python-CIMIS 1.3.5

A comprehensive Python client for the California Irrigation Management Information System (CIMIS) API

Python CIMIS Client

A comprehensive Python client library for the California Irrigation Management Information System (CIMIS) API. This library provides easy access to CIMIS weather station data, spatial data, and station information with built-in CSV export functionality.

📚 Documentation

📖 Complete Documentation - Detailed guides, API reference, and examples

📋 Table of Contents

• 🚀 Quick Start
• ✨ Features
• 📦 Installation
• 🔑 API Key Setup
• 📖 Usage Examples
  • Daily Weather Data
  • Hourly Weather Data
  • Station Information
  • CSV Export
  • Error Handling
• 📊 Available Data Items
• 🛠️ Advanced Usage
• 🔧 Configuration
• 📚 API Reference
• 🤝 Contributing

🚀 Quick Start

from python_cimis import CimisClient
from datetime import date

# Initialize the client with your API key
client = CimisClient(app_key="your-api-key-here")

# Get daily weather data for specific stations
weather_data = client.get_daily_data(
    targets=[2, 8, 127],  # Station numbers
    start_date="2023-01-01",
    end_date="2023-01-05"
)

# Export to CSV with all available columns
client.export_to_csv(weather_data, "weather_data.csv")

# Or get data and export in one step
weather_data = client.get_data_and_export_csv(
    targets=[2, 8, 127],
    start_date=date(2023, 1, 1),
    end_date=date(2023, 1, 5),
    filename="comprehensive_weather_data.csv"
)

✨ Features

• 🌐 Comprehensive API Coverage: Access all CIMIS API endpoints including weather data, station information, and zip code data
• 📡 Multiple Data Sources: Support for both Weather Station Network (WSN) and Spatial CIMIS System (SCS) data
• 🎯 Flexible Data Retrieval: Get data by station numbers, zip codes, coordinates, or street addresses
• 📊 Built-in CSV Export: Export all data with comprehensive column coverage by default
• 🐍 Easy to Use: Simple, Pythonic interface with sensible defaults
• ⚠️ Error Handling: Comprehensive exception handling with descriptive error messages
• 📝 Type Hints: Full type hint support for better IDE integration
• ⚡ Performance: Efficient data processing and export functionality

📦 Installation

From PyPI (Recommended)

pip install python-CIMIS

From Source

git clone https://github.com/python-cimis/python-cimis-client.git
cd python-cimis-client
pip install -e .

🔑 API Key Setup

You need a CIMIS API key to use this library.

Get Your API Key

1. Visit the CIMIS website
2. Register for a free account
3. Generate your API key from your account dashboard

Set Up Your API Key

# Option 1: Pass directly to client (not recommended for production)
client = CimisClient(app_key="your-api-key-here")

# Option 2: Use environment variable (recommended)
import os
os.environ['CIMIS_API_KEY'] = 'your-api-key-here'
client = CimisClient(app_key=os.getenv('CIMIS_API_KEY'))

# Option 3: Load from config file
import json
with open('config.json') as f:
    config = json.load(f)
client = CimisClient(app_key=config['cimis_api_key'])

📖 Usage Examples

Daily Weather Data

🏭 By Station Numbers

from python_cimis import CimisClient
from datetime import date, timedelta

client = CimisClient(app_key="your-api-key-here")

# Get data for specific weather stations
weather_data = client.get_daily_data(
    targets=[2, 8, 127],  # Station numbers: Davis, UC Davis, Five Points
    start_date="2023-01-01",
    end_date="2023-01-31"
    # unit_of_measure defaults to "Metric"
)

print(f"Retrieved {len(weather_data.get_all_records())} records")

📮 By Zip Codes

# Get data for specific zip codes
weather_data = client.get_daily_data(
    targets=["95823", "94503", "93624"],
    start_date="2023-01-01",
    end_date="2023-01-31",
    prioritize_scs=True  # Prioritize Spatial CIMIS System data
)

# Export to CSV
client.export_to_csv(weather_data, "zip_code_weather.csv")

🗺️ By Coordinates

# Get data for specific coordinates (latitude, longitude)
weather_data = client.get_data(
    targets=[
        "lat=39.36,lng=-121.74",  # Near Yuba City
        "lat=38.22,lng=-122.82"   # Near Santa Rosa
    ],
    start_date="2023-01-01",
    end_date="2023-01-31",
    data_items=["day-asce-eto", "day-sol-rad-avg"]  # Only ETo and solar radiation
)

