## instance variables

def __init__(self):

self.theta = 90 ## scattering angle

self.D = 163000 ## distance to the LMC in lyrs

self.c = 1 ## speed of light

## methods

def ellipse_location(self,theta,t):

theta_rad = np.radians(theta)

## from solving quadratic eq on paper

r = (np.cos(theta_rad) + 1)/((np.sin(theta_rad))**2 / (self.c*t))

x = r*np.sin(theta_rad) ## trig

z = r*np.cos(theta_rad) ## trig

return r,z,x

def brightness_dim_factor(self,theta,t):

need = self.ellipse_location(theta,t) ## approx. how much extra it travels

dim = 1/need[0]**2 * 1/(self.D - need[1])**2 ## approx just D

#print('Theta= ', theta, ' z= ',need[1],' r= ',need[0], ' x= ', need[2])

return dim

def magnitude_compared_to_known(self,theta,t):

dim_candidate = self.brightness_dim_factor(theta,t)

dim_known = self.brightness_dim_factor(45,400) ## known parameters

factor_difference = dim_known/dim_candidate

mag_difference = math.log(factor_difference,100**(1/5))

mag_candidate = 22.5 + mag_difference ## known one is 22.5

return mag_candidate

def carry_out_for_t_vals(self,theta,tmin,tmax,step):

mags = []

for t in range(tmin,tmax,step):

mags.append(self.magnitude_compared_to_known(theta,t))

return mags

def plot_mags_vs_age(self,theta,tmin,tmax,step):

mags = self.carry_out_for_t_vals(theta,tmin,tmax,step)

t_vals = np.arange(tmin,tmax,step)

plt.plot(t_vals,mags,label=r'$\theta =$%s$^o$' % np.str(theta))

def plot_many_thetas(self,theta1,theta2,theta3,tmin,tmax,step):

plt.figure(figsize=(6,6))

plt.rc('axes', linewidth=2)

self.plot_mags_vs_age(theta1,tmin,tmax,step)

self.plot_mags_vs_age(theta2,tmin,tmax,step)

self.plot_mags_vs_age(theta3,tmin,tmax,step)

plt.xlabel('Age',fontsize=18)

plt.ylabel('Vega Magnitude / arcsec^2',fontsize=18)

plt.tight_layout()

plt.legend()

########################################################

def get_params_for_obs_angle(self,obsangle,t):

obsangle_rad = np.radians(obsangle)

#x = np.tan(obsangle_rad)*(self.D) ## this is the approximation

x = (-1 + np.sqrt(1 + 4*(np.tan(obsangle_rad))**2/(2*self.c*t)*(self.D + self.c*t/2)))/(

np.tan(obsangle_rad)/(self.c*t)) ## solution to quadratic... real

## use light echo equation

z = x**2/(2*self.c*t) - self.c*t/2

r = np.sqrt(x**2 + z**2)

theta = np.degrees(np.arccos(z/r))

#dim = 1/r**2 * 1/self.D**2

dim = self.brightness_dim_factor(theta,t) ### using my previous code

dim_known = self.brightness_dim_factor(45,400) ## known parameters

factor_difference = dim_known/dim

mag_difference = math.log(factor_difference,100**(1/5))

mag_candidate = 22.5 + mag_difference ## known one is 22.5

return mag_candidate, z, r, x, factor_difference, theta

def keeping_obs_angle_constant(self,obsangle,tmin,tmax,step):

mags = []

factors = []

for t in range(tmin,tmax,step):

mags.append(self.get_params_for_obs_angle(obsangle,t)[0])

factors.append(self.get_params_for_obs_angle(obsangle,t)[4])

return mags, factors

def plot_mags_vs_age_obsangle(self,obsangle,tmin,tmax,step):

mags = self.keeping_obs_angle_constant(obsangle,tmin,tmax,step)[0]

t_vals = np.arange(tmin,tmax,step)

plt.plot(t_vals,mags,label=r'Obs Angle =%s$^o$' % obsangle)

def plot_many_obs_angles(self,obsangle1,obsangle2,obsangle3,obsangle4,

obsangle5,obsangle6,obsangle7,tmin,tmax,step):

plt.figure(figsize=(6,6))

plt.rc('axes', linewidth=2)

self.plot_mags_vs_age_obsangle(obsangle1,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle2,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle3,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle4,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle5,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle6,tmin,tmax,step)

self.plot_mags_vs_age_obsangle(obsangle7,tmin,tmax,step)

plt.xlabel('Age',fontsize=18)

plt.ylabel('Vega Magnitude / arcsec^2',fontsize=18)

plt.tight_layout()

plt.legend()

