def Laurent_data(ra, dec, vstar, vHI, vCO, vHa, age, galaxy):
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i = deprojection_geo(
ra, dec, galaxy) #dist0 and theta0 for Laurent but using the
np.savetxt(
'/Users/amandaquirk/Desktop/{}_data_original.ascii'.format(age),
np.c_[ra, dec, dist, theta, vstar, vHI, vCO, vHa],
header=
'ra, dec, r (kpc), theta (rad), vLOS_star (km/s), vLOS_HI (km/s), vLOS_CO (km/s), vLOS_Ha (km/s)',
fmt='%s',
delimiter='\t')
def Laurent_data(vstar, vHI, vCO, vHa, ra, dec, age, galaxy):
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i = deprojection_geo(ra, dec, galaxy) #dist0 and theta0 for Laurent but using the iterative values for my rotaiton curves
# plt.scatter(alpha, beta, c=dist, vmin=0, vmax=15)
# plt.colorbar()
# plt.savefig('/Users/amandaquirk/Desktop/r_{}.png'.format(age))
# plt.close()
# plt.scatter(alpha, beta, c=theta, vmin=-3.2, vmax=3.2)
# plt.colorbar()
# plt.savefig('/Users/amandaquirk/Desktop/theta_{}.png'.format(age))
# plt.close()
np.savetxt('/Users/amandaquirk/Desktop/{}_data_smoothed.ascii'.format(age), np.c_[ra, dec, dist, theta, vstar, vHI, vCO, vHa], header='ra, dec, r (kpc), theta (rad), vLOS_star (km/s), vLOS_HI (km/s), vLOS_CO (km/s), vLOS_Ha (km/s)', fmt='%s', delimiter='\t',)
def RC(vstar, vHI, vCO, vHa, ra, dec, galaxy
): #will return a file of vrots and AD values, 1 RC, and 1 AD hist
#use the titled ring function to get dist, vrots
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i, assigned_vrot = deprojection_geo(
ra, dec, galaxy, return_vrot=True)
#theta/PA check -- should be 0
#print(np.median(np.tan(PA) - np.tan(theta) * np.cos(np.deg2rad(assigned_i))))
vrot_star = vrot_tr(vstar, ra, dec, galaxy)
vrot_HI = vrot_tr(vHI, ra, dec, galaxy)
vrot_Ha = vrot_tr(vHa, ra, dec, galaxy)
vrot_CO = vrot_tr(vCO, ra, dec, galaxy)
#calculate AD
AD_HI = vrot_HI - vrot_star
AD_CO = vrot_CO - vrot_star
AD_Ha = vrot_Ha - vrot_star
return dist, vrot_star, vrot_HI, vrot_Ha, vrot_CO, AD_HI, AD_Ha, AD_CO
def assign_loc(ra, dec, galaxy):
#use the titled ring function to get dist, vrots
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i, assign_vrot = deprojection_geo(
ra, dec, galaxy,
return_vrot=True) #deg, deg, deg, deg, kpc, rad, rad, deg, deg, km/s
#find model HI vel at LOS
vHI = HI_LOS(assign_vrot, theta, assigned_i)
vsys = -180 #km/s
#assign radial bins
radial_bins = np.zeros(len(ra), dtype=object)
#c = np.zeros(len(ra))
for i in range(len(ra)):
if dist[i] * 4 < 15: #arcmin
radial_bins[i] = 'inner'
#c[i] = 10
elif (dist[i] * 4 >= 15) & (dist[i] * 4 < 30): #arcmin
radial_bins[i] = 'middle'
#c[i] = 50
else:
radial_bins[i] = 'outer'
#assign spatial bin
spatial_bins = np.zeros(len(ra), dtype=object)
#c = np.zeros(len(ra))
for i in range(len(ra)):
if vHI[i] < vsys:
spatial_bins[i] = 'NE'
#c[i] = 50
else:
spatial_bins[i] = 'SW'
#checking
# plt.scatter(alpha * 60, beta * 60, c=c, vmin=0, vmax=55)
# plt.colorbar()
# plt.show()
return radial_bins, spatial_bins, assign_vrot, theta, assigned_i
sdss_g = 3.303
sdss_i = 1.698
emap = sfdmap.SFDMap('../Data/sfddata-master/', scaling=1.0)
coords = SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg))
extinct_map = emap.ebv(coords)
g_ext = g_mag - extinct_map * sdss_g
i_ext = i_mag - extinct_map * sdss_i
print('Did the extinction corrections')
#calcualte deprojected geo ==========================================================================================================
