Introduction
LightningChart Python
Setting Up Python Environment
Loading and Processing Data
Visualizing Data with LightningChart Python
Conclusion

Creating an Open Meteo Weather Application with LightningChart Python

Tutorial

Assisted by AI

Learn to integrate the open meteo weather application with LightningChart in Python for powerful weather visualizations and data analysis.

Soroush Sohrabian

Software Developer

Open-Meteo-Weather-Application-Cover

Introduction to the Project

This project develops a Historical Weather Application using LightningChart Python and Open-Meteo API to visualize historical, real-time and forecasting weather data. The goal is creating an interactive dashboard that provides different weather parameters like temperature, humidity, wind speed, pressure, and precipitation trends for Helsinki, Finland.

About the Dataset and Open-Meteo

We used Open-Meteo’s Historical Weather API, which provides past and real-time weather data. Open-Meteo offers free weather data with high precision and supports multiple weather parameters such as:

Temperature (°C)
Precipitation (mm)
Wind Speed and Direction (km/h, °)
Humidity (%)
Soil Moisture and Soil Temperature (for deeper insights)
Air Pressure
Cloud Coverage

The dataset covers the past 24 hours and provides hourly forecasts, which makes it ideal for monitoring weather patterns.

LightningChart Python

LightningChart Python is a high-performance data visualization library that offers real-time and different interactive charting. It allows the creation of 2D and 3D visualizations that makies it ideal for real-time weather monitoring.

LCPython1

Features and Chart Types Used in the Project

In this project, we used several LightningChart components:

Gauge Chart: Displays real-time temperature.
Polar Chart: Shows wind direction and speed.
3D Chart: Visualizes weather conditions dynamically.
Bar Chart: Represents soil temperature, moisture, and cloud coverage.
Multi-Line Chart: Monitors environmental trends like pressure, humidity, and wind speed.

Performance Characteristics

LightningChart Python provides:

GPU acceleration that enables smooth visualization even with large datasets.
Real-time updates which makes it ideal for monitoring live weather changes.
Low CPU and memory usage which improves dashboard responsiveness.

Setting Up Python Environment

Before running the project, install Python and the other required libraries using:

pip install requests pandas lightningchart trimesh pytz

Overview of Libraries Used

NumPy & Pandas – Handling and processing numerical data.
LightningChart – Creating real-time, interactive visualizations.
Trimesh: For 3D weather condition models.
Requests: To fetch data from the Open-Meteo API.
pytz: To manage timezone conversions.

Setting Up Your Development Environment

Set up a virtual environment:

python -m venv rf_analysis_env
source rf_analysis_env/bin/activate  # On Windows: rf_analysis_env\Scripts\activate

Use Visual Studio Code (VSCode) for a streamlined development experience.

Loading and Processing Data

To create this Open Meteo weather application, we will fetch the weather data using the following function:

def fetch_weather_data():
    API_URL = "https://api.open-meteo.com/v1/forecast"
    params = {
        "latitude": 60.1699,
        "longitude": 24.9384,
        "hourly": "temperature_2m,precipitation,wind_speed_10m",
        "past_days": 1,
        "forecast_days": 0,
        "timezone": "auto",
    }
    response = requests.get(API_URL, params=params)
    return pd.DataFrame(response.json()["hourly"])

Handling and Preprocessing the Data

Convert timestamps to local Helsinki time.
Handle missing values and outliers.
Aggregate hourly data for daily analysis.

Visualizing Data with LightningChart Python

Here are some examples that shows how we created key visualizations:

Wind Direction & Speed (Polar Chart)

polar_chart = dashboard.PolarChart(column_index=0, row_index=0, row_span=4, column_span=4)
polar_chart.set_title("Current Wind Direction & Speed")
polar_chart.add_heatmap_series(sectors=12, annuli=5)

Real-Time Temperature (Gauge Chart)

gauge_chart = dashboard.GaugeChart(column_index=10, row_index=0, row_span=4, column_span=2)
gauge_chart.set_title("Current Temperature (°C)")
gauge_chart.set_interval(start=-30, end=50).set_value(0)

3D Weather Condition Visualization

chart_3d = dashboard.Chart3D(row_index=0, column_index=8, row_span=4, column_span=2)
chart_3d.set_title("Current Weather Condition")
chart_3d.set_camera_location(0, 1, 5)

Environmental Data Trends (Multi-Line Chart)

line_chart = dashboard.ChartXY(column_index=4, row_index=4, row_span=4, column_span=8)
line_chart.set_title("Environmental Data Trends")
series = line_chart.add_line_series(data_pattern="ProgressiveX")

Weekly Forecasting Dashboard

The following dashboard displays how the Open Meteo weather application compiles using the Open Meteo API.

Ppen-Meteo-Weather-Application-Forcasting-Weather

Customizing the Open Meteo Weather Application Visualizations

Color Palettes: Used different gradient colors to show better data intensities.
Legends & Labels: Improved readability of weather parameters.

Conclusion

This Open Meteo weather application project demonstrates how to leverage LightningChart Python with Open-Meteo API to visualize a real-time, historical and forecasting weather monitoring system. The dashboard visualizes weather trends that enable us to improve data-driven decision-making for weather analysis.

Continue learning with LightningChart

Using Scale Breaks in Data Visualization

Using Scale Breaks in Data Visualization Starting from LightningChart® .NET version 8, X axes has supported Scale breaks. Scale breaks allow excluding specific X ranges, e.g. inactive trading hours/dates or machinery off-production hours. In effect, scale breaks allow...

Lighting

This article covers basics of Lighting in Data Visualization.

Cleaning Memory Resources Correctly

Cleaning Memory Resources Correctly