def __init__(self, mass, distance, age, Av, Rin=5):
self.mass = mass #Msun
self.dist = distance #pc
self.age = age #Myr
self.Av = Av #mag
#derived variables
#self.Teff = MtoTfunc(self.mass, self.age)
#self.radius = MtoRfunc(self.mass, self.age)
#1d interpolation method done
#can't incorporate age errors this way
self.Teff = a.mass_to_Teff(self.mass, self.age)
self.radius = a.Teff_to_params(self.Teff, self.age)[1]
self.SpTy = a.Teff_to_SpTy(self.Teff)
self.Rin = Rin * self.radius
#ideal mdot, Lacc
self.ideal_mdot = a.empiricalMdot(self.mass)
self.ideal_Lacc = a.Mdot_to_Lacc(self.ideal_mdot, self.mass,
self.radius, self.Rin)
self.mdot = self.ideal_mdot
def build_figure(observed, df):
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=df["Mass (M$_\odot$)"],
y=df["Mdot (M$_\odot$)"],
mode='markers',
name="Simulated",
opacity=0.7,
marker=dict(color='salmon'),
))
fig.add_trace(
go.Scatter(
x=observed["Object Mass M_Solar"],
y=observed["Accretion Rate M_solar yr-1"],
mode='markers',
name='Observed',
opacity=0.6,
marker=dict(symbol='x', color='#44749D'),
))
fig.add_trace(
go.Scatter(
x=observed["Object Mass M_Solar"],
y=a.empiricalMdot(observed["Object Mass M_Solar"]),
mode='lines',
name='Empirical Relationship',
opacity=0.9,
marker=dict(color='gray'),
))
fig.update_layout(
width=1200,
height=675,
title={
'text': "Accretion Monte Carlo Error Propagation",
'y': 0.9,
'x': 0.46,
'xanchor': 'center',
'yanchor': 'top',
},
xaxis_title="Mass (solar masses)",
yaxis_title="Mass Accretion Rate (solar masses/yr)",
legend_title=None,
font=dict(),
xaxis=dict(tickmode='array',
tickvals=[0, 0.01, 0.05, 0.1, 0.5, 1, 1.5, 2],
showgrid=False),
yaxis=dict(
showexponent='all',
tickmode='array',
tickvals=[1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6],
ticktext=[1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6],
showgrid=True),
)
fig.update_xaxes(type="log")
fig.update_yaxes(type="log")
return fig
def build_residuals(observed, df):
residual_fig = go.Figure()
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = np.log10(df['Mdot (M$_\\odot$)']) - np.log10(
a.empiricalMdot(df['Mass (M$_\\odot$)']))
residual_fig.add_trace(
go.Scatter(
x=df["Mass (M$_\odot$)"],
y=simulated_difference,
mode='markers',
name="Simulated",
opacity=0.7,
marker=dict(color='salmon'),
))
residual_fig.add_trace(
go.Scatter(
x=observed["Object Mass M_Solar"],
y=observed_difference,
mode='markers',
name='Observed',
opacity=0.6,
marker=dict(symbol='x', color='#44749D'),
))
residual_fig.add_trace(
go.Scatter(
x=observed["Object Mass M_Solar"],
y=np.zeros(len(observed["Object Mass M_Solar"])),
mode='lines',
name='Empirical Relationship',
opacity=0.9,
marker=dict(color='gray'),
))
residual_fig.update_layout(
width=1200,
height=575,
title={
'text': "Residuals",
'y': 0.9,
'x': 0.46,
'xanchor': 'center',
'yanchor': 'top',
},
xaxis_title="Mass (solar masses)",
yaxis_title="Residual (log space)",
legend_title=None,
font=dict(),
xaxis=dict(tickmode='array',
tickvals=[0, 0.01, 0.05, 0.1, 0.5, 1, 1.5, 2],
showgrid=False),
yaxis=dict(showexponent='all',
tickmode='array',
tickvals=[-3, -2, -1, 0, 1, 2, 3],
showgrid=True),
)
residual_fig.update_xaxes(type="log")
