mwhv2.github.io/dis_codes.html at main · mwhv2/mwhv2.github.io · GitHub

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<!DOCTYPE html>
<html>
<head>
    <title>MWL - Dissertation Codes</title>
    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
    <link rel="stylesheet" href="styles.css">
</head>
<body>
<div class="container">
<header>
    <div class="head1">Matt Wentzel-Long</div>
    <hr>
</header>
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        <a class="nav-link" href="index.html">Home</a>
        <a class="nav-link" href="cv.html">Curriculum Vitae</a>
        <a class="nav-link" href="dis_codes.html""><b>Dissertation Codes</b></a>
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<div class="body_sec">
<h2>Hertzsprung-Russell Diagram</h2>
<img src="Images/MIST_HRD_IRXha.png" style="width:70%;">
<button type="button" class="collapsible">Code</button>
<div class="content">
<pre class="prettyprint">
# This program reads in isochrone and PMS evolutionary track data from the Mesa
# Isochrones and Stellar Tracks (MIST)of Choi et al. (2016) and overlays
# luminosity and temperature data of a sample of YSOs (including uncertainties)
# to create a Hertzsprung-Russell Diagram (H-R diagram). All MIST resources,
# including the read_mist_models routine, can be downloaded at:
# http://waps.cfa.harvard.edu/MIST/index.html

import read_mist_models
import numpy as np
import matplotlib.pyplot as plt
import pandas

iso = read_mist_models.ISO('/MIST_iso_5ec68f6b31e2b.iso')

# List of evolutionary tracks and isochrones to be plotted
mtracks = ['TRK_0.5M.txt', 'TRK_0.7M.txt', 'TRK_1.0M.txt', 'TRK_1.5M.txt',
           'TRK_2.0M.txt', 'TRK_3.0M.txt', 'TRK_4.0M.txt', 'TRK_5.0M.txt',
           'TRK_7.0M.txt', 'TRK_10.0M.txt', 'TRK_15.0M.txt', 'TRK_24.0M.txt']
Mlist = ['0.5', '0.7', '1.0', '1.5', '2.0', '3.0', '4.0', '5.0', '7.0', '10.0',
         '15.0', '24.0']

lag = np.array([5.0, 6.0, 6.39, 6.7, 7.0, 7.3, 7.8])
Age = np.round(10**(lag)/10**6, 1)

# Read in data for sources to be overlayed on the diagram as a dataframe
fl = pandas.read_csv("/ClusterMembers.txt",
                     sep='\t')

# Initialize figure
fig = plt.figure(figsize=(9,7))
ax1 = fig.add_subplot(111)

# Plot the evolutionary tracks and isochrones via loops and label each
# on the plot
for i in range(len(mtracks)):
    data=pandas.read_csv('/MIST/'+mtracks[i],
                         sep='\t')
    lums=data.values[:,0]
    tems=data.values[:,1]
    ax1.plot(tems,lums,linewidth=0.8,color='gray')
    plt.text(tems[0]-0.001,lums[0],Mlist[i]+r'$M_{\odot}$',fontsize=11)

# Conditional statements below are for small adjustments for the labeling
# of certain isochrones
for i in range(len(lag)):
    age_ind = iso.age_index(lag[i])
    logTeff = iso.isos[age_ind]['log_Teff']
    logL = iso.isos[age_ind]['log_L']
    if lag[i]==6.0:
        ax1.plot(logTeff[40:250], logL[40:250], '--', color='gray',
                 linewidth=0.8)
        plt.text(logTeff[40], logL[40], str(Age[i])+"Myr", fontsize=11)
    else:
        ax1.plot(logTeff[40:214], logL[40:214], '--', color='gray',
                 linewidth=0.8)
        plt.text(logTeff[40], logL[40], str(Age[i])+"Myr", fontsize=11)

# Indicate the groups of cluster members to be plotted and the marker types
indices=['i','h']
labels=['IRX',r'H$\alpha$']
marks=['^','*']
lw=1.0

