| **Name, NGC** | **Name, UGC** | **Type** | $i,^{\circ}$ | $\sigma$ source |
| NGC 338 | UGC 624 | Sab | 64±4 | Z |
| NGC 1167 | UGC 2487 | SA0 | 38±2 | Z (CALIFA?) |
| NGC 2985 | UGC 5253 | (R)SA(r)ab | 36±2 | N08 |
| NGC 3898 | UGC 6787 | SA(s)ab | 61±8 | N08 (ATLASS3D?) |
| NGC 4258 | UGC 7353 | SABb | 65±5 | H98 (ATLASS3D?) |
| NGC 4725 | UGC 7989 | SABa | 50±6 | H99 (ATLASS3D?) |
| NGC 5533 | UGC 9133 | SA(rs)ab | 52±1 | N08 (CALIFA?) |
N08 - https://ui.adsabs.harvard.edu/#abs/2008MNRAS.388.1381N/abstract
Все картинки:
In [1]:
from IPython.display import Image, display
from prettypandas import PrettyPandas
import pandas as pd
import os
import sys
from PIL import Image as Im
%pylab
%matplotlib inline
Using matplotlib backend: Qt5Agg
Populating the interactive namespace from numpy and matplotlib
In [2]:
summary_imgs_path = '..\\pics\\notebook_summary\\'
In [3]:
for img in os.listdir(summary_imgs_path):
display(Image(summary_imgs_path + img))
Одна большая композитная картинка:
In [4]:
images = []
for img in os.listdir(summary_imgs_path):
im = Im.open(summary_imgs_path + img)
images.append(im)
widths, heights = zip(*(i.size for i in images))
total_height = sum(heights)
max_width = max(widths)
new_im = Im.new('RGB', (max_width, total_height))
offset = 0
for im in images:
new_im.paste(im, (0,offset))
offset += im.size[1]
new_im.save('summary_all.jpg')
In [5]:
# странное поведение - если тут поменять размер с 19 до 20, то картинку нельзя будет "открыть в новой вкладке", так что я просто ее сохранил
# import matplotlib.pylab as plt
# fig = plt.figure(figsize=[19, 4*6])
# plt.imshow(new_im)
# plt.xticks([])
# plt.yticks([])
# plt.show()
Неустойчивость в далеких спиралях:
In [6]:
for img in os.listdir('..\\pics\\instab_spirals\\'):
display(Image('..\\pics\\instab_spirals\\' + img))
In [7]:
images = []
imnames = []
for img in os.listdir('..\\pics\\instab_spirals\\'):
if '5533' not in img:
im = Im.open('..\\pics\\instab_spirals\\' + img)
images.append(im)
imnames.append(img)
fig, axes = plt.subplots(1, 2, figsize=[18, 9])
for ind, im in enumerate(images):
axes[ind].imshow(im)
axes[ind].set_xticks([])
axes[ind].set_yticks([])
plt.tight_layout()
plt.show()
In [8]:
images = []
imnames = []
for img in os.listdir('..\\pics\\RF13\\'):
im = Im.open('..\\pics\\RF13\\' + img)
images.append(im)
imnames.append(img)
fig, axes = plt.subplots(7, 1, figsize=[20, 40])
for ind, im in enumerate(images):
axes[ind].imshow(im)
axes[ind].set_title(imnames[ind], fontsize=30)
plt.show()
Видно, что ситуация для всех похожая более-менее и все выглядит неплохо.
Ссылка на таблицу на docs.google.
НЕ ПОСЛЕДНЯЯ ВЕРСИЯ
In [9]:
data_sources = pd.read_csv('sources_data.csv')
Без ссылок и замечаний (формат в ячейках - $i$, $D(Mpc)$, $PA$):
In [10]:
def highlights(s):
return ['background-color: #D8D8D8' if i in [0, 1, 2, 3, 12, 18, 22,] else '' for i in range(len(s))]
s = data_sources[[c for c in data_sources.columns[:-2]]].style.apply(highlights)
s
Out[10]:
source
N338
N1167
N2985
N3898
N4258
N4725
N5533
0
UGC
624
2487
5253
6787
7353
7989
9133
1
HYPERLEDA
75.6, 76.560 \pm 17.629, 107.3
49., -, 70.