return residual_fig
def UVExcessErrorProp(self,
SpTyUnc,
distUnc,
ageUnc,
AvUnc,
bc,
bcUnc,
UVExcessUnc,
numMC,
Rin=5,
RinUnc=0,
variability=0,
age_scatter=False):
#propagate errors forward and obtain uncertainty distributions
if age_scatter == True:
#self.shifted_ideal_mdot = a.empiricalMdot(self.mass, intercept=-a.age_to_intercept_func(self.age))
self.shifted_ideal_mdot = a.empiricalMdot(
self.mass, intercept=-a.age_intercept_exponential(self.age))
self.ideal_UVExcess = a.Mdot_to_UVExcess(self.shifted_ideal_mdot,
bc, self.dist, self.mass,
self.radius, self.Av,
self.Rin)
else:
self.ideal_UVExcess = a.Mdot_to_UVExcess(self.ideal_mdot, bc,
self.dist, self.mass,
self.radius, self.Av,
self.Rin)
#add in uncertainties to observable itself
if numMC == 1:
self.UVExcessUncDist = np.random.normal(self.ideal_UVExcess,
UVExcessUnc *
self.ideal_UVExcess,
size=numMC)[0]
else:
self.UVExcessUncDist = np.random.normal(self.ideal_UVExcess,
UVExcessUnc *
self.ideal_UVExcess,
size=numMC)
#add in uncertainties to variables specific to observable
x = np.linspace(0, 20000, 100000)
if bcUnc == 0:
if numMC == 1:
self.bcUncDist = bc
else:
self.bcUncDist = np.ones(numMC) * bc
else:
self.bcUncDist = a.uncdist(x, bc, bcUnc, numMC)
#build uncertainty distributions
if SpTyUnc == 0:
if numMC == 1:
self.SpTyUncDist = self.SpTy
else:
self.SpTyUncDist = np.ones(numMC) * self.SpTy
else:
self.SpTyUncDist = a.uncdist(x, self.SpTy, SpTyUnc, numMC)
if distUnc == 0:
if numMC == 1:
self.distUncDist = self.dist
else:
self.distUncDist = np.ones(numMC) * self.dist
else:
self.distUncDist = a.uncdist(x, self.dist, distUnc, numMC)
if ageUnc == 0:
if numMC == 1:
self.ageUncDist = self.age
else:
self.ageUncDist = np.ones(numMC) * self.age
else:
self.ageUncDist = a.uncdist(x, self.age, ageUnc, numMC)
if AvUnc == 0:
if numMC == 1:
self.AvUncDist = self.Av
else:
self.AvUncDist = np.ones(numMC) * self.Av
else:
self.AvUncDist = a.uncdist(x, self.Av, AvUnc, numMC)
if RinUnc == 0:
if numMC == 1:
self.RinUncDist = Rin
else:
self.RinUncDist = np.ones(numMC) * Rin
else:
self.RinUncDist = a.uncdist(x, Rin, RinUnc, numMC)
self.TeffUncDist = a.SpTy_to_Teff(self.SpTyUncDist)
self.massUncDist = a.Teff_to_params(self.TeffUncDist,
(self.ageUncDist))[0]
self.radiusUncDist = a.Teff_to_params(self.TeffUncDist,
(self.ageUncDist))[1]
self.RinUncDist = self.RinUncDist * self.radiusUncDist
#self.RinUncDist = Rin*self.radiusUncDist
#Generate synthetic Mdot distribution
#Generate synthetic Lacc distribution
self.mdot = a.UVExcess_to_Mdot(self.UVExcessUncDist, self.bcUncDist,
self.distUncDist, self.massUncDist,
self.radiusUncDist, self.AvUncDist,
self.RinUncDist)
if variability != 0:
log_mdot = np.log10(self.mdot)
if numMC == 1:
draw = np.random.normal(log_mdot, variability, size=numMC)[0]
else:
draw = np.random.normal(log_mdot, variability, size=numMC)
self.mdot = 10**draw
'''
#specify lower and upper bounds for the truncated gaussian
lower, upper = 0,1
mu, sigma = (self.mdot), (self.mdot*variability)
variability_distribution = st.truncnorm(
(lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma)
if numMC == 1:
self.mdot = variability_distribution.rvs(numMC)[0]
