SWESR

A Python package for downloading, analyzing, and visualizing snow data.

Installation

pip install comparator

Or install directly from the source:

git clone https://github.com/yourusername/comparator.git
cd comparator
pip install -e .

Usage

Command line interface

# Basic usage with default parameters
comparator

# Specify custom parameters
comparator --lat 48.6 --lon -121.4 --south 46.6 --west -121.4 --doi 10.5067/PP7T2GBI52I2 --data-dir data --output-dir output --boundary-file SkagitBoundary.json --variables SWE_Post:mean,SCA_Post:mean

Python API

import os
from dotenv import load_dotenv
import geopandas as gpd
from comparator import core, data_processing, visualization

# Load environment variables (for Earthdata credentials)
load_dotenv()

# Set parameters
lat = 48.6
lon = -121.4
south = 46.6
west = -121.4
doi = "10.5067/PP7T2GBI52I2"
data_dir = "data"
output_dir = "output"

# Create a map
map_path = core.create_map(lat, lon, south, west, False, os.path.join(output_dir, "map.html"))

# Search for data
results = core.search_earth_data(doi, lon, lat)
subset, files, years = core.filter_snow_data(results)

# Download data
downloaded_files = core.download_earth_data(subset, data_dir)

# Load the dataset
dataset = data_processing.load_dataset(downloaded_files)

# Load the Skagit boundary
try:
    boundary_gdf = gpd.read_file("SkagitBoundary.json")
except Exception as e:
    print(f"Error loading boundary: {e}")
    boundary_gdf = None

# Extract variables
variable_dict = {"SWE_Post": "mean", "SCA_Post": "mean"}
extracted_data = data_processing.extract_variables(dataset, variable_dict, output_dir)

# Plot data
swe_mean = visualization.plot_skagit_basin_data(
    dataset, "SWE_Post", "mean", 0, boundary_gdf, output_dir
)
sca_mean = visualization.plot_skagit_basin_data(
    dataset, "SCA_Post", "mean", 0, boundary_gdf, output_dir
)

# Plot comparison
if swe_mean is not None and sca_mean is not None:
    visualization.plot_comparison(
        [swe_mean, sca_mean],
        ["SWE", "SCA"],
        "Skagit Basin Mean SWE and SCA",
        os.path.join(output_dir, "skagit_basin_swe_sca_comparison.png")
    )

Requirements

• Python 3.8+
• NASA Earthdata account (set credentials in .env file)

Environment Variables

Create a .env file with your Earthdata credentials:

EARTHDATA_USERNAME=your_username
EARTHDATA_PASSWORD=your_password

Open source licensing

Statement from Schmidt Sciences:

Schmidt Sciences expects that any code from projects funded by Schmidt Sciences be released as open source under an OSI-approved permissive license (such as Apache v2.0 or MIT). We recommend that projects avoid using GPL due to known complexities associated with it. We encourage projects to publish data used for peer-reviewed scientific articles along with the code used to produce the results. Additionally, we recommend avoiding any license modifications for simplicity, and alignment with standard practices.