37.9,-,177
56.3,-,107
68.3, 7.656 \pm 0.124,150
45.4,13.804 \pm 3.179,35.7
60.3,33.33 \pm 0.45,28.2
2
NED scale dist
62
66
19
18.4
8.97
20.5
56.1
3
Dispersions
nan
nan
nan
nan
nan
nan
nan
4
Noordermeer 08
nan
nan
37, 21.1, 356-340
69, 18.9, 107-118
nan
nan
53, 54.3, 24-45
5
Zasov 08
nan
36, 67, 70
nan
nan
nan
nan
nan
6
Zasov 12
64, 65, 108
nan
nan
nan
nan
nan
nan
7
Heraudeau 1998
nan
nan
nan
nan
70.3, -480, 150
nan
nan
8
Heraudeau 1999
nan
nan
44.2, -1328, 0.
49.2, -1153, 107
nan
46.5, -1225,35
nan
9
CALIFA 2016
nan
40.5,-,62
nan
nan
nan
nan
nan
10
Dumas 07 SAURON
nan
nan
38,22.4,-2
nan
nan
nan
nan
11
Pignatelli 01
nan
nan
54., 17.1, 107
nan
nan
nan
nan
12
Gas data
nan
nan
nan
nan
nan
nan
nan
13
Noordermeer 05 HI
59, 65.1
36., 67.4
38, 21.1
67, 18.9
nan
51., 18.2
53, 54.3
14
Lavezzi 98 CO
+'
nan
nan
nan
nan
nan
nan
15
Courteau 97 Vel_21cm
68, 71.22, -
nan
nan
nan
nan
nan
nan
16
Eymeren 11 Vel_HI
nan
nan
nan
nan
69.5, 7.8, 331.25
44.19,25.91,32.45
51.9,54.3,30.36
17
Yim 16 CO+HI
nan
nan
nan
nan
66., 8.0?,
44., 26.8
nan
18
SF
nan
nan
nan
nan
nan
nan
nan
19
Epinat 08 GHASP
nan
nan
36\pm 5, 21.1, 176
53\pm 2, 18.9, 112
nan
nan
nan
20
Hameed 05 H_alpha
nan
nan
42., 22.4
46., 21.9
nan
43., 12.4
nan
21
SPITZER
nan
nan
nan
nan
+'
+'
nan
22
Photometry
nan
nan
nan
nan
nan
nan
nan
23
Noordermer, Hulst 07 BR (I)
64, -, -72
38, -, 70
36, -, -3
nan
nan
nan
52, -, 26
24
CALIFA 16 gri
nan
-,-,72
nan
nan
nan
nan
-,-,27.5
25
Heidt 01 JHK
nan
nan
32-40,28.,2-160
63-74,37.5,102-106
nan
63,13.0, 44.5
nan
26
Gutierrez 2012 R
nan
nan
36, 21.1, 178
53., 18.9, 107
nan
nan
nan
27
Mendez-Abreu 08 J
nan
nan
29.5, 22.4, 1.5
60., 21.9, 106.9
nan
nan
50., 54., 26.3
28
S4G 15
nan
nan
33.9, -,175
nan
nan
56., -,34.
