danforthcenter · nfahlgren · Oct 27, 2025 · Oct 27, 2025 · Oct 27, 2025 · Oct 27, 2025
diff --git a/.deepsource.toml b/.deepsource.toml
@@ -6,6 +6,7 @@ exclude_patterns = [
   "setup.py",
   "tests/conftest.py",
   "tests/*/conftest.py",
+  "tests/testdata/*",
   "versioneer.py"
 ]
 

diff --git a/.github/workflows/continuous-integration.yml b/.github/workflows/continuous-integration.yml
@@ -6,10 +6,10 @@ name: builds
 on:
   push:
     # Run when main is updated
-    branches: [ main ]
+    branches: [ main, v5.0 ]
   pull_request:
     # Run on pull requests against main
-    branches: [ main ]
+    branches: [ main, v5.0 ]
 
 jobs:
   build:

diff --git a/docs/Points.md b/docs/Points.md
@@ -3,7 +3,7 @@
 Using [Jupyter Notebooks](jupyter.md) it is possible to interactively click to collect coordinates from an image, which can be used in various downstream applications. Left click on the image to collect a point. Right click removes the
 closest collected point.
 
-**plantcv.Points**(*img, figsize=(12, 6)*)
+**plantcv.Point**(*img, figsize=(12, 6)*)
 