#############################################################

def scattering_function(self,obsangle,tmin,tmax,step):

S = {}

wmin={'LMCavg':4000.0,'LMC2':4000.0,'SMCbar':4000.0,'MWG':3500.0}

wmax={'LMCavg':7999.0,'LMC2':7999.0,'SMCbar':7999.0,'MWG':9999.0}

lamb = 7000

Sval_of_known = 9.354e-23

## the scattering function stuff

dusttype = 'LMCavg'

S[dusttype]=S_tableclass()

dustfilename = S[dusttype].getdustfilename(dusttype)

#print('Loading dust properties...')

S[dusttype].loadtable(dustfilename)

#if lamb<wmin[dusttype] or lamb>wmax[dusttype]: continue

##########

Svals = []

mags = []

factors = []

for t in range(tmin,tmax,step):

vals = self.get_params_for_obs_angle(obsangle,t)

z = vals[1]

r = vals[2]

x = vals[3]

theta = np.degrees(np.arccos(z/r))

Sval = S[dusttype].S_cm2(theta,lamb)

Svals.append(Sval)

factor_difference = Sval_of_known/Sval

factors.append(factor_difference)

mag_difference = math.log(factor_difference,100**(1/5))

mag_candidate = 22.5 + mag_difference ## known one is 22.5

mags.append(mag_candidate)

return mags,factors

def plot_scattering_function_for_obsangles(self,obsangle1,obsangle2,obsangle3,obsangle4,

obsangle5,obsangle6,obsangle7,tmin,tmax,step):

tvals = np.arange(tmin,tmax,step)

plt.figure(figsize=(6,6))

plt.rc('axes', linewidth=2)

plt.plot(tvals,self.scattering_function(obsangle1,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle1)

plt.plot(tvals,self.scattering_function(obsangle2,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle2)

plt.plot(tvals,self.scattering_function(obsangle3,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle3)

plt.plot(tvals,self.scattering_function(obsangle4,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle4)

plt.plot(tvals,self.scattering_function(obsangle5,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle5)

plt.plot(tvals,self.scattering_function(obsangle6,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle6)

plt.plot(tvals,self.scattering_function(obsangle7,tmin,tmax,step)[0],

label=r'Obs Angle =%s$^o$' % obsangle7)

plt.xlabel('Age',fontsize=18)

plt.ylabel('Vega Magnitude / arcsec^2',fontsize=18)

plt.tight_layout()

plt.legend()

##############################################################

########### MULTIPLY THE FACTORS ############################

def both_factors(self,obsangle,tmin,tmax,step):

dist_factors = self.keeping_obs_angle_constant(obsangle,tmin,tmax,step)[1]

S_factors = self.scattering_function(obsangle,tmin,tmax,step)[1]

factors_combined = [dist_factors[i] * S_factors[i] for i in range(0,len(dist_factors))]

mag_difference = [math.log(i,100**(1/5)) for i in factors_combined]

mags = [22.5 + i for i in mag_difference]

return mags, factors_combined

def plot_both_factors_for_obsangles(self,obsangle1,obsangle2,obsangle3,obsangle4,

obsangle5,obsangle6,obsangle7,tmin,tmax,step):

tvals = np.arange(tmin,tmax,step)

plt.figure(figsize=(6,6))

plt.rc('axes', linewidth=2)

plt.plot(tvals,self.both_factors(obsangle1,tmin,tmax,step)[0], color='k')

#label='Candidate1')

plt.plot(tvals,self.both_factors(obsangle2,tmin,tmax,step)[0], color='k')

#label='Candidate 2')

plt.plot(tvals,self.both_factors(obsangle3,tmin,tmax,step)[0], color='k')

#label='Candidate 3')

plt.plot(tvals,self.both_factors(obsangle4,tmin,tmax,step)[0], color='k')

#label='Candidate 4')

plt.plot(tvals,self.both_factors(obsangle5,tmin,tmax,step)[0], color='k')

#label='Candidate 5')

plt.plot(tvals,self.both_factors(obsangle6,tmin,tmax,step)[0], color='k')