nan
29
YOSHINO 08 VIJ
nan
nan
nan
nan
59.3,7.8
nan
nan
30
Fisher 10 S4G
nan
nan
nan
nan
-,8.17,-
-,13.24,-
nan
31
nan
nan
nan
nan
nan
nan
nan
nan
32
HUBBLE
nan
nan
+'
nan
nan
+'
nan
Ссылки:
In [11]:
PrettyPandas(data_sources[[c for c in (data_sources.columns[0], data_sources.columns[-2])]])
Out[11]:
source
link
0
UGC
nan
1
HYPERLEDA
nan
2
NED scale dist
nan
3
Dispersions
nan
4
Noordermeer 08
https://ui.adsabs.harvard.edu/#abs/2008MNRAS.388.1381N/abstract https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1513N/abstract https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1480N/abstract
5
Zasov 08
https://ui.adsabs.harvard.edu/#abs/2008ARep...52...79Z/abstract
6
Zasov 12
https://ui.adsabs.harvard.edu/#abs/2012AstBu..67..362Z/abstract
7
Heraudeau 1998
http://adsabs.harvard.edu/cgi-bin/bib_query?1998A%26AS..133..317H
8
Heraudeau 1999
http://adsabs.harvard.edu/cgi-bin/bib_query?1999A%26AS..136..509H
9
CALIFA 2016
ftp://ftp.caha.es/CALIFA/dataproducts/Stellar_Kinematics_V1200/FalconBarroso_etal_2016.pdf
10
Dumas 07 SAURON
https://ui.adsabs.harvard.edu/#abs/2007MNRAS.379.1249D/abstract
11
Pignatelli 01
https://ui.adsabs.harvard.edu/#abs/2001MNRAS.323..188P/abstract
12
Gas data
nan
13
Noordermeer 05 HI
https://ui.adsabs.harvard.edu/#abs/2005A&A...442..137N/abstract
14
Lavezzi 98 CO
https://ui.adsabs.harvard.edu/#abs/1998AJ....115..405L/abstract
15
Courteau 97 Vel_21cm
https://ui.adsabs.harvard.edu/#abs/1997AJ....114.2402C/abstract
16
Eymeren 11 Vel_HI
https://arxiv.org/pdf/1103.4928v1.pdf
17
Yim 16 CO+HI
https://arxiv.org/pdf/1608.06735v1.pdf
18
SF
nan
19
Epinat 08 GHASP
https://arxiv.org/pdf/0808.0132v1.pdf
20
Hameed 05 H_alpha
http://iopscience.iop.org/article/10.1086/430211/pdf
21
SPITZER
nan
22
Photometry
nan
23
Noordermer, Hulst 07 BR (I)
https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1480N/abstract
24
CALIFA 16 gri
http://adsabs.harvard.edu/abs/2016arXiv161005324M
25
Heidt 01 JHK
http://www.aanda.org/articles/aa/pdf/2001/10/aa10227.pdf
26
Gutierrez 2012 R
https://ui.adsabs.harvard.edu/#abs/2011AJ....142..145G/abstract
27
Mendez-Abreu 08 J
https://ui.adsabs.harvard.edu/#abs/2008A&A...478..353M/abstract
28
S4G 15
http://adsabs.harvard.edu/abs/2015ApJS..219....4S
29
YOSHINO 08 VIJ
http://pasj.oxfordjournals.org/content/60/3/493.full.pdf
30
Fisher 10 S4G
http://iopscience.iop.org/article/10.1088/0004-637X/716/2/942/pdf
31
nan
nan
32
HUBBLE
nan
По колонкам - Влияние наклона, варьирования $c_g$ от 4 до 15 (20 бинов), убирание молек. газа.
In [12]:
images = []
imnames = []
for img in os.listdir('..\\pics\\cg\\'):
im = Im.open('..\\pics\\cg\\' + img)
images.append(im)
imnames.append(img)
fig, axes = plt.subplots(7, 3, figsize=[16, 30])
for ind, im in enumerate(images):
axes[ind][0].imshow(im)
axes[ind][0].set_title(imnames[ind], fontsize=20)
images = []
imnames = []
for img in os.listdir('..\\pics\\incl_summary\\'):
im = Im.open('..\\pics\\incl_summary\\' + img)
images.append(im)
imnames.append(img)
for ind, im in enumerate(images):
axes[ind][1].imshow(im)
images = []
imnames = []
for img in os.listdir('..\\pics\\He\\'):
im = Im.open('..\\pics\\He\\' + img)
images.append(im)
imnames.append(img)
for ind, im in enumerate(images):
axes[ind][2].imshow(im)
fig.tight_layout()
plt.show()
Content source: Amarchuk/2FInstability
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