-
Notifications
You must be signed in to change notification settings - Fork 0
/
Anglada Plot.py
294 lines (234 loc) · 15.6 KB
/
Anglada Plot.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 22 11:19:43 2022
@author: tiqui
"""
import pandas as pd
from astropy import units as u
from astropy.coordinates import SkyCoord
import pylab as pl
import matplotlib as mpl
import numpy as np
from astropy.io import ascii
from scipy.optimize import curve_fit
###
# Uploading the data from webpages and csv files
###
### For the whole data UCHII and HII
#data_CORNISH = 'https://cornish.leeds.ac.uk/cgi-bin/public/catalogue_query.py?title=hii_all&type=CSV&artifacts=0'
### For HII regions only (sample of 37 sources)
data_CORNISH_HII = 'https://cornish.leeds.ac.uk/cgi-bin/public/catalogue_query.py?title=hii_only&type=CSV&artifacts=0'
### For UCHII regions only (sample of 239 sources)
data_CORNISH = 'https://cornish.leeds.ac.uk/cgi-bin/public/catalogue_query.py?title=uchii_only&type=CSV&artifacts=0'
df_CORNISH = pd.read_csv(data_CORNISH, usecols=[0,3,4])
df_CORNISH.rename(columns = {'#Name':'Source_Name', 'RA_deg':'RA(J2000)_C','Dec_deg':'Dec(J2000)_C'}, inplace = True)
df_CORNISH_HII = pd.read_csv(data_CORNISH_HII , usecols=[0,3,4])
df_CORNISH_HII.rename(columns = {'#Name':'Source_Name', 'RA_deg':'RA(J2000)','Dec_deg':'Dec(J2000)'}, inplace = True)
path_CORNISH_HII_txt = "Data/Tables/CORNISH_All_HII.txt"
data_CORNISH_HII_txt = pd.read_csv(path_CORNISH_HII_txt, header=0, sep='')
df_CORNISH_HII_txt = data_CORNISH_HII_txt[0]
path_CORNISH_III = "Data/Tables/CORNISH_All_UCHII.txt"
data_CORNISH_III = pd.read_csv(path_CORNISH_III, header=0, sep='')
df_CORNISH_III = data_CORNISH_III[0]
### CSV for the SOMA Survey
data_SOMA = 'Data/Tables/SOMA_final_table - MAiN_TABLE.csv'
df_SOMA = pd.read_csv(data_SOMA, usecols=[0,1,2])
df_SOMA = df_SOMA.dropna()
df_SOMA.rename(columns = {'DEC(J2000)':'Dec(J2000)'}, inplace = True)
###
# Changing units for the coordinates on both dataframes
###
for ind in df_CORNISH.index:
ra = df_CORNISH['RA(J2000)_C'][ind]
dec = df_CORNISH['Dec(J2000)_C'][ind]
c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
ra = c.ra.to_string(u.hour)
dec = c.dec.to_string(u.degree, alwayssign=True)
df_CORNISH['RA(J2000)_C'][ind] = ra
df_CORNISH['Dec(J2000)_C'][ind] = dec
for ind in df_CORNISH_HII.index:
ra = df_CORNISH_HII['RA(J2000)'][ind]
dec = df_CORNISH_HII['Dec(J2000)'][ind]
c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
ra = c.ra.to_string(u.hour)
dec = c.dec.to_string(u.degree, alwayssign=True)
