analysis.antennaeKDE module

Plots the Antennae using Kernel Density Estimation to map the density

"""Plots the Antennae using Kernel Density Estimation to map the density"""

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

from analysis.colormaps import red_map
from analysis import utils

def plotKDE(i, theta, phi, view):
    """Plot the Antennae galaxies using kernel density estimation

    Parameters:
        i (int): Timestep at which to plot them
        theta (float): polar angle
        phi (float): azimuthal angle
        view (float): rotation along the line of sight
    """
    data = utils.loadData('antennae', 13500)
    r_vec = data['r_vec'][::]
    
    # Rotate to view from (theta, phi, view) viewpoint
    M1 = np.array([[1, 0, 0],
        [0, np.cos(theta), np.sin(theta)],
        [0, -np.sin(theta), np.cos(theta)]])
    M2 = np.array([[np.cos(phi), np.sin(phi), 0],
        [-np.sin(phi), np.cos(phi), 0],
        [0, 0, 1]])
    M3 = np.array([[np.cos(view), np.sin(view), 0],
        [-np.sin(view), np.cos(view), 0],
        [0, 0, 1]])
    M = np.matmul(M2, np.matmul(M1, M3))
    r_vec = np.tensordot(r_vec, M, axes=[1,0])
    
    # Plotting
    plt.figure(figsize=(4,5), dpi=400)
    # Perform Kernel Density Estimation to colour by density
    xy = np.vstack([r_vec[:,0],r_vec[:,1]])
    c = gaussian_kde(xy)(xy)
    # Densest points are plotted last
    idx = c.argsort()
    x, y, c = r_vec[:,0][idx], r_vec[:,1][idx], c[idx]

    plt.scatter(x, y, c=c, s=3, edgecolor='', cmap=red_map)
    plt.axis('square')
    plt.show()
    

plotKDE(135, 1.86, 4.10, 5.10)

Functions

def plotKDE(i, theta, phi, view)

Plot the Antennae galaxies using kernel density estimation

Parameters

i : int

Timestep at which to plot them

theta : float

polar angle

phi : float

azimuthal angle

view : float

rotation along the line of sight

Source code

def plotKDE(i, theta, phi, view):
    """Plot the Antennae galaxies using kernel density estimation

    Parameters:
        i (int): Timestep at which to plot them
        theta (float): polar angle
        phi (float): azimuthal angle
        view (float): rotation along the line of sight
    """
    data = utils.loadData('antennae', 13500)
    r_vec = data['r_vec'][::]
    
    # Rotate to view from (theta, phi, view) viewpoint
    M1 = np.array([[1, 0, 0],
        [0, np.cos(theta), np.sin(theta)],
        [0, -np.sin(theta), np.cos(theta)]])
    M2 = np.array([[np.cos(phi), np.sin(phi), 0],
        [-np.sin(phi), np.cos(phi), 0],
        [0, 0, 1]])
    M3 = np.array([[np.cos(view), np.sin(view), 0],
        [-np.sin(view), np.cos(view), 0],
        [0, 0, 1]])
    M = np.matmul(M2, np.matmul(M1, M3))
    r_vec = np.tensordot(r_vec, M, axes=[1,0])
    
    # Plotting
    plt.figure(figsize=(4,5), dpi=400)
    # Perform Kernel Density Estimation to colour by density
    xy = np.vstack([r_vec[:,0],r_vec[:,1]])
    c = gaussian_kde(xy)(xy)
    # Densest points are plotted last
    idx = c.argsort()
    x, y, c = r_vec[:,0][idx], r_vec[:,1][idx], c[idx]

    plt.scatter(x, y, c=c, s=3, edgecolor='', cmap=red_map)
    plt.axis('square')
    plt.show()