nathanlunceford/weather_analytics_byu

BYU Schools Weather Data Viewer

The BYU Schools Weather Data Viewer app collects weather data from the Open Meteo API using the openmeteo_requests package in Python.

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Features within the App

Weather Variables

The app supports 13 different weather options:

• —temperature_2m
• —relative_humidity_2m
• —dew_point_2m
• —apparent_temperature
• —precipitation
• —rain
• —snowfall
• —snow_depth
• —weather_code
• —pressure_msl
• —surface_pressure

Dashboard Options

The dashboard provides various options to filter and arrange the data:

• —Select School: Choose to view data for all schools or a specific school (BYU Idaho, BYU Provo, BYU Hawaii).
• —Weather Variable: Select a weather variable to focus on.
• —Timezone: Choose from Mountain Time or Pacific Time.
• —Temperature Unit: Select between Celsius and Fahrenheit for temperature measurements.
• —Precipitation Unit: Select between millimeters (mm) and inches for precipitation measurements.
• —Start Date: Pick the start date for a 15-day interval.

Key Performance Indicators (KPIs)

The app displays KPIs for the selected weather variable, including:

• —Highest value recorded for each school within the selected period.
• —Lowest value recorded for each school within the selected period.

Data Visualization

The app provides various visualizations to analyze the weather data:

• —Combined Weather Data Line Chart: Shows the weather data over time for all schools.
• —Individual School Data: Detailed data and charts for each selected school.
• —Daily Highs and Lows: Line charts showing daily high and low values over the selected period.
• —Forecast vs. Actual Comparisons: Scatter plots, line charts, and heat maps comparing forecasted and actual values for each school.
• —Additional Visualizations: Includes scatter plots with trendlines, boxplots, and violin plots.

Starting the App

We use Streamlit as our prototype dashboard tool, which is embedded in a Docker container.

Steps to Run the App

1. 1.Clone the Repository:

   git clone https://huggingface.co/spaces/ds460/luncefordn_weather_app
   cd luncefordn_weather_app

1. 1.Using Docker Compose:

• —Simply run docker compose up to leverage the docker-compose.yaml file, syncing your local folder with the container folder where the Streamlit app is running.

1. 1.Using Docker Commands:
2. 2.Build the Docker image:

     docker build -t streamlit .

• —Start the container with the appropriate port and volume settings:

     docker run -p 8501:8501 -v "$(pwd):/app:rw" streamlit

This will start the Streamlit app on http://localhost:8501.