Student Name	Nishant Bansal
Organization	OSGeo - Open Source Geospatial Foundation
Mentor Name	Anna Petrasova, Corey White, Vaclav Petras
GitHub Fork	View Repo
GSoC proposal	View Proposal

Abstract

Currently, most GRASS tools produce data in plain text by default. This means that users who want to process the output in Python need to create custom parsing code. To make this easier, several modules could be improved by oﬀering JSON as an alternative output format. Recently, the Parson library was added to the GRASS codebase to support JSON output in addition to plain text. Building on the advancements made during the last GSoC, my project aims to expand JSON output capabilities to more tools using the Parson library. I will also work on making the JSON structure introduced in the previous GSoC more useful and consistent. Additionally, I plan to add support for SHELL and CSV output formats to the relevant tools where appropriate.

Goal

Working with mentors to finalize the structure of the JSON output.
Adding an option to choose the output format (plain text or JSON) for each updated tool.
Make the JSON structure introduced during the last GSoC more useful and consistent across tools.
Developing Python test cases to ensure that the JSON output works correctly and to avoid future issues.
Providing basic documentation and example JSON outputs for each modified tool.
Adding support for SHELL and CSV output formats to the relevant tools where appropriate.
Develop simple examples demonstrating how to use JSON output in a data science workflow, such as reading JSON data into pandas or python.

Timeline

Period	Timeline	Tasks	Status
Bonding Period	May 8 - June 1	Reviewed the work done during the last GSoC to stabilize the JSON output. Discussed the priority tools for adding JSON support. Read GRASS documentation to add Python and JSON examples to the documentation.	OK OK OK
Official Coding Period
	Week 1 (June 2 - June 8)	Add JSON support for g.mapset #5810 Fix uninitialized zone_value in r.univar #5821 Add JSON support for r.what.color #5822 Update test file in r.info #5842 Improve interface for r.info #5859	OK OK OK OK OK
	Week 2 (June 9 - June 15)	Add JSON support for g.version #5863 Add JSON support for i.group #5870 Add test file in r.stats #5879 Add tests for JSON output in r.stats #5892	OK OK OK OK
	Week 3 (June 16 - June 22)	Update the JSON output schema of v.distance #5898 Fix uninitialized window variable in r.stats #5900 Add test file in g.list #5906 Add JSON support for g.list #5921 Update the JSON output schema of g.region #5929	OK OK OK OK OK
	Week 4 (June 23 - June 29)	Update the JSON output schema of v.to.db #5946 Update the JSON output schema of r.profile #5962 Add SHELL and CSV output support for r.univar #5966	OK OK OK
	Week 5 (June 30 - July 6)	Add test file in r.regression.line #5990 Add JSON support for r.regression.line #5993 Add test file in r.stats.quantile #6002 Add JSON support for r.stats.quantile #6007 Add test file in r.volume #6027 Add JSON support for r.volume #6036	OK OK OK OK OK OK
	Week 6 (July 7 - July 13)	Add JSON support for db.columns #6042 Add test file in m.measure #6051 Add test file in db.connect #6052 Add JSON support for m.measure #6053 Add JSON support for db.connect #6059	OK OK OK OK OK
	Week 7 (July 14 - July 20)	Add test file in r.regression.multi #6066 Add test file in v.db.connect #6067 Add JSON support for r.regression.multi #6075 Add JSON support for v.db.connect #6077 Submit MidTerm evaluation.	OK OK OK OK OK
	Week 8 (July 21 - July 27)	Add JSON support for g.findfile #6091 Add test file in r3.info #6098 Add JSON support for r3.info #6103 Add test file in v.what.rast #6109	OK OK OK OK
	Week 9 (July 28 - August 3)	Update format to CSV instead of shell in v.category #6132 Add CSV output support to r.stats #6133 Add CSV headers to r.volume #6134 Add JSON support for v.what.rast #6140 Add CSV output support to r.profile #6145 Update test cases in r.distance #6150	OK OK OK OK OK OK
	Week 10 (August 4 - August 10)	Address review comments on my open PRs.	OK
	Week 11 (August 11 - August 17)	Add JSON and CSV support for r.distance #6204 Add more test cases in v.class #6205 Add JSON and CSV support for v.class #6210	OK OK OK
	Week 12 (August 18 - August 24)	Update JSON output format for v.what #6252 Address review comments on my open PRs.	OK OK
Evaluation Period
Evaluation Period	August 25 - September 1	Address review comments on my open PRs. Submit code, final evaluation and write the final GSoC report.	OK OK

Reports

Community Bonding Period

What did I get done this week?