 **returns** interactive image class
 
@@ -25,8 +25,8 @@ closest collected point.
 ```python
 from plantcv import plantcv as pcv
 
-# Create an instance of the Points class
-marker = pcv.Points(img=img, figsize=(12,6))
+# Create an instance of the Point class
+marker = pcv.Point(img=img, figsize=(12,6))
 
 # Click on the plotted image to collect coordinates
 

diff --git a/docs/astro_color_matrix.md b/docs/astro_color_matrix.md
@@ -0,0 +1,48 @@
+## Astrobotany Calibration Sticker Color Matrix
+
+Returns a color matrix with the standard *R*, *G*, *B* values compatible with Astrobotany Calibration Stickers.
+
+Source: [https://astrobotany.com/product/airi-bio-imaging-spectrum-5cm/](https://astrobotany.com/product/airi-bio-imaging-spectrum-5cm/)
+
+**plantcv.transform.astro_color_matrix**()
+
+**returns** color_matrix
+
+- **Context**
+    - A standard matrix can be used most readily while doing [affine](transform_affine_color_correction.md) color correction. 
+
+- **Returns**
+    - color_matrix - a *n* x 4 matrix containing the standard red, green, and blue
+    values for each color chip
+
+- **Example use below:**
+
+```python
+
+from plantcv import plantcv as pcv
+
+astro_color_matrix = pcv.transform.astro_color_matrix()
+
+# use fixed point notation for printing the matrix
+np.set_printoptions(precision=2, suppress=True)
+
+print(astro_color_matrix)
+
+        [[ 10.,    0.18,   0.23,   0.5 ],
+         [ 20.,    0.34,   0.62,   0.25],
+         [ 30.,    0.71,   0.25,   0.21],
+         [ 40.,    0.89,   0.81,   0.2 ],
+         [ 50.,    0.21,   0.22,   0.22],
+         [ 60.,    0.91,   0.95,   0.93],
+         [ 70.,    0.82,   0.86,   0.86],
+         [ 80.,    0.72,   0.75,   0.73],
+         [ 90.,    0.64,   0.67,   0.64],
+         [100.,    0.57,   0.58,   0.56],
+         [110.,    0.48,   0.49,   0.48],
+         [120.,    0.39,   0.4 ,   0.39],
+         [130.,    0.33,   0.32,   0.32],
+         [140.,    0.27,   0.28,   0.27],
+         [150.,    0.22,   0.23,   0.23]]
+
+```
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/transform/color_correction.py)
diff --git a/docs/db-exporter.md b/docs/db-exporter.md
diff --git a/docs/find_color_card.md b/docs/find_color_card.md
diff --git a/docs/img/documentation_images/correct_color_imgs/astro-sticker.png b/docs/img/documentation_images/correct_color_imgs/astro-sticker.png
diff --git a/docs/img/documentation_images/correct_color_imgs/corrected.png b/docs/img/documentation_images/correct_color_imgs/corrected.png
diff --git a/docs/img/documentation_images/photosynthesis_read_cropreporter/4_gfp_frames.png b/docs/img/documentation_images/photosynthesis_read_cropreporter/4_gfp_frames.png
diff --git a/docs/img/documentation_images/photosynthesis_read_cropreporter/5_rfp_frames.png b/docs/img/documentation_images/photosynthesis_read_cropreporter/5_rfp_frames.png
diff --git a/docs/img/documentation_images/photosynthesis_read_cropreporter/6_aph_frames.png b/docs/img/documentation_images/photosynthesis_read_cropreporter/6_aph_frames.png
diff --git a/docs/img/documentation_images/sharpen/sharp1.png b/docs/img/documentation_images/sharpen/sharp1.png
diff --git a/docs/img/documentation_images/sharpen/sharp5.png b/docs/img/documentation_images/sharpen/sharp5.png
diff --git a/docs/img/documentation_images/transform_detect_color_card/cropped_color_card.png b/docs/img/documentation_images/transform_detect_color_card/cropped_color_card.png
diff --git a/docs/img/documentation_images/transform_detect_color_card/detected_color_card.png b/docs/img/documentation_images/transform_detect_color_card/detected_color_card.png
diff --git a/docs/img/documentation_images/visualize_color_correction_scatter/am003_sv_ex.png b/docs/img/documentation_images/visualize_color_correction_scatter/am003_sv_ex.png
diff --git a/.../img/documentation_images/visualize_color_correction_scatter/am003_sv_input.png b/.../img/documentation_images/visualize_color_correction_scatter/am003_sv_input.png
diff --git a/docs/index.md b/docs/index.md
@@ -44,7 +44,6 @@ Check out our tutorials in our [Tutorial Gallery](https://plantcv.org/tutorials)
 * [Hyperspectral Image Processing](https://plantcv.org/tutorials/hyperspectral)
 * [Machine Learning Tutorial](https://plantcv.org/tutorials/naive-bayes)
 * [Parallel Image Processing](pipeline_parallel.md)
-* [Exporting Data for Downstream Analysis](db-exporter.md)
 
 ## Contributing 
 

diff --git a/docs/json2csv.md b/docs/json2csv.md
@@ -0,0 +1,14 @@
+## Converting JSON results to CSV
+
+The `plantcv.parallel.json2csv` function is used to convert `PlantCV` results files from json into csv format.
+Most of the time you will not need to call this function directly.
+
+Parallel results from [`plantcv-run-workflow`](pipeline_parallel.md) automatically are converted from json into 2 CSV files, one for single-value traits (where each object is described by one quantity like area or height) and one for multi-value traits (where each object is described by several quantities like hue).
+
+For other json results you can use this function to make tabular data:
+
+```python
+from plantcv import parallel as pcvpar
+pcvpar.json2csv("your_file.json", "new_file.csv")
+
+```
diff --git a/docs/jupyter.md b/docs/jupyter.md
@@ -144,5 +144,5 @@ pcv.outputs.save_results(filename=args.result, outformat="json")
 
 In the case where creating a parallel workflow is not convenient (e.g. image sets where ROIs or other parameters must be adjusted between images), it is possible to run all images individually through Jupyter Notebook and then combine the JSON outputs into a larger JSON. The resulting file will look and function similarly to the result.json of a parallel workflow and can be used for downstream data analysis.
 
-For all images whose data should be combined, the output JSONs should be moved to a new, separate folder. The files can then be combined using the process outlined on the [Process Results](parallel_process_results.md) page and plantcv-utils json2csv ([documentation](https://plantcv.readthedocs.io/en/stable/tools/#convert-output-json-data-files-to-csv-tables)).
+For all images whose data should be combined, the output JSONs should be moved to a new, separate folder. The files can then be combined using the process outlined on the [Process Results](parallel_process_results.md) page and [`plantcv.parallel.json2csv`](json2csv.md).
 
diff --git a/docs/output_measurements.md b/docs/output_measurements.md
@@ -115,8 +115,8 @@ Example (abbreviated) JSON data:
 }
 ```
 
-Data in this structure can be converted to tables for downstream analysis using the provided script 
-`plantcv-utils json2csv`, see [Accessory Tools](tools.md) for more details.
+Data in this structure can be converted to tables for downstream analysis using the 
+[`plantcv.parallel.json2csv`](json2csv.md) function.
 
 ## Summary of Output Metadata
 
@@ -172,7 +172,6 @@ Functions that automatically store data to the [`Outputs` class](outputs.md) are
 [morphology.segment_path_length](segment_pathlength.md), 
 [morphology.segment_tangent_angle](segment_tangent_angle.md), 
 [report_size_marker_area](report_size_marker.md), 
-[transform.find_color_card](find_color_card.md), 
 [transform.detect_color_card](transform_detect_color_card.md)
 [watershed_segmentation](watershed.md), 
 [within_frame](within_frame.md), and

diff --git a/docs/parallel_config.md b/docs/parallel_config.md
@@ -47,7 +47,7 @@ Validate parameters/structure of configuration data.
 * **input_dir**: (str, required): path/name of input images directory (validates that it exists).
 