🏠 By Addresses

# Get data for specific addresses
weather_data = client.get_data(
    targets=[
        "addr-name=State Capitol,addr=1315 10th Street Sacramento, CA 95814",
        "addr-name=SF City Hall,addr=1 Dr Carlton B Goodlett Pl, San Francisco, CA 94102"
    ],
    start_date="2023-01-01",
    end_date="2023-01-31",
    data_items=["day-asce-eto", "day-sol-rad-avg"]
)

Hourly Weather Data

# Get hourly data (only available from WSN stations)
# Note: Hourly data requests should be for shorter periods due to data volume
from datetime import datetime, timedelta

yesterday = datetime.now() - timedelta(days=1)
start_date = yesterday.strftime('%Y-%m-%d')
end_date = start_date  # Same day for hourly data

hourly_data = client.get_hourly_data(
    targets=[2, 8, 127],
    start_date=start_date,
    end_date=end_date
)

# Export hourly data (automatically uses clean column names)
client.export_to_csv(hourly_data, "hourly_weather.csv")

Station Information

# Get all available stations
all_stations = client.get_stations()
print(f"Total stations: {len(all_stations)}")

# Get specific station information
station = client.get_stations(station_number="2")
print(f"Station: {station[0].name} in {station[0].city}")

# Export station information to CSV
client.export_stations_to_csv(all_stations, "all_stations.csv")

# Get zip code information
station_zips = client.get_station_zip_codes()
spatial_zips = client.get_spatial_zip_codes()

CSV Export

# Basic export with all columns (default)
client.export_to_csv(weather_data, "complete_data.csv")

# Export with automatic filename generation
filename = client.export_to_csv(weather_data)
print(f"Data exported to: {filename}")

# Export daily and hourly data separately (recommended)
mixed_data = client.get_data(
    targets=[2, 8],
    start_date="2023-01-01",
    end_date="2023-01-02"
)
result = client.export_to_csv(mixed_data, "weather_data.csv")
# Creates: weather_data_daily.csv and weather_data_hourly.csv

# One-step data retrieval and export
client.get_data_and_export_csv(
    targets=[2, 8, 127],
    start_date="2023-01-01",
    end_date="2023-01-05",
    filename="january_weather.csv"
)

Error Handling

from python_cimis import (
    CimisClient, 
    CimisAPIError, 
    CimisAuthenticationError,
    CimisDataError,
    CimisConnectionError
)

client = CimisClient("your-api-key")

try:
    weather_data = client.get_daily_data(
        targets=[999999],  # Invalid station
        start_date="2023-01-01",
        end_date="2023-01-01"
    )
except CimisAuthenticationError:
    print("❌ Invalid API key or authentication failed")
except CimisConnectionError:
    print("❌ Network connection error")
except CimisDataError as e:
    print(f"❌ Data error: {e}")
except CimisAPIError as e:
    print(f"❌ API Error: {e}")
except Exception as e:
    print(f"❌ Unexpected error: {e}")

Custom Data Items

# Specify custom data items for specific needs
temperature_only = [
    "day-air-tmp-avg", 
    "day-air-tmp-max", 
    "day-air-tmp-min"
]

eto_and_weather = [
    "day-asce-eto",
    "day-precip", 
    "day-sol-rad-avg",
    "day-wind-spd-avg",
    "day-rel-hum-avg"
]

# Get only temperature data
temp_data = client.get_daily_data(
    targets=[2, 8, 127],
    start_date="2023-01-01",
    end_date="2023-01-31",
    data_items=temperature_only
)

# Get ETo calculation inputs
eto_data = client.get_daily_data(
    targets=[2, 8, 127],
    start_date="2023-01-01",
    end_date="2023-01-31",
    data_items=eto_and_weather
)

📊 Available Data Items

🌅 Daily Data Items (WSN + SCS)

Category	Data Items	Description
🌡️ Temperature	day-air-tmp-avg, day-air-tmp-max, day-air-tmp-min	Average, maximum, and minimum air temperature
💧 Humidity	day-rel-hum-avg, day-rel-hum-max, day-rel-hum-min	Relative humidity measurements
🌿 Evapotranspiration	day-eto, day-asce-eto, day-asce-etr	Reference evapotranspiration calculations
☀️ Solar Radiation	day-sol-rad-avg, day-sol-rad-net	Solar radiation measurements
💨 Wind	day-wind-spd-avg, day-wind-run	Wind speed and directional components
🌍 Soil	day-soil-tmp-avg, day-soil-tmp-max, day-soil-tmp-min	Soil temperature at various depths
🌧️ Other	day-precip, day-dew-pnt, day-vap-pres-avg	Precipitation, dew point, vapor pressure

⏰ Hourly Data Items (WSN only)