Introduced myself on the OSGeo Discourse mailing list.
Created both my personal Wiki page and the project Wiki page, adding all necessary project-related information.
Read GRASS documentation to support the addition of Python and JSON examples.
Worked on the following pull requests:
- #5688
- #5773
- #5775
- #5784
- #5785

What do I plan on doing next week?

Add JSON output support to the g.mapset and r.what.color modules.
If time permits, I will begin improving the r.info interface: Issue #5793

Am I blocked on anything?

No, I am not currently blocked on anything.

Project Discussions and Meetings

14 May 2025: Introductory meeting discussing JSON support and overall project milestones.
21 May 2025: Introduction with fellow developers. Discussed priority tools, adding Python/Pandas examples, and approaches for finalizing the JSON schema for each module.

Week-1

What did I get done this week?

g.mapset: Added JSON output support, now you can use -p and -l flags to get JSON-formatted results.
r.univar: Fixed a bug causing an uninitialized zone_value.
r.what.color: Implemented JSON output support. (See PR #5822)
r.info (regression tests): To maintain backward compatibility while updating its interface, I added strong regression tests.
r.info (interface improvements): Began improving the module’s interface. (See PR #5859)

What do I plan on doing next week?

Address review comments on my open PRs.
Add JSON output support to g.version (see issue #5816).
Add JSON output support to i.group (see issue #5815).
If time permits, write tests for r.stats (see issue #5814).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-2

What did I get done this week?

g.version: Implemented JSON output support. (See PR #5863)
i.group: Implemented JSON output support. (See PR #5870)
r.stats (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
r.stats: Added tests for JSON output; this uncovered a bug, which was fixed alongside.

What do I plan on doing next week?

Address review comments on my open PRs.
Update JSON output format of v.distance (see issue #5846).
Add JSON output support to g.list (see issue #5848).
If time permits, fix the JSON output format of g.region (see issue #5828).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-3

What did I get done this week?

v.distance: Updated the JSON output schema. (See PR #5898)
r.stats: Fixed a bug causing an uninitialized window variable.
g.list (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
g.list: Implemented JSON output support. (See PR #5921)
g.region: Updated the JSON output schema. (See PR #5929)

What do I plan on doing next week?

Address review comments on my open PRs.
Update JSON output format of v.to.db (see issue #5711).
Update JSON output format and documentation of r.profile (see issue #5834).
If time permits, add shell and csv output formats to r.univar (see issue #5824).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-4

What did I get done this week?

v.to.db: Updated the JSON output schema. (See PR #5946)
r.profile: Updated the JSON output schema. (See PR #5962)
r.univar: Implemented SHELL and CSV output support. (See PR #5966)

What do I plan on doing next week?

Add JSON output support to r.regression.line (see issue #5950).
Add JSON output support to r.stats.quantile (see issue #5951).
Add JSON output support to r.volume (see issue #5949).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-5

What did I get done this week?

r.regression.line (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
r.regression.line: Implemented JSON output support. (See PR #5993)
r.stats.quantile (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
r.stats.quantile: Implemented JSON output support. (See PR #6007)
r.volume (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
r.volume: Implemented JSON output support. (See PR #6036)

What do I plan on doing next week?

Add JSON output support to m.measure (see issue #6006).
Add JSON output support to db.columns (see issue #6008).
Add JSON output support to db.connect (see issue #6009).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-6

What did I get done this week?

db.columns: Implemented JSON output support. (See PR #6042)
m.measure (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
db.connect (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
m.measure: Implemented JSON output support. (See PR #6053)
db.connect: Implemented JSON output support. (See PR #6059)

What do I plan on doing next week?

Add JSON output support to v.db.connect (see issue #6014).
Add JSON output support to r.regression.multi (see issue #6010).
If time permits, add JSON output support to g.findfile (see issue #6005).
If time permits, add JSON output support to r3.info (see issue #5995).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-7

What did I get done this week?

r.regression.multi (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
v.db.connect (regression tests): To maintain backward compatibility while adding JSON format, I added strong regression tests.
r.regression.multi: Implemented JSON output support. (See PR #6075)
v.db.connect: Implemented JSON output support. (See PR #6077)

What do I plan on doing next week?