##indices=['x']
##labels=['Xray']
##marks=['x']
##lw=1.5

##indices = ['k']
##labels = ['Proper Motion']
##marks = ['p']
##lw = 0.6

# This loop will plot the sources with the indices specified above
for i in range(len(indices)):
    data = fl.loc[fl['index']==indices[i]]
    LogL = data['LogL']
    eL = data['eL']
    LogT = data['LogT']
    sTer = np.array([data['eTlow'], data['eTup']])
    ax1.scatter(LogT, LogL, marker=marks[i], label=labels[i] ,s=50,
                linewidths=lw, zorder=2)
    ax1.errorbar(LogT, LogL, yerr=eL, xerr=sTer, fmt='none', elinewidth=0.6,
                 capsize=2, capthick=0.5, zorder=1, ecolor='k')

# Format axes, labels, and legend
ax1.minorticks_on()
ax1.tick_params(direction="in", length=5)
ax1.tick_params(which='minor', direction="in")
ax1.tick_params(which='both', bottom=True, top=True, left=True, right=True,
                labelsize=12)
plt.xlabel(r'$LogT_{eff}(K)$', fontsize=12)
plt.ylabel(r'$Log(L/L_{\odot})$', fontsize=12)
plt.legend(loc=3,frameon=False, fontsize=12)
plt.xlim(4.6, 3.4)
plt.savefig('MIST_HRD_IRXha.png',dpi=600)
plt.show()

plt.close(fig)

</pre>
</div>
<h2>Plotly-based H-R Diagram</h2>
<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless"
src="Images/HRD_errors.html" height="600" width="100%"></iframe>

<button type="button" class="collapsible">Code</button>
<div class="content">
<pre class="prettyprint">
# This Jupyter notebook creates an interactive Hertzsprung-Russell diagram which displays different
# groups of young stars (based on their youth indicators). The background image of pre-main-sequence
# tracks and isochrones will distort if zoomed (giviving incorrect age/mass estimates).

# Import libraries
import numpy as np
import pandas
import plotly.graph_objects as go
import plotly.io as pio
from plotly.offline import iplot
from PIL import Image

# Read in data for sources to be overlayed on the diagram as a dataframe
fl = pandas.read_csv("/ClusterMembers.txt",sep='\t')

# Separate data to be used in individual traces
irx_name = fl.loc[fl['index']=='i']['Name']
irx_LogL = fl.loc[fl['index']=='i']['LogL']
irx_LogT = fl.loc[fl['index']=='i']['LogT']

ha_name = fl.loc[fl['index']=='h']['Name']
ha_LogL = fl.loc[fl['index']=='h']['LogL']
ha_LogT = fl.loc[fl['index']=='h']['LogT']

x_name = fl.loc[fl['index']=='x']['Name']
x_LogL = fl.loc[fl['index']=='x']['LogL']
x_LogT = fl.loc[fl['index']=='x']['LogT']

p_name = fl.loc[fl['index']=='k']['Name']
p_LogL = fl.loc[fl['index']=='k']['LogL']
p_LogT = fl.loc[fl['index']=='k']['LogT']

# Separate symmetric luminosity errors and asymmetric temperature errors
irx_eL = fl.loc[fl['index']=='i']['eL']
irx_eTup = fl.loc[fl['index']=='i']['eTup']
irx_eTlow = fl.loc[fl['index']=='i']['eTlow']

h_eL = fl.loc[fl['index']=='h']['eL']
h_eTup = fl.loc[fl['index']=='h']['eTup']
h_eTlow = fl.loc[fl['index']=='h']['eTlow']

x_eL = fl.loc[fl['index']=='i']['eL']
x_eTup = fl.loc[fl['index']=='i']['eTup']
x_eTlow = fl.loc[fl['index']=='i']['eTlow']

p_eL = fl.loc[fl['index']=='k']['eL']
p_eTup = fl.loc[fl['index']=='k']['eTup']
p_eTlow = fl.loc[fl['index']=='k']['eTlow']

