Our first application of this project is to create art with the images from these buoys, and use them to generate a tapestry of the beautiful oceans.
Previously, our code was written as shown below, as an outline for a class structure. We will keep the class structure, but we will be using a different approach to the project.
class BuoyImage:
def __init__(self, location, weather_conditions, image_data):
self.location = location
self.weather_conditions = weather_conditions
self.image_data = image_data
def get_images(self):
# Retrieve the images from the NOAA API
pass
def stitch_images(self):
# Stitch the images together
pass
def blend_images(self):
# Blend the images over time
pass
# Create a GAN to generate images
class GAN:
def __init__(self, image_data):
self.image_data = image_data
def generate_images(self):
# Generate images using a GAN
pass
def blend_images(self):
# Blend the images over time
pass
class PanoramicImage:
def __init__(self, stitched_image_data, horizon_line, time_lapse_data):
self.stitched_image_data = stitched_image_data
self.horizon_line = horizon_line
self.time_lapse_data = time_lapse_data
def blend_images(self):
# Blend the images over time
pass
def detect_horizon(self):
# Detect the horizon line
pass
def create_time_lapse(self):
# Create a time-lapse animation
pass
class Website:
def __init__(self, layout, content):
self.layout = layout
self.content = content
def generate_html(self):
# Generate the HTML for the website
pass
def generate_css(self):
# Generate the CSS for the website
pass
def generate_javascript(self):
# Generate the JavaScript for the website
pass
Create sunsets over the sea using the images from the NOAA API.
Find images of storms and hurricanes, and create a time-lapse of the storm.
PySeas is a Python project aimed at analyzing buoy data. The project is structured into several directories, each serving a specific purpose in the data analysis pipeline.
src: This directory contains the main scripts of the project. It includes phase_one.py and phase_two.py, which perform initial data loading, cleaning, and visualization.
notebooks: This directory contains Jupyter notebooks that demonstrate the usage of the project modules. For example, PyBuoy.ipynb shows how to use the PyBuoy module to fetch and analyze buoy data.
utils: This directory typically contains utility scripts used across the project. These can include data processing functions, helper functions, and other reusable code snippets.
- Clone the repository to your local machine.
- Navigate to the project directory.
- Install the required dependencies listed in the requirements.txt file. You can do this by running pip install -r requirements.txt in your terminal.
- Run the scripts in the src directory. For example, you can run python src/phase_one.py to execute the first phase of the data analysis pipeline.
PySeas is licensed under the MIT License. See LICENSE for more information.
Contributions are welcome! Please see CONTRIBUTING.md for more information.