)

primary, lateral = plant[10]
pts = np.concatenate([primary.numpy(), lateral.numpy()], axis=0).reshape(-1, 2)
convex_hull = sr.convhull.get_convhull(pts)

YoungerMonocotPipeline

1. Computing traits for a single plant:

import sleap_roots as sr

plant = sr.Series.load(
    "tests/data/rice_3do/0K9E8BI.h5",
    primary_name="longest_3do_6nodes",
    lateral_name="main_3do_6nodes"
)
pipeline = sr.YoungerMonocotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)

2. Computing traits for a batch of plants:

import sleap_roots as sr

plant_paths = sr.find_all_series("tests/data/rice_3do")
plants = [
    sr.Series.load(
        plant_path,
        primary_name="longest_3do_6nodes",
        lateral_name="main_3do_6nodes"
    ) for plant_path in plant_paths]

pipeline = sr.YoungerMonocotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)

3. Computing individual traits:

import sleap_roots as sr
import numpy as np

plant = sr.Series.load(
    "tests/data/rice_3do/0K9E8BI.h5",
    primary_name="longest_3do_6nodes",
    lateral_name="main_3do_6nodes"
)

primary, lateral = plant[10]
pts = np.concatenate([primary.numpy(), lateral.numpy()], axis=0).reshape(-1, 2)
convex_hull = sr.convhull.get_convhull(pts)

Development

For development, first clone the repository:

git clone https://github.com/talmolab/sleap-roots && cd sleap-roots

Then, to create a new conda environment and install the package in editable mode:

conda env create -f environment.yml

This will create a conda environment called sleap-roots.

If you have an existing conda environment (such as where you installed SLEAP), you can just install in editable mode directly. First, activate your environment and then:

pip install -e ".[dev]"

Note: The [dev] makes sure that the development-only dependencies are also installed.

To start fresh, just delete the environment:

conda env remove -n sleap-roots

To run tests, first activate the environment:

conda activate sleap-roots

Then run pytest with:

pytest tests

Acknowledgments

This repository was created by the Talmo Lab and Busch Lab at the Salk Institute for Biological Studies as part of the Harnessing Plants Initiative.

Contributors

• Elizabeth Berrigan
• Lin Wang
• Talmo Pereira

Citation

Coming soon.