# Initialize the figure and define traces to be plotted
fig = go.Figure()

trace = go.Scatter(x=irx_LogT, y=irx_LogL, hovertext=irx_name,
                 error_y=dict(type='data', array=irx_eL, visible=True, thickness=1),
                 error_x=dict(type='data', symmetric=False, array=irx_eTup, arrayminus=irx_eTlow),
                   mode='markers', name='IRX')
trace2 = go.Scatter(x=ha_LogT, y=ha_LogL, hovertext=ha_name,
                  error_y=dict(type='data', array=h_eL, visible=True, thickness=1),
                  error_x=dict(type='data', symmetric=False, array=h_eTup, arrayminus=h_eTlow),
                    mode='markers', name='Ha')
trace3 = go.Scatter(x=x_LogT, y=x_LogL, hovertext=x_name,
                  error_y=dict(type='data', array=x_eL, visible=True, thickness=1),
                  error_x=dict(type='data', symmetric=False, array=x_eTup, arrayminus=x_eTlow),
                    mode='markers', name='Xray')
trace4 = go.Scatter(x=p_LogT, y=p_LogL, hovertext=p_name,
                  error_y=dict(type='data', array=p_eL, visible=True, thickness=1),
                  error_x=dict(type='data', symmetric=False, array=p_eTup, arrayminus=p_eTlow),
                    mode='markers', name='Proper Motion', visible=False)

data = [trace, trace2, trace3, trace4]

updatemenus = list([
    dict(active=0,
         showactive = True,
         buttons=list([
            dict(label = "All",
                 method = "update",
                 args = [{"visible": [True, True, True, False]}]),
            dict(label = "IRX",
                 method = "update",
                 args = [{"visible": [True, False, False, False]}]),
            dict(label = "Halpha",
                 method = "update",
                 args = [{"visible": [False, True, False, False]}]),
            dict(label = "Xray",
                 method = "update",
                 args = [{"visible": [False, False, True, False]}]),
            dict(label = "ProperMotion",
                 method = "update",
                 args = [{"visible": [False, False, False, True]}])
            ]))])

# Update plot layout and superimpose background image of tracks and isochrones
# Note: Image via PIL allows background image to be saved as data in HTML file
layout = dict(title="Cluster Member Hertzsprung-Russell Diagram",
              showlegend=True,
              xaxis=dict(title='Log(Temperature)', range=[4.6,3.4]),
              yaxis=dict(title='Log(Luminosity)', range=[-2,5.25]),
              updatemenus=updatemenus, hovermode="closest", template="plotly_dark",
              images=[dict(
              source=Image.open("Background.png"),
              xref="paper",
              yref="paper",
              x=-0.16,
              y=1.15,
              sizex=1.3,
              sizey=1.3,
              sizing="stretch",
              opacity=0.5,
              layer="below")])

fig = dict(data=data, layout=layout)
iplot(fig)
pio.write_html(fig, file="HRD_errors.html")
</pre>
</div>

<h2>Distance to IC 1805</h2>
<img src="Images/ClusterDistance_example.png" style="width:60%;">
<button type="button" class="collapsible">Code</button>
<div class="content">
<pre class="prettyprint">
# This code imports a sample of stellar distances (in pc) and fits
# a Gaussian distribution to the sample. A figure is
# outputted which shows the sample, best-fit Gaussian, and displays
# the best fit parameters (i.e., mean cluster distance and standard
# deviation).

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy import asarray as ar
import numpy as np
import pandas as pd
import math
import matplotlib.backends.backend_pdf

# Open a PDF for the figure
pdf = matplotlib.backends.backend_pdf.PdfPages("DistHist.pdf")

# Import the stellar distances from a csv file
data = pd.read_csv('/dHist.csv')
d = data['Dist'].values

# Bin the stellar distances according to the Freedman & Diaconis rule
# then compute the midpoints of each bin
w = 2*(np.percentile(d, 75)-np.percentile(d, 25))/len(d)**(1/3)
bs = math.ceil((d.max()-d.min())/w)
hs, bins = np.histogram(d, bs)

mids=[]
for i in range(len(bins)-1):
    mids.append((bins[i]+bins[i+1])/2)

# Define a Gaussian function and use SciPy to fit the Gaussian to
# the imported data
def gaus(x, a, x0, sig):
    return a*np.exp(-(x-x0)**2/(2*sig**2))
mean = np.average(d)
sigma = np.std(d)
popt,pcov = curve_fit(gaus, mids, hs, p0=[1, mean, sigma])

