Install & Compatibility
Where this runs
tested against v0.0.3 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.910 runs
installs and imports cleanly · install 0.0s · import 0.000s · 233.9MB
glibcpy 3.10–3.910 runs
installs and imports cleanly · install 7.8s · import 0.000s · 225MB
233MB installed
● package 233MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
CRError
✓ from kim_convergence import CRError
✗ from kim_convergence import ConvergenceDetector
HeidelbergerWelch
✓ from kim_convergence import HeidelbergerWelch
✗ from kim_convergence import ConvergenceDetector
MSER_m
✓ from kim_convergence import MSER_m
✗ from kim_convergence import ConvergenceDetector
This quickstart demonstrates how to use `ConvergenceDetector` to identify the point at which a time series of data converges. It initializes a detector with 'rmse' criteria, a window size, and a threshold, then applies it to a sample 2D NumPy array.
import numpy as np
from kim_convergence import ConvergenceDetector
# Generate some example converging data (e.g., from a simulation)
np.random.seed(42)
time_series_data = np.concatenate([
np.random.rand(50, 2) + np.array([10.0, 20.0]), # Initial transient
np.random.rand(50, 2) * 0.1 + np.array([10.5, 20.5]) # Converged state
])
# Initialize the detector with desired criteria
# 'rmse' (Root Mean Square Error) is a common choice
detector = ConvergenceDetector(
criteria='rmse',
window_size=10, # Number of data points in the comparison windows
threshold=0.1 # Maximum allowed difference for convergence
)
# Detect convergence
convergence_step = detector.detect_convergence(time_series_data)
if convergence_step is not None:
print(f"Convergence detected at step: {convergence_step}")
print(f"Data considered converged from index: {convergence_step}")
else:
print("Convergence not detected within the given time series.")
Upgrade
Version history
0.0.3latest on PyPI · released Jul 28, 2025
Audit
Dependencies
numpyrequiredCore numerical operations and array handling.
scipyrequiredUsed for statistical tests like Levene's test if specified as a convergence criterion.