else:
self.mdot = variability_distribution.rvs(numMC)
'''
self.Lacc = a.Mdot_to_Lacc(self.mdot, self.massUncDist,
self.radiusUncDist, self.RinUncDist)
def residuals(observed, simulated):
#load in data
logmass = np.log10(simulated['Mass (M$_\\odot$)'])
logMdot = np.log10(simulated['Mdot (M$_\\odot$)'])
logaccepted_relation = np.log10(simulated['"true" Mdot (M$_\\odot$)'])
observed_mass = np.log10(observed['Object Mass M_Solar'])
observed_mdot = np.log10(observed['Accretion Rate M_solar yr-1'])
#Figure Dimensions
left, width = 0.1, 0.4
bottom, height = 0.1, 0.7
spacing = 0.0075
#create figure
fig = plt.figure(figsize=(16, 12))
#Residual Plot
rect_frame2 = [left, bottom, width, height]
rect_ax2_hist = [left + width + spacing, bottom, 0.3, height]
#create axes
frame2 = fig.add_axes(rect_frame2)
ax2_hist = fig.add_axes(rect_ax2_hist)
ax = plt.gca()
#Calculate Residuals
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = np.log10(simulated['Mdot (M$_\\odot$)']) - np.log10(
a.empiricalMdot(simulated['Mass (M$_\\odot$)']))
#Residual Marginal Distribution
#remove ticks
ax2_hist.tick_params(axis="y", labelleft=False)
#bins by hand
residualbinwidth = 0.25
residualmax = np.max(np.abs(observed_difference))
residuallim = (int(residualmax / residualbinwidth) + 1) * residualbinwidth
residualbins = np.arange(-residuallim, residuallim + residualbinwidth,
residualbinwidth)
#plot
ax2_hist.hist(observed_difference,
bins=residualbins,
orientation='horizontal',
color='black',
density=True,
alpha=0.225,
label='Observed')
ax2_hist.hist(simulated_difference,
bins=residualbins,
orientation='horizontal',
color='darkseagreen',
density=True,
alpha=0.3,
label='Simulated')
ax2_hist.set_ylim(-3.3, 3.2)
ax2_hist.axhline(0, color='tomato', label='Empirical Relationship')
ax2_hist.legend()
#Residual Plot
frame2.axhline(0, color='tomato', label='Empirical Relationship')
frame2.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame2.set_ylabel('Residual (log space)', size=16)
frame2.set_ylim(-3.3, 3.2)
frame2.scatter(np.log10(observed['Object Mass M_Solar']),
observed_difference,
color='black',
marker='x',
alpha=0.4,
label='Observed Residuals')
frame2.scatter(np.log10(simulated['Mass (M$_\\odot$)']),
simulated_difference,
color='darkseagreen',
alpha=0.4,
label='Simulated Residuals')
frame2.legend(frameon=True)
return fig
def MarginalDistribution(observed, simulated):
#load in data
logmass = np.log10(simulated['Mass (M$_\\odot$)'])
logMdot = np.log10(simulated['Mdot (M$_\\odot$)'])
logaccepted_relation = np.log10(simulated['"true" Mdot (M$_\\odot$)'])
observed_mass = np.log10(observed['Object Mass M_Solar'])
observed_mdot = np.log10(observed['Accretion Rate M_solar yr-1'])
#Figure Dimensions
left, width = 0.1, 0.7
bottom, height = 0.3, 0.55
spacing = 0.0075
#create figure
fig = plt.figure(figsize=(16, 12))
#Mass vs Mdot
rect_frame1 = [left, bottom, width, height]
#Marginal Distributions
rect_histx = [left, bottom + height + spacing, width, 0.1]
rect_histy = [left + width + spacing, bottom, 0.1, height]
#create axes
frame1 = fig.add_axes(rect_frame1)
ax_histx = fig.add_axes(rect_histx, sharex=frame1)
