Add jupyter notebooks for code instruction

[eberrigan]

• Adjust the pyproject.toml so that the notebooks folder is not automatically registered as a package in our flat layout structure
• Add notebooks with example usage of each pipeline for calculating individual traits, traits of a series, traits of a batch of series and traits of batches of grouped series for the MultipleDicotPipeline
• MultipleDicotPipeline example usage
• DicotPipeline example usage
• YoungerMonocotPipeline example usage
• OlderMonocotPipelline example usage

Summary by CodeRabbit

• New Features
  • Introduced a Jupyter notebook MultipleDicotPipeline.ipynb for loading root predictions, processing with a custom pipeline, and visualizing results.
  • Added a new property group to the Series class representing the group name for the series from the CSV.
• Chores
  • Updated package configuration to specify inclusion of sleap_roots and exclusion of notebooks.

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Commits

Files that changed from the base of the PR and between bf493eb and 77294cf.

Walkthrough

The recent update introduces a new Jupyter notebook, MultipleDicotPipeline.ipynb, focused on processing root predictions and visualizing results. The pyproject.toml file now specifies package inclusions and exclusions for setuptools, enhancing project organization.

Changes

Files	Change Summary
notebooks/MultipleDicotPipeline.ipynb	Introduces a Jupyter notebook for loading root predictions, custom processing, and visualization.
pyproject.toml	Added configuration to specify package inclusion and exclusion for setuptools.
README.md	Updated Series.load calls, added new imports, tutorials section, and citation information.
sleap_roots/series.py	Added a group property to the Series class for group names from CSV.

🐇✨
In the code's deep maze, where roots entwine,
A notebook emerges, its design refined.
Configurations aligned, exclusions defined,
Changes embraced, in the digital vine.
Here's to the updates, under the moon's soft sign,
May data flow smooth, like aged wine.
🌿📊🌟

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Actionable comments posted: 1

Review Status

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between c5544ba and f3023e1.

Files selected for processing (2)

• notebooks/MultipleDicotPipeline.ipynb (1 hunks)
• pyproject.toml (1 hunks)

Additional Context Used

Additional comments not posted (3)

notebooks/MultipleDicotPipeline.ipynb (3)

4-18: The imports are well-organized and appropriate for the tasks demonstrated in the notebook.

---

22-30: The paths for CSV and folder locations are hardcoded, which might limit the notebook's usability in different environments or datasets. Consider making these paths configurable, for example, by using input fields or environment variables, to enhance the notebook's flexibility.

---

34-52: The operation to find all h5 files is correctly implemented. However, consider adding error handling or feedback for cases where no h5 files are found, to improve the user experience. For example, you could add a conditional check on all_h5s and print a message if it's empty.

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Commits

Files that changed from the base of the PR and between f3023e1 and f7d2f2f.

Files selected for processing (1)

• notebooks/MultipleDicotPipeline.ipynb (1 hunks)

Files skipped from review as they are similar to previous changes (1)

• notebooks/MultipleDicotPipeline.ipynb

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Review Status

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between f7d2f2f and 8ebefa3.

Files selected for processing (2)

• notebooks/MultipleDicotPipeline.ipynb (1 hunks)
• pyproject.toml (1 hunks)

Files skipped from review as they are similar to previous changes (2)

• notebooks/MultipleDicotPipeline.ipynb
• pyproject.toml

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 74.90%. Comparing base (cae212d) to head (77294cf).
Report is 1 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #72   +/-   ##
=======================================
  Coverage   74.90%   74.90%           
=======================================
  Files          13       13           
  Lines        1307     1307           
=======================================
  Hits          979      979           
  Misses        328      328           

☔ View full report in Codecov by Sentry.
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Actionable comments posted: 5

Review Status

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 8ebefa3 and fde13d2.

Files selected for processing (2)

• README.md (7 hunks)
• sleap_roots/series.py (1 hunks)

Additional comments not posted (1)

README.md (1)

130-149: The tutorials section is a valuable addition. Enhance its visibility by adding a table of contents at the beginning of the README with a link to this section.

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Review Status

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between fde13d2 and bf493eb.

Files selected for processing (1)

• README.md (7 hunks)

Additional comments not posted (4)

README.md (4)

33-34: Consider adding a brief comment above the primary_name and lateral_name parameters to explain their purpose and usage, as previously suggested.

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"
)

---

49-50: Similar to the previous comment, consider adding explanatory comments for the primary_name and lateral_name parameters in batch processing examples, as previously suggested.

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]

---

62-62: Add a comment explaining the purpose of importing get_all_pts_array for clarity, as previously suggested.

import numpy as np
+ # Import utility for combining primary and lateral root points
from sleap_roots.points import get_all_pts_array

---

130-149: The new section on using Jupyter notebooks for tutorials is clear and informative. However, consider replacing "for use on your own data" with "for use with your own data" for grammatical correctness, as previously suggested.

- You can use the test data located at `tests/data` or copy the notebooks elsewhere for use on your own data!
+ You can use the test data located at `tests/data` or copy the notebooks elsewhere for use with your own data!