pywildfire

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Authors: Pahul Brar, Fiona Chang, Lillian Milroy, & Darwin Zhang

Predict wildfire intensity!

Installation

$ pip install pywildfire

Usage

pywildfire can be used to:

• Download a zip file from a provided URL to extract data and store it in a local directory
• Returns a list of relevant features from a correlation matrix and a target variable
• Calculate the Root Mean Squared Error (RMSE) given observed and predicted values
• Scale numeric values in a dataframe

Example Usage of pywildfire

from pywildfire.pywildfire import calculate_rmse 
from pywildfire.pyfeats import relevant_features
from pywildfire.pyprep import download_extract_data 

# Read a url online and download to files

url = "https://example.com/data.zip"
download_extract_data("url", "./data")

# Identify relevant features based on correlation coefficients.

corr_matrix = pd.DataFrame({
    'A': {'A': 1.0, 'B': -0.8, 'C': 1.0, 'D': -0.8},
    'B': {'A': -0.8, 'B': 1.0, 'C': -0.8, 'D': 1.0},
    'C': {'A': 1.0, 'B': -0.8, 'C': 1.0, 'D': -0.8},
    'D': {'A': -0.8, 'B': 1.0, 'C': -0.8, 'D': 1.0}
})

target_var = 'A'
relevant_features(corr_matrix, target_var)

# Calculate the Root Mean Squared Error (RMSE) between observed and predicted values.

observed = [4, 8, 5, 3, 7]
predicted = [16, 4, 7, 9, 3]
calculate_rmse(observed, predicted)

Contributing

Are you looking to contribute? Please view our contributing guidelines in CONTRIBUTING.md. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

pywildfire was created by Pahul Brar, Fiona Chang, Lillian Milroy, & Darwin Zhang. It is licensed under the terms of the MIT license.

Credits

pywildfire was created with cookiecutter and the py-pkgs-cookiecutter template.