ax_histy = fig.add_axes(rect_histy, sharey=frame1)
ax = plt.gca()
#Marginal Distributions
# no labels
ax_histx.tick_params(axis="x", labelbottom=False)
ax_histy.tick_params(axis="y", labelleft=False)
# determine nice limits by hand:
binwidth = 0.25
xymax = max(np.max(np.abs(observed_mass)), np.max(np.abs(observed_mdot)))
lim = (int(xymax / binwidth) + 1) * binwidth
bins = np.arange(-lim, lim + binwidth, binwidth)
#Plot histograms
ax_histx.hist(observed_mass,
bins=bins,
color='black',
density=True,
alpha=0.225,
label='Observed')
ax_histy.hist(observed_mdot,
bins=bins,
orientation='horizontal',
color='black',
density=True,
alpha=0.225,
label='Observed')
ax_histx.hist(logmass,
bins=bins,
color='darkseagreen',
density=True,
alpha=0.3,
label='Simulated')
ax_histy.hist(logMdot,
bins=bins,
orientation='horizontal',
color='darkseagreen',
density=True,
alpha=0.3,
label='Simulated')
ax_histx.legend()
ax_histy.legend()
#Mass vs Mdot
frame1.scatter(logmass,
logMdot,
color='darkseagreen',
s=130,
alpha=0.4,
label='Simulated')
frame1.scatter(observed_mass,
observed_mdot,
color='black',
s=50,
marker='x',
alpha=0.4,
label='Observed')
frame1.plot(logmass,
logaccepted_relation,
color='tomato',
label='Empirical Relationship')
frame1.axvline(x=np.log10(0.012),
color='black',
linestyle='-.',
label='Deuterium Burning Limit')
frame1.axvline(x=np.log10(0.1),
color='black',
linestyle=':',
label='Hydrogen Burning Limit')
frame1.set_xlim(-2.6, 0.6)
frame1.set_ylim(-14, -5.75)
frame1.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame1.set_ylabel('log(Mass Accretion Rate) (M$_\odot$/yr)', size=16)
frame1.text(-2.3, -6.6, 'Planets', size=15, fontstyle='italic')
frame1.text(-1.64, -6.6, 'Brown Dwarfs', size=15, fontstyle='italic')
frame1.text(-0.29, -6.6, 'Stars', size=15, fontstyle='italic')
ax_histx.set_title('Monte Carlo Error Propagation',
size=20,
fontweight='heavy')
frame1.legend(loc='lower right',
prop={
'family': 'sans-serif',
'style': 'normal',
'size': 16
},
frameon=False,
shadow=True)
#Residual Plot
rect_frame2 = [left, bottom - 0.2, width, bottom - 0.1 - spacing]
rect_ax2_hist = [
left + width + spacing, bottom - 0.2, 0.1, bottom - 0.1 - spacing
]
#create axes
frame2 = fig.add_axes(rect_frame2)
ax2_hist = fig.add_axes(rect_ax2_hist)
ax = plt.gca()
#Calculate Residuals
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = np.log10(simulated['Mdot (M$_\\odot$)']) - np.log10(
a.empiricalMdot(simulated['Mass (M$_\\odot$)']))
#Residual Marginal Distribution
#remove ticks
ax2_hist.tick_params(axis="y", labelleft=False)
#bins by hand
residualbinwidth = 0.25
residualmax = np.max(np.abs(observed_difference))
residuallim = (int(residualmax / residualbinwidth) + 1) * residualbinwidth
residualbins = np.arange(-residuallim, residuallim + residualbinwidth,
residualbinwidth)
#plot
ax2_hist.hist(observed_difference,
bins=residualbins,
orientation='horizontal',
color='black',
density=True,
alpha=0.225,
label='Observed')
ax2_hist.hist(simulated_difference,
bins=residualbins,
orientation='horizontal',
color='darkseagreen',
density=True,
alpha=0.3,
label='Simulated')
ax2_hist.set_ylim(-3.3, 3.2)
ax2_hist.axhline(0, color='tomato')
