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paper_figs.py
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paper_figs.py
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import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.lines import Line2D
import seaborn as sns
import numpy as np
import pandas as pd
import re
import io
from urllib import parse, request
from kilopop.kilonovae import bns_kilonova as saeev
from kilopop.kilonovae import bns_kilonovae_population_distribution as s22p
from tqdm import tqdm
from interpolate_bulla_sed import BullaSEDInterpolator
from scipy.interpolate import SmoothBivariateSpline
from dns_mass_distribution import Galaudage21, Farrow19
from monte_carlo_sims import get_ejecta_mass, get_range, MAX_MASS, MIN_MASS
from sed_to_lc import mej_dyn_grid_high, mej_dyn_grid_low, mej_wind_grid_high, mej_wind_grid_low
from astropy import units as u
from astropy import constants as const
from astropy.coordinates import Distance, SkyCoord
from astropy.io import ascii
from matplotlib import cm
from interpolate_bulla_sed import BullaSEDInterpolator
from interpolate_bulla_sed import uniq_mej_dyn, uniq_mej_wind, phases, lmbd, uniq_cos_theta, uniq_phi
from sed_to_lc import SEDDerviedLC, lsst_bands
from rates_models import LVK_UG
np.random.seed(seed=42)
def makeScalingLawsPlot():
i = BullaSEDInterpolator(from_source=False)
i.computeFluxScalingLaws(plot=True)
def makeDnsMassHistograms():
n = 10000
# recycled, slow
m1_Galaudage21, m2_Galaudage21 = Galaudage21(n)
m1_Golomb21, m2_Golomb21 = Farrow19(n)
bins = np.arange(MIN_MASS, MAX_MASS, 0.01)
np.random.normal()
plt.hist(m1_Galaudage21, histtype=u'step', label=r'$m_{recycled}$', linewidth=2, density=True, bins=bins)
plt.hist(m2_Galaudage21, histtype=u'step', label=r'$m_{slow}$ - Galaudage et al.', linewidth=2, density=True, bins=bins)
plt.hist(m2_Golomb21, histtype=u'step', label=r'$m_{slow}$ - Farrow et al.', linewidth=2, density=True, bins=bins)
plt.legend()
plt.xlabel(r"$\mathrm{M_{\odot}}$", fontsize='x-large')
plt.ylabel("Relative count", fontsize='x-large')
plt.tight_layout()
#plt.savefig(f'paper_figures/dns_mass_dist.pdf')
plt.show()
def makeEjectaDistribution():
df = pd.read_csv('O4-DECam-r-23mag-Abbott/trials_df.csv')
mej_wind = df['mej_wind']
mej_dyn = df['mej_dyn']
sns.jointplot(x=mej_wind, y=mej_dyn)
plt.xlabel(r'$\mathrm{m_{ej}^{wind}}$')
plt.ylabel(r'$\mathrm{m_{ej}^{dyn}}$')
plt.tight_layout()
plt.savefig('paper_figures/mej_scatter.pdf')
plt.show()
def makeTrialsEjectaHistogram():
# 170817 params
GW170817_mej_wind = 10**-1.28
GW170817_mej_wind_errors = [[10**-1.28 - 10**-1.63],[10**-0.86 - 10**-1.28]]
GW170817_mej_dyn = 10**-2.27
GW170817_mej_dyn_errors = [[10**-2.27 - 10**-2.81],[10**-1.26 - 10**-2.27]]
df_Galaudage = pd.read_csv('O4-DECam-r-23mag-Galaudage/trials_df.csv')
df_Farrow = pd.read_csv('O4-DECam-r-23mag-Farrow/trials_df.csv')
mej_wind = df_Farrow['mej_wind']
mej_dyn = df_Farrow['mej_dyn']
sns.kdeplot(x=mej_wind, y=mej_dyn, levels=[0.2, 0.5, 0.8])
mej_wind = df_Galaudage['mej_wind']
mej_dyn = df_Galaudage['mej_dyn']
sns.kdeplot(x=mej_wind, y=mej_dyn, levels=[0.2, 0.5, 0.8])
plt.xlabel(r'$\mathrm{m_{ej}^{wind}} (M_{\odot})$', fontsize='x-large')
plt.ylabel(r'$\mathrm{m_{ej}^{dyn}} (M_{\odot})$', fontsize='x-large')
colors = ['C0', 'C1']
lines = [Line2D([0], [0], color=colors[0]), Line2D([0], [0], color=colors[1])]
labels = ['Farrow et al.', 'Galaudage et al.']
