pydropcountr 1.1.0

Python library for interacting with DropCountr.com water usage monitoring

PyDropCountr 💧

A Python library for interacting with DropCountr.com water usage monitoring systems.

Features

• 🔐 Authentication: Secure login with session management
• 🏠 Service Discovery: List and get details for your service connections
• 📊 Usage Data: Fetch water usage data with flexible date ranges
• 🐍 Pythonic API: Clean, type-safe interface with Pydantic models
• ⏰ Smart Dates: Accepts both Python datetime objects and ISO strings
• 🖥️ CLI Tool: Command-line interface for quick usage reports

Installation

pip install pydropcountr

For development:

git clone https://github.com/yourusername/pydropcountr.git
cd pydropcountr
uv install --dev

CLI Usage

PyDropCountr includes a command-line tool for quick usage reports:

Quick Start

# Set credentials as environment variables (recommended)
export DROPCOUNTR_EMAIL="[e-mail]"
export DROPCOUNTR_PASSWORD="yourpassword"

# Get yesterday's usage + last 7 days (default behavior)
dropcountr usage

# Get last 30 days
dropcountr usage --days=30

# Get specific date range
dropcountr usage --start_date=2025-06-01 --end_date=2025-06-15

# List all service connections
dropcountr services

CLI Options

# All commands support these authentication options:
--email=[e-mail] --password=yourpass

# Usage command options:
dropcountr usage [options]
  --service_id=1234567      # Use specific service (defaults to first)
  --start_date=YYYY-MM-DD   # Start date
  --end_date=YYYY-MM-DD     # End date  
  --days=30                 # Days back from today
  --period=day              # Granularity: 'day' or 'hour'

# Get help for any command:
dropcountr usage --help
dropcountr services --help

Environment Variables

export DROPCOUNTR_EMAIL="[e-mail]"
export DROPCOUNTR_PASSWORD="yourpassword"

Python API Usage

Quick Start

from pydropcountr import DropCountrClient
from datetime import datetime

# Create client and login
client = DropCountrClient()
success = client.login('[e-mail]', 'yourpassword')

if success:
    # Discover your service connections
    services = client.list_service_connections()
    print(f"Found {len(services)} service connections:")
    
    for service in services:
        print(f"  {service.id}: {service.name} at {service.address}")
    
    # Get usage data for a service
    service_id = services[0].id
    start_date = datetime(2025, 6, 1)
    end_date = datetime(2025, 6, 30, 23, 59, 59)
    
    usage = client.get_usage(service_id, start_date, end_date)
    if usage:
        print(f"\\nUsage data: {usage.total_items} records")
        for record in usage.usage_data[:5]:  # Show first 5 days
            print(f"  {record.start_date.date()}: {record.total_gallons:.1f} gallons")

List Service Connections

# Get all service connections for the authenticated user
services = client.list_service_connections()
if services:
    print(f"Found {len(services)} service connections:")
    for service in services:
        print(f"  {service.id}: {service.name} at {service.address}")

Get Service Connection Details

# Get details for a specific service connection
service = client.get_service_connection(1064520)
if service:
    print(f"Service: {service.name}")
    print(f"Address: {service.address}")
    print(f"Account: {service.account_number}")
    print(f"Status: {service.status}")

Fetch Usage Data

from datetime import datetime

# Get daily usage data for June 2025 using Python datetime objects
usage = client.get_usage(
    service_connection_id=1258809,
    start_date=datetime(2025, 6, 1),           # Python datetime object
    end_date=datetime(2025, 6, 30, 23, 59, 59), # Python datetime object
    period='day'  # Can be 'day', 'hour', etc.
)

if usage:
    print(f"Total records: {usage.total_items}")
    for record in usage.usage_data[:3]:
        print(f"{record.start_date.date()}: {record.total_gallons} gallons")

# Alternative: You can still use ISO datetime strings
usage = client.get_usage(
    service_connection_id=1258809,
    start_date='2025-06-01T00:00:00.000Z',
    end_date='2025-06-30T23:59:59.000Z',
    period='day'
)

API Reference

DropCountrClient

The main client for interacting with DropCountr.

