| Name | sleap-roots JSON |
| Version |
0.0.9
JSON |
| download |
| home_page | None |
| Summary | Analysis tools for SLEAP-based plant root phenotyping. |
| upload_time | 2024-04-23 18:33:54 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | BSD-3-Clause |
| keywords |
sleap
plants
roots
phenotyping
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# sleap-roots
[](https://github.com/talmolab/sleap-roots/actions/workflows/ci.yml)
[](https://codecov.io/gh/talmolab/sleap-roots)
[](https://github.com/talmolab/sleap-roots/releases/)
[](https://pypi.org/project/sleap-roots)
Analysis tools for [SLEAP](https://sleap.ai)-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
Detailed trait documentation per pipeline is available here:
[sleap-roots HackMD](https://hackmd.io/DMiXO2kXQhKH8AIIcIy--g)
### `DicotPipeline`
**1. Computing traits for a single plant:**
```py
import sleap_roots as sr
plant = sr.Series.load(
"tests/data/canola_7do/919QDUH.h5",
# Specify the names of the primary and lateral roots for trait calculation
primary_name="primary",
lateral_name="lateral"
)
pipeline = sr.DicotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)
```
**2. Computing traits for a batch of plants:**
```py
import sleap_roots as sr
plant_paths = sr.find_all_series("tests/data/soy_6do")
plants = [
sr.Series.load(
plant_path,
# Specify the names of the primary and lateral roots for trait calculation
primary_name="primary",
lateral_name="lateral",
) for plant_path in plant_paths]
pipeline = sr.DicotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)
```
**3. Computing individual traits:**
```py
import sleap_roots as sr
import numpy as np
import numpy as np
# Import utility for combining primary and lateral root points
from sleap_roots.points import get_all_pts_array
plant = sr.Series.load(
"tests/data/canola_7do/919QDUH.h5",
primary_name="primary",
lateral_name="lateral"
)
frame_index = 0
primary_pts = plant.get_primary_points(frame_index)
lateral_pts = plant.get_lateral_points(frame_index)
pts = get_all_pts_array(primary_pts, lateral_pts)
convex_hull = sr.convhull.get_convhull(pts)
```
### `YoungerMonocotPipeline`
**1. Computing traits for a single plant:**
```py
import sleap_roots as sr
plant = sr.Series.load(
"tests/data/rice_3do/0K9E8BI.h5",
primary_name="primary",
lateral_name="crown"
)
pipeline = sr.YoungerMonocotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)
```
**2. Computing traits for a batch of plants:**
```py
import sleap_roots as sr
plant_paths = sr.find_all_series("tests/data/rice_3do")
plants = [
sr.Series.load(
plant_path,
primary_name="primary",
lateral_name="crown"
) for plant_path in plant_paths]
pipeline = sr.YoungerMonocotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)
```
**3. Computing individual traits:**
```py
import sleap_roots as sr
import numpy as np
from sleap_roots.points import get_all_pts_array
plant = sr.Series.load(
"tests/data/rice_3do/0K9E8BI.h5",
primary_name="primary",
lateral_name="crown"
)
frame_index = 0
primary_pts = plant.get_primary_points(frame_index)
lateral_pts = plant.get_lateral_points(frame_index)
pts = get_all_pts_array(primary_pts, lateral_pts)
convex_hull = sr.convhull.get_convhull(pts)
```
## Tutorials
Jupyter notebooks are located in this repo at `sleap-roots/notebooks`.
To use them, activate your conda environment which includes JupyterLab (recommended):
```
conda activate sleap-roots
```
Clone this repository if you haven't already:
```
git clone https://github.com/talmolab/sleap-roots.git && cd sleap-roots
```
Then you can change directories to the location of the notebooks, and open Jupyter Lab:
```
cd notebooks
jupyter lab
```
Go through the commands in the notebooks to learn about each pipeline.
You can use the test data located at `tests/data` or copy the notebooks elsewhere for use with your own data!