Address review comments on my open PRs.
Add JSON output support to g.findfile (see issue #6005).
Add JSON output support to r3.info (see issue #5995).
If time permits, add JSON output support to v.what.rast (see issue #6024).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-8

What did I get done this week?

g.findfile: Implemented JSON output support. (See PR #6091)
r3.info (regression tests): Added regression tests to ensure backward compatibility with new JSON support and fixed a flag-handling bug.
r3.info: Implemented JSON output support. (See PR #6103)
v.what.rast (regression tests): Added regression tests to ensure backward compatibility with new JSON support and fixed a sorting bug.

What do I plan on doing next week?

Address review comments on my open PRs.
Add JSON output support to v.what.rast (see issue #6024).
Update format to CSV instead of shell in v.category (see issue #6025).
Add CSV output support to r.stats (see issue #5835).
If time permits, add CSV output support to r.profile (see issue #5958).
If time permits, add CSV headers to r.volume (see issue #6049).

Am I blocked on anything?

No, I don’t have any blockers this week. However, I’ll be a little busy with some personal work from August 3 to August 10, so I’ll try to complete next week’s tasks this week itself.

Week-9

What did I get done this week?

v.category: Updated format to CSV instead of shell. (See PR #6132)
r.stats: Implemented CSV output support. (See PR #6133)
r.volume: Implemented CSV column headers support. (See PR #6134)
v.what.rast: Implemented JSON output support. (See PR #6140)
r.profile: Implemented CSV output support. (See PR #6145)
r.distance (regression tests): Updated regression tests to ensure backward compatibility with new JSON support. (See PR #6150)

What do I plan on doing next week?

I’ll be a bit busy with some personal work from August 3 to 10, so I completed next week’s tasks last week itself. I’ve already informed my mentors about my limited availability.

Am I blocked on anything?

Yes, I’ll be a bit busy with some personal work from August 3 to 10.

Week-10

What did I get done this week?

This week I was busy with some personal work, so I did not complete any additional tasks. However, I had already finished this week’s tasks last week. This week, I mostly focused on handling reviews for those PRs.

What do I plan on doing next week?

Add JSON output support to r.distance (see issue #6135).
Add JSON output support to v.class (see issue #6136).

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-11

What did I get done this week?

r.distance: Implemented JSON and CSV output support. (See PR #6204)
v.class: Updated regression tests to ensure backward compatibility with new JSON support and fixed a out-of-bounds bug. (See PR #6205)
v.class: Implemented JSON and CSV output support. (See PR #6210)

What do I plan on doing next week?

Address review comments on my open PRs.
Discuss next week’s tasks with my mentors, if no issues are assigned, I’ll identify tools that currently lack JSON support and evaluate which ones are feasible to add it to.
Write the final GSoC report summarizing the work completed, changes merged, and next steps.

Am I blocked on anything?

No, I am not currently blocked on anything.

Week-12

What did I get done this week?

v.what: Updated JSON output format. (See PR #6252)
Addressed review comments on my open PRs.

What do I plan on doing next week?

Address review comments on my open PRs.
Write the final GSoC report summarizing the work completed, changes merged, and next steps.

Am I blocked on anything?

No, I am not currently blocked on anything.

Final Report

Title	Add JSON output to different tools in C for GRASS
Organization	OSGeo (Open Source Geospatial Foundation)
Mentor Name	Anna Petrasova, Corey White, Vaclav Petras
Program	Google Summer of Code 2025
Contributions	Pull Requests

Abstract

During my GSoC project, I addressed the challenge that most GRASS tools produced only plain text output, forcing users to write custom parsing code for Python-based workflows. The task was to make these tools more data-science friendly by adding structured output formats. To achieve this, I extended the use of the Parson library to implement consistent JSON output across multiple modules, refined the JSON structure introduced in the previous GSoC, and added optional support for SHELL and CSV outputs where appropriate. I developed comprehensive Python test cases, ensured backward compatibility, and provided documentation with practical examples such as loading JSON into pandas. As a result, GRASS users can now directly consume structured outputs in modern workflows, reducing parsing overhead, improving interoperability, and paving the way for broader adoption of GRASS in data science contexts.