plt.legend(lines, labels, loc='upper left', prop={'size': 13})
plt.vlines(mej_wind_grid_low, ymin=mej_dyn_grid_low, ymax=mej_dyn_grid_high, color='black', linestyle='dotted')
plt.vlines(mej_wind_grid_high, ymin=mej_dyn_grid_low, ymax=mej_dyn_grid_high, color='black', linestyle='dotted')
plt.hlines(mej_dyn_grid_low, xmin = mej_wind_grid_low, xmax= mej_wind_grid_high, color='black', linestyle='dotted')
plt.hlines(mej_dyn_grid_high, xmin = mej_wind_grid_low, xmax= mej_wind_grid_high, color='black', linestyle='dotted')
plt.errorbar(GW170817_mej_wind, GW170817_mej_dyn, xerr= GW170817_mej_wind_errors, yerr=GW170817_mej_dyn_errors, marker='*', c='black',ecolor='black', markersize= 15)
plt.text(0.06 , 0.006 ,'SSS17a', c='black')
plt.loglog()
plt.savefig('paper_figures/mej_scatter_hist.pdf')
plt.show()
def makeTrialsAvPlot():
df = pd.read_csv('O4-DECam-r-23mag-Nitz/trials_df.csv')
av = df['a_v']
bins = np.arange(0, np.max(av), step=0.01)
plt.hist(av, density=True, bins=bins, histtype=u'step', linewidth=3)
plt.xlabel(r'$A_V$', fontsize='x-large')
plt.ylabel('Density', fontsize='x-large')
plt.tight_layout()
plt.savefig('paper_figures/av_hist.pdf')
def makeInterceptSurface():
df_linear = pd.read_csv("data/m3_linear_scaling_laws.csv")
phi = df_linear['phi']
cos_theta = df_linear['cos_theta']
c = df_linear['intercept']
fig = plt.figure(constrained_layout=True)
fig.suptitle("Surface for intercept (c) values", fontsize='x-large')
#fig.set_size_inches(12.75, 10)
ax1 = fig.add_subplot(projection='3d')
ax1.plot(phi, cos_theta, c, 'ro')
func = SmoothBivariateSpline(phi, cos_theta, c)
print('c', func.get_residual())
x_grid = np.arange(start=14.5, stop=76, step=0.1)
y_grid = np.arange(start=-0.05, stop=1.1, step=0.01)
z_grid = func(x_grid, y_grid).T
xx, yy = np.meshgrid(x_grid, y_grid)
ax1.plot_surface(xx, yy, z_grid, cmap=cm.plasma,rstride=1, cstride=1, edgecolor='none')
ax1.set_xlabel(r'$\Phi$ (degrees)', fontsize='x-large')
ax1.set_ylabel(r'cos $\Theta$', fontsize='x-large')
ax1.set_zlabel('c', fontsize='x-large')
ax1.dist = 13
plt.locator_params(nbins=4)
fig.savefig('paper_figures/c_surface.pdf', bbox_inches='tight')
fig.savefig('paper_figures/c_surface.png', bbox_inches='tight')
def makeSlopeSurface():
df_linear = pd.read_csv("data/m3_linear_scaling_laws.csv")
phi = df_linear['phi']
cos_theta = df_linear['cos_theta']
m = df_linear['slope']
fig = plt.figure(constrained_layout=True)
fig.suptitle("Surface for slope (m) values", fontsize='x-large')
#fig.set_size_inches(12.75, 10)
ax1 = fig.add_subplot(projection='3d')
ax1.plot(phi, cos_theta, m, 'ro')
func = SmoothBivariateSpline(phi, cos_theta, m)
print('m', func.get_residual())
x_grid = np.arange(start=14.5, stop=76, step=0.1)
y_grid = np.arange(start=-0.05, stop=1.1, step=0.01)
z_grid = func(x_grid, y_grid).T
xx, yy = np.meshgrid(x_grid, y_grid)
ax1.plot_surface(xx, yy, z_grid, cmap=cm.plasma,rstride=1, cstride=1, edgecolor='none')
ax1.set_xlabel(r'$\Phi (degrees)$', fontsize='x-large')
ax1.set_ylabel(r'cos $\Theta$', fontsize='x-large')
ax1.set_zlabel('m', fontsize='x-large', labelpad = 5)
ax1.dist = 13
plt.locator_params(nbins=4)
fig.savefig('paper_figures/m_surface.pdf', bbox_inches='tight')
fig.savefig('paper_figures/m_surface.png', bbox_inches='tight')
def makeExponentSurface():