 
-* **json**: (str, required): path/name of output JSON data file (appends new data if it already exists).
+* **results**: (str, required): path/name of output JSON data file (appends new data if it already exists).
 
 
 * **filename_metadata**: (list, required): list of metadata terms used to construct filenames. for example: 
@@ -92,9 +92,9 @@ extensions should be combined (if using phenofront data this must be length 1 an
   `{"imgtype": "VIS", "frame": ["0", "90"]"}`).
 
 
-* **metadata_regex**: (dict, default = `None`): a dictionary of filepath terms (keys) and values, any specified keys
+* **metadata_regex**: (dict, default = `None`): a dictionary of metadata terms (keys) and values, any specified keys
 will be used for regex based filtering (e.g. 
-  `{"filepath1": "first[p|P]athPattern.*", "basename": "^starts_with.*"}`).
+  `{"dir1": "first[p|P]athPattern.*", "basename": "^starts_with.*"}`).
 
 
 * **timestampformat**: (str, default = '%Y-%m-%dT%H:%M:%S.%fZ'): a date format code compatible with strptime C library. 
@@ -118,7 +118,10 @@ metadata terms are listed [here](pipeline_parallel.md).
 image group (created by `groupby`), or `"auto"` to generate a numbered image sequence `image1, image2, ...`. The resulting
 names are used to access individual image filepaths in a workflow.
 
-* **cleanup**: (bool, default =`True`): remove temporary job directory if `True`.
+* **checkpoint**: (bool, default = `True`): restart from where a previous run left off and/or keep checkpointing files in
+case jobs fail for any reason. For details see the checkpointing section of ['workflow_inputs'](parallel_workflow_inputs.md).
+
+* **cleanup**: (bool, default =`True`): remove `config.tmp_dir` directory (including all temporary job directories and checkpoint files) after a complete run if `True`.
 
 
 * **append**: (bool, default = `False`): if `False`, will delete previous results stored in the specified JSON file.
@@ -135,7 +138,8 @@ names are used to access individual image filepaths in a workflow.
 
 
 * **metadata_terms**: (dict, default: as-is): a dictionary of metadata terms used to assign values in image filenames
-  (or metadata files) to metadata terms (should not be modified here). Terms from `filename_metadata` that are not present in the default dictionary of terms are added automatically.
+  (or metadata files) to metadata terms (should not be modified here). Terms from `filename_metadata`
+  that are not present in the default dictionary of terms are added automatically.
 
 
 ### Cluster configuration
@@ -202,7 +206,7 @@ config.import_config(config_file="my_config.json")
 
 # Change configuration values directly in Python as needed. At a minimum you must specify input_dir, json, filename_metadata, workflow.
 config.input_dir = "./my_images"
-config.json = "output.json"
+config.results = "output.json"
 config.filename_metadata = ["plantbarcode", "timestamp"]
 config.workflow = "my_workflow.py"
 

diff --git a/docs/parallel_inspect_config.md b/docs/parallel_inspect_config.md
@@ -26,3 +26,11 @@ config.input_dir = "./my_images"
 