The state of integration BEFORE the start of GSoC

Before the start of GSoC, the integration of GRASS with data science workflows was limited due to several challenges. The JSON outputs introduced in the previous GSoC contained deeply nested hierarchies, making them difficult to use directly with libraries such as pandas or in broader data science pipelines. While some tools already supported JSON (e.g., r.univar, r.info, v.distance, g.region, v.to.db, r.profile, v.what), many important and highly demanded modules lacked JSON support, including r3.info, g.mapset, r.what.color, g.version, i.group, g.list, r.regression.line, r.stats.quantile, r.volume, m.measure and many more. This lack of consistency limited the seamless adoption of GRASS in analytics and machine learning contexts. Moreover, there was no clear documentation of the JSON schema or guidance on how to parse and utilize these outputs effectively in python or pandas, which made it difficult for users to understand the use cases and integrate GRASS outputs into modern data science workflows.

The state of integration AFTER GSoC

After GSoC, the integration state of JSON and alternative output formats in GRASS has significantly improved. I refactored the JSON structures for tools such as r.univar, r.info, v.distance, g.region, v.to.db, r.profile, v.what, and others, removing unnecessary nesting introduced during the previous GSoC. This makes their outputs much easier to consume directly in Python or with pandas. In addition, JSON support has been extended to many essential and frequently used modules, including r3.info, g.mapset, r.what.color, g.version, i.group, g.list, r.regression.line, r.stats.quantile, r.volume, m.measure, and many more. I also added detailed documentation of the JSON schema and provided examples showing how to parse and use these outputs effectively with Python and pandas. Together, these improvements make the adoption of GRASS in modern data science workflows far more seamless and accessible.

Enhancements made per tool

r.univar: Simplified JSON structure for univariate statistics, removed unnecessary hierarchy, and added new output format options (CSV, shell).
r.what.color: Added JSON support for querying raster map colors, with a Python parsing example.
g.mapset: Added JSON support for reporting the current mapset, with a Python example.
r.info: Updated JSON structure for raster map metadata, removing hierarchy for easier parsing. Additionally, added PLAIN and SHELL output formats for easier parsing and integration.
g.region: Simplified JSON output for geographic region boundaries, with examples.
g.version: Added JSON support for GRASS version information, with examples.
i.group: Added JSON support for listing imagery groups, with examples.
r.stats: Added JSON and CSV support for raster area statistics, with Python and CLI examples.
v.distance: Refined JSON schema for nearest element queries, with pandas integration example.
g.list: Added JSON support for listing GRASS database files, with Python example.
v.to.db: Updated JSON schema for printing vector attribute values, with Python example.
r.profile: Simplified JSON output, added CSV support, and included a matplotlib example for profile visualization.
r.regression.line: Updated JSON schema for linear regression between rasters, with Python parsing example.
r.stats.quantile: Updated JSON schema for category quantiles, with Python example.
r.volume: Added JSON support for raster volume calculations, fixed missing headers in CSV output, and provided pandas examples.
db.columns: Added JSON output for listing database table columns, with Python example.
m.measure: Added JSON output for measuring lengths and areas, with Python example.
db.connect: Added JSON output for DB connection info, with Python example.
r.regression.multi: Added JSON output for multiple regression between rasters, with Python example.
v.db.connect: Added JSON output for vector map attribute connections, with Python example.
g.findfile: Added JSON output for GRASS database file search, with Python example.
r3.info: Added JSON output for 3D raster map info, with Python example. Additionally, added PLAIN and SHELL output formats for easier parsing and integration.
v.what.rast: Added JSON output for extracting raster values at vector points, with pandas integration example.
r.distance: Added JSON and CSV support for locating closest points between raster objects, with pandas and CLI examples.
v.class: Added JSON and CSV support for attribute data, with pandas and CLI examples.
v.what: Migrated from manually written JSON to Parson-based JSON output, simplified schema, and provided Python parsing examples.

All these changes were accompanied by regression tests to ensure that new JSON/CSV/SHELL/PLAIN output options do not break existing functionality. Additionally, I introduced comprehensive Python test cases for these outputs, making the enhancements reliable and future-proof. During this work, I also fixed several bugs in modules such as r.stats, r3.info, v.what.rast, and v.class, strengthening the stability of GRASS as a whole.

Conclusion

To sum up, I’m glad to have met the three core objectives of my project with the constant support of my mentors:

Brought JSON output to several important and long-requested modules, added regression tests, and ensured reliable future test coverage.
Enhanced the JSON schema introduced in last year’s GSoC by reducing unnecessary hierarchy, making it more consistent across tools, and providing Python/Pandas examples for practical use.
Expanded CSV and SHELL output formats to additional tools where they could bring value.

