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Quiver Plot Script

Description

This script generates quiver plots based on wind speed and direction data. It creates plots where either the length or the color of the arrows represent the wind speed.

Prerequisites

Ensure you have the following libraries installed:

  • numpy
  • matplotlib
  • netCDF4
  • basemap
  • datetime
  • imageio
  • Pillow

You can install all the necessary libraries using pip:

pip install numpy matplotlib netCDF4 basemap datetime imageio Pillow

Usage

  1. Set up your data paths:

    • Update the direction_file_path and speed_file_path variables in the script to point to the appropriate datasets for the years 2023 and 2024.

  2. Run the script:

    • You can run the script directly to generate plots for specific dates.

    • Example:

      python quiver.py

    • The script will generate plots for the following dates:

      • November 1st, 2023
      • November 15th, 2023
      • December 1st, 2023
      • December 15th, 2023
      • January 1st, 2024
      • January 15th, 2024
      • January 31st, 2024

  3. Generate GIFs:

    • The script also includes functionality to create GIFs from the generated plots.
    • Ensure the file_path variable is set to the directory where your plots are saved.
    • Example: 
      python quiver.py

File Structure

  • quiver.py: The main script file.

Example Plots

The script will save the plots in the specified file_path.

Streamline Plot Script

Description

This script generates streamline plots based on wind speed and direction data. It creates images and compiles them into a GIF animation showing wind streamlines over time.

Prerequisites

Ensure you have the following libraries installed:

  • numpy
  • xarray
  • imageio
  • matplotlib
  • basemap
  • scipy

You can install all the necessary libraries using pip:

pip install numpy xarray imageio matplotlib basemap scipy

Usage

  1. Set up your data paths:

    • Update the dir_data2, speed_data2, dir_data1, and speed_data1 variables in the script to point to the appropriate datasets for the years 2023 and 2024.

  2. Run the script:

    • You can run the script directly to generate streamline plots for specific days.

    • Example:

      python streamlines.py

    • The script will generate plots for the following days:

      • November 1st, 2023
      • November 11th, 2023
      • November 21st, 2023
      • December 1st, 2023
      • December 11th, 2023
      • December 21st, 2023
      • December 31st, 2023
      • January 9th, 2024
      • January 19th, 2024
      • January 29th, 2024

  3. Generate GIF:

    • The script includes functionality to create a GIF from the generated plots.
    • Ensure the file_path variable is set to the directory where your plots are saved.
    • Example: 
      python streamlines.py

File Structure

  • streamlines.py: The main script file.

Example Plots

The script will save the plots in the specified directory and create a GIF animation.

Colormap Visualization Script

Description

This script generates colormap visualizations for various weather data variables. It creates plots for specific weather events and saves them as images.

Prerequisites

Ensure you have the following libraries installed:

  • xarray
  • matplotlib
  • numpy
  • cartopy
  • os

You can install all the necessary libraries using pip:

pip install xarray matplotlib numpy cartopy

Usage

  1. Set up your data paths:

    • Update the load_single_weather_data function in the script to point to the appropriate dataset paths for the year 2024.

  2. Run the script:

    • You can run the script directly to generate colormap plots for specified weather events.

    • Example:

      python colormap (2).py

    • The script will generate plots for the following weather events:

      • Drought and Wildfires on specified days (153, 168, 183, 198, 213, 228)

  3. Output:

    • The generated plots will be saved in the specified output_dir directory.

File Structure

  • colormap (2).py: The main script file.

Example Plots

The script will save the plots in the specified directory.

TreeMap Vizualization

There are three interactive visualizations created using D3.js. Each visualization renders data from the athlete_events.csv file. Follow the instructions below to interact with each dashboard.

1. Event Participation by Year and City

This visualization shows a treemap of event participation organized by year and city. You can interact with the following features:

  • Tiling Method: Select from Squarify, Slice, Dice, or Slice-Dice layouts.
  • Top Years Display: Limit the number of years displayed (e.g., top 5, 10, 15, etc.).

How to Use:

  1. Open Viz1_Event_Participation.html in a web browser.
  2. Select a tiling method from the dropdown menu.
  3. Choose the number of top years to display.
  4. The chart will update based on your selections.

Main File: Viz1_Event_Participation.html

2. Medal Distribution by Age Group and Sex

This visualization displays a treemap showing medal distribution by age group and sex. You can switch between different layout methods using the dropdown menu.

How to Use:

  1. Open Viz3.html in a web browser.
  2. Select the desired tiling method (Squarify, Slice, Dice, or Slice-Dice).
  3. The treemap will adjust to reflect your selection, displaying medal distributions by age groups and sex.

Main File: Viz3.html

3. Participation Over Time in Volleyball

This visualization tracks participation in volleyball events over time, grouped by countries. The layout and the number of years to display are customizable.

