gen3-metadata 1.4.0

User friendly tools for downloading and manipulating gen3 metadata

gen3-metadata

User friendly tools for downloading and manipulating gen3 metadata.

Python

Installation

git clone https://github.com/AustralianBioCommons/gen3-metadata.git
bash build.sh

A full usage notebook is available in example_notebook.ipynb. Make sure to select .venv as the kernel.

Fetch all metadata

fetch_all_metadata is the primary entry point. It walks the data dictionary in dependency order and fetches data for every node, returning a dot-accessible object of JSON dicts. Call .to_df() on the result to get pandas DataFrames instead.

from gen3_metadata.gen3_metadata_parser import fetch_all_metadata

key_file = "path/to/credentials.json"
result = fetch_all_metadata(key_file, "program1", "project1")

# Access each node as raw JSON
result.subject          # dict
result.demographic      # dict

# Or get DataFrames
dfs = result.to_df()
dfs.subject             # pandas DataFrame
dfs.demographic         # pandas DataFrame

Filtering by data release

fetch_all_metadata accepts a data_release argument that filters each node's records by release. The default is "latest" — each node is inspected for a data_release_date field and only records matching the max ISO date are returned. The selected version and date are logged per node.

# Default: per node, keep records with the max data_release_date
result = fetch_all_metadata(key_file, "program1", "project1")
# node 'subject': selected data_release_date=2024-06-01 data_release='v2.3' (123/22494 records)
# node 'demographic': selected data_release_date=2024-06-01 data_release='v2.3' (123/22494 records)
# ...

# Pin to a specific release (exact, case-sensitive match on data_release field)
result = fetch_all_metadata(key_file, "program1", "project1", data_release="v2.3")

# Disable filtering — return every record, no filter logs
result = fetch_all_metadata(key_file, "program1", "project1", data_release=None)

Behavior per node:

data_release value	Behavior
"latest" (default)	Keep records with the max data_release_date (ISO 8601). Log the selected date and version.
any other string	Keep records where data_release equals that string exactly. Log the selected version and date.
None	No filtering. No filter log lines emitted.

Nodes that have neither a data_release nor a data_release_date field (for example lookup/link nodes like program or project) are passed through unchanged, with an info log noting they were not filtered. Unparseable ISO dates in "latest" mode are skipped with a warning.

The same data_release argument is also available on Gen3MetadataParser.fetch_data and fetch_data_json for single-node fetches.

Logging and error handling

fetch_all_metadata prints per-node progress to stdout by default (verbose=True). If you want richer diagnostic output — filter decisions, authentication steps, debug messages — call configure_logging() once in your REPL or notebook to attach a stderr handler to the gen3_metadata logger:

import logging
import gen3_metadata

gen3_metadata.configure_logging()              # INFO by default
# gen3_metadata.configure_logging(logging.DEBUG)  # or more verbose

Each module logs under its own gen3_metadata.<module> name, so you can filter with standard logging machinery.

Network calls inside fetch_all_metadata are timeout-guarded (30s per request) and wrapped with friendly error messages. A VPN/connectivity failure produces a clean one-liner instead of a urllib3 traceback:

fetch_all_metadata: starting for program1/CDAH
fetch_all_metadata: fetching data dictionary from cad.staging.biocommons.org.au...
RuntimeError: Could not reach cad.staging.biocommons.org.au to fetch the Gen3
data dictionary. Check VPN / network connectivity. Underlying error: ...

Individual nodes that time out or return an HTTP error during the fetch loop are logged and skipped; the overall call completes with a final summary of which nodes succeeded and which failed.

List nodes

get_node_order returns a topologically sorted list of node names from the data dictionary (parents before children).

from gen3_metadata.gen3_metadata_parser import get_node_order

nodes = get_node_order("path/to/credentials.json")
# ['program', 'project', 'subject', 'sample', 'demographic', ...]

Fetch a single node

If you only need one node, use Gen3MetadataParser + fetch_data_json. It returns the raw JSON response as a dict. Convert to a DataFrame yourself if you need one.

import pandas as pd
from gen3_metadata.gen3_metadata_parser import Gen3MetadataParser

key_file = "path/to/credentials.json"
parser = Gen3MetadataParser(key_file)
parser.authenticate()

# Default: filters to latest data_release_date
json_data = parser.fetch_data_json("program1", "project1", node_label="medical_history")
json_data  # {'data': [...]}

# Pin to a specific release, or pass data_release=None to disable filtering
json_data = parser.fetch_data_json(
    "program1", "project1", node_label="medical_history", data_release="v2.3"
)

# Convert to DataFrame if desired:
df = pd.json_normalize(json_data["data"])

Running Tests

pytest -vv tests/

---

R

As of v1.3.0 the R package is fully standalone — no Python interpreter, no reticulate, and no Python gen3_metadata package required. A single devtools::install_github(...) is all you need, which makes containerized RStudio deployments significantly simpler.