df_linear = pd.read_csv("data/m3_power_scaling_laws.csv")
phi = df_linear['phi']
cos_theta = df_linear['cos_theta']
n = df_linear['exponent']
fig = plt.figure(constrained_layout=True)
fig.suptitle("Surface for exponent (n) values", fontsize='x-large')
#fig.set_size_inches(12.75, 10)
ax1 = fig.add_subplot(projection='3d')
ax1.plot(phi, cos_theta, n, 'ro')
func = SmoothBivariateSpline(phi, cos_theta, n)
print('n', func.get_residual())
x_grid = np.arange(start=14.5, stop=76, step=0.1)
y_grid = np.arange(start=-0.05, stop=1.1, step=0.01)
z_grid = func(x_grid, y_grid).T
xx, yy = np.meshgrid(x_grid, y_grid)
ax1.plot_surface(xx, yy, z_grid, cmap=cm.plasma,rstride=1, cstride=1, edgecolor='none')
ax1.set_xlabel(r'$\Phi (degrees)$', fontsize='x-large')
ax1.set_ylabel(r'cos $\Theta$', fontsize='x-large')
ax1.zaxis.set_major_formatter('{x:2<2.3f}')
ax1.set_zlabel('n', fontsize='x-large', labelpad = 5)
plt.locator_params(nbins=4)
ax1.dist = 13
fig.savefig('paper_figures/n_surface.pdf', bbox_inches='tight')
fig.savefig('paper_figures/n_surface.png', bbox_inches='tight')
def makeGW170817PhotometryPlotVillar():
# Pass band stuff
bands = ['g','r','i']
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
# Best fit parameters for GW 170817 - https://iopscience.iop.org/article/10.3847/1538-4357/ab5799
mej_wind = 10**-1.28
mej_dyn = 10**-2.27
phi = 49.5
cos_theta = 0.7337298645
# coordinates for GW170817
c = SkyCoord(ra = "13h09m48.08s", dec = "−23deg22min53.3sec")
d = 43*u.Mpc
av = 0.0
# LC from sed
GW170817 = SEDDerviedLC(mej_dyn=mej_dyn, mej_wind = mej_wind, phi = phi, cos_theta = cos_theta, dist=d, coord=c, av = av)
lcs = GW170817.getAppMagsInPassbands(lsst_bands)
from astropy.io import ascii
data = ascii.read('data/Villar_170817.txt')
data = data[data['Used'] == "*"]
data_g = data[data['Band']=='g']
data_r = data[data['Band']=='r']
data_i = data[data['Band']=='i']
plt.errorbar(data_g['Phase'].data, np.array(data_g['Mag'].data, dtype=float) + 2, yerr=np.array(data_g['e_Mag'].data, dtype=float) , label='g + 2',c=colors[0], fmt='o')
plt.errorbar(data_r['Phase'].data, np.array(data_r['Mag'].data, dtype=float), yerr=np.array(data_r['e_Mag'].data, dtype=float), label='r',c=colors[1], fmt='o')
plt.errorbar(data_i['Phase'].data, np.array(data_i['Mag'].data, dtype=float) - 2, yerr=np.array(data_i['e_Mag'].data, dtype=float), label='i - 2',c=colors[2], fmt='o')
plt.plot(phases[:60], lcs[f'lsstg'][:60] + 2, label = f'lsstg + 2', c=colors[0])
plt.plot(phases[:60], lcs[f'lsstr'][:60], label = f'lsstr', c=colors[1])
plt.plot(phases[:60], lcs[f'lssti'][:60] - 2, label = f'lssti - 2', c=colors[2])
plt.xlabel('Phase (Days)', fontsize='x-large')
plt.ylabel('Apparent Mag', fontsize='x-large')
#plt.axhline(y=24, label = "Rubin 10s exposure", linestyle='dotted', color='red')
plt.xlim(left= 0, right=7)
plt.gca().invert_yaxis()
plt.legend()
plt.grid(linestyle="--")
plt.tight_layout()
plt.savefig(f'paper_figures/GW170817LC_villar.pdf')
def makeBNSRangePlot():
ligo_range_O4 = get_range('ligo', 'O4')
virgo_range_O4 = get_range('virgo', 'O4')
kagra_range_O4 = get_range('kagra', 'O4')
ligo_range_O5 = get_range('ligo', 'O5')
virgo_range_O5 = get_range('virgo', 'O5')
kagra_range_O5 = get_range('kagra', 'O5')
print(f'Min O4 \nLIGO {ligo_range_O4(m1=1,m2=1, inclination=10)}\nVirgo {virgo_range_O4(m1=1,m2=1)}\nKagra {kagra_range_O4(m1=1,m2=1)}')
