pds3_utils

A module of utilities to work with PDS3 data, built on top of pvl.

Dependencies

The following dependencies must be met:

• python 3
• pandas
• pyyaml
• pvl

Installation

pip

pip install pds3_utils

should do the job, although creating a dedicated environment is recommended (see below).

conda

First, clone this repository. If you are using conda, the dependencies can be installed in a new environment using the provided environment file:

conda env create -f environment.yml

The newly created environment can be activated with:

conda activate pds3_utils

Otherwise, please make sure the dependencies are installed with your system package manager, or a tool like pip. Use of a conda environment or virtualenv is recommended!

The package can then be installed with:

python setup.py install

Usage

index_products(directory='.', pattern='*.lbl', recursive=True):

This function loads the labels of products with pattern in directory (recursively if requested) and retrieves key hard-coded meta-data and loads them into a pandas dataframe:

• PRODUCT_ID
• INSTRUMENT_ID
• DATA_SET_ID
• MISSION_ID
• START_TIME
• STOP_TIME

Database (class)

The Database class is used to create one or more tables (pandas DataFrames) from PDS3 meta-data according to a configuration file. This can be retrieved, saved to/loaded from disk and used in later analysis.

This class is instantiated with:

files=None, directory='.', config_file=default_config, recursive=True

where files, directory and recursive are as above. config_file gives the filepath to a YAML formatted file which defines which additional meta-data are retrieved.

The configuration file has a format like:

INSTRUMENT_ID:
  table_name:
    prod_id: "TEST_RAW"
    keywords:
      altitude:
        path: 'ALTITUDE'
      pixel_x:
        path: 'IMAGE_POI/IMAGE_POI_PIXEL'
        index: 0
      pixel_y:
        path: 'IMAGE_POI/IMAGE_POI_PIXEL'
        index: 1

In this case when using this configuration, any PDS3 products in directory with file pattern files will be checked for INSTRUMENT_ID. For those that match, each table_name entry will be used to defined a unique table built from products with PRODUCT_ID containing prod_id. Keywords then specify fields to be scraped from the labels.

For simple key/pair entries, it is only necessary to specify the keyword (e.g. altitude above) which will be used as the field name in the database, and the path (e.g. ALTITUDE). If the keyword has an attached unit, this wil be ignored and the value used. For keywords that have lists, the index parameter can be used to select which one is mapped into the corresponding field.

Putting this together with a few examples:

SC_TARGET_POSITION_VECTOR        = (-23.936 <km>, -43.917 <km>, -31.456 <km>)

to retrieve the 3 components, one would specify something like:

      sc_position_x:
        path: 'SC_TARGET_POSITION_VECTOR'
        index: 0
      sc_position_x:
        path: 'SC_TARGET_POSITION_VECTOR'
        index: 1
      sc_position_x:
        path: 'SC_TARGET_POSITION_VECTOR'
        index: 2

In the case of groups, the input file should use a slash to describe the path, for example:

GROUP                            = SCIENCE_ACTIVITY
ROSETTA:MISSION_PHASE            = ("LTP001", "MTP006", "STP015")
ROSETTA:RATIONALE_DESC           = "NUCLEUS"
ROSETTA:OPERATIONAL_ACTIVITY     = "TAG_NUCLEUS_SHAPE"
ROSETTA:ACTIVITY_NAME            = "STP015_SHAP4S_002"
END_GROUP                        = SCIENCE_ACTIVITY

to get the mission phase here, one would use:

      ltp:
        path: 'SCIENCE_ACTIVITY/ROSETTA:ROSETTA:MISSION_PHASE'
        index: 0
      mtp:
        path: 'SCIENCE_ACTIVITY/ROSETTA:ROSETTA:MISSION_PHASE'
        index: 1
      stp:
        path: 'SCIENCE_ACTIVITY/ROSETTA:ROSETTA:MISSION_PHASE'
        index: 2

Example

The Jupyter notebook included with this repository shows an example of how to use the code. To view the notebook, click here.