Category	Data Items	Description
🌡️ Temperature	hly-air-tmp, hly-soil-tmp	Hourly air and soil temperature
💧 Humidity	hly-rel-hum	Hourly relative humidity
🌿 Evapotranspiration	hly-eto, hly-asce-eto, hly-asce-etr	Hourly ET calculations
☀️ Solar	hly-sol-rad, hly-net-rad	Hourly solar radiation
💨 Wind	hly-wind-spd, hly-wind-dir, hly-res-wind	Hourly wind measurements
🌧️ Other	hly-precip, hly-dew-pnt, hly-vap-pres	Hourly precipitation and atmospheric data

📝 Note: When exporting hourly data to CSV, column names automatically have the "Hly" prefix removed for cleaner formatting (e.g., HlyAirTmp becomes AirTmp_Value).

🛠️ Advanced Usage

Batch Processing Multiple Locations

from concurrent.futures import ThreadPoolExecutor
import pandas as pd

def get_station_data(station_id):
    """Get data for a single station"""
    try:
        data = client.get_daily_data(
            targets=[station_id],
            start_date="2023-01-01",
            end_date="2023-01-31"
        )
        return station_id, data
    except Exception as e:
        print(f"Error for station {station_id}: {e}")
        return station_id, None

# Process multiple stations in parallel
stations = [2, 8, 127, 54, 6]
results = {}

with ThreadPoolExecutor(max_workers=3) as executor:
    futures = [executor.submit(get_station_data, station) for station in stations]
    for future in futures:
        station_id, data = future.result()
        if data:
            results[station_id] = data

Data Analysis Integration

import pandas as pd
import matplotlib.pyplot as plt

# Get weather data
weather_data = client.get_daily_data(
    targets=[2],  # Davis station
    start_date="2023-01-01",
    end_date="2023-12-31",
    data_items=["day-air-tmp-avg", "day-asce-eto", "day-precip"]
)

# Export to CSV
csv_file = client.export_to_csv(weather_data, "davis_2023.csv")

# Load into pandas for analysis
df = pd.read_csv(csv_file)
df['Date'] = pd.to_datetime(df['Date'])

# Basic analysis
print(f"Average temperature: {df['DayAirTmpAvg_Value'].mean():.1f}°C")
print(f"Total precipitation: {df['DayPrecip_Value'].sum():.1f}mm")
print(f"Total ETo: {df['DayAsceEto_Value'].sum():.1f}mm")

# Simple visualization
plt.figure(figsize=(12, 6))
plt.plot(df['Date'], df['DayAirTmpAvg_Value'])
plt.title('Daily Average Temperature - Davis Station 2023')
plt.ylabel('Temperature (°C)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Custom Data Validation

def validate_weather_data(weather_data):
    """Validate weather data quality"""
    records = weather_data.get_all_records()
    
    issues = []
    for record in records:
        # Check for missing critical data
        if 'DayAsceEto' in record.data_values:
            eto_value = record.data_values['DayAsceEto'].value
            qc_flag = record.data_values['DayAsceEto'].qc
            
            if not eto_value or eto_value == '':
                issues.append(f"Missing ETo data for {record.date}")
            elif qc_flag and qc_flag.strip() not in [' ', '']:
                issues.append(f"Quality issue for ETo on {record.date}: {qc_flag}")
    
    return issues

# Use validation
weather_data = client.get_daily_data(targets=[2], start_date="2023-01-01", end_date="2023-01-31")
issues = validate_weather_data(weather_data)
if issues:
    print("Data quality issues found:")
    for issue in issues[:5]:  # Show first 5 issues
        print(f"  - {issue}")

🔧 Configuration

Environment Variables

# Set your API key as an environment variable (recommended)
export CIMIS_API_KEY="your-api-key-here"

# Optional: Set default timeout
export CIMIS_TIMEOUT="30"

Client Configuration

# Configure client with custom settings
client = CimisClient(
    app_key="your-api-key",
    timeout=60,  # Custom timeout in seconds
    base_url="https://et.water.ca.gov/api"  # Custom base URL if needed
)

# Check client configuration
print(f"API Key configured: {'Yes' if client.app_key else 'No'}")
print(f"Timeout: {client.timeout} seconds")

📚 API Reference

CimisClient Class

from python_cimis import CimisClient

client = CimisClient(
    app_key: str,           # Your CIMIS API key (required)
    timeout: int = 30,      # Request timeout in seconds
    base_url: str = None    # Custom API base URL (optional)
)