df_CORNISH_HII['RA(J2000)'][ind] = ra
df_CORNISH_HII['Dec(J2000)'][ind] = dec
###
# Working on the dataframes, joins and drop nan values and other values
###
df_CORNISH_SOMA = pd.merge(df_CORNISH_III, df_SOMA, on='RA(J2000)', how='inner', suffixes=('', '_y'))
df_CORNISH_SOMA.drop(df_CORNISH_SOMA.filter(regex='_y$').columns, axis=1, inplace=True)
df_CORNISH_alone = pd.merge(df_CORNISH_III, df_SOMA, on='RA(J2000)', how='left', indicator=True)
df_CORNISH_alone = df_CORNISH_alone[(df_CORNISH_alone['_merge'] != 'both')].reset_index(drop=True)
df_CORNISH_alone.drop(df_CORNISH_alone.filter(regex='_y$').columns, axis=1, inplace=True)
df_CORNISH_alone.drop(['SOMA SOURCE','_merge'], axis=1, inplace=True)
df_CORNISH_alone.dropna(subset=['Dist_h(kpc)'], inplace=True)
df = df_CORNISH_SOMA = pd.merge(df_CORNISH_alone, df_CORNISH, on='Source_Name', how='inner')
# Optimal aperture calculated using Zoie algorithm without the correction for crowded regions
optimal_aperture_values = [93.0, 21.0, 93.0, 15.0, 24.0, 34.0, 28.0, 27.0, 33.0, 13.0, 65.0, 14.0, 38.0, 21.0, 13.0, 13.0, 74.0, 15.0, 14.0, 84.0, 0.0, 79.0, 17.0, 47.0, 43.0, 50.0, 31.0, 37.0, 92.0, 13.0, 24.0, 15.0, 14.0, 27.0, 44.0, 18.0, 94.0, 77.0, 14.0, 13.0, 77.0, 92.0, 18.0, 92.0, 91.0, 23.0, 38.0, 14.0, 50.0, 19.0, 15.0, 14.0, 16.0, 41.0, 14.0, 41.0, 34.0, 15.0, 13.0, 15.0, 78.0, 83.0, 65.0, 88.0, 85.0, 88.0, 89.0, 89.0, 95.0, 91.0, 91.0, 70.0, 14.0, 85.0, 94.0, 13.0, 96.0, 77.0, 17.0, 13.0, 17.0, 14.0, 15.0, 81.0, 31.0, 24.0, 15.0, 28.0, 94.0, 17.0, 41.0, 21.0, 20.0, 12.0, 37.0, 80.0, 41.0, 95.0, 18.0, 89.0, 24.0, 18.0, 44.0, 13.0, 93.0, 17.0, 18.0, 20.0, 17.0, 21.0, 79.0, 23.0, 21.0, 35.0, 25.0, 28.0, 18.0, 14.0, 21.0, 25.0, 21.0, 21.0, 92.0, 21.0, 71.0, 21.0, 82.0, 83.0, 86.0, 86.0, 86.0, 18.0, 17.0, 91.0, 13.0, 15.0, 14.0, 13.0, 70.0, 15.0, 53.0, 16.0, 16.0, 21.0, 13.0, 28.0, 78.0, 13.0, 14.0, 14.0, 14.0, 15.0, 18.0, 20.0, 45.0, 14.0, 94.0, 92.0, 89.0, 94.0, 84.0, 84.0, 84.0, 84.0, 85.0, 5.0, 85.0, 85.0, 86.0, 91.0, 91.0, 87.0, 91.0, 62.0, 20.0, 13.0, 15.0, 80.0, 14.0, 13.0, 85.0, 20.0, 15.0, 52.0, 83.0, 82.0, 87.0, 38.0, 88.0, 90.0, 18.0, 97.0, 88.0, 88.0, 68.0, 20.0, 13.0, 18.0, 31.0, 38.0, 87.0, 78.0, 14.0]
optimal_aperture_values_HII = [63.0, 35.0, 53.0, 89.0, 48.0, 89.0, 28.0, 30.0, 14.0, 28.0, 5.0, 82.0, 92.0, 25.0, 85.0, 42.0, 48.0, 33.0, 27.0, 54.0, 25.0, 27.0, 5.0, 33.0, 93.0, 5.0, 40.0, 86.0, 53.0, 5.0, 85.0, 28.0, 25.0, 93.0, 88.0, 60.0, 77.0]
df['Optimal_apert'] = optimal_aperture_values
