nerc-rates 1.0.1

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NERC Rates

This repository stores rates, outages and invoicing configuration for the New England Research Cloud.

To make use of the rates and outages data, install the package and import the modules:

Usage

Loading Rates

from nerc_rates import rates
rate_data = rates.load_from_url()
rate_data.get_value_at("CPU SU Rate", "2024-06", Decimal)

Loading Outages

from nerc_rates import outages
outage_data = outages.load_from_url()
outage_data.get_outages_during("2024-05-01", "2024-06-01", "NERC OpenStack")

Loading Rates from file

from nerc_rates import rates
rate_data = rates.load_from_file("rates.yaml")
rate_data.get_value_at("CPU SU Rate", "2024-06", Decimal)

Loading Outages from file

from nerc_rates import outages
outage_data = outages.load_from_file("outages.yaml")
outage_data.get_outages_during("2024-05-01", "2024-06-01", "NERC OpenStack")

How To Verify Rates and Outages Data

This guide explains how the system automatically verifies the integrity and correctness of rates and outages data when it is loaded. The data models are designed with built-in validation rules to catch common errors, such as overlapping date ranges, incorrect data types, or duplicate entries.

How Verification Works

When you load data using rates.load_from_file() or outages.load_from_file(), the system attempts to parse your YAML data into its internal data models. During this process, several validation checks are performed automatically:

• Rates Verification Checks:

  • Date Range Order: Ensures that date_until is always after date_from within a RateValue.
  • No Overlapping Date Ranges: Verifies that there are no overlapping timeframes for RateValue entries within a RateItem's history.
  • Rate Type Consistency: Checks that value strings can be correctly converted to the RateType specified for the RateItem (e.g., a Decimal rate is a valid number, bool rates are "true" or "false").
  • Unique Rate Names: Ensures that each RateItem has a unique name across the entire rates list.

• Outages Verification Checks:

  • Time Range Order: Ensures that time_until is always after time_from within an OutageTimeFrames.
  • Unique Affected Services: Verifies that affected_services within an OutageTimeFrames do not contain duplicate service names.
  • Unique Outage URLs: Checks for duplicate url entries across all OutageItems in the outages list.
  • UTC Timezone Enforcement: Ensures all outage datetime values (time_from, time_until) are provided with UTC timezone information and are not naive datetimes.

If any of these validation rules are violated, the loading process will raise a ValueError or TypeError, providing a clear message about what went wrong and where.

Example: Rates Verification

Let's say you have a rates.yaml file with an overlapping date range, which is an invalid configuration:

- name: CPU SU Rate
  history:
    - value: 0.013
      from: 2023-06
      until: 2024-06
    - value: 0.15
      from: 2024-05 # This date range overlaps with the previous one

When you try to load this data, you would see an error like this:

from nerc_rates import rates
try:
    rate_data = rates.load_from_file("rates.yaml")
except ValueError as e:
    print(f"Rates data validation failed: {e}")

Expected Output:

Rates data validation failed: date ranges overlap

Example: Outages Verification

Similarly, if your outages.yaml contains an outage with a naive datetime (missing timezone information), it will trigger a validation error:

- url: http://example.com/outage-1
  timeframes:
    - from: 2024-01-01T12:00:00 # Missing timezone info (e.g., Z or +00:00)
      until: 2024-01-01T14:00:00Z
      affected_services: ["NERC OpenStack"]

Loading this file would result in:

from nerc_rates import outages
try:
    outage_data = outages.load_from_file("outages.yaml")
except ValueError as e:
    print(f"Outages data validation failed: {e}")

Expected Output:

Outages data validation failed: Naive datetime without timezone information is not allowed. Please provide timezone information (e.g., '2024-01-01T12:00:00Z' or '2024-01-01T12:00:00+00:00')

How To Run Tests

This section guides you through setting up and running the test suite for this project using pytest. Running tests is crucial for verifying that all components of the system are working as expected and that any changes introduced do not break existing functionality.

Prerequisites

• Python 3.8+ installed
• pip installed for dependency management

Steps

1. Install Dependencies: Navigate to the root directory of the project in your terminal and install the project dependencies.

   pip install -r requirements.txt
   pip install -r test-requirements.txt
   

2. Run Tests: After installing the dependencies, you can run the test suite from the project's root directory:

   pytest
   

   This command will discover and execute all tests located in the tests/ directory.

Additional Information

• Specific Tests: To run a specific test file or test case, you can provide its path to pytest:

  pytest tests/test_rates.py
  pytest tests/test_rates.py::test_get_cpu_rate
  

• Verbose Output: Use the -v flag for more detailed output during test execution:

  pytest -v