# Create the Gaussian curve to overlay onto the figure
fit=gaus(mids, popt[0], popt[1], popt[2])

# Create and format the figure
fig, ax = plt.subplots()
n2, bins, patches = plt.hist(d,bins)
plt.plot(mids, fit, color='k', linestyle='--', linewidth=1)
ax.minorticks_on()
ax.tick_params(direction="in", length=5)
ax.tick_params(which='minor', direction="in")
ax.tick_params(which='both', bottom=True, top=True, left=True, right=True)
plt.xlabel('Distance (pc)', fontsize=12)
plt.ylabel('No. of Sources', fontsize=12)
plt.xlim(right=4000)
plt.text(2200,40,"Mean Distance (pc)="+str(round(popt[1]))+r'$\pm$'+str(round(popt[2])))
pdf.savefig()
pdf.close()

</pre>
</div>

<h2>MCMC Circumstellar Disk Parameters</h2>
<img src="Images/MCMC.png" style="width:100%;">
<button type="button" class="collapsible">Code</button>
<div class="content">
<pre class="prettyprint">

# This program will estimate four parameters, via Markov chain Monte Carlo sampling,
# of disk-bearing YSOs using a flared blackbody disk model.
# Input photometry should be dereddened and in units of Watts cm^-2 mu^-1.
# All documentation for the "emcee" program by Foreman-Mackey et al. (2013) can be
# found here: https://emcee.readthedocs.io/en/stable/#

import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.backends.backend_pdf
import emcee
import corner
from scipy.integrate import quad

# Define constants to be used in disk model
h = 6.626e-34
c = 2.998e8
hc = h * c
bnum = 2 * np.pi * h * c ** 2
k = 1.381e-23
G = 6.674e-11
RsunAU = 0.0046525  # Solar radius in AU
au2 = (1.496e11) ** 2  # Conversion between meters and AU

# Set burn-in and sampling iterations
burn = 500
steps = 800

# Wavelengths for PanSTARRS, 2MASS, AllWISE, and Spitzer passbands
# (in order of increasing wavelength)
lam = [
    0.486,
    0.617,
    0.752,
    0.866,
    0.962,
    1.235,
    1.662,
    2.159,
    3.4,
    3.55,
    4.5,
    4.6,
    5.74,
    7.92,
    23.68,
]
lam2 = np.array(lam) * 10 ** (-6.0)

# Define Planck distribution, prior, and total log probability
def bbmodel(Teff, lamb):
    bbmodel = (
        (bnum / lamb ** 5)
        * (np.exp(hc / (lamb * k * Teff)) - 1) ** (-1.0)
        * 10 ** (-10.0)
    )
    return bbmodel


def prior(theta):
    f, hole, p, out = theta
    if 37.0 < f < 42.0 and RstarAU < hole < 2.0 and 0.4 < p < 1.5 and 0.3 < out < 2.8:
        return 0.0
    return -np.inf


def lnprob(theta):
    lp = prior(theta)
    if not np.isfinite(lp):
        return -np.inf
    return lp + lnlike(theta)


# Open a text document that will record best fit parameter values
pfile = open("/params.txt", "w")

# Open text file containing all information of sources to be fitted:
# dereddened photometries, extinction, temeprature, and stellar radius.
# This code will individually pass each source to the emcee sampler,
# and collect the numerical results in a text file while also creating
# individual PDFs of figures.
file = open("/IRXsources.txt")
for line in file:
    row = line.replace("\n", "").split("\t")
    name = row[0]
    av = float(row[1])
    obs = [
        float(row[2]),
        float(row[4]),
        float(row[6]),
        float(row[8]),
        float(row[10]),
        float(row[12]),
        float(row[14]),
        float(row[16]),
        float(row[18]),
        float(row[20]),
        float(row[22]),
        float(row[24]),
        float(row[26]),
        float(row[28]),
        float(row[30]),
    ]
    err = [
        float(row[3]),
        float(row[5]),
        float(row[7]),
        float(row[9]),
        float(row[11]),
        float(row[13]),
        float(row[15]),
        float(row[17]),
        float(row[19]),
        float(row[21]),
        float(row[23]),
        float(row[25]),
        float(row[27]),
        float(row[29]),
        float(row[31]),
    ]
    sptyp = row[32]
    T = float(row[33])
    Rstar = float(row[34])
    n2 = row[35]