df_CORNISH_HII['Optimal_apert'] = optimal_aperture_values_HII
# Remove the bad sources from the sample
list_bad_sources = ['G019.6087-00.2351', 'G019.6090-00.2313', 'G024.4721+00.4877', 'G032.7966+00.1909', 'G032.7982+00.1937', 'G038.6465-00.2260', 'G034.0901+00.4365', 'G011.0328+00.0274', 'G012.4317-01.1112', 'G018.1460-00.2839', 'G024.8497+00.0881', 'G030.6881-00.0718', 'G030.7532-00.0511', 'G034.2544+00.1460', 'G043.1460+00.0139', 'G043.1489+00.0130', 'G043.1520+00.0115', 'G043.1651-00.0283', 'G043.1652+00.0129', 'G043.1657+00.0116', 'G043.1665+00.0106', 'G043.1674+00.0128', 'G043.1677+00.0196', 'G043.1684+00.0124', 'G043.1699+00.0115', 'G043.1701+00.0078', 'G043.1706-00.0003', 'G043.1716+00.0001', 'G043.1720+00.0080', 'G043.1763+00.0248', 'G048.6099+00.0270', 'G049.4640-00.3511', 'G049.4891-00.3763', 'G049.4905-00.3688', 'G010.6297-00.3380', 'G014.5988+00.0198', 'G018.8250-00.4675', 'G023.8985+00.0647', 'G025.7157+00.0487', 'G026.0083+00.1369', 'G028.4518+00.0027', 'G043.1684+00.0087', 'G048.9296-00.2793']
list_bad_sources_index = []
for i in df.index:
source = df['Source_Name'][i]
if source in list_bad_sources:
list_bad_sources_index.append(i)
df.drop(list_bad_sources_index, inplace=True)
df = df.reset_index(drop=True)
# Drop HII regions that don't have distance data, and bad sources (G045.4790+00.1294)
df_CORNISH_HII_txt.dropna(subset=['Dist_h(kpc)'], inplace=True)
df_CORNISH_HII_txt.drop([29], inplace=True)
df_CORNISH_HII_txt = df_CORNISH_HII_txt.reset_index(drop=True)
###
# Anglada plot
###
# Paths for the three different average methods
path_all = 'Data/Fitting Results'
table_name = path_all+'/table_aver_4p_bad_sources.txt'
table_UCHII_verf = ascii.read(table_name)
path_SOMA = 'Data/Fitting Results/average_goodmodels_SOMA_sources_linear_flu+bkg.txt'
SOMA_table = ascii.read(path_SOMA)
path_all = 'Data/Fitting Results'
table_name = path_all+'/table_aver_4p.txt'
table_HII = ascii.read(table_name)
path_Anglada = 'Data/Anglada Models/'
# Add results of the fitting (Lbol) to the dataframe
Source_Name = table_UCHII_verf['Source_Name']
df_Lbol = pd.DataFrame(Source_Name, columns=['Source_Name'])
df_Lbol['lbol'] = table_UCHII_verf['lbol']
df_UCHII = pd.merge(df, df_Lbol, on='Source_Name', how='inner', suffixes=('', '_y'))
df_UCHII.drop(df_UCHII.filter(regex='_y$').columns, axis=1, inplace=True)
# Remove bad Sources HII regions (G045.4790+00.1294)
table_HII.remove_row(19)
# Uploading data from models and SOMA data points
data_dict_Lit = {}
Lit_sources = []
infile='Data/Anglada Models/Reference_table.txt'