## 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](https://talmolab.org) and [Busch Lab](https://busch.salk.edu) at the Salk Institute for Biological Studies as part of the [Harnessing Plants Initiative](https://www.salk.edu/harnessing-plants-initiative/).
### Contributors
- Elizabeth Berrigan
- Lin Wang
- Talmo Pereira
### Citation
E.M. Berrigan, L. Wang, H. Carrillo, K. Echegoyen, M. Kappes, J. Torres, A. Ai-Perreira, E. McCoy, E. Shane, C.D. Copeland, L. Ragel,
C. Georgousakis, S. Lee, D. Reynolds, A. Talgo, J. Gonzalez, L. Zhang, A.B. Rajurkar, M. Ruiz, E. Daniels, L. Maree, S. Pariyar, W. Busch, T.D. Pereira.
"Fast and Efficient Root Phenotyping via Pose Estimation", *Plant Phenomics* 0: DOI:[10.34133/plantphenomics.0175](https://doi.org/10.34133/plantphenomics.0175).
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"description": "# sleap-roots\n\n[](https://github.com/talmolab/sleap-roots/actions/workflows/ci.yml)\n[](https://codecov.io/gh/talmolab/sleap-roots)\n[](https://github.com/talmolab/sleap-roots/releases/)\n[](https://pypi.org/project/sleap-roots)\n\nAnalysis tools for [SLEAP](https://sleap.ai)-based plant root phenotyping.\n\n## Installation\n```\npip install sleap-roots\n```\n\nIf you are using conda (recommended):\n```\nconda create -n sleap-roots python=3.9\nconda activate sleap-roots\npip install sleap-roots\n```\n\n## Usage\n\nDetailed trait documentation per pipeline is available here:\n[sleap-roots HackMD](https://hackmd.io/DMiXO2kXQhKH8AIIcIy--g)\n\n\n### `DicotPipeline`\n\n**1. Computing traits for a single plant:**\n\n```py\nimport sleap_roots as sr\n\nplant = sr.Series.load(\n \"tests/data/canola_7do/919QDUH.h5\",\n # Specify the names of the primary and lateral roots for trait calculation\n primary_name=\"primary\",\n lateral_name=\"lateral\"\n)\npipeline = sr.DicotPipeline()\ntraits = pipeline.compute_plant_traits(plant, write_csv=True)\n```\n\n**2. Computing traits for a batch of plants:**\n\n```py\nimport sleap_roots as sr\n\nplant_paths = sr.find_all_series(\"tests/data/soy_6do\")\nplants = [\n sr.Series.load(\n plant_path,\n # Specify the names of the primary and lateral roots for trait calculation\n primary_name=\"primary\",\n lateral_name=\"lateral\",\n ) for plant_path in plant_paths]\n\npipeline = sr.DicotPipeline()\nall_traits = pipeline.compute_batch_traits(plants, write_csv=True)\n```\n\n**3. Computing individual traits:**\n\n```py\nimport sleap_roots as sr\nimport numpy as np\nimport numpy as np\n# Import utility for combining primary and lateral root points\nfrom sleap_roots.points import get_all_pts_array\n\nplant = sr.Series.load(\n \"tests/data/canola_7do/919QDUH.h5\",\n primary_name=\"primary\",\n lateral_name=\"lateral\"\n)\n\nframe_index = 0\nprimary_pts = plant.get_primary_points(frame_index)\nlateral_pts = plant.get_lateral_points(frame_index)\npts = get_all_pts_array(primary_pts, lateral_pts)\nconvex_hull = sr.convhull.get_convhull(pts)\n```\n\n### `YoungerMonocotPipeline`\n\n**1. Computing traits for a single plant:**\n\n```py\nimport sleap_roots as sr\n\nplant = sr.Series.load(\n \"tests/data/rice_3do/0K9E8BI.h5\",\n primary_name=\"primary\",\n lateral_name=\"crown\"\n)\npipeline = sr.YoungerMonocotPipeline()\ntraits = pipeline.compute_plant_traits(plant, write_csv=True)\n```\n\n**2. Computing