Explore kernels

In this example, we plot the kernel options provided.

# Code source: Lucy Owen
# License: MIT

import os

import numpy as np
from matplotlib import pyplot as plt

# load
import timecorr as tc

# load helper functions
from timecorr.helpers import plot_weights

# Configure matplotlib for CI environments
if os.environ.get("CI") or os.environ.get("GITHUB_ACTIONS"):
    import matplotlib

    matplotlib.use("Agg")  # Use non-interactive backend in CI

# define number of timepoints
T = 100

# define width
width = 10

# define functions
laplace = {"name": "Laplace", "weights": tc.laplace_weights, "params": {"scale": width}}
delta = {"name": r"$\delta$", "weights": tc.eye_weights, "params": tc.eye_params}
gaussian = {
    "name": "Gaussian",
    "weights": tc.gaussian_weights,
    "params": {"var": width},
}
mexican_hat = {
    "name": "Mexican hat",
    "weights": tc.mexican_hat_weights,
    "params": {"sigma": width},
}


# Helper function to show plots conditionally
def show_plot():
    """Show plot only in interactive environments, not in CI."""
    if not (os.environ.get("CI") or os.environ.get("GITHUB_ACTIONS")):
        plt.show()


# plot delta
plot_weights(delta["weights"](T), title="Delta")
show_plot()
plt.clf()

# plot gaussian
plot_weights(gaussian["weights"](T), title="Gaussian")
show_plot()
plt.clf()

# plot laplace
plot_weights(laplace["weights"](T), title="Laplace")
show_plot()
plt.clf()

# plot mexican hat
plot_weights(mexican_hat["weights"](T), title="Mexican hat")
show_plot()
plt.clf()