postprocessing-seismo-lib 0.1.7

A library for building and parsing Seismology API message bodies.

postprocessing_seismo_lib

postprocessing_seismo_lib is a lightweight Python library for building and parsing structured API messages, especially for use with nested JSON structures used in event-based data systems. Currently, the library works on building out the Response format for seismology associator outputs, or extracting the body out of its Response format.

Features

• Extract the body section from a structured JSON file using extract_body_from_file
• Create request for a body object using wrap_data, with provided associator or pickfilter files.
• Validates if input and output formats are to specification using wrap_data

Use cases of this library

1. Individual users
2. Pipeline scripts

Example Scenarios

Extraction of body

The below function allows for extracting out the body from an output response file:

from postprocessing_seismo_lib import extract_body_from_file

body_data = extract_body_from_file("output_response_association.json")
body_data = extract_body_from_file("output_response_pickfilter.json")

where as an example, output_response_association.json is:

{
  "status": 404,
  "headers": {
    "Content-Type": "application/json"
  },
  "body": {
    "id": "78604159",
    "format": "none.noeventsfound",
    "data": []
  }
}

Creation of the request for a body object

The below function creates the request from the body object, which can be extracted from the above function. All four variables listed below need to be specified:

from postprocessing_seismo_lib import wrap_data

#creating the request for the associator input
wrap_data(
    input_file_path='[xxxx_file_containing_filtered_picks].json',
    output_file_path='output_associator.json',
    evid='[Name of choice]',
    module='associator'
)

#creating the request for the pickfilter input
wrap_data(
    input_file_path='[xxxx_file_containing_picks].json',
    output_file_path='output_pickfilter.json',
    evid='[Name of choice]',
    module='pickfilter'
)

The request format will be different across each module. Currently, the module takes in 'associator' and 'pickfilter' but this will be expanded in future updates.

Specifically, this function reads a list of pick dictionaries from a JSON file specified by input_file_path, validates them against a schema, wraps the data into a module-specific JSON structure, validates the output, and writes it to a new file specified by output_file_path. Any errors are logged to a file named wrap_data_errors.log.

As an example, our input_file_path='[xxxx_file_containing_picks].json' might look like this (as a list of dictionaries):

[
    {
        "Amplitude": {
            "Amplitude": 1039.6302490234,
            "SNR": 11.074
        },
        "Filter": [
            {
                "HighPass": 1.0,
                "Type": "HighPass"
            }
        ],
        "Onset": "emergent",
        "Phase": "S",
        "Picker": "deep-learning",
        "Polarity": "no-result",
        "Quality": [
            {
                "Standard": "PhaseNet",
                "Value": 0.851
            },
            {
                "Standard": "hypoinverse",
                "Value": 2
            }
        ],
        "Site": {
            "Channel": "HHE",
            "Location": "",
            "Network": "CI",
            "Station": "WOR"
        },
        "Source": {
            "AgencyID": "CI",
            "Author": "hypoPN"
        },
        "Time": "2025-04-22T21:51:15.148Z",
        "Type": "Pick"
    },
    {
        ...
    }
]

and its output would be the necessary format to POST into the associator API endpoint:

{
  "RetrieveParameters": {
    "pickFile": "Ryan_testingAgainPicks_picks.json",
    "pickDataStr": [
      {
        "Amplitude": {
          "Amplitude": 1039.6302490234,
          "SNR": 11.074
        },
        "Filter": [
          {
            "HighPass": 1.0,
            "Type": "HighPass"
          }
        ],
        "Onset": "emergent",
        "Phase": "S",
        "Picker": "deep-learning",
        "Polarity": "no-result",
        "Quality": [
          {
            "Standard": "PhaseNet",
            "Value": 0.851
          },
          {
            "Standard": "hypoinverse",
            "Value": 2
          }
        ],
        "Site": {
          "Channel": "HHE",
          "Location": "",
          "Network": "CI",
          "Station": "WOR"
        },
        "Source": {
          "AgencyID": "CI",
          "Author": "hypoPN"
        },
        "Time": "2025-04-22T21:51:15.148Z",
        "Type": "Pick"
      },
      ...
    ]
  }
}