This journey has been both rewarding and educational, and I’m grateful for the opportunity to contribute to GRASS through GSoC 2025. I especially want to thank Anna Petrasova and the GRASS Development Team for their guidance and support. I’m excited to keep building on this work and further extend JSON and other features in the future.

Future Work

There is significant scope to extend structured output support across GRASS tools. In particular, future efforts should focus on:

Expanding JSON output to additional modules, with details tracked here: GRASS JSON Outputs Project.
Refining and standardizing the JSON schema to better support complex workflows and integrations.
Exploring and implementing other output formats (e.g., SHELL, CSV, PLAIN) where they can improve usability and automation.

I also plan to continue contributing to GRASS by extending JSON support to more modules and engaging in community discussions around schema design and format integration.

Log of Pull Requests

Below is a list of pull requests submitted to the GRASS repository during GSoC 2025:

Pull Request	Description	Date	Status
#5688	r.univar: Fix JSON structure	May 20, 2025	Merged
#5773	r.what.color: Add test file	May 25, 2025	Merged
#5775	g.mapset: Add test file	May 26, 2025	Merged
#5784	r.info: Add Python example	May 28, 2025	Closed
#5785	g.region: Add Python example	May 28, 2025	Merged
#5810	g.mapset: Add JSON support	June 2, 2025	Merged
#5821	r.univar: Fix uninitialized zone_value	June 3, 2025	Merged
#5822	r.what.color: Add JSON support	June 3, 2025	Merged
#5842	r.info: Update test file	June 5, 2025	Merged
#5859	r.info: Improve interface	June 7, 2025	Merged
#5863	g.version: Add JSON support	June 9, 2025	Merged
#5870	i.group: Add JSON support	June 10, 2025	Merged
#5879	r.stats: Add test file	June 12, 2025	Merged
#5892	r.stats: Add tests for JSON output	June 14, 2025	Merged
#5898	v.distance: Update JSON format	June 16, 2025	Merged
#5900	r.stats: fix uninitialised window value	June 17, 2025	Merged
#5906	g.list: Add test file	June 17, 2025	Merged
#5921	g.list: Add JSON support	June 20, 2025	Merged
#5929	g.region: Update JSON format	June 21, 2025	Merged
#5946	v.to.db: Update JSON format	June 23, 2025	Merged
#5962	r.profile: Update JSON format	June 25, 2025	Merged
#5966	r.univar: Add shell and CSV format	June 25, 2025	Merged
#5990	r.regression.line: Add test file	June 30, 2025	Merged
#5993	r.regression.line: Add JSON support	June 30, 2025	Merged
#6002	r.stats.quantile: Add test file	July 1, 2025	Merged
#6007	r.stats.quantile: Add JSON support	July 1, 2025	Merged
#6027	r.volume: Add test file	July 4, 2025	Merged
#6036	r.volume: Add JSON support	July 6, 2025	Merged
#6042	db.columns: Add JSON support	July 7, 2025	Merged
#6051	m.measure: Add test file	July 10, 2025	Merged
#6052	db.connect: Add test file	July 10, 2025	Merged
#6053	m.measure: Add JSON support	July 11, 2025	Merged
#6059	db.connect: Add JSON support	July 12, 2025	Merged
#6066	r.regression.multi: Add test file	July 15, 2025	Merged
#6067	v.db.connect: Add test file	July 15, 2025	Merged
#6075	r.regression.multi: Add JSON support	July 18, 2025	Merged
#6077	v.db.connect: Add JSON support	July 19, 2025	Merged
#6091	g.findfile: Add JSON support	July 21, 2025	Merged
#6098	r3.info: Add test file	July 22, 2025	Merged
#6103	r3.info: Add JSON support	July 23, 2025	Merged
#6109	v.what.rast: Add test file	July 24, 2025	Merged
#6132	v.category: Use CSV format instead of shell format	July 28, 2025	Merged
#6133	r.stats: add CSV support	July 28, 2025	Merged
#6134	r.volume: add csv column headers	July 28, 2025	Merged
#6140	v.what.rast: Add JSON support	July 29, 2025	Merged
#6145	r.profile: Add CSV support	July 30, 2025	Merged
#6150	r.distance: Update existing test cases	August 1, 2025	Merged
#6204	r.distance: Add JSON and CSV support	August 12, 2025	Merged
#6205	v.class: Add more test cases and fix out-of-bounds bug	August 12, 2025	Merged
#6210	v.class: Add JSON and CSV support	August 15, 2025	Merged
#6252	v.what: Update JSON output format	August 23, 2025	Merged

Additional Links

Ttile	Link
Project Wiki Page	Add JSON output to different tools in C for GRASS
Github Fork	GitHub Fork
GSoC Page	Link

Examples & Images

Below is a list of some of the tasks I completed during my GSoC. Please note that this is not a complete list. For more details on these and other tools, please check out the GRASS documentation from the PR links provided above.

