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SAPPHIRE API Client

Tests

Maintenance Status

🟢 Active – Ongoing development as part of SAPPHIRE Forecast Tools

Python client for the SAPPHIRE Forecast Tools API. This package provides a convenient interface for reading from and writing to the SAPPHIRE preprocessing and postprocessing databases.

Installation

uv pip install git+https://github.com/hydrosolutions/sapphire-api-client.git

Usage

Preprocessing Client

from sapphire_api_client import SapphirePreprocessingClient
import pandas as pd

# Initialize client (defaults to localhost:8000)
client = SapphirePreprocessingClient()

# Or specify a different base URL
client = SapphirePreprocessingClient(base_url="https://api.example.com")

# Read runoff data
df = client.read_runoff(
    horizon="day",
    code="12345",
    start_date="2024-01-01",
    end_date="2024-01-31"
)

# Write runoff data from DataFrame
records = SapphirePreprocessingClient.prepare_runoff_records(
    df=my_dataframe,
    horizon_type="day",
    code="12345"
)
count = client.write_runoff(records)
print(f"Wrote {count} records")

Short-Term Forecast Client

from sapphire_api_client import SapphireShortTermForecastClient

client = SapphireShortTermForecastClient()

# Read short-term forecasts
forecasts = client.read_short_term_forecasts(
    horizon="pentad",
    code="12345",
    model="TFT"
)

# Write short-term forecasts
records = SapphireShortTermForecastClient.prepare_short_term_forecast_records(
    df=my_dataframe,
    horizon_type="pentad",
    code="12345"
)
client.write_short_term_forecasts(records)

# Read linear regression forecasts
lr_forecasts = client.read_lr_forecasts(horizon="pentad", code="12345")

Long-Term Forecast Client

from sapphire_api_client import SapphireLongTermForecastClient

client = SapphireLongTermForecastClient()

# Read long-term forecasts (monthly, quarterly, seasonal)
forecasts = client.read_long_term_forecasts(
    horizon_type="month",
    horizon_value=7,
    code="15013",
    model="GBT"
)

# Write long-term forecasts
records = SapphireLongTermForecastClient.prepare_long_term_forecast_records(
    df=my_dataframe,
    horizon_type="month",
    horizon_value=7,
    model_type="GBT"
)
client.write_long_term_forecasts(records)

Combined Postprocessing Client

For convenience, SapphirePostprocessingClient combines both short-term and long-term forecast clients into a single interface. It also provides skill metric methods.

from sapphire_api_client import SapphirePostprocessingClient

client = SapphirePostprocessingClient()

# All short-term, long-term, and skill metric methods are available
forecasts = client.read_short_term_forecasts(horizon="pentad", code="12345")
long_forecasts = client.read_long_term_forecasts(horizon_type="month", code="15013")
metrics = client.read_skill_metrics(horizon="pentad", code="12345")

Client Architecture

SapphireAPIClient (base)                    # HTTP methods, retry logic, batching
├── SapphirePreprocessingClient             # Runoff, hydrograph, meteo, snow
└── SapphirePostprocessingBase              # Skill metrics (shared)
    ├── SapphireShortTermForecastClient     # Short-term forecasts + LR forecasts
    ├── SapphireLongTermForecastClient      # Long-term forecasts (quantile predictions)
    └── SapphirePostprocessingClient        # Facade combining both forecast families

Use the focused clients (SapphireShortTermForecastClient, SapphireLongTermForecastClient) when you only need one forecast family. Use SapphirePostprocessingClient when you need access to both.

Features

  • Automatic retry with exponential backoff on transient failures
  • Batch posting for large datasets
  • Input validation with clear error messages
  • Strict error handling (fails fast on persistent errors)
  • Type-safe record preparation from pandas DataFrames
  • Support for all SAPPHIRE data types:
    • Runoff (daily time series)
    • Hydrograph (statistical summaries)
    • Meteorological data (temperature, precipitation)
    • Snow data (height, SWE, runoff)
    • Short-term forecasts with confidence bounds (day, pentad, decade, month, season, year)
    • Long-term forecasts with quantile predictions (month, quarter, season)
    • Linear regression forecasts
    • Skill metrics (MAE, RMSE, NSE, KGE, bias, R2, PBIAS)

Configuration

The client can be configured with:

  • base_url: API base URL (default: http://localhost:8000)
  • auth_token: Optional Bearer token for authentication
  • max_retries: Maximum retry attempts (default: 3)
  • batch_size: Records per batch for bulk writes (default: 1000)

Authentication

The client supports Bearer token authentication for controlled access.

Security best practice: Store tokens in environment variables, never hardcode them.

import os

# Load token from environment variable
auth_token = os.environ.get("SAPPHIRE_API_TOKEN")

# Hydromet with full read/write access
client = SapphirePreprocessingClient(
    base_url="https://api.hydromet.example.org",
    auth_token=auth_token,
)

# Check if client is authenticated
if client.is_authenticated:
    print("Using authenticated access")

Access levels (configured server-side):

  • Hydromet operators: Full read/write access to all data
  • External organizations: Read-only access to forecasts for specific sites/horizons

The server-side auth service controls which resources each token can access.

Error Handling

The client raises SapphireAPIError on failures:

from sapphire_api_client import SapphireAPIError

try:
    client.write_runoff(records)
except SapphireAPIError as e:
    print(f"API error: {e}")
    # Handle failure - the operation should be retried or investigated

Contributing

git clone https://github.com/hydrosolutions/sapphire-api-client.git
cd sapphire-api-client
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

Run tests:

uv run pytest

License

MIT License. See the LICENSE file for details.