for line in open(infile, 'r'):
l1 = line.split()
if l1==[]: continue
skipChars = ['#']
if line[0] in skipChars: continue
if not l1[0] in data_dict_Lit:
data_dict_Lit[l1[0]] = {}
Lit_sources.append(l1[0])
data_dict_Lit[l1[0]] = {'Dist': l1[1],
'Flux': l1[2],
'Radio_lum': l1[3],
'Lum_bol': l1[4],
'Pdot': l1[5],
'Refs':l1[6]}
Unresolved_Kurtz_94 = ['G10.841-2.592', 'G28.200-0.049', 'G48.61+0.02', 'G76.383-0.621',\
'G138.295+1.555', 'G189.030+0.784', 'G189.876+0.516',\
'G206.543-16.347']
scale= 1.36
scale_k = 0.95
Anglada_Lbol = []
Anglada_Radio = []
Kur_Lbol = []
Kur_Radio = []
for source in Lit_sources:
if data_dict_Lit[source]['Refs'] == 'Anglada_95' and data_dict_Lit[source]['Lum_bol'] != 'na':
Anglada_Lbol.append(float(data_dict_Lit[source]['Lum_bol']))
Anglada_Radio.append(float(data_dict_Lit[source]['Radio_lum'])/scale)
### Values for the Data points from the SOMA Radio I, II and III
# SOMA 1
lbol_S1 = [1.4e4, 3.9e4, 5.6e4, 8.7e4, 4.2e5, 6.9e4, 4.9e4, 2.1e5]
lbol_max_S1 = [2.1e4, 5.6e4, 8.6e4, 19e4, 7.0e5, 14e4, 7.3e4, 4.7e5]
lbol_min_S1 = [0.9e4, 2.7e4, 3.7e4, 4.0e4, 2.5e5, 3.3e4, 3.3e4, 0.9e5]
Radio_flux_S1 = [0.14, 0.77, 1.15, 0.74, 91.0, 0.06, np.nan, 0.42]
Dist_S1 = [2.0, 2.0, 1.68, 2.2, 8.4, 1.64, 0.7, 2.65]
# SOMA 2
lbol_S2 = [5.5e5, 5.5e5, 3.0e5, 2.6e5, 2.6e5, 1.2e5, 1.4e5]
lbol_max_S2 = [9.7e5, 9.4e5, 3.0e5, 6.6e5, 4.6e5, 3.4e5, 5.1e5]
lbol_min_S2 = [3.1e5, 3.3e5, 2.9e5, 1.0e5, 1.4e5, 0.4e5, 0.4e5]
Radio_flux_S2 = [1702.69, np.nan, 89.70, 8.53, np.nan, 0.04, 0.78]
Dist_S2 = [7.4, 5.5, 10.2, 1.7, 4.1, 5.55, 2.1]
# SOMA 3
lbol_S3 = [1.5e4, 4.2e3, 1.9e3, 6.6e2, 4.7e3, 6.72e2, 6.3e2, 5.3e3, 5.5e2, 4.8e2, 4.4e2, 6.7e2]
lbol_min_S3 = [7.2e4, 11e3, 1.9e3, 14e2, 9.3e3, 6.7e2, 21e2, 34e3, 14e2, 12e2, 9.6e2, 6.7e2]
lbol_max_S3 = [0.3e4, 1.6e3, 1.9e3, 3.2e2, 2.3e3, 6.7e2, 1.9e2, 0.8e3, 2.1e2, 2.0e2, 2.0e2, 6.7e2]
Radio_flux_S3 = [260e-3, 233e-3, 634e-2, 153e-4, 313e-4, 264e-4, 258e-4, 276e-4, 272e-4, 160e-3, 161e-3, 178e-4]
Dist_S3 = [1.8, 0.764, 0.39, 0.73, 0.776, 0.776, 2.40, 2.40, 2.40, 0.75, 0.75, 0.75]
# Anglada plot
pl.figure(9, figsize=(9,9))
Fig = 'Anglada_plot'
fig = pl.gcf()
ax = fig.add_axes([.15,.15, .8, .75])
# To make the Anglada and Kurtz points
pl.loglog(Anglada_Lbol, Anglada_Radio, '^y', markersize=8, markeredgewidth=1.5, alpha=0.5,
label = 'Jets low-Mass YSO: Anglada et al. 1995')
#pl.loglog(Kur_Lbol, Kur_Radio, 'xk', markersize=8, markeredgewidth=1.5, alpha=0.5,
# label = 'UC/HC HII: Kurtz et al. 1994')
# Values from Monge
T_e = 1e4 #K
nu = 6.0 #GHz at 5 cm