star = (flag1 == -1) & (
flag2 == -1) #let's look at things that are most likely to be stars
isolated = isolation_tag == 0
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i = deprojection_geo(
ra, dec, 'M33', unit='deg')
#calculate color and minimum brightnesses
color = g_ext - i_ext
bright = i_mag < 22 #what we'll target for spectroscopy
minimum_bright = i_mag < 24
# #calculate bins and make CMD =======================================================================================================
# MS = (color < -0.5) & (i_ext > 20)
# HeB = (color > 0.75) & (color < 1.3) & (i_ext < 21.5)
# AGB = (i_ext < 20.75) & (i_ext > 20) & (color > 1.6)
# RGB = (color > 0.5) & (i_ext > 21)
# # RGB_poly = Polygon([(1.4, 21), (2.4, 21.25), (3.55, 21.9), (3, 21.89), (2.6, 21.95), (1.9, 22.5), (1.8, 22.8), (1.5, 23.4), (1.2, 24.9), (.43, 23.25), (1.1, 22)])
# # HeB_poly = Polygon([(.8, 22), (1.1, 19.5), (1.4, 18), (1.8, 19), (1.6, 20), (1.1, 22)])
# # AGB_poly = Polygon([(1.4, 21), (2.4, 21.25), (3.55, 21.9), (3.55, 20), (1.8, 20)])
# # RGB_flag = np.zeros(len(i_ext))
def outputs(vstar, vHI, vCO, vHa, ra, dec, age, IDs, galaxy, region): #will return a file of vrots and AD values, 1 RC, and 1 AD hist
#use the titled ring function to get dist, vrots
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i = deprojection_geo(ra, dec, galaxy)
#theta/PA check -- should be 0
#print(np.median(np.tan(PA) - np.tan(theta) * np.cos(np.deg2rad(assigned_i))))
vrot0_star = vrot_0(vstar, ra, dec, galaxy)
vrot_star = vrot_tr(vstar, ra, dec, galaxy)
#vrot0_HI = vrot_0(vHI, ra, dec, galaxy)
vrot_HI = vrot_tr(vHI, ra, dec, galaxy)
#vrot0_Ha = vrot_0(vHa, ra, dec, galaxy)
vrot_Ha = vrot_tr(vHa, ra, dec, galaxy)
#vrot0_CO = vrot_0(vCO, ra, dec, galaxy)
vrot_CO = vrot_tr(vCO, ra, dec, galaxy)
#calculate AD
if region == 'inner':
cut = dist <= 4
elif region == 'outer':
print('yes')
cut = dist > 4
else:
cut = dist > 0 #no cut
AD_HI = vrot_HI[cut] - vrot_star[cut]
AD_CO = vrot_CO[cut] - vrot_star[cut]
AD_Ha = vrot_Ha[cut] - vrot_star[cut]
#save the data here
np.savetxt('/Volumes/Titan/M33/Data/{}_vrot_ad_data.txt'.format(age), np.c_[IDs, dist, vrot_star, vrot_HI, vrot_CO, vrot_Ha, AD_HI, AD_CO, AD_Ha], fmt='%s', delimiter='\t', header='ID, dist (kpc), star vrot (km/s), HI vrot, CO vrot, Ha vrot, AD wrt HI, AD wrt CO, AD wrt Ha')
#rotation curve
# single_plot()
# plt.scatter(dist, vrot_star, c='r', label='{}'.format(age))
# #plt.scatter(dist, vrot0_star, c='b') #comp for sanity check of the rotation velocities
# plt.scatter(dist, vrot_HI, c='darkgrey', label='HI', s=3, alpha=.8)
# #plt.scatter(dist, vrot_CO, c='teal', label='CO', alpha=.8)
# #plt.scatter(dist, vrot_Ha, c='b', label='Ha', alpha=.8)
# plt.xlabel('Deprojected R [kpc]')
# plt.ylabel('V rot titled ring [kpc]')
# plt.ylim(0, 300)
# plt.legend()
# plt.show()
# plt.savefig('/Volumes/Titan/M33/Plots/{}_rc.png'.format(age))
# plt.close()
#ad hist
#get rid of nans in the AD list
AD_HI = AD_HI[~np.isnan(AD_HI)]
AD_CO = AD_CO[~np.isnan(AD_CO)]
AD_Ha = AD_Ha[~np.isnan(AD_Ha)]
print(np.nanmedian(AD_HI))