print(f'Max O4 \nLIGO {ligo_range_O4(m1=2.05,m2=2.05, inclination=10)}\nVirgo {virgo_range_O4(m1=2.05,m2=2.05)}\nKagra {kagra_range_O4(m1=2.05,m2=2.05)}')
print(f'Min O5 \nLIGO {ligo_range_O5(m1=1,m2=1)}\nVirgo {virgo_range_O5(m1=1,m2=1)}\nKagra {kagra_range_O5(m1=1,m2=1)}')
print(f'Max O5 \nLIGO {ligo_range_O5(m1=2.05,m2=2.05)}\nVirgo {virgo_range_O5(m1=2.05,m2=2.05)}\nKagra {kagra_range_O5(m1=2.05,m2=2.05)}')
masses = np.arange(1, 2.06, 0.1)
lo4_array = np.zeros((len(masses), len(masses)))
vo4_array = np.zeros((len(masses), len(masses)))
ko4_array = np.zeros((len(masses), len(masses)))
lo5_array = np.zeros((len(masses), len(masses)))
vo5_array = np.zeros((len(masses), len(masses)))
ko5_array = np.zeros((len(masses), len(masses)))
for i, m1 in enumerate(masses):
for j, m2 in enumerate(masses):
print(m1, m2, ligo_range_O4(m1 = m1, m2 = m2))
lo4_array[i][j] = ligo_range_O4(m1 = m1, m2 = m2)
vo4_array[i][j] = virgo_range_O4(m1 = m1, m2 = m2)
ko4_array[i][j] = kagra_range_O4(m1 = m1, m2 = m2)
lo5_array[i][j] = ligo_range_O5(m1 = m1, m2 = m2)
vo5_array[i][j] = virgo_range_O5(m1 = m1, m2 = m2)
ko5_array[i][j] = kagra_range_O5(m1 = m1, m2 = m2)
cmap=cm.get_cmap('plasma')
max_range_O4 = max([np.max(lo4_array), np.max(vo4_array), np.max(ko4_array)])
min_range_O4 = min([np.min(lo4_array), np.min(vo4_array), np.min(ko4_array)])
h, w = 12, 5.5
fig, axes = plt.subplots(nrows=3, ncols=1)
fig.set_size_inches(w, h)
normalizer=Normalize(0, max_range_O4)
im=cm.ScalarMappable(norm=normalizer, cmap=cmap)
axes[0].imshow(lo4_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[0].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[0].set_ylabel("LIGO Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
axes[1].imshow(vo4_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[1].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[1].set_ylabel("Virgo Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
axes[2].imshow(ko4_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[2].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[2].set_ylabel("Virgo Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
cbar = fig.colorbar(im, ax=axes.ravel().tolist())
cbar.set_label('Horizon distances (MPc)', fontsize='x-large')
plt.savefig('paper_figures/O4_range.pdf')
plt.show()
max_range_O5 = max([np.max(lo5_array), np.max(vo5_array), np.max(ko5_array)])
min_range_O5 = min([np.min(lo5_array), np.min(vo5_array), np.min(ko5_array)])
fig, axes = plt.subplots(nrows=3, ncols=1)
fig.set_size_inches(w, h)
normalizer=Normalize(0, max_range_O5)
im=cm.ScalarMappable(norm=normalizer, cmap=cmap)
axes[0].imshow(lo5_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[0].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[0].set_ylabel("LIGO Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
axes[1].imshow(vo5_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[1].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[1].set_ylabel("Virgo Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
axes[2].imshow(ko5_array, extent=[min(masses),max(masses),min(masses),max(masses)], origin="lower",norm=normalizer, cmap=cmap)
axes[2].set_xlabel(r"$m_{1} (M_{\odot})$", fontsize='x-large')
axes[2].set_ylabel("KAGRA Range\n\n" + r"$m_{2} (M_{\odot})$", fontsize='x-large')
