NGP IRIS 👀

NGP IRIS, or just Iris, is a tool for interacting with a Hitachi Content Platform (HCP) using S3 in the boto3 package. NGP Iris is designed with two use cases in mind:

• A simple, clear, real-time interaction with NGPr file management
• Improving process flow for performing off-site data analysis by using automated transfer scripts

Both of these cases can be achieved as either a Python package or as a Command Line Interface (CLI).

Getting started

Prerequisites

• Python 3.14
• NGPr credentials (see "NGPr credentials")

Installation

Iris can be installed via PyPi by running the following:

pip install NGPIris

If you wish, you can also install Iris with the following steps:

1. Clone this repository
2. Open a terminal in your local copy of the repository
3. Run pip install .. This will install Iris along with the required Python packages in your Python environment

NGPr credentials

In order to use Iris, a JSON file containing your credentials for the NGPr. The template of the JSON file can be found in credentials/credentials_template.json. Depending on your needs, you can either enter only the credentials for the HCP, only for the HCI or both. Do note that you can't leave some parts of either the HCP or HCI credentials empty:

{
  "hcp" : {
      "endpoint" : "some_endpoint",
      "aws_access_key_id" : "",    
      "aws_secret_access_key" : "" 
  },
  "hci" : {
      "username" : "some_user",
      "password" : "some_password",
      "address" : "some_address",
      "auth_port" : "some_auth_port",
      "api_port" : "some_api_port"
  }
}

This will prompt Iris to complain about incomplete credentials (since the entries aws_access_key_id and aws_secret_access_key are empty). Of course, the same error would occur if the reverse between the HCP and HCI fields would be true.

NOTE: the endpoint field should not contain https:// or any port number.

Technical package documentation

A thorough package documentation can be found in the technical documentation page.

Basic usage

Iris can be used as a Python package or by using the command line. The following sections cover some examples of how Iris might be used as a package and how to use its various commands. However, we highly recommend checking out the tutorial containing more example use cases.

As a Python package

Connect to HCP

In order to connect to the HCP, we first need to create an HCPHandler object and mount it to some bucket:

from NGPIris.hcp import HCPHandler

hcp_h = HCPHandler("credentials.json")

hcp_h.mount_bucket("myBucket")

If you are unsure which buckets you are allowed to see, you can use hcp_h.list_buckets() in order to list all available buckets to you.

When you have successfully mounted a bucket, you can then do different operations onto the bucket. Object names on the bucket can be listed by typing print(hcp_h.list_objects(True)).

Upload files

# Upload a single file to HCP
hcp_h.upload_file("myFile")

# Upload folder contents to HCP
hcp_h.upload_folder("myFiles/")

Download files

# Download a single object from HCP
hcp_h.download_file("myFile")

Connect to HCI

In order to connect to the HCI, we first need to create an HCIHandler object and request an authorization token:

from NGPIris.hci import HCIHandler

hci_h = HCIHandler("credentials.json")

hci_h.request_token()

Note that the token is stored inside of the HCIHandler object called hci_h. We can now request a list of indexes that are available by typing print(hci_h.list_index_names()). We can also look up information about a certain index with print(hci_h.look_up_index("myIndex")). It is recommended to combine the use of the pretty print module pprint and the json module for this output, as it is mostly unreadable otherwise:

from NGPIris.hci import HCIHandler
from pprint import pprint
import json

hci_h = HCIHandler("credentials.json")

hci_h.request_token()

pprint(
    json.dumps(
        hci_h.look_up_index("myIndex"), 
        indent = 4
    )
)

Miscellaneous utilities (utils.py)

The utils module can be contains two functions: one for converting a string to base64 encoding and one for MD5 encoding.

As a command line tool

NGP Iris comes with two commands: iris and iris_generate_credentials_file. The latter command is used solely to generate the .json credentials file. Running iris_generate_credentials_file --help we get the following:

Usage: iris_generate_credentials_file [OPTIONS]

  Generate blank credentials file for the HCI and HCP.

  WARNING: This file will store sensisitve information (such as passwords) in
  plaintext.

Options:
  --path TEXT  Path for where to put the new credentials file
  --name TEXT  Custom name for the credentials file
  --help       Show this message and exit.

The iris command is used for communicating with the HCP and HCI. This includes upload and download to and from the HCP/HCI. Running iris --help yields the following:

Usage: iris [OPTIONS] CREDENTIALS COMMAND [ARGS]...

  NGP Intelligence and Repository Interface Software, IRIS.

  CREDENTIALS refers to the path to the JSON credentials file.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  delete-folder    Delete a folder from an HCP bucket/namespace.
  delete-object    Delete an object from an HCP bucket/namespace.
  download         Download files from an HCP bucket/namespace.
  list-buckets     List the available buckets/namespaces on the HCP.
  list-objects     List the objects in a certain bucket/namespace on the...
  simple-search    Make simple search using substrings in a...
  test-connection  Test the connection to a bucket/namespace.
  upload           Upload files to an HCP bucket/namespace.

Each sub-command has its own help message and is displayed by iris path/to/your/credentials.json sub-command --help

Testing

Assuming that the repository has been cloned, run the following tests:

pytype

pytest

Generating new documentation

With force flag -f:

cd docs/
sphinx-apidoc ../NGPIris/ -o . -F -f 

Without force flag

cd docs/
sphinx-apidoc ../NGPIris/ -o . -F

Compiling the documentation

cd docs/
make html