ax2_hist.legend()
#Residual Plot
frame2.axhline(0, color='tomato')
frame2.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame2.set_ylabel('Residual (log space)', size=16)
frame2.set_ylim(-3.3, 3.2)
frame2.scatter(np.log10(observed['Object Mass M_Solar']),
observed_difference,
color='black',
marker='x',
alpha=0.4,
label='Observed Residuals')
frame2.scatter(np.log10(simulated['Mass (M$_\\odot$)']),
simulated_difference,
color='darkseagreen',
alpha=0.4,
label='Simulated Residuals')
frame2.legend(frameon=True)
return fig
def MoneyPlot(observed, simulated):
logmass = np.log10(simulated['Mass (M$_\\odot$)'])
logMdot = np.log10(simulated['Mdot (M$_\\odot$)'])
logaccepted_relation = np.log10(simulated['"true" Mdot (M$_\\odot$)'])
observed_mass = np.log10(observed['Object Mass M_Solar'])
observed_mdot = np.log10(observed['Accretion Rate M_solar yr-1'])
fig = plt.figure(figsize=(16, 12))
frame1 = fig.add_axes((.1, .3, .8, .6))
ax = plt.gca()
ax.scatter(logmass,
logMdot,
color='darkseagreen',
s=130,
alpha=0.4,
label='Simulated')
ax.scatter(observed_mass,
observed_mdot,
color='black',
s=50,
marker='x',
alpha=0.4,
label='Observed')
ax.plot(logmass,
logaccepted_relation,
color='tomato',
label='Empirical Relationship')
ax.axvline(x=np.log10(0.012),
color='black',
linestyle='-.',
label='Deuterium Burning Limit')
ax.axvline(x=np.log10(0.1),
color='black',
linestyle=':',
label='Hydrogen Burning Limit')
ax.set_xlim(-2.6, 0.6)
ax.set_xlabel('log(Mass) (M$_\odot$)', size=16)
ax.set_ylabel('log(Mass Accretion Rate) (M$_\odot$/yr)', size=16)
ax.text(-2.3, -6.6, 'Planets', size=15, fontstyle='italic')
ax.text(-1.64, -6.6, 'Brown Dwarfs', size=15, fontstyle='italic')
ax.text(-0.29, -6.6, 'Stars', size=15, fontstyle='italic')
ax.set_title('Monte Carlo Error Propagation', size=20, fontweight='heavy')
ax.legend(loc='lower right',
prop={
'family': 'sans-serif',
'style': 'normal',
'size': 16
},
frameon=False,
shadow=True)
frame2 = fig.add_axes((.1, .1, .8, .2))
ax = plt.gca()
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = np.log10(simulated['Mdot (M$_\\odot$)']) - np.log10(
a.empiricalMdot(simulated['Mass (M$_\\odot$)']))
ax.axhline(0, color='tomato')
ax.set_xlabel('log(Mass) (M$_\odot$)', size=16)
ax.set_ylabel('Residual (log space)', size=16)
ax.scatter(np.log10(observed['Object Mass M_Solar']),
observed_difference,
color='black',
marker='x',
alpha=0.4,
label='Observed Residuals')
ax.scatter(np.log10(simulated['Mass (M$_\\odot$)']),
simulated_difference,
color='darkseagreen',
alpha=0.4,
label='Simulated Residuals')
ax.legend(frameon=True)
return fig
def linefluxErrorProp(self,
SpTyUnc,
distUnc,
ageUnc,
AvUnc,
linefluxUnc,
numMC,
Rin=5,
RinUnc=0,
line=None,
A=None,
AUnc=None,
B=None,
BUnc=None,
variability=0,
age_scatter=False):
#propagate errors forward and obtain uncertainty distributions
if line == None:
A = A
aUnc = AUnc
B = B
bUnc = BUnc
elif line == 'Ha':
A = 1.13 #+/- 0.05
aUnc = 0.05
B = 1.74 #+/- 0.19
bUnc = 0.19
elif line == 'Pab':
A = 1.06 #+/- 0.07
aUnc = 0.07
B = 2.76 #+/- 0.34
bUnc = 0.34
elif line == 'Brg':
A = 1.19 #+/- 0.10
aUnc = 0.10
B = 4.02 #+/- 0.51
bUnc = 0.51
else:
print('Line not found.')