How to Use:

  1. Open Viz4.html in a web browser.
  2. Choose a tiling method from the dropdown.
  3. Select the number of top years to visualize.
  4. The visualization will update to reflect the changes.

Main File: Viz4.html

Dependencies

Each HTML file relies on D3.js for rendering the treemaps. D3.js is loaded from a CDN, so an active internet connection is required to load the visualizations. Ensure that athlete_events.csv is located in the same directory as the HTML files when running them locally.

Usage

  1. Download the files and place them in the same directory as athlete_events.csv.
  2. Open each HTML file in a browser to interact with the visualizations.
  3. Use the dropdown menus to change the display options.

Contour Analysis Script

This Python script loads various meteorological data files in netCDF format and displays their available variables. The script is designed to work with data such as precipitation, temperature, and humidity from different datasets, which can be used for further analysis or visualization.

Prerequisites

The script requires Python 3 and the following Python packages:

  • netCDF4 for working with netCDF data files
  • xarray for handling multi-dimensional arrays
  • matplotlib for visualizations
  • numpy for numerical operations

Install these packages via:

pip install netCDF4 xarray matplotlib numpy

Usage

  1. Prepare Data Files: Place the required netCDF files (e.g., sph_2023.nc, pr_2024.nc) in the script’s directory, or update the file paths in the script if they are stored elsewhere.
  2. Run the Script: Execute the script to load each netCDF dataset and list available variables within each file. This will help verify the structure and contents of each dataset for further analysis or plotting.
python contour.py
  1. Viewing Variables: The script will print the variables in each dataset to the console, allowing you to inspect the specific parameters contained within each netCDF file.

Modifying for Contour Plots

To add contour plots, identify relevant variables (e.g., lat, lon, temperature) and use matplotlib.pyplot.contour() for visualization. Here’s an example snippet:

import matplotlib.pyplot as plt

# Example contour plot
data = data_sph['temperature_variable_name'][:]  # Replace with actual variable name
plt.contour(data)
plt.title("Contour Plot")
plt.show()

Please update the example file paths in the code if your data files are named differently or located in a different directory.

python contour.py

COLORMAP Analysis

This project provides a set of Python scripts for visualizing weather data, specifically maximum temperature, using different color mapping and normalization techniques. The visualizations include continuous and logarithmic color maps for temperature data across various days, as well as additional visualizations for other weather-related variables.

Files and Descriptions

color_log.py

This script generates an animated visualization of daily maximum temperature data using a logarithmic color scale. The dataset is visualized over a series of days, showing how the temperature changes over time with colors scaled logarithmically for better contrast in large temperature ranges.

  • Dependencies: numpy, matplotlib, netCDF4
  • Usage:
    1. Load daily maximum temperature data from NetCDF files.
    2. Define a series of days to visualize.
    3. Calculate minimum and maximum temperature values globally for logarithmic scaling.
    4. Use matplotlib.animation to create an animated GIF (temperature_animation_logarithmic.gif).

color_cont.py

This script creates a continuous color scale animation for daily maximum temperature data. Unlike color_log.py, this script uses a linear color normalization, providing a direct visual comparison of temperature changes without logarithmic scaling.

  • Dependencies: numpy, matplotlib, netCDF4
  • Usage:
    1. Similar to color_log.py, but applies a linear color scale to the temperature data.
    2. Saves the animation as a GIF file (temperature_animation_continuous.gif).

colormap (2).py

This script is used for generating weather data plots with customizable color maps and scaling options. It includes support for different weather variables (e.g., wind speed, solar radiation, precipitation) and offers options for both continuous and discrete color scales. Additionally, it uses cartopy to create geographic projections.

  • Dependencies: xarray, matplotlib, numpy, cartopy
  • Usage:
    1. Load specific weather variable data from NetCDF files.
    2. Choose a color scale (continuous or discrete) and normalization method (e.g., logarithmic, linear).
    3. Generate plots for specified days and save as images with geographic context.

Prerequisites

Ensure the following Python packages are installed:

pip install numpy matplotlib netCDF4 xarray cartopy

Running the Scripts

To create the animations, run color_log.py and color_cont.py. Ensure that the NetCDF files are correctly located in the specified path.

python color_log.py
python color_cont.py

To create customized weather event plots, use colormap (2).py:

python colormap\ \(2\).py

Output:

  1. temperature_animation_continuous
  2. temperature_animation_logarithmic

AUTHOR CONTRIBUTIONS

  1. Task 1 By Areen Vaghasiya

    • Color maps, Tree maps

  2. Task 2 By Aryan Vaghasiya

    • Node Link diagrams, Contour maps

  3. Task 3 By Rutul Patel

    • Parallel Coordinate Plots, Quiver Plots and StreamLine Plots