traits for a batch of plants:**\n\n```py\nimport sleap_roots as sr\n\nplant_paths = sr.find_all_series(\"tests/data/rice_3do\")\nplants = [\n sr.Series.load(\n plant_path,\n primary_name=\"primary\",\n lateral_name=\"crown\"\n ) for plant_path in plant_paths]\n\npipeline = sr.YoungerMonocotPipeline()\nall_traits = pipeline.compute_batch_traits(plants, write_csv=True)\n```\n\n**3. Computing individual traits:**\n\n```py\nimport sleap_roots as sr\nimport numpy as np\nfrom sleap_roots.points import get_all_pts_array\n\nplant = sr.Series.load(\n \"tests/data/rice_3do/0K9E8BI.h5\",\n primary_name=\"primary\",\n lateral_name=\"crown\"\n)\n\nframe_index = 0\nprimary_pts = plant.get_primary_points(frame_index)\nlateral_pts = plant.get_lateral_points(frame_index)\npts = get_all_pts_array(primary_pts, lateral_pts)\nconvex_hull = sr.convhull.get_convhull(pts)\n```\n\n## Tutorials\nJupyter notebooks are located in this repo at `sleap-roots/notebooks`.\n\nTo use them, activate your conda environment which includes JupyterLab (recommended):\n```\nconda activate sleap-roots\n```\n\nClone this repository if you haven't already:\n```\ngit clone https://github.com/talmolab/sleap-roots.git && cd sleap-roots\n```\n\nThen you can change directories to the location of the notebooks, and open Jupyter Lab:\n```\ncd notebooks\njupyter lab\n```\n\nGo through the commands in the notebooks to learn about each pipeline. \nYou can use the test data located at `tests/data` or copy the notebooks elsewhere for use with your own data!\n\n\n## Development\nFor development, first clone the repository:\n```\ngit clone https://github.com/talmolab/sleap-roots && cd sleap-roots\n```\n\nThen, to create a **new conda environment** and install the package in editable mode:\n```\nconda env create -f environment.yml\n```\nThis will create a conda environment called `sleap-roots`.\n\nIf you have an **existing conda environment** (such as where you installed SLEAP), you\ncan just install in editable mode directly. First, activate your environment and then:\n```\npip install -e \".[dev]\"\n```\n*Note:* The `[dev]` makes sure that the development-only dependencies are also\ninstalled.\n\nTo **start fresh**, just delete the environment:\n```\nconda env remove -n sleap-roots\n```\n\nTo **run tests**, first activate the environment:\n```\nconda activate sleap-roots\n```\nThen run `pytest` with:\n```\npytest tests\n```\n\n## Acknowledgments\n\nThis repository was created by the [Talmo Lab](https://talmolab.org) and [Busch Lab](https://busch.salk.edu) at the Salk Institute for Biological Studies as part of the [Harnessing Plants Initiative](https://www.salk.edu/harnessing-plants-initiative/).\n\n### Contributors\n\n- Elizabeth Berrigan\n- Lin Wang\n- Talmo Pereira\n\n### Citation\n\nE.M. Berrigan, L. Wang, H. Carrillo, K. Echegoyen, M. Kappes, J. Torres, A. Ai-Perreira, E. McCoy, E. Shane, C.D. Copeland, L. Ragel, \nC. Georgousakis, S. Lee, D. Reynolds, A. Talgo, J. Gonzalez, L. Zhang, A.B. Rajurkar, M. Ruiz, E. Daniels, L. Maree, S. Pariyar, W. Busch, T.D. Pereira. \n\"Fast and Efficient Root Phenotyping via Pose Estimation\", *Plant Phenomics* 0: DOI:[10.34133/plantphenomics.0175](https://doi.org/10.34133/plantphenomics.0175).\n",
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