# single_plot()
# plt.hist(AD_HI, bins=range(-100, 200, 10), label='HI' + r'$={}$'.format(round(np.median(AD_HI),2)) + r'$\rm \ km \ s^{-1}$', density=True, histtype='step', linewidth=1.6, linestyle='--', stacked=True, fill=False, color='darkgrey')
# plt.hist(AD_CO, bins=range(-100, 200, 10), label='CO' + r'$={}$'.format(round(np.median(AD_CO),2)) + r'$\rm \ km \ s^{-1}$', density=True, histtype='step', linewidth=1.6, linestyle='--', stacked=True, fill=True, hatch='//', color='teal')
# plt.hist(AD_Ha, bins=range(-100, 200, 10), label='Ha' + r'$={}$'.format(round(np.median(AD_Ha),2)) + r'$\rm \ km \ s^{-1}$', density=True, histtype='step', linewidth=1.6, stacked=True, fill=False, color='blue')
# plt.legend()
# plt.savefig('/Volumes/Titan/M33/Plots/{}_ad_{}.png'.format(age, region))
# plt.close()
return AD_HI
def projected_AD(age_label, name, two_comps=False, observed=False):
if observed == False:
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i, assigned_vrot = deprojection_geo(
ra[age_label * good_qual],
dec[age_label * good_qual],
'M33',
return_vrot=True)
data_offset = offset(assigned_vrot, theta, assigned_i,
vel[age_label * good_qual])
data = data_offset.reshape(-1, 1)
gmm = GMM(n_components=1).fit(data)
#check how our model did
x = np.linspace(-200, 200, len(data_offset))
logprob = gmm.score_samples(x.reshape(-1, 1))
pdf = np.exp(logprob)
init_vals = [max(pdf), gmm.means_[0][0], 20] #a, mu, sigma
best_vals_gmm, covar = curve_fit(gaussian, x, pdf, p0=init_vals)
plt.hist(data_offset,
bins=range(-200, 200, 15),
density=True,
alpha=.5,
histtype='stepfilled',
label='data')
plt.plot(x,
pdf,
'-k',
label=r'$\mu\ \rm =\ {}\ km\ s^-1$'.format(
round(best_vals_gmm[1])))
#plt.plot(x, pdf, '-k', label=r'$\rm \mu= {}, \sigma= {}$'.format(round(best_vals_gmm[1]), round(best_vals_gmm[2])))
plt.legend()
plt.xlabel('LOS offset: vHI - vstar (km/s)')
plt.title(name)
#plt.title('BIC = {}, AIC = {}'.format(round(gmm.bic(data)), round(gmm.aic(data))))
plt.savefig(
'/Users/amandaquirk/Desktop/{}_mixture_model.png'.format(name))
plt.close()
if two_comps == True:
gmm2 = GMM(n_components=2).fit(data)
x = np.linspace(-200, 200, 1000)
responsibilities = gmm2.predict_proba(x.reshape(-1, 1))
logprob = gmm2.score_samples(x.reshape(-1, 1))
pdf = np.exp(logprob)
pdf_individual = responsibilities * pdf[:, np.newaxis]
mus, sigmas, halo_label, disk_label = do_fit_labels(
gmm2, x, pdf_individual, 1)
plt.hist(data_offset,
bins=range(-200, 200, 15),
density=True,
alpha=.5,
histtype='stepfilled',
label='data')
plt.plot(x, pdf, '-k', label='mixture model')
plt.plot(x,
pdf_individual[:, halo_label],
'--r',
label=r'$\rm halo, \mu= {}, \sigma= {}$'.format(
round(mus[halo_label]), round(sigmas[halo_label])))
plt.plot(x,
pdf_individual[:, disk_label],
':r',
label=r'$\rm disk, \mu= {}, \sigma= {}$'.format(
round(mus[disk_label]), round(sigmas[disk_label])))
plt.legend()
plt.xlabel('LOS offset: vHI - vstar (km/s)')
plt.title('BIC = {}, AIC = {}'.format(round(gmm2.bic(data)),
round(gmm2.aic(data))))
plt.savefig(
'/Users/amandaquirk/Desktop/{}_mixture_model2.png'.format(
name))
plt.close()
probs = gmm2.predict_proba(data_offset.reshape(-1, 1))
halo_prob = probs[:, halo_label]
disk_prob = probs[:, disk_label]
np.savetxt(
'/Volumes/Titan/M33/Data/{}_disk_halo_probs_offset.txt'.format(
name),
np.c_[ID[age_label * good_qual], halo_prob, disk_prob],
header='ID, halo offset prob, disk offset prob',
fmt='%s')
else:
data_offset1 = HI[age_label * good_qual] - vel[age_label * good_qual]
data_offset = data_offset1[~np.isnan(data_offset1)]
data = data_offset.reshape(-1, 1)
gmm = GMM(n_components=1).fit(data)