Installation

Always-latest from main:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("AustralianBioCommons/gen3-metadata", subdir = "gen3metadata-R")

Pinned to a specific release (recommended for reproducible environments such as Docker images):

if (!require("devtools")) install.packages("devtools")
devtools::install_github(
    "AustralianBioCommons/gen3-metadata",
    subdir = "gen3metadata-R",
    ref    = "v1.4.0"
)

CRAN dependencies are installed automatically with the package. If your environment requires installing them manually:

install.packages(c("httr", "jsonlite", "jose", "glue"))

In a Dockerfile (RStudio container)

Add a single layer to your image:

RUN R -e 'if (!require("devtools")) install.packages("devtools", repos="https://cloud.r-project.org"); \
          devtools::install_github("AustralianBioCommons/gen3-metadata", subdir = "gen3metadata-R", ref = "v1.4.0")'

No Python or pip step is required.

Loading

library("gen3metadata")

Fetch all metadata

fetch_all_metadata is the primary entry point. It walks the data dictionary in dependency order and fetches data for every node, returning a metadata_collection object where nodes are accessible via $. Call to_df() on it to get data.frames instead.

result <- fetch_all_metadata("path/to/credentials.json", "program1", "AusDiab")

# Access each node as raw JSON (nested list)
result$subject
result$demographic

# Or get data.frames
dfs <- to_df(result)
dfs$subject         # data.frame
dfs$demographic     # data.frame

Filtering by data release

fetch_all_metadata accepts a data_release argument that mirrors the Python API. The default is "latest" — each node is inspected for a data_release_date field and only records matching the max ISO date are kept. The selected version and date are emitted per node via message().

# Default: per node, keep records with the max data_release_date
result <- fetch_all_metadata("path/to/credentials.json", "program1", "AusDiab")
# node 'subject': selected data_release_date=2024-06-01 data_release='v2.3' (123/22494 records)
# node 'demographic': selected data_release_date=2024-06-01 data_release='v2.3' (123/22494 records)
# ...

# Pin to a specific release (exact, case-sensitive match on data_release field)
result <- fetch_all_metadata(
    "path/to/credentials.json", "program1", "AusDiab",
    data_release = "v2.3"
)

# Disable filtering — return every record, no filter messages
result <- fetch_all_metadata(
    "path/to/credentials.json", "program1", "AusDiab",
    data_release = NULL
)

# Suppress the per-node message output while keeping the filter active
result <- suppressMessages(
    fetch_all_metadata("path/to/credentials.json", "program1", "AusDiab")
)

Behavior per node:

data_release value	Behavior
"latest" (default)	Keep records with the max data_release_date (ISO 8601). Emit selected date and version.
any other string	Keep records where data_release equals that string exactly. Emit selected version and date.
NULL	No filtering. No filter messages emitted.

Nodes that have neither a data_release nor a data_release_date field are passed through unchanged with a message. Unparseable ISO dates in "latest" mode are skipped with a warning.

The same data_release argument is also available on fetch_data() for single-node fetches.

Progress messages and error handling

fetch_all_metadata emits per-node progress via message() (visible in interactive sessions and captured by knitr/RMarkdown by default). A DNS/VPN failure produces a clean stop() error rather than an httr traceback, e.g.:

fetch_all_metadata: starting for program1/AusDiab
fetch_all_metadata: fetching data dictionary...
Error: Could not fetch Gen3 data dictionary. Check VPN / network connectivity. Underlying error: ...

All network calls (authentication, dictionary fetch, per-node fetch) have a 30-second timeout, so a flaky network surfaces quickly instead of hanging indefinitely. To silence the progress output:

result <- suppressMessages(fetch_all_metadata(...))

List nodes

get_node_order returns a topologically sorted character vector of node names from the data dictionary.

nodes <- get_node_order("path/to/credentials.json")
# [1] "program" "project" "subject" "sample" "demographic" ...

Fetch a single node

If you only need one node, use Gen3MetadataParser + fetch_data. It returns the raw JSON data as a nested list. Convert to a data.frame yourself if you need one.

key_file_path <- "path/to/credentials.json"

gen3 <- Gen3MetadataParser(key_file_path)
gen3 <- authenticate(gen3)

# Default: filters to latest data_release_date
data <- fetch_data(gen3,
                   program_name = "program1",
                   project_code = "AusDiab",
                   node_label = "subject")

# Pin to a specific release, or pass data_release = NULL to disable filtering
data <- fetch_data(gen3,
                   program_name = "program1",
                   project_code = "AusDiab",
                   node_label = "subject",
                   data_release = "v2.3")

# data is a list of records

# Convert to a data.frame if desired:
df <- do.call(rbind, lapply(data, as.data.frame))