#label='Candidate 6')

plt.plot(tvals,self.both_factors(obsangle7,tmin,tmax,step)[0], color='k',

label='Candidates')

plt.axvline(x=10000,linestyle='--',color='b',label='Candidates Published \nMinimum Age')

plt.axhline(y=23.7,color='r',label='Approx. LE Surface \nBrightness')

plt.xlabel('Age',fontsize=18)

plt.ylabel(r'Surface Brightness (mag/arcsec$^2$)',fontsize=18)

plt.tight_layout()

plt.legend(loc='lower right',prop={'size': 15})

plt.show()

def plot_factors_for_obsangles(self,obsangle1,obsangle2,obsangle3,obsangle4,

obsangle5,obsangle6,obsangle7,tmin,tmax,step):

tvals = np.arange(tmin,tmax,step)

plt.figure(figsize=(6,6))

plt.rc('axes', linewidth=2)

plt.plot(tvals,self.both_factors(obsangle1,tmin,tmax,step)[1], color='k')

#label='Candidate1')

plt.plot(tvals,self.both_factors(obsangle2,tmin,tmax,step)[1], color='k')

#label='Candidate 2')

plt.plot(tvals,self.both_factors(obsangle3,tmin,tmax,step)[1], color='k')

#label='Candidate 3')

plt.plot(tvals,self.both_factors(obsangle4,tmin,tmax,step)[1], color='k')

#label='Candidate 4')

plt.plot(tvals,self.both_factors(obsangle5,tmin,tmax,step)[1], color='k')

#label='Candidate 5')

plt.plot(tvals,self.both_factors(obsangle6,tmin,tmax,step)[1], color='k')

#label='Candidate 6')

plt.plot(tvals,self.both_factors(obsangle7,tmin,tmax,step)[1], color='k',

label='Candidates')

plt.axvline(x=10000,linestyle='--',label='Candidates Published \nMinimum Age')

#plt.axhline(y=24,color='r',label='Approx. LE Surface \nBrightness')

plt.xlabel('Age',fontsize=18)

plt.ylabel('Factor Difference from Brightest \nKnown LEs in LMC',fontsize=18)

plt.tight_layout()

plt.legend(loc='upper right',prop={'size': 15})

def Scat_function_value(self,t,theta):

S = {}

lamb = 7000

Sval_of_known = 9.354e-23

## the scattering function stuff

dusttype = 'LMCavg'

S[dusttype]=S_tableclass()

dustfilename = S[dusttype].getdustfilename(dusttype)

#print('Loading dust properties...')

S[dusttype].loadtable(dustfilename)

#if lamb<wmin[dusttype] or lamb>wmax[dusttype]: continue

##########

#mag_candidate, z, r, x, factor_difference = self.get_params_for_obs_angle(obsangle,t)

#theta = np.degrees(np.arccos(z/r))

Sval = S[dusttype].S_cm2(theta,lamb)

return Sval

def get_SB_vs_obsangle_for_given_age(self,t,obsanglemin,obsanglemax,step):

S = {}

wmin={'LMCavg':4000.0,'LMC2':4000.0,'SMCbar':4000.0,'MWG':3500.0}

wmax={'LMCavg':7999.0,'LMC2':7999.0,'SMCbar':7999.0,'MWG':9999.0}

lamb = 7000

Sval_of_known = 9.354e-23

## the scattering function stuff

dusttype = 'LMCavg'

S[dusttype]=S_tableclass()

dustfilename = S[dusttype].getdustfilename(dusttype)

#print('Loading dust properties...')

S[dusttype].loadtable(dustfilename)

#if lamb<wmin[dusttype] or lamb>wmax[dusttype]: continue

##########

### hold some values, especially for sanity checks

Svals = []

factors = []

mag_vals = []

Sfactor = []

thetas = []

zvals = []

dist_factors = []

obsangles = np.arange(obsanglemin,obsanglemax,step)

for obsangle in obsangles:

### get the factor from the 1/r^2 relationship (WRITTEN ABOVE)

mag_candidate, z, r, x, factor_difference, theta = self.get_params_for_obs_angle(obsangle,t)

zvals.append(z)

dist_factors.append(factor_difference)

theta = np.degrees(np.arccos(z/r))

thetas.append(theta)

#### FROM THE ORIGINAL SCRIPT TO CALCULATE THE S VALUE

Sval = S[dusttype].S_cm2(theta,lamb)