return
if age_scatter == True:
#self.shifted_ideal_mdot = a.empiricalMdot(self.mass, intercept=-a.age_to_intercept_func(self.age))
self.shifted_ideal_mdot = a.empiricalMdot(
self.mass, intercept=-a.age_intercept_exponential(self.age))
self.ideal_lineflux = a.Mdot_to_lineflux(self.shifted_ideal_mdot,
self.dist,
self.mass,
self.radius,
self.Av,
Rin=self.Rin,
line=line,
A=A,
B=B)
else:
self.ideal_lineflux = a.Mdot_to_lineflux(self.ideal_mdot,
self.dist,
self.mass,
self.radius,
self.Av,
Rin=self.Rin,
line=line,
A=A,
B=B)
#add in uncertainties to observable itself
if numMC == 1:
self.linefluxUncDist = np.random.normal(self.ideal_lineflux,
linefluxUnc *
self.ideal_lineflux,
size=numMC)[0]
else:
self.linefluxUncDist = np.random.normal(self.ideal_lineflux,
linefluxUnc *
self.ideal_lineflux,
size=numMC)
x = np.linspace(0, 1000, 100000)
#add in uncertainties to variables specific to observable
#self.AUncDist = a.uncdist(x, A, aUnc, numMC) #A
#self.BUncDist = a.uncdist(x, B, bUnc, numMC) #B
#Ignore uncertainties of the empirical fit
self.AUncDist = A #A
self.BUncDist = B #B
#build uncertainty distributions
if SpTyUnc == 0:
if numMC == 1:
self.SpTyUncDist = self.SpTy
else:
self.SpTyUncDist = np.ones(numMC) * self.SpTy
else:
self.SpTyUncDist = a.uncdist(x, self.SpTy, SpTyUnc, numMC)
if distUnc == 0:
if numMC == 1:
self.distUncDist = self.dist
else:
self.distUncDist = np.ones(numMC) * self.dist
else:
self.distUncDist = a.uncdist(x, self.dist, distUnc, numMC)
if ageUnc == 0:
if numMC == 1:
self.ageUncDist = self.age
else:
self.ageUncDist = np.ones(numMC) * self.age
else:
self.ageUncDist = a.uncdist(x, self.age, ageUnc, numMC)
if AvUnc == 0:
if numMC == 1:
self.AvUncDist = self.Av
else:
self.AvUncDist = np.ones(numMC) * self.Av
else:
self.AvUncDist = a.uncdist(x, self.Av, AvUnc, numMC)
if RinUnc == 0:
if numMC == 1:
self.RinUncDist = Rin
else:
self.RinUncDist = np.ones(numMC) * Rin
else:
self.RinUncDist = a.uncdist(x, Rin, RinUnc, numMC)
self.TeffUncDist = a.SpTy_to_Teff(self.SpTyUncDist)
self.massUncDist = a.Teff_to_params(self.TeffUncDist,
(self.ageUncDist))[0]
self.radiusUncDist = a.Teff_to_params(self.TeffUncDist,
(self.ageUncDist))[1]
self.RinUncDist = self.RinUncDist * self.radiusUncDist
#Generate synthetic Mdot distribution
#Generate synthetic Lacc distribution
self.mdot = a.lineflux_to_Mdot(self.linefluxUncDist,
self.distUncDist,
self.massUncDist,
self.radiusUncDist,
self.AvUncDist,
Rin=self.RinUncDist,
A=self.AUncDist,
B=self.BUncDist)
if variability != 0:
#specify lower and upper bounds for the truncated gaussian
lower, upper = 0, 1
mu, sigma = (self.mdot), (self.mdot * variability)
variability_distribution = st.truncnorm((lower - mu) / sigma,
(upper - mu) / sigma,
loc=mu,
scale=sigma)
if numMC == 1:
self.mdot = variability_distribution.rvs(numMC)[0]