Using `r.profile` JSON output in Python

The following example demonstrates how the JSON output of r.profile can be directly loaded into a pandas DataFrame and used for plotting with matplotlib.

import grass.script as gs
import pandas as pd
import matplotlib.pyplot as plt

# Run r.profile command
elevation = gs.read_command(
    "r.profile",
    input="elevation",
    coordinates="641712,226095,641546,224138,641546,222048,641049,221186",
    format="json",
    flags="gc",
)

# Load the JSON data into a dataframe
df = pd.read_json(elevation)
print(df)

# Create the scatter plot
plt.figure(figsize=(10, 6))
plt.scatter(df["distance"], df["value"], c=df["color"], marker="o")
plt.title("Profile of Distance vs. Elevation with Color Coding")
plt.xlabel("Distance (meters)")
plt.ylabel("Elevation")
plt.grid(True)
plt.show()

Sample DataFrame Output (first 10 rows)

         easting       northing   distance      value    color
0  641712.000000  226095.000000   0.000000  84.661507  #71FF00
1  641711.154826  226085.036106   9.999675  84.511765  #6FFF00
2  641710.309651  226075.072213  19.999349  84.241806  #6BFF00
3  641709.464477  226065.108319  29.999024  84.304214  #6CFF00
4  641708.619302  226055.144426  39.998699  84.174400  #6AFF00
5  641707.774128  226045.180532  49.998374  84.112534  #6AFF00
6  641706.928954  226035.216639  59.998048  84.011208  #68FF00
7  641706.083779  226025.252745  69.997723  83.834084  #66FF00
8  641705.238605  226015.288851  79.997398  83.870193  #67FF00
9  641704.393430  226005.324958  89.997073  84.194908  #6BFF00

Visualization

Matplotlib scatter plot generated from the r.profile JSON output:

Using `v.class` JSON output with Pandas

The following example demonstrates how the JSON output of v.class can be directly loaded into a pandas DataFrame.

import pandas as pd
import grass.script as gs

data = gs.parse_command(
    "v.class",
    map="bridges",
    column="YEAR_BUILT",
    algorithm="std",
    nbclasses=5,
    format="json",
)

df = pd.DataFrame(data["intervals"])
print(df)

Sample DataFrame output (intervals)

          from           to  frequency
0  1891.000000  1938.829689        750
1  1938.829689  1954.722836       1841
2  1954.722836  1970.615983       5556
3  1970.615983  1986.509130       1788
4  1986.509130  1997.000000       1003

Using `v.what.rast` JSON output with Pandas

The following example demonstrates how the JSON output of v.what.rast can be directly loaded into a Pandas DataFrame to display categories and values.

import grass.script as gs
import pandas as pd

data = gs.parse_command(
    "v.what.rast", map="bridges", raster="elevation", flags="p", format="json"
)

df = pd.DataFrame(data)
print(df)

Sample DataFrame output

    category       value
0       4635   86.851212
1       4648   87.883255
2       4688  103.382004
3       4709   67.387856
4       4986   90.972832
5       4991   86.550629
6       5027   84.256355
7       5060   79.500443
8       5071   78.732864
9       5089   85.703636
10      5096  115.967323
11      5130   81.055870
12      5150   92.812927
13      5166   88.149483
14      5184   76.426331

Test Suite Update

Added 6 tests to the test suite, including both regression tests and future-proof tests to ensure functionality remains reliable in future updates.