Svals.append(Sval)

Sval_factor_difference = Sval_of_known/Sval

Sfactor.append(Sval_factor_difference)

full_factor_difference = factor_difference * Sval_factor_difference

factors.append(full_factor_difference)

mag_difference = math.log(full_factor_difference,100**(1/5))

mag = 22.5 + mag_difference ## 22.5 was the actual event

mag_vals.append(mag)

return mag_vals, Sfactor, dist_factors, factors, thetas, zvals, Svals

### THE PLOT OF SB VS OBSANGLES FOR DIFFERENT AGES

def plot_SB_vs_obsangle_for_given_age(self,t1,t2,t3,t4,t5,t6,t7,obsanglemin,obsanglemax,step):

obsangles = np.arange(obsanglemin,obsanglemax,step)

plt.figure(figsize=(6,6))

plt.rc('axes',linewidth=2)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t1,obsanglemin,obsanglemax,step)[0],

color='r',label='%s yrs' % t1)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t2,obsanglemin,obsanglemax,step)[0],

color='orange',label='%s yrs' % t2)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t3,obsanglemin,obsanglemax,step)[0],

color='y',label='%s yrs' % t3)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t4,obsanglemin,obsanglemax,step)[0],

color='g',label='%s yrs' % t4)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t5,obsanglemin,obsanglemax,step)[0],

color='b',label='%s yrs' % t5)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t6,obsanglemin,obsanglemax,step)[0],

color='purple',label='%s yrs' % t6)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t7,obsanglemin,obsanglemax,step)[0],

color='m',label='%s yrs' % t7)

plt.axhline(y=23.3,color='k',linestyle='--',label='Approx. LE Surface \nBrightness')

plt.xlabel('Observed Angular Separation',fontsize=18)

plt.ylabel(r'Surface Brightness (mag/arcsec$^2$)',fontsize=18)

plt.tight_layout()

plt.legend(loc='lower right',prop={'size': 15})

#### THE PLOT OF Z DIMENSION WITH OBS ANGLE ####################

def plot_z_vs_obsangle_for_given_age(self,t1,t2,t3,t4,t5,t6,t7,obsanglemin,obsanglemax,step):

obsangles = np.arange(obsanglemin,obsanglemax,step)

plt.figure(figsize=(6,6))

plt.rc('axes',linewidth=2)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t1,obsanglemin,obsanglemax,step)[5],

color='r',label='%s yrs' % t1)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t2,obsanglemin,obsanglemax,step)[5],

color='orange',label='%s yrs' % t2)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t3,obsanglemin,obsanglemax,step)[5],

color='y',label='%s yrs' % t3)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t4,obsanglemin,obsanglemax,step)[5],

color='g',label='%s yrs' % t4)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t5,obsanglemin,obsanglemax,step)[5],

color='b',label='%s yrs' % t5)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t6,obsanglemin,obsanglemax,step)[5],

color='purple',label='%s yrs' % t6)

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t7,obsanglemin,obsanglemax,step)[5],

color='m',label='%s yrs' % t7)

#plt.axhline(y=1630,linestyle='--',color='k',label='Edge of LMC')

#plt.axhline(y=-1630,linestyle='--',color='k')

plt.axhspan(-1630,1630,color='skyblue',alpha=0.5,label='Thickness of LMC')

#plt.axhline(y=2000,linestyle='-.',color='k',label='Edge of 30 Dor')

plt.xlabel('Observed Angular Separation',fontsize=18)

plt.ylabel('z (lyrs)',fontsize=18)

plt.tight_layout()

plt.legend(loc='upper left',prop={'size': 15})

######## A SANITY CHECK PLOT ###############

def plot_SB_factors_for_given_age(self,t,obsanglemin,obsanglemax,step):

obsangles = np.arange(obsanglemin,obsanglemax,step)

fig = plt.figure()

ax1 = fig.add_subplot(111)

#plt.figure(figsize=(6,6))

#plt.rc('axes',linewidth=2)

ax1.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(t,obsanglemin,obsanglemax,step)[3],

color='r',label='%s yrs' % t)

ax1.set_xlabel('Observational Angle',fontsize=18)

ax2 = ax1.twiny()

ax2.plot(self.get_SB_vs_obsangle_for_given_age(t,obsanglemin,obsanglemax,step)[4],

self.get_SB_vs_obsangle_for_given_age(t,obsanglemin,obsanglemax,step)[3],

color='r',label='%s yrs' % t)

xticksneeded = np.array([obsangles[0],obsangles[10],obsangles[20],

obsangles[30],obsangles[40],obsangles[50]])

ax2.set_xticks(xticksneeded)

