evo-lake-trend-analysis

Performs non-parametric Mann-Kendall trend detection on water temperature time series.

Key Concepts

• Uses pymannkendall library for Mann-Kendall tests
• Sen's slope attribute: result.slope
• P-value attribute: result.p
• NaN values MUST be dropped before passing to pymannkendall
• For annual data (low autocorrelation risk), original_test is appropriate
• For data with autocorrelation, use hamed_rao or yue_wang methods

Usage

import sys
sys.path.insert(0, '/app/environment/skills/evo-lake-trend-analysis/scripts')
from utils import run_mann_kendall_trend, save_trend_result

# Run trend test on water temperature series
trend = run_mann_kendall_trend(merged_df['WaterTemperature'], method='original')

# Save to CSV (columns: slope, p-value)
result_df = save_trend_result(trend, '/root/output/trend_result.csv')

Key Functions

• run_mann_kendall_trend(series, method, alpha) — runs MK test, returns dict with slope, p_value, trend
• save_trend_result(trend_dict, output_path) — saves slope and p-value to CSV

Output Format

trend_result.csv:

slope,p-value
0.0245,0.034

Import Pattern (avoiding naming conflicts)

When using multiple skills that each have utils.py, use importlib to avoid conflicts:

import importlib.util

def load_module(name, path):
    spec = importlib.util.spec_from_file_location(name, path)
    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)
    return mod

data_utils = load_module('data_utils', '/app/environment/skills/evo-lake-data-pipeline/scripts/utils.py')
trend_utils = load_module('trend_utils', '/app/environment/skills/evo-lake-trend-analysis/scripts/utils.py')
factor_utils = load_module('factor_utils', '/app/environment/skills/evo-lake-factor-attribution/scripts/utils.py')