#check how our model did
x = np.linspace(-200, 200, len(data_offset))
logprob = gmm.score_samples(x.reshape(-1, 1))
pdf = np.exp(logprob)
init_vals = [max(pdf), gmm.means_[0][0], 20] #a, mu, sigma
best_vals_gmm, covar = curve_fit(gaussian, x, pdf, p0=init_vals)
plt.hist(data_offset,
bins=range(-200, 200, 15),
density=True,
alpha=.5,
histtype='stepfilled',
label='data')
plt.plot(x,
pdf,
'-k',
label=r'$\mu\ \rm =\ {}\ km\ s^-1$'.format(
round(best_vals_gmm[1])))
#plt.plot(x, pdf, '-k', label=r'$\rm \mu= {}, \sigma= {}$'.format(round(best_vals_gmm[1]), round(best_vals_gmm[2])))
plt.legend()
plt.xlabel('LOS offset: vHI - vstar (km/s)')
plt.title(name)
#plt.title('BIC = {}, AIC = {}'.format(round(gmm.bic(data)), round(gmm.aic(data))))
plt.savefig(
'/Users/amandaquirk/Desktop/{}_mixture_model_observed.png'.format(
name))
plt.close()
if two_comps == True:
gmm2 = GMM(n_components=2).fit(data)
x = np.linspace(-200, 200, 1000)
responsibilities = gmm2.predict_proba(x.reshape(-1, 1))
logprob = gmm2.score_samples(x.reshape(-1, 1))
pdf = np.exp(logprob)
pdf_individual = responsibilities * pdf[:, np.newaxis]
mus, sigmas, halo_label, disk_label = do_fit_labels(
gmm2, x, pdf_individual, 1)
plt.hist(data_offset,
bins=range(-200, 200, 15),
density=True,
alpha=.5,
histtype='stepfilled',
label='data')
plt.plot(x, pdf, '-k', label='mixture model')
plt.plot(x,
pdf_individual[:, halo_label],
'--r',
label=r'$\rm halo, \mu= {}, \sigma= {}$'.format(
round(mus[halo_label]), round(sigmas[halo_label])))
plt.plot(x,
pdf_individual[:, disk_label],
':r',
label=r'$\rm disk, \mu= {}, \sigma= {}$'.format(
round(mus[disk_label]), round(sigmas[disk_label])))
plt.legend()
plt.xlabel('LOS offset: vHI - vstar (km/s)')
plt.title('BIC = {}, AIC = {}'.format(round(gmm2.bic(data)),
round(gmm2.aic(data))))
plt.savefig(
'/Users/amandaquirk/Desktop/{}_mixture_model2_observed.png'.
format(name))
plt.close()
probs = gmm2.predict_proba(data_offset.reshape(-1, 1))
halo_prob = probs[:, halo_label]
disk_prob = probs[:, disk_label]
np.savetxt(
'/Volumes/Titan/M33/Data/{}_disk_halo_probs_offset_observed.txt'
.format(name),
np.c_[ID[age_label * good_qual][~np.isnan(data_offset1)],
halo_prob, disk_prob],
header='ID, halo offset prob, disk offset prob',
fmt='%s')
80) & (aband * const.c.to(u.km / u.s).value > -80) & (err > -1)
in_m33 = np.invert(np.in1d(ID, fg_id))
good_qual = qual * in_m33
#pull RGB data
RGB = age == 'RGB'
RGB = RGB * not_weak_CN * not_C #remove stars misclassified
# #calculate the offset from the HI as caluclated via the equation in Kam 2017 ===========================================
# '''
# Karrie interpolates this table; it might be best to do that step later. Skipping for now. (If I do that step here, I should do that step in main deprojection geo function)
# '''
# #pull the deprojected geometry, including the vrot of the TR
xi, eta, alpha, beta, dist, PA, theta, assigned_PA, assigned_i, assigned_vrot = deprojection_geo(
ra[RGB * good_qual], dec[RGB * good_qual], 'M33', return_vrot=True)
#calculate LOS HI velocity according to Kam 2017
def HI_LOS(vrot, theta, i): #km/s, rad, deg
vsys = -180 #km/s
return vsys + vrot * np.cos(theta) * np.sin(np.deg2rad(i)) #km/s
#calculate LOS offset
def offset(vrot, theta, i, vel): #km/s, rad, deg, km/s
vHI = HI_LOS(vrot, theta, i)
return vHI - vel #km/s
RGB_offset = offset(assigned_vrot, theta, assigned_i, vel[RGB * good_qual])