## round the values

thetavalsneeded = [np.round(np.degrees(np.arccos(self.get_params_for_obs_angle(xticksneeded[i],t)[1]/

self.get_params_for_obs_angle(xticksneeded[i],t)[2])),1) for i in range(0, len(xticksneeded))] ## how to get theta

ax2.set_xticklabels(thetavalsneeded)

#ax2.set_xlabel('Theta',fontsize=18)

ax1.set_ylabel('Factors Multiplied Together',fontsize=18)

ax2.set_xlim(ax1.get_xlim())

ax2.set_xlabel('Theta')

print(thetavalsneeded)

#ax2.plot(self.get_SB_vs_obsangle_for_given_age(t,obsanglemin,obsanglemax,step)[4],

# self.get_SB_vs_obsangle_for_given_age(t,obsanglemin,obsanglemax,step)[3],

# color='r')

#ax2.set_xlabel('Theta')

plt.tight_layout()

plt.legend(loc='upper right',prop={'size': 15})

def make_area_prob_plot(self):

#plt.axvspan(0, 0.49475, alpha=0.2, color='gray',label='400')

plt.axvspan(0, 0.49475, alpha=0.2, color='gray',label='600')

plt.axvspan(0, 0.4435, alpha=0.2, color='gray',label='800')

#plt.axvspan(0, 0.4267, alpha=0.2, color='gray',label='200')

plt.axvspan(0, 0.3665, alpha=0.2, color='gray',label='1000')

plt.axvspan(0, 0.245, alpha=0.2, color='gray',label='1200')

plt.axhspan(500,1200, alpha=0.2, color='gray', label='Ages')

plt.axhspan(500,1000, alpha=0.2, color='gray', label='Ages')

plt.axhspan(500,800, alpha=0.2, color='gray', label='Ages')

plt.axhspan(500,600, alpha=0.2, color='gray', label='Ages')

plt.xlim(-0.1,1.1)

plt.ylim(0,2000)

plt.xlabel('Observed Angular Separation')

plt.ylabel('Age')

#def make_better_area_prob_plot(self):

#fill([0,0])

######## MAKE A CONTOUR PLOT WITH AGE AND ANG SEP AS INDEP VARS #######

def contour_age_ang_sep_surface_brightness(self,minAge,maxAge,stepAge,minAng,maxAng,stepAng):

x = np.linspace(minAge,maxAge,5000)

y = np.linspace(minAng,maxAng,5000)

X,Y = np.meshgrid(x,y)

#Z = np.empty_like(X)

#for lengthIter in range(0,len(X)):

# for xIter in range(0,len(X[0])):

# Z[lengthIter][xIter] = self.get_params_for_obs_angle(Y[lengthIter][0],X[lengthIter][xIter])[0] ## the 0 is to get the mag

Zzvals = np.empty_like(X)

for lengthIter in range(0,len(X)):

for xIter in range(0,len(X[0])):

Zzvals[lengthIter][xIter] = self.get_params_for_obs_angle(Y[lengthIter][0],X[lengthIter][xIter])[1]

#Zthetavals = np.empty_like(X)

#for lengthIter in range(0,len(X)):

# for xIter in range(0,len(X[0])):

# Zthetavals[lengthIter][xIter] = self.get_params_for_obs_angle(Y[lengthIter][0],X[lengthIter][xIter])[5] ## this is for theta

#points = []

#values = []

#for i in range(0,len(x)):

# item = [x[i], y[i]]

# points.append(item)

# values.append(self.get_params_for_obs_angle(y[i],x[i])[0])

#points = np.array(points)

#values = np.array(values)

#print(points)

#grid_z0 = griddata(points, values, (X, Y), method='nearest')

plt.figure()

plt.rc('text', usetex=True)

#plt.imshow(grid_z0.T, extent=[minAge,maxAge,minAng,maxAng], origin='lower',interpolation='none',aspect='auto')

#plt.title('Nearest')

plt.imshow(Zzvals,extent=[minAge,maxAge,minAng,maxAng], vmin=-8000, vmax=8000, origin='lower',interpolation='none',aspect='auto',cmap='RdBu')