    # Estimate protostellar radius in AU and the thin disk grazing angle coefficient
    RstarAU = Rstar * RsunAU
    A = 0.4 * RstarAU

    # Open PDFs that will record fit SEDs and Corner plots for each source
    pdf = matplotlib.backends.backend_pdf.PdfPages("/MCMCdisk_sample_" + n2 + ".pdf")
    pdf2 = matplotlib.backends.backend_pdf.PdfPages("/MCMCcorner_sample_" + n2 + ".pdf")

    # Trim photometry and wavelength lists to weed out zeroes
    obs2 = []
    err2 = []
    lamB = []
    for i in range(len(obs)):
        if obs[i] != 0 and err[i] != 0:
            obs2.append(obs[i])
            err2.append(err[i])
            lamB.append(lam[i])

    lamB2 = (np.array(lamB)) * 10 ** (-6.0)
    fluxm = np.array(obs2)
    err2 = np.array(err2)
    fluxm2 = np.log10(fluxm)
    err3 = fluxm2 - np.log10(fluxm - err2)
    errax = err3

    # These ranges can be adjusted to skip PanSTARRS photometry if necessary
    fluxm2 = fluxm2[1:]
    err3 = err3[1:]
    lb = lamB[1:]
    lb2 = lamB2[1:]

    obs3 = np.log10(np.array(obs2))
    llam = np.log10(np.array(lamB))

    # Estimate normalization factor for stellar blackbody curve
    scale2 = obs[1] / (lam[1] * np.pi * bbmodel(T, lam2[1]))

    # Define the likelihood function.
    # This is done within the for-loop because some variables here are
    # determined from each individual source.
    def lnlike(theta):
        f, hole, p, out = theta
        totes = []
        sflux = []
        bb = np.pi * scale2 * bbmodel(T, lb2)
        blfl = lb * bb
        sflux = blfl
        for i in range(len(fluxm2)):
            l1 = lb2[i]
            integ = lambda r: r * bbmodel(
                (0.5 * (A / r + 0.03 * r ** (2 / 7))) ** (1 / 4)
                * T
                * (RstarAU / (r)) ** (p),
                l1,
            )
            q = quad(integ, hole, 10 ** (out), epsabs=0)
            flux = np.pi * lb[i] * au2 * q[0]
            totes.append(flux)
        totes = np.array(totes) * 10 ** (-f)
        total = totes + sflux
        total = np.log10(total)
        return -0.5 * (
            np.sum((fluxm2 - total) ** 2 / err3 ** 2 + np.log(2 * np.pi * err3 ** 2))
        )

    # Set up the sampler and create distributions of walkers
    ndim, nwalkers = 4, 50
    pos1 = np.random.normal(40.0, 1.0, nwalkers)
    pos2 = np.random.normal(0.2, 0.01, nwalkers)
    pos3 = np.random.normal(0.625, 0.1, nwalkers)
    pos4 = np.random.normal(1.8, 0.1, nwalkers)
    pos = np.stack((pos1, pos2, pos3, pos4), axis=-1)

    # Run MCMC
    sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)

    print("Burn-in")
    posB, prob, state = sampler.run_mcmc(pos, burn, thin_by=1)
    sampler.reset()
    print("Sizzling")

    print("Running MCMC...RadTrans")
    sampler.run_mcmc(posB, steps, thin_by=1)
    print("Done.")