# Lyman Continuum from YSO, making the teal (blue) line
infile_M603 = path_Anglada+'stel_Mc60.Sigma0.3.dat'
M_star_M603, r_star_M603, L_star_M603, T_star_M603, rad_lum_star_M603, Q_star_M603 = np.loadtxt(infile_M603, unpack=True ,usecols=[0, 1, 2, 3, 4, 5])
infile_M601 = path_Anglada+'stel_Mc60.Sigma1.dat'
M_star_M601, r_star_M601, L_star_M601, T_star_M601, rad_lum_star_M601, Q_star_M601 = np.loadtxt(infile_M601, unpack=True ,usecols=[0, 1, 2, 3, 4, 5])
infile_M602 = path_Anglada+'stel_Mc60.Sigma3.dat'
M_star_M602, r_star_M602, L_star_M602, T_star_M602, rad_lum_star_M602, Q_star_M602 = np.loadtxt(infile_M602, unpack=True ,usecols=[0, 1, 2, 3, 4, 5])
pl.loglog(L_star_M601, np.multiply(rad_lum_star_M601,1.07789612350129e-44), '-', color= 'teal', linewidth=3, label='_nolegend_', alpha=0.8)
pl.loglog(L_star_M602, np.multiply(rad_lum_star_M602,1.07789612350129e-44), '-', color= 'teal', linewidth=3, label='_nolegend_', alpha=0.8)
pl.loglog(L_star_M603, np.multiply(rad_lum_star_M603,1.07789612350129e-44), '-', color= 'teal', linewidth=3, label='_nolegend_', alpha=0.8)
# Lyman Continuum from ZAMS, making the black line
cluster_file = path_Anglada+'lbin_cluster_Lbol-Nlym.dat'
logL_bol_cl, logNe_05_cl, logNe_95_cl = np.loadtxt(cluster_file, unpack=True)
rad_lum_cesaroni_data = 2.08e-46 * 10**(np.array(logNe_95_cl))* nu**-0.1 * T_e**0.45
pl.loglog(10**pl.array(logL_bol_cl), rad_lum_cesaroni_data, 'k-', linewidth=3)
pl.annotate(r'$S_{\nu}d^{2} =\,8 \times 10^{-3}(L_{bol})^{0.6}$', xy=(5e-1,5e-0), annotation_clip=False, fontsize='20')
pl.annotate(r'Lyman Continuum from ZAMS', xy=(4e-1,1e3), annotation_clip=False, fontsize='18')
pl.annotate(r'Lyman Continuum from YSO', xy=(1.5e2,2e-4), annotation_clip=False, fontsize='18', color='teal')
# CORNISH UCHII and HII data points
pl.scatter(x=df_UCHII['lbol'], y=df_UCHII['Flux (mJy)']*df_UCHII['Dist_h(kpc)']**2, marker = 'o', label='CORNISH HII',
color='blue', zorder=10, s=40)
pl.scatter(x=table_HII['lbol'], y=df_CORNISH_HII_txt['Flux (mJy)']*df_CORNISH_HII_txt['Dist_h(kpc)']**2, marker = 'o',
color='blue', zorder=10, s=40)
# To plot the SOMA data points with the errorbar (See Radio SOMA I, II and III)
sct1 = pl.scatter(x=lbol_S1, y=np.multiply(Radio_flux_S1,np.multiply(Dist_S1,Dist_S1)), marker = 'o',
label='SOMA I and II', color='#d62728', zorder=5, s=80)
#plt.errorbar(sct1, xerr=[[np.subtract(lbol_S1,lbol_min_S1)], [np.subtract(lbol_max_S1,lbol_S1)]], color = 'grey',
# elinewidth=2, capsize=7, capthick=3, alpha=0.5)
sct2 = pl.scatter(x=lbol_S2, y=np.multiply(Radio_flux_S2,np.multiply(Dist_S2,Dist_S2)), marker = 'o',