# vmin=-8000, vmax=8000,

cb = plt.colorbar()

cb.set_label(r"z (lyrs)", fontsize=20,fontweight='bold')

#cb.set_label(r"Surface Brightness (mag/arcsec$^2$)", fontsize=20,fontweight='bold')

#SUGGESTION = plt.contour(X,Y,Z,levels=[23.7,23.7+0.5,23.7+1.5],colors=['k','k','k'],alpha=0.5,linewidths=[3,2,1])

CS = plt.contour(X,Y,Zzvals,levels=[3*-815,-1630,-815,0,815,1630,3*815],colors=['black','black',

'g','g','g','black','black'],linewidths=[0.5,1,2,2,2,1,0.5])

plt.clabel(CS, fmt = '%2.1d', colors = 'k', fontsize=14) #contour line labels

#plt.colorbar()

#THETAS = plt.contour(X,Y,Zthetavals,levels=[45,75,90,100],colors=['skyblue','skyblue','skyblue','skyblue'],

# linewidths=[1,2,3,2])

#plt.clabel(THETAS, fmt = '%2.1d', colors = 'k', fontsize=14) #contour line labels

plt.plot([10000],[0.85],marker='o',color='k') ## candidate 2

plt.plot([12500],[1.49],marker='o',color='k') ## candidate 3

plt.plot([12500],[1.44],marker='o',color='k') ## candidate 4

plt.plot([25000],[1.92],marker='o',color='k') ## candidate 5

plt.plot([25000],[1.18],marker='o',color='k') ## candidate 6

#### If you want error bars on the ages

#plt.errorbar([10000],[0.85],xerr=3000,marker='o',color='white') ## candidate 2

#plt.errorbar([12500],[1.49],xerr=3000,marker='o',color='white') ## candidate 3

#plt.errorbar([12500],[1.44],xerr=3000,marker='o',color='white') ## candidate 4

#plt.errorbar([25000],[1.921],xerr=5000,marker='o',color='white') ## candidate 5

#plt.errorbar([25000],[1.18],xerr=5000,marker='o',color='white') ## candidate 6

plt.xlabel('Age',fontsize=20,fontweight='bold')

plt.ylabel('Angular Separation',fontsize=20,fontweight='bold')

plt.ylim(0,2)

plt.xscale('log')

plt.show()

def plot_Scat_function_candidates(self):

allThetas = GetThetas.returnallThetas()

angles = np.arange(0,179,0.5)

val90 = self.Scat_function_value(500,90)

SvalsNorm = [self.Scat_function_value(500,i)/val90 for i in angles]

plt.rc('text', usetex=True)

plt.rc('font', family='serif')

plt.plot(angles,SvalsNorm,color='g')

plt.axvline(x = 153.6, color='b')

plt.axvline(x = 143.1, color='b')

plt.axvline(x = 144.4, color='b')

plt.axvline(x = 155.7, color='b')

plt.axvline(x = 164, color='b', label='Candidates')

plt.axvline(x = 48, linestyle='dashed', color='r',label=r'Brightnest Known SNR LE Example')

#plt.axvspan(30,110,color='k',alpha=0.25,label='Observed LEs from Literature')

plt.hist(allThetas)

plt.xlabel(r'$\theta$', fontsize=20)

plt.ylabel(r'Scattering Efficiency (Norm. to 90$^o$)',fontsize=20)

plt.legend(loc='upper right',fontsize=16)

plt.yscale('log')

def plot_z_vs_Ang_Sep_for_ages(self,obsanglemin, obsanglemax, step):

obsangles = np.arange(obsanglemin,obsanglemax,step)

ages = np.arange(200,5000,100)

alphas = np.linspace(0,1,len(ages))

plt.figure(figsize=(6,6))

plt.rc('axes',linewidth=2)

for i in range(0,len(ages)):

plt.plot(obsangles,self.get_SB_vs_obsangle_for_given_age(ages[i],obsanglemin,obsanglemax,step)[5],

color='b',alpha = alphas[i])

plt.axhspan(-1630,1630,color='r',alpha=0.5,label='Thickness of LMC')

plt.ylabel('z (ly)',fontsize=20,fontweight='bold')

plt.xlabel('Angular Separation',fontsize=20,fontweight='bold')

plt.legend(fontsize=18)