Main Methods

Method	Description	Returns
get_daily_data()	Get daily weather data	WeatherData object
get_hourly_data()	Get hourly weather data	WeatherData object
get_data()	Get weather data (flexible)	WeatherData object
get_stations()	Get station information	List[Station]
get_station_zip_codes()	Get WSN zip codes	List[ZipCode]
get_spatial_zip_codes()	Get SCS zip codes	List[SpatialZipCode]
export_to_csv()	Export data to CSV	str (filename)
export_stations_to_csv()	Export stations to CSV	str (filename)
get_data_and_export_csv()	Get data and export in one step	Tuple[WeatherData, str]

Method Parameters

get_daily_data() / get_hourly_data()

client.get_daily_data(
    targets: Union[str, List[str]],           # Station numbers, zip codes, coordinates
    start_date: Union[str, date, datetime],   # Start date (YYYY-MM-DD)
    end_date: Union[str, date, datetime],     # End date (YYYY-MM-DD)
    data_items: Optional[List[str]] = None,   # Specific data items (optional)
    unit_of_measure: str = 'Metric',          # 'Metric' or 'English'
    csv: bool = False,                        # Export to CSV immediately
    filename: Optional[str] = None            # CSV filename (if csv=True)
)

🧩 Data Models

WeatherData

Container for weather data responses with methods:

• get_all_records(): Get all weather records
• get_daily_records(): Get only daily records
• get_hourly_records(): Get only hourly records

WeatherRecord

Individual weather data record with attributes:

• date: Record date (YYYY-MM-DD)
• station: Station identifier
• data_values: Dictionary of data measurements
• scope: 'daily' or 'hourly'

Station

Weather station information with attributes:

• station_nbr: Station number
• name: Station name
• city: City location
• latitude, longitude: Coordinates
• elevation: Station elevation

💡 Best Practices

1. API Key Security

# ✅ Good: Use environment variables
import os
client = CimisClient(app_key=os.getenv('CIMIS_API_KEY'))

# ❌ Avoid: Hardcoding API keys
client = CimisClient(app_key="your-key-here")  # Don't do this!

2. Date Range Management

# ✅ Good: Reasonable date ranges
from datetime import date, timedelta

# For daily data: up to 1 year
start_date = date.today() - timedelta(days=365)
end_date = date.today() - timedelta(days=1)

# For hourly data: keep it short (1-7 days)
start_date = date.today() - timedelta(days=1)
end_date = date.today() - timedelta(days=1)

3. Error Handling

# ✅ Good: Comprehensive error handling
from python_cimis import CimisClient, CimisAPIError

try:
    weather_data = client.get_daily_data(targets=[2], start_date="2023-01-01", end_date="2023-01-31")
except CimisAPIError as e:
    print(f"API Error: {e}")
    # Handle error appropriately

4. Performance Optimization

# ✅ Good: Request specific data items when possible
data_items = ["day-asce-eto", "day-precip", "day-air-tmp-avg"]
weather_data = client.get_daily_data(
    targets=[2, 8, 127],
    start_date="2023-01-01",
    end_date="2023-01-31",
    data_items=data_items  # Reduces response size
)

Requirements

• Python: 3.8+
• Dependencies:
  • requests >= 2.25.0

🤝 Contributing

Contributions are welcome! Here's how you can help:

Development Setup

# Clone the repository
git clone https://github.com/python-cimis/python-cimis-client.git
cd python-cimis-client

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
black python_cimis/
flake8 python_cimis/
mypy python_cimis/

Contributing Guidelines

1. 🍴 Fork the repository
2. 🌟 Create a feature branch (git checkout -b feature/amazing-feature)
3. ✨ Make your changes
4. ✅ Add tests for new functionality
5. 🧪 Run the test suite (pytest)
6. 📝 Update documentation if needed
7. 💾 Commit your changes (git commit -m 'Add amazing feature')
8. 📤 Push to the branch (git push origin feature/amazing-feature)
9. 🔄 Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Contributors

Authors:

• Mahipal Reddy Ramireddy
• M. A. Andrade

Maintainer:

• Precision Irrigation Management Lab (PRIMA)

We welcome contributions from the community! See CONTRIBUTING.md for guidelines.

⚠️ Disclaimer

This library is not affiliated with the California Department of Water Resources or the CIMIS program. It is an independent client library for accessing the public CIMIS API.

🔗 Useful Links

• � Python CIMIS Documentation - Complete library documentation
• �📋 CIMIS Website - Official CIMIS portal
• 📖 CIMIS API Documentation - Official API docs
• 🏛️ California Department of Water Resources - DWR website
• 🐛 Report Issues - Bug reports and feature requests
• 📦 PyPI Package - Package on PyPI

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