cbar = fig.colorbar(im, ax=axes.ravel().tolist())
cbar.set_label('Horizon distances (MPc)', fontsize='x-large')
plt.savefig('paper_figures/O5_range.pdf')
plt.show()
def makeBNSMergerRateHist():
n = 100000
samples, d = LVK_UG(n)
s_5 = np.percentile(samples, 5)
s_95 = np.percentile(samples, 95)
bins = 10 ** np.linspace(np.log10(min(samples)), np.log10(max(samples)), 200)
plt.hist(samples, histtype='step', bins=bins)
plt.axvline(x=s_5, label=r'$\langle R \rangle_{5} = %.2f$' % (s_5), c ='black', linestyle='--')
plt.axvline(x=s_95, label=r"$\langle R \rangle_{95} = %.2f$" % (s_95), c ='black', linestyle='--')
plt.xlabel(r'Rate (R, $GPc^{-3} yr^{-1}$)', fontsize='x-large')
plt.ylabel('Count', fontsize='x-large')
plt.legend()
plt.xscale('log')
plt.tight_layout()
plt.savefig('paper_figures/bns_rates.pdf')
plt.show()
def flatMassDistEjecta():
n_events = 1000
stars = s22p(population_size=n_events, only_draw_parameters=True)
mass1 = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['mass1'] for i in range(n_events)])
mass2 = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['mass2'] for i in range(n_events)])
mej_dyn, mej_wind = get_ejecta_mass(mass1, mass2)
print(mej_dyn, mej_wind )
sns.kdeplot(x=mej_wind, y=mej_dyn, fill=True, cmap="Reds", levels=15)
plt.xlabel(r'$\mathrm{m_{ej}^{wind}}$', fontsize='x-large')
plt.ylabel(r'$\mathrm{m_{ej}^{dyn}}$', fontsize='x-large')
plt.axvline(mej_wind_grid_low, color='black', linestyle='dotted')
plt.axvline(mej_wind_grid_high, color='black', linestyle='dotted')
plt.axhline(mej_dyn_grid_low, color='black', linestyle='dotted')
plt.axhline(mej_dyn_grid_high, color='black', linestyle='dotted')
plt.loglog()
plt.savefig('paper_figures/mej_uniform_scatter_hist.pdf')
plt.tight_layout()
plt.show()
def makeComparisonPlot():
fig, ax = plt.subplots()
# Frostig et al
plt.errorbar(0, 1, yerr=[[1], [2]], marker='o', c='purple', ecolor='purple', label = 'J band')
# Colombo et all
plt.errorbar(1, 2.4, yerr=[[1.8], [3.6]], marker='o', c='purple', ecolor='purple')
# Weizmann et al
plt.errorbar(2, 0.43, yerr=[[0.26], [0.48]], marker='o', c='red', ecolor='red', label = 'r band')
# Colombo et all
plt.errorbar(3, 5.1, yerr=[[3.8], [7.8]], marker='o', c='red', ecolor='red')
# This work - best case
plt.errorbar(4, 2, yerr=[[2], [3]], marker='*', c='red', ecolor='red', markersize='12')
# This work - worst case
plt.errorbar(5, 1, yerr=[[1], [4]], marker='*', c='red', ecolor='red', markersize='12')
plt.ylabel('KN Detections', fontsize='x-large')
labels = [item.get_text() for item in ax.get_xticklabels()]
labels = ['','Frostig et al.\n(over LVK O4)', 'Colombo et al.\n(per year)', 'Weizmann \nKiendrebeogo \net al. (per year)', 'Colombo et al.\n(per year)', 'This work - MM 1\n(over LVK O4)', 'This work - MM2\n(over LVK O4)']
ax.set_xticklabels(labels, rotation=50)
plt.legend()
plt.tight_layout()
plt.savefig('paper_figures/result_comparison.pdf')
plt.show()
if __name__ == '__main__':
#makeScalingLawsPlot()
#makeDnsMassHistograms()
#makeTrialsEjectaScatter()
#makeTrialsEjectaHistogram()
#makeTrialsAvPlot()
makeInterceptSurface()
makeSlopeSurface()
makeExponentSurface()
#makeGW170817PhotometryPlotVillar()
#makeBNSRangePlot()
#makeBNSMergerRateHist()
#flatMassDistEjecta()
#makeComparisonPlot()