Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/open-contracting/cardinal-rs/llms.txt

Use this file to discover all available pages before exploring further.

​
Function Signature

def coverage(file: str) -> dict:
    """
    Count the number of times each field is non-empty in a line-delimited JSON file
    
    Args:
        file: The path to the file, in which each line is JSON text
    
    Returns:
        A dictionary mapping paths to counts
    
    Raises:
        PyRuntimeError: If the file cannot be opened or read
    """

​
Parameters

​
file
str
required
The path to the line-delimited JSON file to analyze. Each line should contain a complete JSON object representing an OCDS release.

​
Return Value

​
dict
dict[str, int]
A dictionary mapping JSON paths to occurrence counts. Keys are paths through the JSON structure, and values are the number of times that path contained non-empty data.

​
Path Format

The returned dictionary uses a specific path notation:
  • The empty string "" corresponds to a line (the root object)
  • Paths ending with / correspond to JSON objects
  • Paths ending with [] correspond to array elements
  • Other paths correspond to object members

​
Empty Values

The function considers the following as “empty” and does not count them:
  • Empty strings: ""
  • Empty arrays: []
  • Empty objects: {}
  • Null values: null
  • Any nodes containing only empty nodes
The command walks the JSON tree, counting only non-empty nodes.

​
Usage Example

from ocdscardinal import coverage

# Analyze field coverage in an OCDS file
result = coverage("releases.jsonl")

# Print the coverage statistics
for path, count in result.items():
    print(f"{path}: {count}")

​
Example Output

Given a file with OCDS releases, the output might look like: 
{
    "": 100,                           # 100 lines (releases)
    "ocid": 100,                       # All releases have an OCID
    "tender/": 100,                    # All releases have a tender object
    "tender/id": 100,                  # All tenders have an ID
    "tender/title": 95,                # 95 tenders have a title
    "awards[]": 80,                    # 80 releases have awards
    "awards[]/id": 80,                 # All awards have an ID
    "awards[]/value/": 80,             # All awards have a value object
    "awards[]/value/amount": 78,       # 78 awards have an amount
    "contracts[]": 60,                 # 60 releases have contracts
    # ... more paths
}

​
Error Handling

The function raises a PyRuntimeError if:
  • The file cannot be opened (e.g., file not found, permission denied)
  • An I/O error occurs while reading the file
from ocdscardinal import coverage

try:
    result = coverage("data.jsonl")
except RuntimeError as e:
    print(f"Error analyzing coverage: {e}")

​
Use Cases

  • Data quality assessment: Identify which fields are consistently populated in your OCDS data
  • Schema validation: Check coverage of required and recommended fields
  • Data completeness reports: Generate statistics on field usage across a dataset
  • Publication planning: Understand which fields publishers are using most frequently

Build docs developers (and LLMs) love

Get started for freeTalk to us