 summary, meta = pcvpar.inspect_dataset(config)
 ```
+
+#### Command Line Interface
+
+`plantcv.parallel.inspect_dataset` can also be run from the command line using `plantcv-run-workflow --dryrun configfile.json`. Used this way it will save the summary and metadata DataFrames to csv files named for your configuration file with `_summary_df.csv` and `_metadata_df.csv` replacing the `.json` extension.
+
+```bash
+plantcv-run-workflow --dryrun configfile.json
+```
diff --git a/docs/parallel_jupyterconfig.md b/docs/parallel_jupyterconfig.md
@@ -0,0 +1,94 @@
+## Running in Parallel from a Jupyter notebook
+
+The `jupyterconfig` class is very similar to the [`WorkflowConfig` class](parallel_config.md) and is used to configure a parallel workflow directly from a Jupyter notebook.
+
+### Quick Start
+
+Initializing a `jupyterconfig` object in a Jupyter notebook will immediately use `nbconvert` to create a python script version of the workflow. When the python script is generated notebook cells marked with `@ignore` as the start of a comment (`# @ignore`) are excluded, which may be useful to avoid making diagnostic plots in parallel or otherwise in keeping your notebook reproducible. Similar to `WorkflowConfig` objects you can overwrite default attributes to meet your particular needs. Once your edits are done the `run` method will run the workflow in parallel. Note that you will need to specify arguments with [`workflow_inputs`](parallel_workflow_inputs.md) how you would when using `WorkflowConfig` to run a script in parallel.
+
+```python
+from plantcv import parallel as pcvpar
+
+jupcon = pcvpar.jupyterconfig()
+jupcon.input_dir = "path/to/images"
+# ... other edits
+jupcon.run()
+
+args = pcvpar.workflow_inputs()
+```
+
+*class* **plantcv.parallel.jupyterconfig**
+
+### Class methods
+
+Inspect the input dataset.
+
+**jupyterconfig.inspect_dataset**()
+
+- **Parameters:**
+    - None
+- **Context:**
+    - Used to check which images will be found by the current configuration using [`inspect_dataset`](parallel_inspect_config.md).
+
+
+Save a configuration file that can be modified and imported to run workflows in parallel.
+
+**jupyterconfig.save_config**()
+
+- **Parameters:**
+    - None
+- **Context:**
+    - Used to create a configuration file that can be edited and imported. The configuration file is named by `jupyterconfig.config`, which defaults to the name of the notebook with the `.json` extension.
+
+
+Run a parallel workflow from Jupyter.
+
+**jupyterconfig.run**()
+
+- **Parameters:**
+    - None
+- **Context:**
+    - Used to run a parallel workflow, similar to the [`plantcv-run-workflow`](pipeline_parallel.md) utility.
+
+
+There are additional methods (`find_notebook`, `nameScript`, `nameResults`, `notebook2script`, `nameConfig`, and `in_notebook`) which are used internally on initializing the `jupyterconfig` object to make and name the different files needed to run in parallel. These generally do not need to be interacted with directly and their generated values can be overwritten if desired.
+
+
+
+### Attributes
+
+* **notebook**: (str, default = `os.environ["JPY_SESSION_NAME"]`): Name of the Jupyter notebook file. This is used to name results, python script, and saved configuration json.
+
+* **workflow**: (str, default = `self.nameScript()`): Name of the python workflow to run in parallel. Default is to name the python script with the same basename as the Jupyter notebook where this is initialized.
+
+* **config**: (str, default = `self.nameConfig()`): Name of the configuration json file to save to run in parallel. Default is to name the json with the same basename as the Jupyter notebook where this is initialized.
+
+* **analysis_script**: (bool, default = `self.notebook2script()`): True if the workflow file exists, false otherwise. Normally this should be True since `jupyterconfig.notebook2script()` writes the python script specified by `jupyterconfig.workflow`. 
+
+Additional attributes are the same as those in the [`WorkflowConfig` documentation](parallel_config.md).
+
+
+### Example
+
+```python
+from plantcv import plantcv as pcv 
+from plantcv import parallel as pcvpar
+
+jupcon = pcvpar.jupyterconfig()
+jupcon.input_dir = "path/to/images"
+jupcon.cluster_config["n_workers"] = 10
+# ... other edits to defaults
+jupcon.run()
+
+# get arguments from plantcv.parallel.workflowinputs
+args = pcvpar.workflow_inputs() # This is necessary for running the notebook in parallel
+# read image
+img, path, filename = pcv.readimage(filename=args.image1)
+# other components of your workflow
+# ...
+# save results
+pcv.outputs.save_results(filename= args.result, outformat="json")
+```
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/parallel/jupyterconfig.py)
+
Original file line number	Diff line number	Diff line change
Expand Up		@@ -144,5 +144,5 @@ pcv.outputs.save_results(filename=args.result, outformat="json")

		In the case where creating a parallel workflow is not convenient (e.g. image sets where ROIs or other parameters must be adjusted between images), it is possible to run all images individually through Jupyter Notebook and then combine the JSON outputs into a larger JSON. The resulting file will look and function similarly to the result.json of a parallel workflow and can be used for downstream data analysis.

		For all images whose data should be combined, the output JSONs should be moved to a new, separate folder. The files can then be combined using the process outlined on the [Process Results](parallel_process_results.md) page and plantcv-utils json2csv ([documentation](https://plantcv.readthedocs.io/en/stable/tools/#convert-output-json-data-files-to-csv-tables)).
		For all images whose data should be combined, the output JSONs should be moved to a new, separate folder. The files can then be combined using the process outlined on the [Process Results](parallel_process_results.md) page and [`plantcv.parallel.json2csv`](json2csv.md).