else:
self.mdot = variability_distribution.rvs(numMC)
self.Lacc = a.Mdot_to_Lacc(self.mdot, self.massUncDist,
self.radiusUncDist, self.RinUncDist)
def MoneyPlot(observed, simulated, numMC=1):
mass = []
for r in range(len(simulated)):
for i in range(numMC):
mass.append(simulated['Mass (M$_\\odot$)'][r])
mass = np.array(mass)
mdot = []
for r in range(len(simulated)):
for i in range(numMC):
if numMC == 1:
mdot.append(simulated['Mdot (M$_\\odot$)'][r])
else:
mdot.append(simulated['Mdot (M$_\\odot$)'][r][i])
mdot = np.array(mdot)
true_mdot = []
for r in range(len(simulated)):
for i in range(numMC):
true_mdot.append(simulated['"true" Mdot (M$_\\odot$)'][r])
true_mdot = np.array(true_mdot)
logmass = np.log10(mass)
logMdot = np.log10(mdot)
logaccepted_relation = np.log10(true_mdot)
observed_mass = np.log10(observed['Object Mass M_Solar'])
observed_mdot = np.log10(observed['Accretion Rate M_solar yr-1'])
fig = plt.figure(figsize=(16, 12))
frame1 = fig.add_axes((.1, .3, .8, .6))
frame1.scatter(logmass,
logMdot,
facecolor='lightsteelblue',
edgecolor='steelblue',
s=130,
alpha=0.4,
label='Simulated')
frame1.scatter(observed_mass,
observed_mdot,
edgecolor='black',
facecolor='black',
s=50,
marker='x',
alpha=0.4,
label='Observed')
frame1.plot(logmass,
logaccepted_relation,
color='tomato',
label='Empirical Relationship')
frame1.axvline(x=np.log10(0.012),
color='black',
linestyle='-.',
label='Deuterium Burning Limit')
frame1.axvline(x=np.log10(0.1),
color='black',
linestyle=':',
label='Hydrogen Burning Limit')
frame1.set_xlim(-2.6, 0.6)
frame1.set_ylim(-14, -5.75)
frame1.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame1.set_ylabel('log(Mass Accretion Rate) (M$_\odot$/yr)', size=16)
frame1.text(-2.3, -6.6, 'Planets', size=15, fontstyle='italic')
frame1.text(-1.64, -6.6, 'Brown Dwarfs', size=15, fontstyle='italic')
frame1.text(-0.29, -6.6, 'Stars', size=15, fontstyle='italic')
frame1.set_title('Monte Carlo Error Propagation',
size=20,
fontweight='heavy')
frame1.legend(loc='lower right',
prop={
'family': 'sans-serif',
'style': 'normal',
'size': 16
},
frameon=False,
shadow=True)
frame2 = fig.add_axes((.1, .1, .8, .2))
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = logMdot - np.log10(a.empiricalMdot(10**logmass))
frame2.axhline(0, color='tomato')
frame2.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame2.set_ylabel('Residual (log space)', size=16)
frame2.set_ylim(-3.3, 3.2)
frame2.scatter(logmass,
simulated_difference,
facecolor='lightsteelblue',
edgecolor='steelblue',
alpha=0.4,
label='Simulated Residuals')
frame2.scatter(np.log10(observed['Object Mass M_Solar']),
observed_difference,
facecolor='black',
edgecolor='black',
marker='x',
alpha=0.4,
label='Observed Residuals')
frame2.legend(frameon=True)
return fig