    # Estimate median and standard deviations for each parameter chain
    samples = sampler.get_chain(discard=0, flat=True)
    f_e, h_e, p_e, o_e = np.median(samples, axis=0)
    f_std, h_std, p_std, o_std = np.std(samples, axis=0)

    # Write best fit parameters to text file
    pfile.write(
        n2
        + "\t"
        + str(round(f_e, 6))
        + "\t"
        + str(round(h_e, 6))
        + "\t"
        + str(round(o_e, 6))
        + "\t"
        + str(round(p_e, 6))
        + "\t"
        + str(round(f_std, 7))
        + "\t"
        + str(round(h_std, 7))
        + "\t"
        + str(round(o_std, 7))
        + "\t"
        + str(round(p_std, 7))
        + "\n"
    )

    # Reset the sampler before the next source
    sampler.reset()

    # Make the corner plot and save to PDF. Note: show_titles=TRUE displays
    # the 0.16,0.5, and 0.84 quantiles
    name2 = name + " " + "(" + sptyp + ")"
    fig = corner.corner(
        samples,
        labels=["f", r"$R_{in}$", "p", r"$R_{out}$"],
        quantiles=[0.16, 0.5, 0.84],
        show_titles=True,
        title_fmt=".4f",
        title_kwargs={"fontsize": 16},
        label_kwargs={"fontsize": 14},
    )
    fig.gca().annotate(
        name2,
        xy=(1.0, 1.0),
        xycoords="figure fraction",
        xytext=(-20, -10),
        textcoords="offset points",
        ha="right",
        va="top",
        fontsize=18,
    )
    pdf2.savefig()
    plt.close(fig)

    # Plot SEDs of the results using medians
    fig = plt.figure()
    ax1 = fig.add_subplot(111)

    # Use a finer spread of wavelengths to produce smoother curves in the theoretical SEDs
    lmb1 = np.arange(0.5, 24.0, 0.1)
    lmb2 = lmb1 * 10 ** (-6)
    lmb3 = np.log10(lmb1)

    # Convert the outer disk radius and standard deviation to AU
    routs = samples[:, 3]
    routs = 10 ** (routs)
    ro_e = np.median(routs)
    ro_std = np.std(routs)

    # Reproduce the star, disk, and star+disk SEDs using median parameter values from the sampler
    totes = []
    sflux = []
    star = []
    bb = np.pi * scale2 * bbmodel(T, lmb2)
    blfl = lmb1 * bb
    sflux = blfl
    star = np.log10(sflux)
    for i in range(len(lmb1)):
        l1 = lmb2[i]
        integ = lambda r: r * bbmodel(
            (0.5 * (A / r + 0.03 * r ** (2 / 7))) ** (1 / 4)
            * T
            * (RstarAU / (r)) ** (p_e),
            l1,
        )
        q = quad(integ, h_e, 10 ** (o_e), epsabs=0)
        flux = np.pi * lmb1[i] * au2 * q[0]
        totes.append(flux)
    totes = np.array(totes) * 10 ** (-f_e)
    total = totes + sflux
    total = np.log10(total)
    disk = np.log10(totes)

    name2 = name + " " + "(" + sptyp + ")"

    # Assemble the actual SED plot, save to PDF, and repeat until the end of the file is reached
    ax1.scatter(llam, obs3, s=20.0, zorder=3)
    ax1.plot(lmb3, star, zorder=2)
    ax1.plot(lmb3, total, "--", color="k", zorder=2)
    ax1.plot(lmb3, disk, color="g", linewidth=1, zorder=2)
    plt.title(name2)
    plt.xlim(-0.4, 1.5)
    plt.ylim(bottom=-22)
    plt.xlabel(r"Log $\lambda$  $(\mu m)$")
    plt.ylabel(r"Log $\lambda F_{\lambda}$ (Watts $cm^{-2})$")
    plt.text(
        0,
        -20,
        "f="
        + str(round(f_e, 4))
        + r"$\pm$"
        + str(round(f_std, 4))
        + "\n"
        + r"$R_{in}$(AU)="
        + str(round(h_e, 4))
        + r"$\pm$"
        + str(round(h_std, 4))
        + "\n"
        + r"$R_{out}$(AU)="
        + str(round(ro_e, 1))
        + r"$\pm$"
        + str(round(ro_std, 1))
        + "\n"
        + "p="
        + str(round(p_e, 2)),
        fontsize="medium",
    )
    plt.text(0, 0, "flared", transform=ax1.transAxes)
    pdf.savefig()
    plt.close(fig)

    pdf.close()
    pdf2.close()

file.close()
pfile.close()


</pre>
</div>

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<footer>Last updated March 2025.</footer>

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