Initialization

# Default: Pacific timezone (America/Los_Angeles)
client = DropCountrClient()

# Custom timezone
client = DropCountrClient(timezone="America/New_York")
client = DropCountrClient(timezone="UTC")

# Using ZoneInfo object
from zoneinfo import ZoneInfo
client = DropCountrClient(timezone=ZoneInfo("Europe/London"))

Authentication

success = client.login(email: str, password: str) -> bool
client.logout() -> None
client.is_logged_in() -> bool

Service Connections

# List all service connections
services = client.list_service_connections() -> List[ServiceConnection] | None

# Get specific service details
service = client.get_service_connection(service_id: int) -> ServiceConnection | None

Usage Data

# Get usage data with datetime objects (recommended)
usage = client.get_usage(
    service_connection_id: int,
    start_date: datetime,
    end_date: datetime,
    period: str = "day"  # or "hour"
) -> UsageResponse | None

# Alternative: Use ISO datetime strings
usage = client.get_usage(
    service_connection_id=1234,
    start_date='2025-06-01T00:00:00.000Z',
    end_date='2025-06-30T23:59:59.000Z'
)

Data Models

All response data is validated using Pydantic models:

ServiceConnection

class ServiceConnection(BaseModel):
    id: int                           # Service connection ID
    name: str                         # Service name
    address: str                      # Service address
    account_number: str | None        # Account number
    service_type: str | None          # Service type
    status: str | None                # Service status
    meter_serial: str | None          # Meter serial number
    api_id: str | None                # API identifier

UsageData

class UsageData(BaseModel):
    during: str                       # Time period (ISO interval)
    total_gallons: float              # Total usage in gallons (≥0)
    irrigation_gallons: float         # Irrigation usage (≥0)
    irrigation_events: float          # Number of irrigation events (≥0)
    is_leaking: bool                  # Leak detection status
    
    # Convenience properties
    start_date: datetime              # Parsed start date (timezone-aware)
    end_date: datetime                # Parsed end date (timezone-aware)

UsageResponse

class UsageResponse(BaseModel):
    usage_data: List[UsageData]       # List of usage records
    total_items: int                  # Total number of items (≥0)
    api_id: str                       # API response identifier
    consumed_via_id: str              # Service connection identifier

Error Handling

The library raises clear exceptions for common issues:

try:
    usage = client.get_usage(service_id, start_date, end_date)
except ValueError as e:
    print(f"Authentication or parameter error: {e}")
except requests.RequestException as e:
    print(f"Network error: {e}")

Common errors:

• ValueError: Not logged in or invalid parameters
• requests.RequestException: Network connectivity issues
• Returns None: API returned unexpected data format

Date and Timezone Handling

The library accepts both Python datetime objects and ISO 8601 strings:

from datetime import datetime

# Recommended: Python datetime objects
start_date = datetime(2025, 6, 1)
end_date = datetime(2025, 6, 30, 23, 59, 59)

# Alternative: ISO datetime strings
start_date = '2025-06-01T00:00:00.000Z'
end_date = '2025-06-30T23:59:59.000Z'

Timezone Behavior

⚠️ Breaking Change in v0.1.3: Timezone handling has been fixed to correctly represent local time.

PyDropCountr now properly handles timezone-aware datetime objects:

• Default timezone: Pacific Time (America/Los_Angeles) - configurable during client initialization
• API timestamps: Despite having 'Z' suffix, timestamps are actually in local time (not UTC)
• Returned datetimes: All start_date and end_date properties are timezone-aware
• Standard library: Uses Python 3.12+ zoneinfo module (no external dependencies)

from datetime import datetime
from zoneinfo import ZoneInfo

# Default Pacific timezone
client = DropCountrClient()
usage = client.get_usage(service_id, start_date, end_date)

# Returned datetimes are timezone-aware in Pacific time
for record in usage.usage_data:
    print(record.start_date)  # 2025-06-01 08:00:00-07:00 (PDT)
    print(record.start_date.tzinfo)  # America/Los_Angeles

# Custom timezone for other regions
client = DropCountrClient(timezone="America/New_York")
# or
client = DropCountrClient(timezone=ZoneInfo("UTC"))

Migration from v0.1.2: If you were previously working around the incorrect UTC timestamps, you'll need to update your code as datetimes are now correctly timezone-aware in local time.

Development

Setup

git clone https://github.com/yourusername/pydropcountr.git
cd pydropcountr
uv install --dev

Testing

uv run python test_login.py

Linting

uv run ruff check
uv run ruff format
uv run mypy pydropcountr.py

Project Structure

pydropcountr/
├── pydropcountr.py       # Main library
├── cli.py                # Command-line interface
├── test_login.py         # Test suite
├── pyproject.toml        # Project configuration
├── README.md             # This file
└── CLAUDE.md            # Development notes

Requirements

• Python 3.12+
• requests >= 2.31.0
• pydantic >= 2.0.0
• fire >= 0.7.0

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linting
5. Submit a pull request

Disclaimer

This is an unofficial library for DropCountr.com. Use at your own risk and respect the service's terms of use.