Using `r.distance` JSON output with Pandas

The following example demonstrates how the JSON output of r.distance can be directly loaded into a Pandas DataFrame to locate closest points.

import pandas as pd
import grass.script as gs

result = gs.parse_command(
    "r.distance", map=["zipcodes", "lakes"], flags="l", format="json"
)
df = pd.json_normalize(result)
print(df)

Sample DataFrame output (first 10 rows, truncated)

       distance  from_cell.category  from_cell.easting  ...  to_cell.easting to_cell.northing  to_cell.label
0  11158.507466               27511      632604.951740  ...    640584.676990    215499.220687       Dam/Weir
1   1037.271198               27511      631734.981693  ...    632314.961724    221839.026534      Lake/Pond
2   2276.988836               27511      630015.040913  ...    630765.015090    219459.099418      Reservoir
3  14614.155031               27513      631055.005106  ...    640584.676990    215499.220687       Dam/Weir
4   3635.876453               27513      631674.983759  ...    635164.863600    227198.862392      Lake/Pond
5   6971.335517               27513      630005.041257  ...    630765.015090    219459.099418      Reservoir
6   8978.927486               27518      632134.967922  ...    640424.682499    215209.229568       Dam/Weir
7      0.000000               27518      630005.041257  ...    630005.041257    220199.076757      Lake/Pond
8    699.975899               27518      630005.041257  ...    630705.017156    219379.101868      Reservoir
9    509.197766               27529      641154.657366  ...    640674.673892    215249.228343       Dam/Weir

[10 rows x 9 columns]

Using `v.distance` JSON output with Pandas

The following example demonstrates how the JSON output of v.distance can be directly loaded into a Pandas DataFrame.

import grass.script as gs
import pandas as pd

data = gs.parse_command(
    "v.distance",
    flags="p",
    from_="busroute_a",
    to="busstopsall",
    upload="dist,to_attr",
    to_column="routes",
    format="json",
)
df = pd.DataFrame(data)

print(df)

Sample DataFrame output

   from_cat  to_cat      dist to_attr
0         1     112  0.142812     8,A
1         2      44  0.102327     9,A

Using `r.volume` JSON output with Pandas

The following example demonstrates how the JSON output of r.volume can be directly loaded into a Pandas DataFrame.

import grass.script as gs
import pandas as pd

data = gs.parse_command(
    "r.volume", input="elevation", clump="geology_30m", flags="p", format="json"
)

df = pd.DataFrame(data)
print(df)

Sample DataFrame output

    category  average         sum   cells        volume  easting  northing
0        217   118.93 86288827.99  725562 8628882798.63   635325    221535
1        262   108.97 21650560.37  198684 2165056037.02   638935    222495
2        270    92.23 63578874.44  689373 6357887443.53   642405    221485
3        405   132.96 33732662.09  253710 3373266208.59   631835    224095
4        583   139.35  3011288.22   21609  301128821.55   630205    224665
5        720   124.30   599618.16    4824   59961816.06   634075    227995
6        766   132.43   936791.20    7074   93679120.08   631425    227845
7        862   118.31  7302317.47   61722  730231746.74   630505    218885
8        910    94.20  4235816.13   44964  423581613.11   639215    216365
9        921   135.22  1693985.23   12528  169398523.05   630755    215445
10       945   127.24     1145.12       9     114512.03   630015    215015
11       946    89.91   365748.34    4068   36574833.85   639085    215255
12       948   129.02   112631.82     873   11263181.57   630185    215115

Test Suite Update

Added 4 tests to the test suite, including both regression tests and future-proof tests to ensure functionality remains reliable in future updates.

GRASS GSoC 2025 Add JSON output to different tools in C

Abstract

Goal

Timeline

Reports

Community Bonding Period

Week-1

Week-2

Week-3

Week-4

Week-5

Week-6

Week-7

Week-8

Week-9

Week-10

Week-11

Week-12

Final Report

Abstract

The state of integration BEFORE the start of GSoC

The state of integration AFTER GSoC

Enhancements made per tool

Conclusion

Future Work

Log of Pull Requests

Additional Links

Examples & Images

Using r.profile JSON output in Python

Sample DataFrame Output (first 10 rows)

Visualization

Using v.class JSON output with Pandas

Sample DataFrame output (intervals)

Using v.what.rast JSON output with Pandas

Sample DataFrame output

Test Suite Update

Using r.distance JSON output with Pandas

Sample DataFrame output (first 10 rows, truncated)

Using v.distance JSON output with Pandas

Sample DataFrame output

Using r.volume JSON output with Pandas

Sample DataFrame output

Test Suite Update

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Using `r.profile` JSON output in Python

Using `v.class` JSON output with Pandas

Using `v.what.rast` JSON output with Pandas

Using `r.distance` JSON output with Pandas

Using `v.distance` JSON output with Pandas

Using `r.volume` JSON output with Pandas