def residuals(observed, simulated, numMC=1):
#load in data
mass = []
for r in range(len(simulated)):
for i in range(numMC):
mass.append(simulated['Mass (M$_\\odot$)'][r])
mass = np.array(mass)
mdot = []
for r in range(len(simulated)):
for i in range(numMC):
if numMC == 1:
mdot.append(simulated['Mdot (M$_\\odot$)'][r])
else:
mdot.append(simulated['Mdot (M$_\\odot$)'][r][i])
mdot = np.array(mdot)
true_mdot = []
for r in range(len(simulated)):
for i in range(numMC):
true_mdot.append(simulated['"true" Mdot (M$_\\odot$)'][r])
true_mdot = np.array(true_mdot)
logmass = np.log10(mass)
logMdot = np.log10(mdot)
logaccepted_relation = np.log10(true_mdot)
observed_mass = np.log10(observed['Object Mass M_Solar'])
observed_mdot = np.log10(observed['Accretion Rate M_solar yr-1'])
#Figure Dimensions
left, width = 0.1, 0.4
bottom, height = 0.1, 0.7
spacing = 0.0075
#create figure
fig = plt.figure(figsize=(16, 12))
#Residual Plot
rect_frame2 = [left, bottom, width, height]
rect_ax2_hist = [left + width + spacing, bottom, 0.3, height]
#create axes
frame2 = fig.add_axes(rect_frame2)
ax2_hist = fig.add_axes(rect_ax2_hist)
ax = plt.gca()
#Calculate Residuals
observed_difference = np.log10(
observed['Accretion Rate M_solar yr-1']) - np.log10(
a.empiricalMdot(observed['Object Mass M_Solar']))
simulated_difference = logMdot - np.log10(a.empiricalMdot(10**logmass))
#Residual Marginal Distribution
#remove ticks
ax2_hist.tick_params(axis="y", labelleft=False)
#bins by hand
residualbinwidth = 0.25
residualmax = np.max(np.abs(observed_difference))
residuallim = (int(residualmax / residualbinwidth) + 1) * residualbinwidth
residualbins = np.arange(-residuallim, residuallim + residualbinwidth,
residualbinwidth)
#plot
ax2_hist.hist(observed_difference,
bins=residualbins,
orientation='horizontal',
histtype='stepfilled',
facecolor='gray',
edgecolor='black',
density=True,
alpha=0.4,
label='Observed')
ax2_hist.hist(simulated_difference,
bins=residualbins,
orientation='horizontal',
histtype='stepfilled',
facecolor='lightsteelblue',
edgecolor='royalblue',
density=True,
alpha=0.3,
label='Simulated')
ax2_hist.set_ylim(-3.3, 3.2)
ax2_hist.axhline(0, color='tomato', label='Empirical Relationship')
ax2_hist.tick_params(labelsize=13.5)
#Residual Plot
frame2.axhline(0, color='tomato', label='Empirical Relationship')
frame2.set_xlabel('log(Mass) (M$_\odot$)', size=16)
frame2.set_ylabel('Residual (log space)', size=16)
frame2.set_ylim(-3.3, 3.2)
frame2.scatter(logmass,
simulated_difference,
facecolor='lightsteelblue',
edgecolor='steelblue',
alpha=0.4,
label='Simulated Residuals')
frame2.scatter(np.log10(observed['Object Mass M_Solar']),
observed_difference,
color='black',
marker='x',
alpha=0.4,
label='Observed Residuals')
frame2.tick_params(labelsize=13.5)
frame2.legend(frameon=True, fontsize=15)
return fig