color='#d62728', zorder=5, s=80)
#plt.errorbar(sct2, xerr=[[np.subtract(lbol_S2,lbol_min_S2)], [np.subtract(lbol_max_S2,lbol_S2)]], color = 'grey',
# elinewidth=2, capsize=7, capthick=3, alpha=0.5)
sct3 = pl.scatter(x=lbol_S3, y=np.multiply(Radio_flux_S3,np.multiply(Dist_S3,Dist_S3)), marker = 'o',
label='SOMA III', color='#8c92ac', zorder=5, s=80)
#plt.errorbar(sct3, xerr=[[np.subtract(lbol_S3,lbol_min_S3)], [np.subtract(lbol_max_S3,lbol_S3)]], color = 'grey',
# elinewidth=2, capsize=7, capthick=3, alpha=0.5)
# To plot the data points from Tanaka 2016
infile_A = path_Anglada+'A.dat'
Mst, Lst, Sdd_1GHz, Sdd_8GHz, Sdd_10GHz = np.loadtxt(infile_A, unpack=True ,usecols=[0, 3, 4, 5, 6])
pl.loglog(Lst, Sdd_8GHz, 's', color='gold', markersize=8, markeredgewidth=1.5, alpha=0.5, label = 'Model TTZ16')
infile_Al = path_Anglada+'Al.dat'
Mst, Lst, Sdd_1GHz, Sdd_8GHz, Sdd_10GHz = np.loadtxt(infile_Al, unpack=True ,usecols=[0, 3, 4, 5, 6])
pl.loglog(Lst, Sdd_8GHz, 's', color='gold', markersize=8, markeredgewidth=1.5, alpha=0.5, label = '_nolegend_')
infile_Ah = path_Anglada+'Ah.dat'
Mst, Lst, Sdd_1GHz, Sdd_8GHz, Sdd_10GHz = np.loadtxt(infile_Ah, unpack=True ,usecols=[0, 3, 4, 5, 6])
pl.loglog(Lst, Sdd_8GHz, 's', color='gold', markersize=8, markeredgewidth=1.5, alpha=0.5, label = '_nolegend_')
infile_B = path_Anglada+'B.dat'
Mst, Lst, Sdd_1GHz, Sdd_8GHz, Sdd_10GHz = np.loadtxt(infile_B, unpack=True ,usecols=[0, 3, 4, 5, 6])
pl.loglog(Lst, Sdd_8GHz, 's', color='gold', markersize=8, markeredgewidth=1.5, alpha=0.5, label = '_nolegend_')
infile_C = path_Anglada+'C.dat'
Mst, Lst, Sdd_1GHz, Sdd_8GHz, Sdd_10GHz = np.loadtxt(infile_C, unpack=True ,usecols=[0, 3, 4, 5, 6])
pl.loglog(Lst, Sdd_8GHz, 's', color='gold', markersize=8, markeredgewidth=1.5, alpha=0.5, label = '_nolegend_')
pl.legend(fontsize=10, loc=0, ncol=1)
pl.xlabel('$L_\mathrm{bol}\,(L_\odot)$', fontsize='20')
pl.ylabel(r'$S_{\nu}D^2$ ($mJy$ $kpc^2$)', fontsize='20')
pl.gca().set_ylim(1e-4,1.0e6)
pl.gca().set_xlim(1e-1,1.9e6)
pl.gca().tick_params(axis='both', reset=True, length=10, width=1, which='major', direction='in', labelsize=18)
pl.gca().tick_params(axis='both', reset=True, length=4, width=1, which='minor', direction='in', labelsize=18)
## Theretical fit from the low mass sources (- line)
plot_lum_v2= pl.array(pl.gcf().gca().get_xlim())
rad_lum_an_v2= 8*10**(-3)*plot_lum_v2**(0.6)
pl.loglog(plot_lum_v2, rad_lum_an_v2,'--k')
pl.show()
#fig.savefig('Anglada Plot.png')#,dpi=300,bbox_inches='tight')