scatter_plot_snr.py

import h5py
import numpy as np
import os
import matplotlib.pyplot as plt
import pickle
from plot_styles import apply_physrev_style
from astropy.cosmology import Planck18
from astropy.cosmology import z_at_value
from astropy import units as u
from scipy.stats import gumbel_r
# Apply the style

def get_detection_threshold(normalized, alpha, gumbel=True, list_hyp=False):
    """Compute detection threshold for given significance level alpha."""
    if gumbel:
        if list_hyp:
            detection_threshold = [gumbel_r(*gumbel_r.fit(el)).isf(alpha) for el in normalized.T]
        else:
            detection_threshold = gumbel_r(*gumbel_r.fit(np.max(normalized, axis=1))).isf(alpha)
    else:
        
        if list_hyp:
            detection_threshold = np.quantile(normalized, 1-alpha/len(tpl_vector), axis=0)
        else:
            detection_threshold = np.quantile(np.max(normalized, axis=1), 1-alpha)
    return detection_threshold

# New function for interactive plotting
def plot_mass_vs_distance_or_redshift(
    snrs=[30], alpha=1e-4, y_axis="Redshift", x_axis="Primary Mass", colorbar_var="ef"):
    """
    Interactive plot for mass vs distance/redshift.
    snrs: list of SNRs to include
    alpha: false alarm rate
    y_axis: 'Redshift' or 'Luminosity Distance'
    x_axis: 'Primary Mass' or 'Secondary Mass'
    colorbar_var: 'e0', 'ef', 'm1', or 'm2'
    Returns: matplotlib figure
    """
    noise_file = "paper_results_tdi.h5"
    if not os.path.exists(noise_file):
        raise FileNotFoundError(f"Noise file {noise_file} not found.")
    with h5py.File(noise_file, 'r') as f:
        all_best_losses_noise = f['all_best_losses_noise'][()]
        tpl_vector = f['tpl_vector'][()]
    mean_noise = all_best_losses_noise.mean(axis=0)
    std_noise = all_best_losses_noise.std(axis=0)
    normalized = (all_best_losses_noise - mean_noise) / std_noise

    results_detection = []
    snr_values = []
    for snr in snrs:
        cache_file = f"paper_scatter_cache_{snr}.pkl"
        if not os.path.exists(cache_file):
            continue
        with open(cache_file, "rb") as f:
            results_, snr_ = pickle.load(f)
            results_detection.extend(results_)
            snr_values.extend(snr_)
    snr_values = np.array(snr_values)
    detection_threshold = get_detection_threshold(normalized, alpha)
    detected = np.array([np.max((r['losses'] - mean_noise)/std_noise) > detection_threshold for r in results_detection])
    norm_ds = np.asarray([np.max((r['losses'] - mean_noise)/std_noise) for r in results_detection])

    m1_values = np.array([r['m1'] for r in results_detection])
    m2_values = np.array([r['m2'] for r in results_detection])
    distances = np.array([r['dist'] for r in results_detection])
    e0_values = np.array([r['e0'] for r in results_detection])
    ef_values = np.array([r['ef'] for r in results_detection])

    mask = np.isin(snr_values, snrs)
    det_mask = mask & detected
    not_det_mask = mask & ~detected

    filtered_distances = distances[det_mask]
    z_values = np.array([z_at_value(Planck18.luminosity_distance, d*u.Gpc) for d in filtered_distances])
    filtered_z = z_values
    filtered_m1 = m1_values[det_mask]/(1 + z_values)
    filtered_m2 = m2_values[det_mask]/(1 + z_values)
    filtered_ef = ef_values[det_mask]
    filtered_e0 = e0_values[det_mask]

    # Map app.py dropdown input to variable
    colorbar_map = {
        r"Final eccentricity": (filtered_ef, 'Final Eccentricity $e_f$', 'plasma'),
        r"Initial eccentricity": (filtered_e0, 'Initial Eccentricity $e_0$', 'cividis'),
        r"Primary mass": (filtered_m1, 'Primary Mass [M$_\odot$]', 'viridis'),
        r"Secondary mass": (filtered_m2, 'Secondary Mass [M$_\odot$]', 'viridis'),
    }
    color_data, color_label, cmap = colorbar_map.get(colorbar_var, (filtered_ef, 'Final Eccentricity ($e_f$)', 'plasma'))

    fig, ax = plt.subplots(figsize=(7, 5))
    if x_axis == "Primary Mass":
        x = filtered_m1
        xlabel = r'Source frame primary mass [M$_\odot$]'
    else:
        x = filtered_m2
        xlabel = r'Source frame secondary mass [M$_\odot$]'

    if y_axis == "Redshift":
        y = filtered_z
        ylabel = 'Redshift'
    else:
        y = filtered_distances
        ylabel = 'Luminosity Distance [Gpc]'

    scatter = ax.scatter(x, y, c=color_data, cmap=cmap, alpha=0.7, marker='o')
    cbar = plt.colorbar(scatter, ax=ax)
    cbar.set_label(color_label)
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    return fig